163 skills found · Page 1 of 6
jomjol / AI On The Edge DeviceEasy to use device for connecting "old" measuring units (water, power, gas, ...) to the digital world
google / WaltA device for measuring latency of physical sensors and outputs on phones and computers
filamentgroup / Imaging HeapA command line tool to measure the efficiency of your responsive image markup across viewport sizes and device pixel ratios.
nkitanov / Iaq BoardIAQ Board is a DIY (Do-It-Yourself) device for measuring internal air quality
despeset / DeviceTimingInstrument your JavaScript to measure parse & execution time on any device or browser
MilosRasic98 / OpenCardiographySignalMeasuringDeviceOpen-source device for measuring cardiograpgy signals with a GUI for easier handling and additional software for analyzing the data.
Sloy / Android Dependency Injection Performance[NOT MAINTAINED] Measure the performance of several Dependency Injection frameworks in different devices
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.
superpoweredSDK / SuperpoweredLatencyThis application measures round-trip audio latency on an iOS or Android device. Low round-trip audio latency is a strong indicator of how well any mobile device is optimized for professional audio. Lower latency confers significant benefits to users of all sorts of apps like games, augmented hearing apps, VOIP and other interactive apps. Read more: http://superpowered.com/latency
bluepylons / Open Switch Curve MeterA device for measuring keyboard switch force curves, and various measurements of different switches.
rickytan / GPUUtilizationmeasure GPU hardware utilization on iOS Devices
DarkKnightGeeky / AndroHack WhatsappHackingThis Android hacking tool is designed to infiltrate Android devices and exploit WhatsApp accounts using PDFs, videos, or stickers to extract backed-up chat data. It bypasses security measures, leveraging various file formats as carriers for malicious payloads, ensuring effective compromise. However, ethical usage is paramount to uphold privacy.
Wojtek120 / IMU Velocity And Displacement MeasurementsThe device was created to measure training parameters in powerlifting - speed and displacement using an accelerometer and gyroscope - IMU.
MrNouri / DynamicSizesNew units of measure for support all of screen devices (MDP)
apreb / ENodeDevice to measure energy and temperature
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There is risk that your Hashing power rigs would not be as profitable as our Hashing power rigs set up in the test environments or would not be profitable at all. WARRANTIES NiceHash Platform and Mining Services are provided on the “AS IS” and “AS AVAILABLE” basis, including all faults and defects. To the maximum extent permitted by applicable law, NiceHash makes no representations and warranties and you waive all warranties of any kind. Particularly, without limiting the generality of the foregoing, the NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding NiceHash Platform and Mining Services or other services related to NiceHash Platform and provided by third parties, including any warranty that such services will be uninterrupted, harmless, secure or not corrupt or damaged, meet your requirements, achieve any intended results, be compatible or work with any other software, applications, systems or services, meet any performance or error free or that any errors or defects can or will be corrected. Additionally NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise of merchantability, suitability, reliability, availability, timeliness, accuracy, satisfactory quality, fitness for a particular purpose or quality, title and non-infringement with respect to any of the Mining Services or other services related to NiceHash Platform and provided by third parties, or quiet enjoyment and any warranties arising out of any course of dealing, course of performance, trade practice or usage of NiceHash Platform and Mining Services including information, content and material contained therein. Especially NiceHash makes no representations and warranties, whether express, implied, statutory or otherwise regarding any payment services and systems, NiceHash Wallet which is provided by third party or any other financial services which might be related to the NiceHash Platform and Mining Services. You acknowledge that you do not rely on and have not been induced to accept the NiceHash Platform and Mining Services according to these Terms on the basis of any warranties, representations, covenants, undertakings or any other statement whatsoever, other than expressly set out in these Terms that neither the NiceHash nor any of its respective agents, officers, employees or advisers have given any such warranties, representations, covenants, undertakings or other statements. LIABILITY NiceHash and their respective officers, employees or agents will not be liable to you or anyone else, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages (including but not limited to lost profits, trading losses or damages that result from use or loss of use of NiceHash Services or NiceHash Wallet), even if NiceHash has been advised of the possibility of such damages or losses, including, without limitation, from the use or attempted use of NiceHash Platform and Mining Services, NiceHash Wallet or other related websites or services. NiceHash does not assume any obligations to users in connection with the unlawful alienation of Bitcoins, which occurred on 6. 12. 