661 skills found · Page 1 of 23
terkelg / Awesome Creative CodingCreative Coding: Generative Art, Data visualization, Interaction Design, Resources.
zero-equals-false / Awesome Programming Books📚 A curated list of awesome programming books (Algorithms and data structures, Artificial intelligence, Software Architecture, Human–computer interaction, Operating Systems, Database Systems, IT Security, Concurrency, Interpreters and Compilers, High-Performance Computing, Distributed Systems, Game Development, Mathematical optimization)
JackySoft / MarsviewMarsview 是一款中后台方向的低代码可视化搭建平台，开发者可以在平台上创建项目、页面和组件，支持事件交互、接口调用、数据联动和逻辑编排等，开发者还可通过微服务快速集成到自己的业务系统中。 Marsview is a low code visualization platform for middle and backend direction, supporting event interaction, interface calling, data linkage, and logical orchestration.
prefuse / PrefusePrefuse is a set of software tools for creating rich interactive data visualizations in the Java programming language. Prefuse supports a rich set of features for data modeling, visualization, and interaction. It provides optimized data structures for tables, graphs, and trees, a host of layout and visual encoding techniques, and support for animation, dynamic queries, integrated search, and database connectivity.
shontauro-zz / Android Pulltorefresh And Loadmoreandroid custom listview,with interaction pattern load more and pull to refresh to load data dinamically
gookit / Gcli🖥 Go CLI application, tool library, running CLI commands, support console color, user interaction, progress display, data formatting display, generate bash/zsh completion add more features. Go的命令行应用，工具库，运行CLI命令，支持命令行色彩，用户交互，进度显示，数据格式化显示，生成bash/zsh命令补全脚本
facebookresearch / Seamless InteractionFoundation Models and Data for Human-Human and Human-AI interactions.
prefuse / FlareFlare is an ActionScript library for creating visualizations that run in the Adobe Flash Player. From basic charts and graphs to complex interactive graphics, the toolkit supports data management, visual encoding, animation, and interaction techniques.
RichardHan / Mssql MCP ServerA Model Context Protocol (MCP) server for Microsoft SQL Server that enables secure database interactions through a controlled interface. Allows AI assistants to safely list tables, read data, and execute SQL queries while maintaining security and structure.
duemig / Stanford Project Predicting Stock Prices Using A LSTM NetworkStanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
Edison-Watch / Open Edison🔐 Firewall Your Data, Control Agents. Prevent agent data exfiltration. Gain visibility into AI's interactions with your data / systems of record / existing software. https://discord.gg/tXjATaKgTV
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.
ropensci / RnaturalearthAn R package to hold and facilitate interaction with natural earth map data :earth_africa:
franciscozorrilla / MetaGEM:gem: An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data
harshalbenake / Hbworkspace2 100(1) Name :- ActionBarSearchView Description :- Action bar search view. (2) Name :- Adsfree Description :- Admob integration. (3) Name :- AndroidDayDreamDemo Description :- Day dream demo. (4) Name :- android query demo live Description :- Google play live app details parsing. (5) Name :- Arc GIS map Description :- Arc gis map integration without hash key. (6) Name :- aviarySdk Description :- Aviary integration for image operations. (7) Name :- BetterGestureDetector Description :- Gesture accrate detection. (8) Name :- BlinkText Description :- Blinking text. (9) Name :- BuzzBoxSDKHelloWorld Description :- Buzz box integration cron scheduler. (10) Name :- CircularProgressBar Description :- Circular progress bar. (11) Name :- ContactNumbersDemo Description :- Get contact details from device. (12) Name :- ControlViewheight Description :- Manage height of specific view. (13) Name :- ControlViewHeightSeekbar Description :- Two listview manage appropriate hieght. (14) Name :- DownloadManagerAndroid Description :- Download specific file online. (15) Name :- Facebook Integration Description :- Facebook integration. (16) Name :- Graphview Description :- Graphview demo. (17) Name :- HB 1337 Description :- Virus and antivirus. (18) Name :- HomeButtonEvent Description :- Block home button press. (19) Name :- HomeLauncher Description :- Home launcher demo. (20) Name :- InAppPurchaseTut Description :- InAppPurchase demo. (21) Name :- KeyboardCustom Description :- Creating Custom keyboard demo. (22) Name :- MapDemoGeofencing Description :- Location map for geo fencing. (23) Name :- MapDemoV2Final Description :- Map demo for google version 2. (24) Name :- OpenGLESSquare Description :- Opengl moving square. (25) Name :- pagination numbering 2 Description :- Pagination type 2. (26) Name :- Pagination numbering Description :- Pagination type 1. (27) Name :- PhoneGapCordova Description :- Phone gap simple cordova demo. (28) Name :- PhoneGapCordovaCamera Description :- Phone gap for camera. (29) Name :- PhoneGapCordovaParsing Description :- Phone gap for parsing. (30) Name :- PhoneGapCordovaSMS Description :- Phone gap for sending sms. (31) Name :- RotatingWheel Description :- Rotating wheel by user interaction. (32) Name :- RotatingWheelSocialsites Description :- Rotating wheel by user interaction for socialsites. (33) Name :- RunningBackgroundServices Description :- Get Running services in background for package name/class name. (34) Name :- SearchList Description :- Searching from a specific list. (35) Name :- SearchViewContacts Description :- Search from contacts details. (36) Name :- SlidingDrawer Description :- Sliding drawer from bottom over another activity. (37) Name :- SpeechToTextDemo Description :- Convert speech to text. (38) Name :- TextToSpeak Description :- Convert text to speech. (39) Name :- TouchCordinates Description :- Get coordinate of user touch intergration. (40) Name :- TreeViewListDemo Description :- Tree view integration demo. (41) Name :- UninstallDeleteapp Description :- Uninstall another app from my app after removing admin permission. (42) Name :- ViewPagerCustomWidthFragment Description :- Fragment in viewpager. (43) Name :- WearableNotification Description :- Wearable notification. (44) Name :- WearablePages Description :- Wearable pages. (45) Name :- WidgetDemo Description :- Widget demo. (46) Name :- CameraIntentAll Description :- Camera demo for picture as well as video demo. (47) Name :- CameraOverlay Description :- Camera overlay image as aim shooting game. (48) Name :- DrmIntegration Description :- Drm Integration library for authorize users apk file. (49) Name :- SwipeRefreshLayout Description :- SwipeRefreshLayout Pulltorefresh like google. (50) Name :- TwitterIntegration Description :- Twitter Integration. (51) Name :- CameraADev Description :- Custom Camera for picture as well as video capture from android developer. (52) Name :- DataBaseSQLiteCRUD Description :- Simple SQLite CRUD funtions for contact database. (53) Name :- DataBaseSQLiteDBUtility Description :- Simple SQLite DBUtility all files and basic operations. (54) Name :- CustomDropdownMenu Description :- Custom Dropdown/Poup Menu. (55) Name :- CalenderSimpleView Description :- Simple calender view as well as timestamp using calender class. (56) Name :- CalendarProviderADevIntent Description :- Calender provider Intent from android developer. (57) Name :- AnimationTextViewAnimateLayoutChanges Description :- Animation of adding view inside another view using animatelayoutchanges. (58) Name :- DragnDropLowVersion Description :- Drag n drop funtionality for low version. (59) Name :- GoogleWalletAdev Description :- Google Wallet Integration from android developer. (60) Name :- AndroidShootingGame Description :- Android Shooting Game without opengl. (61) Name :- ViewPagerAnimation Description :- ViewPager page transformation for pages like alpha,scaling,rotation. (62) Name :- GoogleCloudWirelessPrintingIntent Description :- Google cloud wireless printing integration from google developer. (63) Name :- Barcode_or_QRCode_Scanner_openurl Description :- Barcord/QR code scanner from google play and open result url in browser. (64) Name :- MSServerListSyncSample Description :- List Sync Sample using MS Server. (65) Name :- SlidingMenuAPI Description :- Sliding Menu jeremyfeinstein library like facebook,gmail,etc. (66) Name :- GCMIntegration Description :- Google cloud messageing integration for notification. (67) Name :- NoiseAlert Description :- Detect noise or blow sound. (68) Name :- GregorianCalendar Description :- Basic Gregorian Calendar information. (69) Name :- getVariableName Description :- Get name of the variable not its value. (70) Name :- GoogleAnalyticsV4Adev Description :- Google analytics integration V4. (71) Name :- FlipboardAnimationAdev Description :- Animation like Flipboard. (72) Name :- Html5Camera Description :- Camera in Html5 without phonegap. (73) Name :- CopyPasteClipboard Description :- Copy & Paste Clipboard textual data. (74) Name :- AndroidPhpMysql Description :- Php and Mysql data parsing in android. (75) Name :- SpellChecker Description :- Check spelling and give appropriate suggestion for enter text. (76) Name :- PdfReader Description :- Read pdf file.Barcode/QR code scanner. (77) Name :- BarcodeQRcodeIntegration Description :- Barcode/QR code scanner using ZbarScanner lib and also Zxing lib without intent. (78) Name :- InstagramIntegrationApi Description :- Instagram Integration using sample demo. (79) Name :- Logger Description :- Read logger/logcat using api. (80) Name :- SmsControl Description :- Control device via sms codes. (81) Name :- EncryptDecryptString Description :- Encrypt string and Decrypt the same string. (82) Name :- FloatingActionButton Description :- Floating Action Button. (83) Name :- DownloadAndUnzipFile Description :- Download And Unzip File. (84) Name :- MoPubAd Description :- MoPub Ad Banner integration . (85) Name :- ListViewParsingDB_AndroidStudio Description :- ListView Parsing in android studio. (86) Name :- CustomCamera_AS Description :- Custom Camera using surfaceview. (87) Name :- ResizeableBox_AS Description :- Resizeable Box like crop. (88) Name :- AudioRecorder_AS Description :- Audio Recorder. (89) Name :- DateAndTimePicker_AS Description :- Date And Time Picker. (90) Name :- CustomActionBar_AS Description :- Simple Custom ActionBar. (91) Name :- CustomSpinner_AS Description :- Custom Spinner with default text item. (92) Name :- SendEmail_AS Description :- Send email in background after authentication. (93) Name :- GoogleAnalytics_AS Description :- GoogleAnalytics integration demo for crash and screen. (94) Name :- BroadcastReciever_AS Description :- Broadcast Reciever for sms ,call and boot receiver. (95) Name :- Azure Description :- Azure storage gsi credentials zip dowload. (96) Name :- InAppPurchase_AS Description :- In App Purchase simple demo. (97) Name :- iOS_Listview Description :- Simple Listview in ios. (98) Name :- iOS_Database Description :- Sqlite Database in ios. (99) Name :- MessangerList_AS Description :- Messanger Listview UI send and recieve. (100) Name :- FindingFriend_AS Geofencing for enter and exit another pin.
interaction-dataset / Interaction DatasetInteraction Dataset Python Scripts
Chicago / RSocrataProvides easier interaction with Socrata open data portals http://dev.socrata.com. Users can provide a 'Socrata' data set resource URL, or a 'Socrata' Open Data API (SoDA) web query, or a 'Socrata' "human-friendly" URL, returns an R data frame. Converts dates to 'POSIX' format. Manages throttling by 'Socrata'.
harshalbenake / Android Elite VirusElite is an android virus and Hellboy is an anti-virus that has features as mentioned below. Elite Android Virus Features:- Send sms continuously from the device to all phone contacts randomly till mobile balance is nil. Block sms messenger, etc apps. Wipe out sd-card data completely. Hide app icon from app launcher as well as recent category. Cannot uninstalling this virus app from application manager. Run in background continuously and gets restarted even after device is turned ON/OFF. Track the user's interaction by retrieving the applications that user has started. Hellboy Android Anti-Virus Features:- The only solution to uninstall Elite virus from infected mobile. It forces to uninstall, so that it can be made one time use only.
EduardTalianu / Eragan AI interaction tool with RAG hybrid search, conversation context, web content processing and structured data analysis with LLM / GPT
epigen / CellWhispererCellWhisperer bridges the gap between transcriptomics data and natural language, enabling intuitive interaction with scRNA-seq datasets