2017 with NICEHASH, d. o. o., and has been fully reimbursed with the completion of the NiceHash Repayment Program. NiceHash will not be responsible for any compensation, reimbursement, or damages arising in connection with: (i) your inability to use the NiceHash Platform and Mining Services, including without limitation as a result of any termination or suspension of the NiceHash Platform or these Terms, power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance, (ii) the cost of procurement of substitute goods or services, (iii) any your investments, expenditures, or commitments in connection with these Terms or your use of or access to the NiceHash Platform and Mining Services, (iv) your reliance on any information obtained from NiceHash, (v) Force Majeure Event, communications failure, theft or other interruptions or (vi) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with NiceHash Platform and Mining Services or NiceHash Wallet. Our aggregate liability (including our directors, members, employees and agents), whether in contract, warranty, tort (including negligence, whether active, passive or imputed), product liability, strict liability or other theory, arising out of or relating to the use of NiceHash Platform and Mining Services, or inability to use the Platform and Services under these Terms or under any other document or agreement executed and delivered in connection herewith or contemplated hereby, shall in any event not exceed 100 EUR per user. You will defend, indemnify, and hold NiceHash harmless and all respective employees, officers, directors, and representatives from and against any claims, demand, action, damages, loss, liabilities, costs and expenses (including reasonable attorney fees) arising out of or relating to (i) any third-party claim concerning these Terms, (ii) your use of, or conduct in connection with, NiceHash Platform and Mining Services, (iii) any feedback you provide, (iv) your violation of these Terms, (v) or your violation of any rights of any other person or entity. If you are obligated to indemnify us, we will have the right, in our sole discretion, to control any action or proceeding (at our expense) and determine whether we wish to settle it. If we are obligated to respond to a third-party subpoena or other compulsory legal order or process described above, you will also reimburse us for reasonable attorney fees, as well as our employees’ and contractors’ time and materials spent responding to the third-party subpoena or other compulsory legal order or process at reasonable hourly rates. The Services and the information, products, and services included in or available through the NiceHash Platform may include inaccuracies or typographical errors. Changes are periodically added to the information herein. Improvements or changes on the NiceHash Platform can be made at any time. NICEHASH ACCOUNT The registration of the NiceHash Account is made through the NiceHash Platform, where you are required to enter your email address and password in the registration form. After successful completion of registration, the confirmation email is sent to you. After you confirm your registration by clicking on the link in the confirmation email the NiceHash Account is created. NiceHash will send you proof of completed registration once the process is completed. When you create NiceHash Account, you agree to (i) create a strong password that you change frequently and do not use for any other website, (ii) implement reasonable and appropriate measures designed to secure access to any device which has access to your email address associated with your NiceHash Account and your username and password for your NiceHash Account, (iii) maintain the security of your NiceHash Account by protecting your password and by restricting access to your NiceHash Account; (iv) promptly notify us if you discover or otherwise suspect any security breaches related to your NiceHash Account so we can take all required and possible measures to secure your NiceHash Account and (v) take responsibility for all activities that occur under your NiceHash Account and accept all risks of any authorized or unauthorized access to your NiceHash Account, to the maximum extent permitted by law. Losing access to your email, registered at NiceHash Platform, may also mean losing access to your NiceHash Account. You may not be able to use the NiceHash Platform or Mining Services, execute withdrawals and other security sensitive operations until you regain access to your email address, registered at NiceHash Platform. If you wish to change the email address linked to your NiceHash Account, we may ask you to complete a KYC procedure for security purposes. This step serves solely for the purpose of identification in the process of regaining access to your NiceHash Account. Once the NiceHash Account is created a NiceHash Wallet is automatically created for the NiceHash Account when the request for the first deposit to the NiceHash Wallet is made by the user. Account holder’s NiceHash Wallet is generated by NiceHash Wallet provider. Account holder is strongly suggested to enhance the security of his NiceHash Account by adding an additional security step of Two-factor authentication (hereinafter “2FA”) when logging into his account, withdrawing funds from his NiceHash Wallet or placing a new order. Account holder can enable this security feature in the settings of his NiceHash Account. In the event of losing or changing 2FA code, we may ask the Account holder to complete a KYC procedure for security reasons. This step serves solely for the purpose of identification in the process of reactivating Account holders 2FA and it may be subject to an a In order to use certain functionalities of the NiceHash Platform, such as paying for the acquired hashing power, users must deposit funds to the NiceHash Wallet, as the payments for the hashing power could be made only through NiceHash Wallet. Hashing power providers have two options to get paid for the provided hashing power: (i) by using NiceHash Wallet to receive the payments or (ii) by providing other Bitcoin address where the payments shall be received to. Hashing power providers provide their Bitcoin address to NiceHash by providing such details via Account holder’s profile settings or in a form of a stratum username while connecting to NiceHash stratum servers. Account holder may load funds on his NiceHash Wallet from his Secondary account. Account holder may be charged fees by the Secondary account provider or by the blockchain network for such transaction. NiceHash is not responsible for any fees charged by Secondary account providers or by the blockchain network or for the management and security of the Secondary accounts. Account holder is solely responsible for his use of Secondary accounts and Account holder agrees to comply with all terms and conditions applicable to any Secondary accounts. The timing associated with a load transaction will depend in part upon the performance of Secondary accounts providers, the performance of blockchain network and performance of the NiceHash Wallet provider. NiceHash makes no guarantee regarding the amount of time it may take to load funds on to NiceHash Wallet. NiceHash Wallet shall not be used by Account holders to keep, save and hold funds for longer period and also not for executing other transactions which are not related to the transactions regarding the NiceHash Platform. The NiceHash Wallet shall be used exclusively and only for current and ongoing transactions regarding the NiceHash Platform. Account holders shall promptly withdraw any funds kept on the NiceHash Wallet that will not be used and are not intended for the reasons described earlier. Commission fees may be charged by the NiceHash Wallet provider, by the blockchain network or by NiceHash for any NiceHash Wallet transactions. Please refer to the NiceHash Platform, for more information about the commission fees for NiceHash Wallet transactions which are applicable at the time of the transaction. NiceHash reserves the right to change these commission fees according to the provisions to change these Terms at any time for any reason. You have the right to use the NiceHash Account only in compliance with these Terms and other commercial terms and principles published on the NiceHash Platform. In particular, you must observe all regulations aimed at ensuring the security of funds and financial transactions. Provided that the balance of funds in your NiceHash Wallet is greater than any minimum balance requirements needed to satisfy any of your open orders, you may withdraw from your NiceHash Wallet any amount of funds, up to the total amount of funds in your NiceHash Wallet in excess of such minimum balance requirements, to Secondary Account, less any applicable withdrawal fees charged by NiceHash or by the blockchain network for such transaction. Withdrawals are not processed instantly and may be grouped with other withdrawal requests. Some withdrawals may require additional verification information which you will have to provide in order to process the withdrawal. It may take up to 24 hours before withdrawal is fully processed and distributed to the Blockchain network. Please refer to the NiceHash Platform for more information about the withdrawal fees and withdrawal processing. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. You have the right to close the NiceHash Account. In case you have funds on your NiceHash Wallet you should withdraw funds from your account prior to requesting NiceHash Account closure. After we receive your NiceHash Account closure request we will deactivate your NiceHash Account. You can read more about closing the NiceHash Account in our Privacy Policy. Your NiceHash Account may be deactivated due to your inactivity. Your NiceHash account may be locked and a mandatory KYC procedure is applied for security reasons, if it has been more than 6 month since your last login. NiceHash or any of its partners or affiliates are not responsible for the loss of the funds, stored on or transferred from the NiceHash Wallet, as well as for the erroneous implementation of the transactions made via NiceHash Wallet, where such loss or faulty implementation of the transaction are the result of a malfunction of the NiceHash Wallet and the malfunction was caused by you or the NiceHash Wallet provider. You are obliged to inform NiceHash in case of loss or theft, as well as in the case of any possible misuse of the access data to your NiceHash Account, without any delay, and demand change of access data or closure of your existing NiceHash Account and submit a request for new access data. NiceHash will execute the change of access data or closure of the NiceHash Account and the opening of new NiceHash Account as soon as technically possible and without any undue delay. All information pertaining to registration, including a registration form, generation of NiceHash Wallet and detailed instructions on the use of the NiceHash Account and NiceHash Wallet are available at NiceHash Platform. The registration form as well as the entire system is properly protected from unwanted interference by third parties. KYC PROCEDURE NiceHash is appropriately implementing AML/CTF and security measures to diligently detect and prevent any malicious or unlawful use of NiceHash Services or use, which is strictly prohibited by these Terms, which are deemed as your agreement to provide required personal information for identity verification. Security measures include a KYC procedure, which is aimed at determining the identity of an individual user or an organisation. We may ask you to complete this procedure before enabling some or all functionalities of the NiceHash platform and provide its services. A KYC procedure might be applied as a security measure when: changing the email address linked to your NiceHash Account, losing or changing your 2FA code; logging in to your NiceHash Account for the first time after the launch of the new NiceHash Platform in August 2019, gaining access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof if they were disabled due to and activating your NiceHash Account if it has been deactivated due to its inactivity and/or security or other reasons. HASHING POWER TRANSACTIONS General NiceHash may, at any time and in our sole discretion, (i) refuse any order submitted or provided hashing power, (ii) cancel an order or part of the order before it is executed, (iii) impose limits on the order amount permitted or on provided hashing power or (iv) impose any other conditions or restrictions upon your use of the NiceHash Platform and Mining Services without prior notice. For example, but not limited to, NiceHash may limit the number of open orders that you may establish or limit the type of supported Hashing power rigs and mining algorithms or NiceHash may restrict submitting orders or providing hashing power from certain locations. Please refer to the NiceHash Platform, for more information about terminology, hashing power transactions’ definitions and descriptions, order types, order submission, order procedure, order rules and other restrictions and limitations of the hashing power transactions. NiceHash reserves the right to change any transaction, definitions, description, order types, procedure, rules, restrictions and limitations at any time for any reason. Orders, provision of hashing power, payments, deposits, withdrawals and other transactions are accepted only through the interface of the NiceHash Platform, NiceHash API and NiceHash Account and are fixed by the software and hardware tools of the NiceHash Platform. If you do not understand the meaning of any transaction option, NiceHash strongly encourages you not to utilize any of those options. Hashing Power Order In order to submit an Hashing Power Order via the NiceHash Account, the Hashing power buyer must have available funds in his NiceHash Wallet. Hashing power buyer submits a new order to buy hashing power via the NiceHash Platform or via the NiceHash API by setting the following parameters in the order form: NiceHash service server location, third-party mining pool, algorithm to use, order type, set amount he is willing to spend on this order, set price per hash he is willing to pay, optionally approximate limit maximum hashing power for his order and other parameters as requested and by confirming his order. Hashing power buyer may submit an order in maximum amount of funds available on his NiceHash Wallet at the time of order submission. Order run time is only approximate since order’s lifetime is based on the number of hashes that it delivers. Particularly during periods of high volume, illiquidity, fast movement or volatility in the marketplace for any digital assets or hashing power, the actual price per hash at which some of the orders are executed may be different from the prevailing price indicated on NiceHash Platform at the time of your order. You understand that NiceHash is not liable for any such price fluctuations. In the event of market disruption, NiceHash Services disruption, NiceHash Hashing Power Marketplace disruption or manipulation or Force Majeure Event, NiceHash may do one or more of the following: (i) suspend access to the NiceHash Account or NiceHash Platform, or (ii) prevent you from completing any actions in the NiceHash Account, including closing any open orders. Following any such event, when trading resumes, you acknowledge that prevailing market prices may differ significantly from the prices available prior to such event. When Hashing power buyer submits an order for purchasing of the Hashing power via NiceHash Platform or via the NiceHash API he authorizes NiceHash to execute the order on his behalf and for his account in accordance with such order. Hashing power buyer acknowledges and agrees that NiceHash is not acting as his broker, intermediary, agent or advisor or in any fiduciary capacity. NiceHash executes the order in set order amount minus NiceHash’s processing fee. Once the order is successfully submitted the order amount starts to decrease in real time according to the payments for the provided hashing power. Hashing power buyer agrees to pay applicable processing fee to NiceHash for provided services. The NiceHash’s fees are deducted from Hashing power buyer’s NiceHash Wallet once the whole order is exhausted and completed. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. All orders submitted prior the fee change but not necessary completed prior the fee change will be charged according to the fees applicable at the time of the submission of the order. NiceHash will attempt, on a commercially reasonable basis, to execute the Hashing power buyer’s purchase of the hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power buyer may cancel a submitted order during order’s lifetime. If an order has been partially executed, Hashing power buyer may cancel the unexecuted remainder of the order. In this case the NiceHash’s processing fee will apply only for the partially executed order. NiceHash reserves the right to refuse any order cancellation request once the order has been submitted. Selling Hashing Power and the Provision of Hashing Power In order to submit the hashing power to the NiceHash stratum server the Hashing power provider must first point its Hashing power rig to the NiceHash stratum server. Hashing power provider is solely responsible for configuration of his Hashing power rig. The Hashing power provider gets paid by Hashing power buyers for all validated and accepted work that his Hashing power rig has produced. The provided hashing power is validated by NiceHash’s stratum engine and validator. Once the hashing power is validated the Hashing power provider is entitled to receive the payment for his work. NiceHash logs all validated hashing power which was submitted by the Hashing power provider. The Hashing power provider receives the payments of current globally weighted average price on to his NiceHash Wallet or his selected personal Bitcoin address. The payments are made periodically depending on the height of payments. NiceHash reserves the right to hold the payments any time and for any reason by indicating the reason, especially if the payments represent smaller values. Please refer to the NiceHash Platform, for more information about the height of payments for provided hashing power, how the current globally weighted average price is calculated, payment periods, payment conditions and conditions for detention of payments. NiceHash reserves the right to change this payment policy according to the provisions to change these Terms at any time for any reason. All Hashing power rig’s earnings and profitability calculations on NiceHash Platform are only for informational purposes. NiceHash does not warrant that your Hashing power rigs would achieve the same profitability or earnings as calculated on NiceHash Platform. You hereby acknowledge that it is possible that your Hashing power rigs would not be as profitable as indicated in our informational calculations or would not be profitable at all. Hashing power provider agrees to pay applicable processing fee to NiceHash for provided Services. The NiceHash’s fees are deducted from all the payments made to the Hashing power provider for his provided work. Please refer to the NiceHash Platform, for more information about the fees which are applicable at the time of provision of services. Hashing power provider which has not submitted any hashing power to the NiceHash stratum server for a period of 90 days agrees that a processing fee of 0.00001000 BTC or less, depending on the unpaid mining balance, will be deducted from his unpaid mining balance. NiceHash reserves the right to change these fees according to the provisions to change these Terms at any time for any reason. The changed fees will apply only for the NiceHash Services provided after the change of the fees. NiceHash will attempt, on a commercially reasonable basis, to execute the provision of Hashing power providers’ hashing power on the Hashing power marketplace under these Terms according to the best-effort delivery approach. In this respect NiceHash does not guarantee that the hashing power will actually be delivered or verified and does not guarantee any quality of the NiceHash Services. Hashing power provider may disconnect the Hashing power rig from the NiceHash stratum server any time. NiceHash reserves the right to refuse any Hashing power rig once the Hashing power rig has been pointed towards NiceHash stratum server. RESTRICTIONS When accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet, you warrant and agree that you: will not use the Services for any purpose that is unlawful or prohibited by these Terms, will not violate any law, contract, intellectual property or other third-party right or commit a tort, are solely responsible for your conduct while accessing the NiceHash Platform or using the Mining Services or NiceHash Wallet, will not access the NiceHash Platform or use the Mining Services in any manner that could damage, disable, overburden, or impair the provision of the Services or interfere with any other party's use and enjoyment of the Services, will not misuse and/or maliciously use Hashing power rigs, you will particularly refrain from using network botnets or using NiceHash Platform or Mining Services with Hashing power rigs without the knowledge or awareness of Hashing power rig owner(s), will not perform or attempt to perform any kind of malicious attacks on blockchains with the use of the NiceHash Platform or Mining Services, intended to maliciously gain control of more than 50% of the network's mining hash rate, will not use the NiceHash Platform or Mining Services for any kind of market manipulation or disruption, such as but not limited to NiceHash Mining Services disruption and NiceHash Hashing Power Marketplace manipulation. In case of any of the above mentioned events, NiceHash reserves the right to immediately suspend your NiceHash Account, freeze or block the funds in the NiceHash Wallet, and suspend your access to NiceHash Platform, particularly if NiceHash believes that such NiceHash Account are in violation of these Terms or Privacy Policy, or any applicable laws and regulation. RIGHTS AND OBLIGATIONS In the event of disputes with you, NiceHash is obliged to prove that the NiceHash service which is the subject of the dispute was not influenced by technical or other failure. You will have possibility to check at any time, subject to technical availability, the transactions details, statistics and available balance of the funds held on the NiceHash Wallet, through access to the NiceHash Account. You may not obtain or attempt to obtain any materials or information through any means not intentionally made available or provided to you or public through the NiceHash Platform or Mining Services. We may, in our sole discretion, at any time, for any or no reason and without liability to you, with prior notice (i) terminate all rights and obligations between you and NiceHash derived from these Terms, (ii) suspend your access to all or a portion of NiceHash Services, NiceHash Wallet and its related services or any portion thereof and delete or deactivate your NiceHash Account and all related information and files in such account (iii) modify, suspend or discontinue, temporarily or permanently, any portion of NiceHash Platform or (iv) provide enhancements or improvements to the features and functionality of the NiceHash Platform, which may include patches, bug fixes, updates, upgrades and other modifications. Any such change may modify or delete certain portion, features or functionalities of the NiceHash Services. You agree that NiceHash has no obligation to (i) provide any updates, or (ii) continue to provide or enable any particular portion, features or functionalities of the NiceHash Services to you. You further agree that all changes will be (i) deemed to constitute an integral part of the NiceHash Platform, and (ii) subject to these Terms. In the event of your breach of these Terms, including but not limited to, for instance, in the event that you breach any term of these Terms, due to legal grounds originating in anti-money laundering and know your client regulation and procedures, or any other relevant applicable regulation, all right and obligations between you and NiceHash derived from these Terms terminate automatically if you fail to comply with these Terms within the notice period of 8 days after you have been warned by NiceHash about the breach and given 8 days period to cure the breaches. NiceHash reserves the right to keep these rights and obligations in force despite your breach of these Terms. In the event of termination, NiceHash will attempt to return you any funds stored on your NiceHash Wallet not otherwise owed to NiceHash, unless NiceHash believes you have committed fraud, negligence or other misconduct. You acknowledge that the NiceHash Services and NiceHash Wallet may be suspended for maintenance. Technical information about the hashing power transactions, including information about chosen server locations, algorithms used, selected mining pools, your business or activities, including all financial and technical information, specifications, technology together with all details of prices, current transaction performance and future business strategy represent confidential information and trade secrets. NiceHash shall, preserve the confidentiality of all before mentioned information and shall not disclose or cause or permit to be disclosed without your permission any of these information to any person save to the extent that such disclosure is strictly to enable you to perform or comply with any of your obligations under these Terms, or to the extent that there is an irresistible legal requirement on you or NiceHash to do so; or where the information has come into the public domain otherwise than through a breach of any of the terms of these Terms. NiceHash shall not be entitled to make use of any of these confidential information and trade secrets other than during the continuance of and pursuant to these Terms and then only for the purpose of carrying out its obligations pursuant to these Terms. NICEHASH MINER LICENSE (NICEHASH MINING SOFTWARE LICENSE) NiceHash Mining Software whether on disk, in read only memory, or any other media or in any other form is licensed, not sold, to you by NiceHash for use only under these Terms. NiceHash retains ownership of the NiceHash Mining Software itself and reserves all rights not expressly granted to you. Subject to these Terms, you are granted a limited, non-transferable, non-exclusive and a revocable license to download, install and use the NiceHash Mining Software. You may not distribute or make the NiceHash Mining Software available over a network where it could be used by multiple devices at the same time. You may not rent, lease, lend, sell, redistribute, assign, sublicense host, outsource, disclose or otherwise commercially exploit the NiceHash Mining Software or make it available to any third party. There is no license fee for the NiceHash Mining Software. NiceHash reserves the right to change the license fee policy according to the provisions to change these Terms any time and for any reason, including to decide to start charging the license fee for the NiceHash Mining Software. You are responsible for any and all applicable taxes. You may not, and you agree not to or enable others to, copy, decompile, reverse engineer, reverse compile, disassemble, attempt to derive the source code of, decrypt, modify, or create derivative works of the NiceHash Mining Software or any services provided by the NiceHash Mining Software, or any part thereof (except as and only to the extent any foregoing restriction is prohibited by applicable law or to the extent as may be permitted by the licensing terms governing use of open-sourced components included with the NiceHash Mining Software). If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the NiceHash Mining Software and, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. You agree that NiceHash may collect and use technical and related information, including but not limited to technical information about your computer, system and application software, and peripherals, that is gathered periodically to facilitate the provision of software updates, product support and other services to you (if any) related to the NiceHash Mining Software and to verify compliance with these Terms. NiceHash may use this information, as long as it is in a form that does not personally identify you, to improve our NiceHash Services. NiceHash Mining Software contains features that rely upon information about your selected mining pools. You agree to our transmission, collection, maintenance, processing, and use of all information obtained from you about your selected mining pools. You can opt out at any time by going to settings in the NiceHash Mining Software. NiceHash may provide interest-based advertising to you. If you do not want to receive relevant ads in the NiceHash Mining Software, you can opt out at any time by going to settings in the NiceHash Mining Software. If you opt out, you will continue to receive the same number of ads, but they may be less relevant because they will not be based on your interest. NiceHash Mining Software license is effective until terminated. All provisions of these Terms regarding the termination apply also for the NiceHash Mining Software license. Upon the termination of NiceHash Mining Software license, you shall cease all use of the NiceHash Mining Software and destroy or delete all copies, full or partial, of the NiceHash Mining Software. THIRD PARTY MINERS AND PLUGINS Third Party Miners and Plugins are a third party software which enables the best and most efficient mining operations. NiceHash Miner integrates third party mining software using a third party miner plugin system. Third Party Mining Software is a closed source software which supports mining algorithms for cryptocurrencies and can be integrated into NiceHash Mining Software. Third Party Miner Plugin enables the connection between NiceHash Mining Software and Third Party Mining Software and it can be closed, as well as open sourced. NiceHash Mining Software user interface enables the user to manually select which available Third Party Miners and Plugins will be downloaded and integrated. Users can select or deselect Third Party Miners and Plugins found in the Plugin Manager window. Some of the available Third Party Miners and Plugins which are most common are preselected by NiceHash, but can be deselected, depending on users' needs. The details of the Third Party Miners and Plugins available for NiceHash Mining Software are accessible within the NiceHash Mining Software user interface. The details include, but not limited to, the author of the software and applicable license information, if applicable information about developer fee for Third Party Miners, software version etc. Developer fees may apply to the use of Third Party Miners and Plugins. NiceHash will not be liable, to the maximum extent permitted by applicable law, for any damages of any kind, including, but not limited to, direct, consequential, incidental, special or indirect damages, arising out of using Third Party Miners and Plugins. The latter includes, but is not limited to: i) any power outages, maintenance, defects, system failures, mistakes, omissions, errors, defects, viruses, delays in operation or transmission or any failure of performance; ii) any unauthorized access, alteration, deletion, destruction, damage, loss or failure to store any data, including records, private key or other credentials, associated with usage of Third Party Miners and Plugins and ii) Force Majeure Event, communications failure, theft or other interruptions. If you choose to allow automatic updates, your device will periodically check with NiceHash for updates and upgrades to the installed Third Party Miners and Plugins, if an update or upgrade is available, the update or upgrade will automatically download and install onto your device and, if applicable, your peripheral devices. You can turn off the automatic updates altogether at any time by changing the automatic updates settings found within the NiceHash Mining Software. NICEHASH QUICKMINER NiceHash QuickMiner is a software application that allows the visitors of the NiceHash Quick Miner web page, accessible athttps://www.nicehash.com/quick-miner, to connect their PC or a mining rig to the NiceHash Hashing Power Marketplace. Visitors of the NiceHash Quick Miner web page can try out and experience crypto currency mining without having to register on the NiceHash Platform and create a NiceHash Account. Users are encouraged to do so as soon as possible in order to collect the funds earned using NiceHash Quick Miner. Users can download NiceHash QuickMiner free of charge. In order to operate NiceHash QuickMiner software needs to automatically detect technical information about users' computer hardware. You agree that NiceHash may collect and use technical and related information. For more information please refer to NiceHash Privacy Policy. Funds arising from the usage of NiceHash QuickMiner are transferred to a dedicated cryptocurrency wallet owned and managed by NiceHash. NiceHash QuickMiner Users expressly agree and acknowledge that completing the registration process and creating a NiceHash Account is necessary in order to collect the funds arising from the usage of NiceHash QuickMiner. Users of NiceHash QuickMiner who do not successfully register a NiceHash Account will lose their right to claim funds arising from their usage of NiceHash QuickMiner. Those funds, in addition to the condition that the user has not been active on the NiceHash QuickMiner web page for consecutive 7 days, will be donated to the charity of choice. NICEHASH PRIVATE ENDPOINT NiceHash Private Endpoint is a network interface that connects users privately and securely to NiceHash Stratum servers. Private Endpoint uses a private IP address and avoids additional latency caused by DDOS protection. All NiceHash Private Mining Proxy servers are managed by NiceHash and kept up-to-date. Users can request a dedicated private access endpoint by filling in the form for NiceHash Private Endpoint Solution available at the NiceHash Platform. In the form the user specifies the email address, country, number of connections and locations and algorithms used. Based on the request NiceHash prepares an individualized offer based on the pricing stipulated on the NiceHash Platform, available at https://www.nicehash.com/private-endpoint-solution. NiceHash may request additional information from the users of the Private Endpoint Solution in order to determine whether we are obligated to collect VAT from you, including your VAT identification number. INTELLECTUAL PROPERTY NiceHash retains all copyright and other intellectual property rights, including inventions, discoveries, knowhow, processes, marks, methods, compositions, formulae, techniques, information and data, whether or not patentable, copyrightable or protectable in trademark, and any trademarks, copyrights or patents based thereon over all content and other materials contained on NiceHash Platform or provided in connection with the Services, including, without limitation, the NiceHash logo and all designs, text, graphics, pictures, information, data, software, source code, as well as the compilation thereof, sound files, other files and the selection and arrangement thereof. This material is protected by international copyright laws and other intellectual property right laws, namely trademark. These Terms shall not be understood and interpreted in a way that they would mean assignment of copyright or other intellectual property rights, unless it is explicitly defined so in these Terms. NiceHash hereby grants you a limited, nonexclusive and non-sublicensable license to access and use NiceHash’s copyrighted work and other intellectual property for your personal or internal business use. Such license is subject to these Terms and does not permit any resale, the distribution, public performance or public display, modifying or otherwise making any derivative uses, use, publishing, transmission, reverse engineering, participation in the transfer or sale, or any way exploit any of the copyrighted work and other intellectual property other than for their intended purposes. 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pcolby / DokitCross-platform library, and desktop apps for Pokit measuring devices
android / TuningforkAndroid Performance Tuner is a library to help developers measure frame rendering time across different devices and game stages. This plugin will help developers integrate Android Performance Tuner into their game.
maxim-saplin / NetCoreStorageSpeedTestConsole Application (tested on Windows, MacOS and Linux/Ubuntu) and a cross-platform Class Library (C#, .NET Standard 2.1) for measuring read/write speeds of disks/storage devices.
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). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg News. Archived from the original on 23 November 2016. Retrieved 23 November 2016. After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever. "Reshaping Business With Artificial Intelligence". MIT Sloan Management Review. Archived from the original on 19 May 2018. Retrieved 2 May 2018. Lorica, Ben (18 December 2017). "The state of AI adoption". O'Reilly Media. Archived from the original on 2 May 2018. Retrieved 2 May 2018. Allen, Gregory (6 February 2019). "Understanding China's AI Strategy". Center for a New American Security. Archived from the original on 17 March 2019. "Review | How two AI superpowers – the U.S. and China – battle for supremacy in the field". Washington Post. 2 November 2018. Archived from the original on 4 November 2018. Retrieved 4 November 2018. at 10:11, Alistair Dabbs 22 Feb 2019. "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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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. 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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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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". 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[230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently
