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vslavik / Diff PdfA simple tool for visually comparing two PDF files
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.
red6 / PdfcompareA simple Java library to compare two PDF files
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gtags", " gtags-cscope", " htags", " htags-server", "glulxe", " glulxe", "gmic", " gmic", "gnuchess", " gnuchess", "gnugo", " gnugo", "gnuit", " .gitaction", " gitaction", " gitdpkgname", " gitfm", " gitkeys", " gitmkdirs", " gitmount", " gitps", " gitregrep", " gitrfgrep", " gitrgrep", " gitunpack", " gitview", " gitwhich", " gitwipe", " gitxgrep", "gnupg", " gpg", " gpg-zip", " gpgsplit", "gnupg2", " addgnupghome", " applygnupgdefaults", " gpg-agent", " gpg-connect-agent", " gpg2", " gpgconf", " gpgparsemail", " gpgscm", " gpgsm", " gpgtar", " gpgv2", " kbxutil", " watchgnupg", "gnuplot", " gnuplot", "gnushogi", " gnushogi", "gnutls", " certtool", " gnutls-cli", " gnutls-cli-debug", " gnutls-serv", " ocsptool", " psktool", " srptool", "golang", " go", " gofmt", "gperf", " gperf", "gpgme", " gpgme-tool", "gpgme-dev", " gpgme-config", "gpgv", " gpgv", "gpsbabel", " gpsbabel", "graphicsmagick", " gm", "graphviz", " acyclic", " bcomps", " ccomps", " circo", " cluster", " diffimg", " dijkstra", " dot", " dot2gxl", " dot_builtins", " edgepaint", " fdp", " gc", " gml2gv", " graphml2gv", " gv2gml", " gv2gxl", " gvcolor", " gvgen", " gvmap", " gvmap.sh", " gvpack", " gvpr", " gxl2dot", " gxl2gv", " mm2gv", " neato", " nop", " osage", " patchwork", " prune", " sccmap", " sfdp", " tred", " twopi", " unflatten", "greed", " greed", "grep", " egrep", " fgrep", " grep", "gst-plugins-base", " gst-device-monitor-1.0", " gst-discoverer-1.0", " gst-play-1.0", "gstreamer", " gst-inspect-1.0", " gst-launch-1.0", " gst-stats-1.0", " gst-typefind-1.0", "gtypist", " gtypist", "gzip", " gunzip", " gzexe", " gzip", " uncompress", " zcat", " zcmp", " zdiff", " zegrep", " zfgrep", " zforce", " zgrep", " zless", " zmore", " znew", "harfbuzz-utils", " hb-ot-shape-closure", " hb-shape", " hb-view", "hashdeep", " hashdeep", " md5deep", " sha1deep", " sha256deep", " tigerdeep", " whirlpooldeep", "hexcurse", " hexcurse", "heyu", " heyu", "hfsutils", " hattrib", " hcd", " hcopy", " hdel", " hdir", " hformat", " hfsutil", " hls", " hmkdir", " hmount", " hpwd", " hrename", " hrmdir", " humount", " hvol", "htop", " htop", "httping", " httping", "hub", " hub", "hunspell", " affixcompress", " analyze", " chmorph", " hunspell", " hunzip", " hzip", " ispellaff2myspell", " makealias", " munch", " unmunch", " wordforms", " wordlist2hunspell", "hydra", " dpl4hydra.sh", " hydra", " hydra-wizard.sh", " pw-inspector", "icecast", " icecast", "iconv", " iconv", "icu-devtools", " derb", " escapesrc", " genbrk", " genccode", " gencfu", " gencmn", " gencnval", " gendict", " gennorm2", " genrb", " gensprep", " icu-config", " icuinfo", " icupkg", " makeconv", " pkgdata", " uconv", "imagemagick", " animate", " compare", " composite", " conjure", " convert", " display", " identify", " import", " magick", " magick-script", " mogrify", " montage", " stream", "imgflo", " imgflo", " imgflo-graphinfo", " imgflo-runtime", "inetutils", " dnsdomainname", " ftp", " logger", " telnet", " tftp", " whois", "inotify-tools", " inotifywait", " inotifywatch", "iperf3", " iperf3", "ired", " bdiff", " ired", " vired", "irssi", " irssi", "jhead", " jhead", "joe", " jmacs", " joe", " jpico", " jstar", " rjoe", "jq", " jq", "jupp", " jmacs", " joe", " jpico", " jstar", " jupp", " rjoe", "kona", " k", "krb5", " compile_et", " gss-client", " gss-server", " k5srvutil", " kadmin", " kadmin.local", " kadmind", " kdb5_util", " kdestroy", " kinit", " klist", " kpasswd", " kprop", " kpropd", " kproplog", " krb5-config", " krb5-send-pr", " krb5kdc", " ksu", " kswitch", " ktutil", " kvno", " sclient", " sim_client", " sim_server", " sserver", " uuclient", " uuserver", "ldc", " dub", " ldc-build-runtime", " ldc2", " ldmd2", " rdmd", "ldns-dev", " ldns-config", "ledger", " ledger", "leptonica", " convertfilestopdf", " convertfilestops", " convertformat", " convertsegfilestopdf", " convertsegfilestops", " converttopdf", " converttops", " fileinfo", " xtractprotos", "less", " less", " lessecho", " lesskey", "lftp", " lftp", " lftpget", "libassuan-dev", " libassuan-config", "libcaca", " caca-config", " cacaclock", " cacademo", " cacafire", " cacaplay", " cacaserver", " cacaview", " img2txt", "libcairo", " cairo-trace", "libcroco", " croco-0.6-config", " csslint-0.6", "libcurl-dev", " curl-config", "libgcrypt", " dumpsexp", " hmac256", " libgcrypt-config", " mpicalc", "libgd", " annotate", " bdftogd", " gd2copypal", " gd2togif", " gd2topng", " gdcmpgif", " gdlib-config", " gdparttopng", " gdtopng", " giftogd2", " pngtogd", " pngtogd2", " webpng", "libgpg-error", " gpg-error", " gpg-error-config", "libgrpc", " check_epollexclusive", " gen_hpack_tables", " gen_legal_metadata_characters", " gen_percent_encoding_tables", " grpc_cpp_plugin", " grpc_create_jwt", " grpc_csharp_plugin", " grpc_node_plugin", " grpc_objective_c_plugin", " grpc_php_plugin", " grpc_print_google_default_creds_token", " grpc_python_plugin", " grpc_ruby_plugin", " grpc_verify_jwt", "libjasper-utils", " imgcmp", " imginfo", " jasper", "libjpeg-turbo-progs", " cjpeg", " djpeg", " jpegtran", " rdjpgcom", " tjbench", " wrjpgcom", "libksba-dev", " ksba-config", "libnpth", " npth-config", "liboggz", " oggz", " oggz-chop", " oggz-codecs", " oggz-comment", " oggz-diff", " oggz-dump", " oggz-info", " oggz-known-codecs", " oggz-merge", " oggz-rip", " oggz-scan", " oggz-sort", " oggz-validate", "libotr", " otr_mackey", " otr_modify", " otr_parse", " otr_readforge", " otr_remac", " otr_sesskeys", "libpng-dev", " libpng-config", " libpng16-config", "librsvg", " rsvg-convert", "libtiff-utils", " fax2ps", " fax2tiff", " pal2rgb", " ppm2tiff", " raw2tiff", " tiff2bw", " tiff2pdf", " tiff2ps", " tiff2rgba", " tiffcmp", " tiffcp", " tiffcrop", " tiffdither", " tiffdump", " tiffinfo", " tiffmedian", " tiffset", " tiffsplit", "libtool", " libtool", " libtoolize", "libxapian-dev", " xapian-config", "libxml2-dev", " xml2-config", "libxml2-utils", " xmlcatalog", " xmllint", "libxslt-dev", " xslt-config", "libzmq", " curve_keygen", "lighttpd", " lighttpd", "littlecms-utils", " jpgicc", " linkicc", " psicc", " tificc", " transicc", "lld", " ld.lld", " lld", " lld-link", "llvm", " llc", " lli", " llvm-ar", " llvm-as", " llvm-bcanalyzer", " llvm-c-test", " llvm-cat", " llvm-config", " llvm-cov", " llvm-cvtres", " llvm-cxxdump", " llvm-cxxfilt", " llvm-diff", " llvm-dis", " llvm-dlltool", " llvm-dsymutil", " llvm-dwarfdump", " llvm-dwp", " llvm-extract", " llvm-lib", " llvm-link", " llvm-lto", " llvm-lto2", " llvm-mc", " llvm-mcmarkup", " llvm-modextract", " llvm-mt", " llvm-nm", " llvm-objdump", " llvm-opt-report", " llvm-pdbutil", " llvm-profdata", " llvm-ranlib", " llvm-readelf", " llvm-readobj", " llvm-rtdyld", " llvm-size", " llvm-split", " llvm-stress", " llvm-strings", " llvm-symbolizer", " llvm-xray", " obj2yaml", " opt", " verify-uselistorder", " yaml2obj", "ltrace", " ltrace", "lua", " lua", " luac", "lynx", " lynx", "lzip", " lzip", "lzop", " lzop", "m4", " m4", "macchanger", " macchanger", "make", " make", "man", " apropos", " demandoc", " makewhatis", " man", " mandoc", " soelim", " whatis", "mariadb", " aria_chk", " aria_dump_log", " aria_ftdump", " aria_pack", " aria_read_log", " innochecksum", " mariadb_config", " msql2mysql", " my_print_defaults", " myisam_ftdump", " myisamchk", " myisamlog", " myisampack", " mysql", " mysql_client_test", " mysql_config", " mysql_convert_table_format", " mysql_find_rows", " mysql_fix_extensions", " mysql_install_db", " mysql_plugin", " mysql_secure_installation", " mysql_setpermission", " mysql_tzinfo_to_sql", " mysql_upgrade", " mysql_waitpid", " mysqlaccess", " mysqladmin", " mysqlbinlog", " mysqlcheck", " mysqld", " mysqld_multi", " mysqld_safe", " mysqld_safe_helper", " mysqldump", " mysqldumpslow", " mysqlhotcopy", " mysqlimport", " mysqlshow", " mysqlslap", " mytop", " perror", " replace", " resolve_stack_dump", " resolveip", "mathomatic", " mathomatic", "mc", " mc", " mcdiff", " mcedit", " mcview", "megatools", " megacopy", " megadf", " megadl", " megaget", " megals", " megamkdir", " megaput", " megareg", " megarm", "memcached", " memcached", "micro", " micro", "minicom", " ascii-xfr", " minicom", " runscript", " xminicom", "mlocate", " locate", " updatedb", "moon-buggy", " moon-buggy", "moria", " moria", "mosh", " mosh", " mosh-client", " mosh-server", " mosh.pl", "mosquitto", " mosquitto", " mosquitto_passwd", " mosquitto_pub", " mosquitto_sub", "mp3splt", " mp3splt", "mpc", " mpc", "mpd", " mpd", "mpv", " mpv", "mtools", " amuFormat.sh", " lz", " mattrib", " mbadblocks", " mcat", " mcd", " mcheck", " mclasserase", " mcomp", " mcopy", " mdel", " mdeltree", " mdir", " mdu", " mformat", " minfo", " mkmanifest", " mlabel", " mmd", " mmount", " mmove", " mpartition", " mrd", " mren", " mshortname", " mshowfat", " mtools", " mtoolstest", " mtype", " mxtar", " mzip", " tgz", " uz", "multitail", " multitail", "mutt", " mutt", " pgpewrap", " pgpring", " smime_keys", "nano", " nano", "ncdc", " ncdc", "ncdu", " ncdu", "ncmpcpp", " ncmpcpp", "ncurses-dev", " ncursesw6-config", "ncurses-utils", " clear", " infocmp", " reset", " tabs", " tic", " toe", " tput", " tset", "ne", " ne", "neovim", " nvim", "net-tools", " arp", " ifconfig", " ipmaddr", " iptunnel", " mii-tool", " nameif", " netstat", " plipconfig", " rarp", " route", " slattach", "netcat", " nc", " ncat", " netcat", "netpbm", " 411toppm", " anytopnm", " asciitopgm", " atktopbm", " avstopam", " bioradtopgm", " bmptopnm", " bmptoppm", " brushtopbm", " cameratopam", " cistopbm", " cmuwmtopbm", " ddbugtopbm", " escp2topbm", " eyuvtoppm", " fiascotopnm", " fitstopnm", " fstopgm", " g3topbm", " gemtopbm", " gemtopnm", " giftopnm", " gouldtoppm", " hdifftopam", " hipstopgm", " hpcdtoppm", " icontopbm", " ilbmtoppm", " imgtoppm", " infotopam", " jbigtopnm", " jpeg2ktopam", " jpegtopnm", " leaftoppm", " lispmtopgm", " macptopbm", " manweb", " mdatopbm", " mgrtopbm", " mrftopbm", " mtvtoppm", " neotoppm", " netpbm-config", " palmtopnm", " pamaddnoise", " pamarith", " pambackground", " pambayer", " pamchannel", " pamcomp", " pamcrater", " pamcut", " pamdeinterlace", " pamdepth", " pamdice", " pamditherbw", " pamedge", " pamendian", " pamenlarge", " pamexec", " pamfile", " pamfix", " pamfixtrunc", " pamflip", " pamfunc", " pamgauss", " pamgradient", " pamlookup", " pammasksharpen", " pammixinterlace", " pammosaicknit", " pamoil", " pampaintspill", " pamperspective", " pampick", " pampop9", " pamrecolor", " pamrgbatopng", " pamrubber", " pamscale", " pamseq", " pamshadedrelief", " pamsharpmap", " pamsharpness", " pamsistoaglyph", " pamslice", " pamsplit", " pamstack", " pamstereogram", " pamstretch", " pamstretch-gen", " pamsumm", " pamsummcol", " pamtable", " pamthreshold", " pamtilt", " pamtoavs", " pamtodjvurle", " pamtofits", " pamtogif", " pamtohdiff", " pamtohtmltbl", " pamtojpeg2k", " pamtompfont", " pamtooctaveimg", " pamtopam", " pamtopdbimg", " pamtopfm", " pamtopng", " pamtopnm", " pamtosrf", " pamtosvg", " pamtotga", " pamtotiff", " pamtouil", " pamtowinicon", " pamtoxvmini", " pamundice", " pamunlookup", " pamvalidate", " pamwipeout", " pbmclean", " pbmlife", " pbmmake", " pbmmask", " pbmminkowski", " pbmpage", " pbmpscale", " pbmreduce", " pbmtext", " pbmtextps", " pbmto10x", " pbmto4425", " pbmtoascii", " pbmtoatk", " pbmtobbnbg", " pbmtocis", " pbmtocmuwm", " pbmtodjvurle", " pbmtoepsi", " pbmtoepson", " pbmtoescp2", " pbmtog3", " pbmtogem", " pbmtogo", " pbmtoibm23xx", " pbmtoicon", " pbmtolj", " pbmtoln03", " pbmtolps", " pbmtomacp", " pbmtomatrixorbital", " pbmtomda", " pbmtomgr", " pbmtomrf", " pbmtonokia", " pbmtopgm", " pbmtopi3", " pbmtopk", " pbmtoplot", " pbmtoppa", " pbmtopsg3", " pbmtoptx", " pbmtosunicon", " pbmtowbmp", " pbmtox10bm", " pbmtoxbm", " pbmtoybm", " pbmtozinc", " pbmupc", " pc1toppm", " pcdovtoppm", " pcxtoppm", " pdbimgtopam", " pfmtopam", " pgmabel", " pgmbentley", " pgmcrater", " pgmdeshadow", " pgmedge", " pgmenhance", " pgmhist", " pgmkernel", " pgmmake", " pgmmedian", " pgmminkowski", " pgmmorphconv", " pgmnoise", " pgmnorm", " pgmoil", " pgmramp", " pgmslice", " pgmtexture", " pgmtofs", " pgmtolispm", " pgmtopbm", " pgmtopgm", " pgmtoppm", " pgmtosbig", " pgmtost4", " pi1toppm", " pi3topbm", " picttoppm", " pjtoppm", " pktopbm", " pngtopam", " pngtopnm", " pnmalias", " pnmarith", " pnmcat", " pnmcolormap", " pnmcomp", " pnmconvol", " pnmcrop", " pnmcut", " pnmdepth", " pnmenlarge", " pnmfile", " pnmflip", " pnmgamma", " pnmhisteq", " pnmhistmap", " pnmindex", " pnminterp", " pnminvert", " pnmmargin", " pnmmercator", " pnmmontage", " pnmnlfilt", " pnmnoraw", " pnmnorm", " pnmpad", " pnmpaste", " pnmpsnr", " pnmquant", " pnmquantall", " pnmremap", " pnmrotate", " pnmscale", " pnmscalefixed", " pnmshear", " pnmsmooth", " pnmsplit", " pnmstitch", " pnmtile", " pnmtoddif", " pnmtofiasco", " pnmtofits", " pnmtojbig", " pnmtojpeg", " pnmtopalm", " pnmtopclxl", " pnmtoplainpnm", " pnmtopng", " pnmtopnm", " pnmtops", " pnmtorast", " pnmtorle", " pnmtosgi", " pnmtosir", " pnmtotiff", " pnmtotiffcmyk", " pnmtoxwd", " ppm3d", " ppmbrighten", " ppmchange", " ppmcie", " ppmcolormask", " ppmcolors", " ppmdcfont", " ppmddumpfont", " ppmdim", " ppmdist", " ppmdither", " ppmdmkfont", " ppmdraw", " ppmfade", " ppmflash", " ppmforge", " ppmglobe", " ppmhist", " ppmlabel", " ppmmake", " ppmmix", " ppmnorm", " ppmntsc", " ppmpat", " ppmquant", " ppmquantall", " ppmrainbow", " ppmrelief", " ppmrough", " ppmshadow", " ppmshift", " ppmspread", " ppmtoacad", " ppmtoapplevol", " ppmtoarbtxt", " ppmtoascii", " ppmtobmp", " ppmtoeyuv", " ppmtogif", " ppmtoicr", " ppmtoilbm", " ppmtojpeg", " ppmtoleaf", " ppmtolj", " ppmtomap", " ppmtomitsu", " ppmtompeg", " ppmtoneo", " ppmtopcx", " ppmtopgm", " ppmtopi1", " ppmtopict", " ppmtopj", " ppmtopjxl", " ppmtoppm", " ppmtopuzz", " ppmtorgb3", " ppmtosixel", " ppmtospu", " ppmtoterm", " ppmtotga", " ppmtouil", " ppmtowinicon", " ppmtoxpm", " ppmtoyuv", " ppmtoyuvsplit", " ppmtv", " ppmwheel", " psidtopgm", " pstopnm", " qrttoppm", " rasttopnm", " rawtopgm", " rawtoppm", " rgb3toppm", " rlatopam", " rletopnm", " sbigtopgm", " sgitopnm", " sirtopnm", " sldtoppm", " spctoppm", " spottopgm", " sputoppm", " srftopam", " st4topgm", " sunicontopnm", " svgtopam", " tgatoppm", " thinkjettopbm", " tifftopnm", " wbmptopbm", " winicontopam", " winicontoppm", " xbmtopbm", " ximtoppm", " xpmtoppm", " xvminitoppm", " xwdtopnm", " ybmtopbm", " yuvsplittoppm", " yuvtoppm", " yuy2topam", " zeisstopnm", "nettle", " nettle-hash", " nettle-lfib-stream", " nettle-pbkdf2", " pkcs1-conv", " sexp-conv", "newsboat", " newsboat", " podboat", "nginx", " nginx", "ninja", " ninja", "nmap", " nmap", " nping", "nodejs", " node", " npm", " npx", "nodejs-current", " node", " npm", " npx", "notmuch", " notmuch", "nyancat", " nyancat", "nzbget", " nzbget", "oathtool", " oathtool", " pskctool", "ocrad", " ocrad", "openjpeg-tools", " opj_compress", " opj_decompress", " opj_dump", "openssh", " scp", " sftp", " source-ssh-agent", " ssh", " ssh-add", " ssh-agent", " ssh-copy-id", " ssh-keygen", " ssh-keyscan", " ssha", " sshd", "openssl-tool", " openssl", "optipng", " optipng", "opus-tools", " opusdec", " opusenc", " opusinfo", "p7zip", " 7z", " 7za", " 7zr", "pango", " pango-view", "par2", " par2", " par2create", " par2repair", " par2verify", "patch", " patch", "patchelf", " patchelf", "pcre-dev", " pcre-config", "pcre2-dev", " pcre2-config", "perl", " corelist", " cpan", " enc2xs", " encguess", " h2ph", " h2xs", " instmodsh", " json_pp", " libnetcfg", " perl", " perlbug", " perldoc", " perlivp", " perlthanks", " piconv", " pl2pm", " pod2html", " pod2man", " pod2text", " pod2usage", " podchecker", " podselect", " prove", " ptar", " ptardiff", " ptargrep", " shasum", " splain", " xsubpp", " zipdetails", "pforth", " pforth", "php", " phar", " phar.phar", " php", " php-cgi", " php-config", " phpdbg", " phpize", "php-fpm", " php-fpm", "pick", " pick", "picolisp", " picolisp", " pil", "pinentry", " pinentry", " pinentry-curses", "pkg-config", " pkg-config", "play-audio", " play-audio", "pngquant", " pngquant", "poppler", " pdfdetach", " pdffonts", " pdfimages", " pdfinfo", " pdfseparate", " pdftocairo", " pdftohtml", " pdftoppm", " pdftops", " pdftotext", " pdfunite", "postgresql", " clusterdb", " createdb", " createuser", " dropdb", " dropuser", " initdb", " pg_archivecleanup", " pg_basebackup", " pg_config", " pg_controldata", " pg_ctl", " pg_dump", " pg_dumpall", " pg_isready", " pg_receivewal", " pg_recvlogical", " pg_resetwal", " pg_restore", " pg_rewind", " pg_test_fsync", " pg_test_timing", " pg_upgrade", " pg_waldump", " pgbench", " postgres", " postmaster", " psql", " reindexdb", " vacuumdb", "potrace", " mkbitmap", " potrace", "privoxy", " privoxy", "procps", " free", " pgrep", " pidof", " pkill", " pmap", " ps", " pwdx", " slabtop", " sysctl", " tload", " top", " uptime", " vmstat", " w", " watch", "profanity", " profanity", "proot", " proot", " termux-chroot", "protobuf", " protoc", "psmisc", " fuser", " killall", " peekfd", " prtstat", " pstree", "pulseaudio", " esdcompat", " pacat", " pacmd", " pactl", " pasuspender", " pulseaudio", "pure-ftpd", " pure-authd", " pure-ftpd", " pure-ftpwho", " pure-mrtginfo", " pure-pw", " pure-pwconvert", " pure-quotacheck", " pure-statsdecode", " pure-uploadscript", "pv", " pv", "pwgen", " pwgen", "python", " 2to3", " 2to3-3.6", " pydoc3", " pydoc3.6", " python", " python3", " python3-config", " python3.6", " python3.6-config", " python3.6m-config", " pyvenv", " pyvenv-3.6", "python2", " idle", " pydoc", " python-config", " python2", " python2-config", " python2.7", " python2.7-config", " smtpd.py", "qalc", " qalc", "qpdf", " fix-qdf", " qpdf", " zlib-flate", "radare2", " r2", " r2agent", " r2pm", " rabin2", " radare2", " radiff2", " rafind2", " ragg2", " ragg2-cc", " rahash2", " rarun2", " rasm2", " rax2", "ragel", " ragel", "rcs", " ci", " co", " ident", " merge", " rcs", " rcsclean", " rcsdiff", " rcsmerge", " rlog", "rdiff", " rdiff", "redir", " redir", "redis", " redis-benchmark", " redis-check-aof", " redis-check-rdb", " redis-cli", " redis-sentinel", " redis-server", "remind", " rem", " rem2ps", " remind", "rgbds", " rgbasm", " rgbfix", " rgbgfx", " rgblink", "rlwrap", " rlwrap", "rsync", " rsync", "rtmpdump", " rtmpdump", " rtmpgw", " rtmpsrv", " rtmpsuck", "ruby", " erb", " gem", " irb", " rake", " rdoc", " ruby", "screen", " screen", " screen-4.6.2", "scrypt", " scrypt", "sed", " sed", "sensible-utils", " select-editor", " sensible-browser", " sensible-editor", " sensible-pager", "sharutils", " shar", " unshar", " uudecode", " uuencode", "silversearcher-ag", " ag", "sl", " sl", "socat", " filan", " procan", " socat", "sox", " play", " rec", " sox", " soxi", "sqlite", " sqlite3", "squid", " purge", " squid", " squidclient", "sshpass", " sshpass", "sslscan", " sslscan", "stag", " stag", "strace", " strace", " strace-log-merge", "stunnel", " stunnel", "subversion", " svn", " svnadmin", " svnbench", " svndumpfilter", " svnfsfs", " svnlook", " svnmucc", " svnrdump", " svnserve", " svnsync", " svnversion", "syncthing", " syncthing", "tar", " tar", "tasksh", " tasksh", "taskwarrior", " task", "tcl", " sqlite3_analyzer", " tclsh", " tclsh8.6", "tcsh", " tcsh", "teckit", " sfconv", " teckit_compile", " txtconv", "termux-api", " termux-audio-info", " termux-battery-status", " termux-camera-info", " termux-camera-photo", " termux-clipboard-get", " termux-clipboard-set", " termux-contact-list", " termux-dialog", " termux-download", " termux-infrared-frequencies", " termux-infrared-transmit", " termux-location", " termux-media-scan", " termux-notification", " termux-notification-remove", " termux-share", " termux-sms-inbox", " termux-sms-send", " termux-storage-get", " termux-telephony-call", " termux-telephony-cellinfo", " termux-telephony-deviceinfo", " termux-toast", " termux-tts-engines", " termux-tts-speak", " termux-vibrate", " termux-wifi-connectioninfo", " termux-wifi-scaninfo", "termux-elf-cleaner", " termux-elf-cleaner", "teseq", " reseq", " teseq", "tesseract", " tesseract", "texinfo", " info", " install-info", " makeinfo", " pdftexi2dvi", " pod2texi", " texi2any", " texi2dvi", " texi2pdf", " texindex", "texlive-bin", " a2ping", " a5toa4", " adhocfilelist", " afm2afm", " afm2pl", " afm2tfm", " aleph", " allcm", " allec", " allneeded", " arara", " arlatex", " authorindex", " autoinst", " autosp", " bbl2bib", " bbox", " bg5+latex", " bg5+pdflatex", " bg5conv", " bg5latex", " bg5pdflatex", " bibdoiadd", " bibexport", " bibmradd", " bibtex", " bibtex8", " bibzbladd", " bundledoc", " cachepic", " cef5conv", " cef5latex", " cef5pdflatex", " cefconv", " ceflatex", " cefpdflatex", " cefsconv", " cefslatex", " cefspdflatex", " cfftot1", " checkcites", " checklistings", " chktex", " chkweb", " cjk-gs-integrate", " context", " contextjit", " convbkmk", " ctangle", " ctanify", " ctanupload", " ctie", " cweave", " de-macro", " depythontex", " detex", " devnag", " deweb", " diadia", " disdvi", " dosepsbin", " dt2dv", " dtxgen", " dv2dt", " dvi2fax", " dvi2tty", " dviasm", " dvicopy", " dvidvi", " dvigif", " dvihp", " dvilj", " dvilj2p", " dvilj4", " dvilj4l", " dvilj6", " dvipdfm", " dvipdfmx", " dvipdft", " dvipng", " dvipos", " dvips", " dvired", " dvitomp", " dvitype", " e2pall", " ebb", " ebong", " epspdf", " epspdftk", " epstopdf", " eptex", " euptex", " exceltex", " extconv", " extractbb", " fig4latex", " findhyph", " fmtutil", " fmtutil-sys", " fmtutil-user", " fontinst", " fragmaster", " gbklatex", " gbkpdflatex", " getmapdl", " gftodvi", " gftopk", " gftype", " gsftopk", " hbf2gf", " ht", " htcontext", " htlatex", " htmex", " httex", " httexi", " htxelatex", " htxetex", " inimf", " initex", " installfont-tl", " jamo-normalize", " kanji-config-updmap", " kanji-config-updmap-sys", " kanji-config-updmap-user", " kanji-fontmap-creator", " komkindex", " kpseaccess", " kpsepath", " kpsereadlink", " kpsestat", " kpsetool", " kpsewhere", " kpsewhich", " kpsexpand", " lacheck", " latex-git-log", " latex-papersize", " latex2man", " latex2nemeth", " latexdiff", " latexdiff-vc", " latexfileversion", " latexindent", " latexmk", " latexpand", " latexrevise", " lily-glyph-commands", " lily-image-commands", " lily-rebuild-pdfs", " listbib", " listings-ext.sh", " ltx2crossrefxml", " ltxfileinfo", " ltximg", " lua2dox_filter", " luaotfload-tool", " luatex", " luatools", " lwarpmk", " m-tx", " make4ht", " makedtx", " makeglossaries", " makeglossaries-lite", " makeindex", " match_parens", " mathspic", " mf", " mf-nowin", " mf2pt1", " mfplain", " mft", " mk4ht", " mkgrkindex", " mkindex", " mkjobtexmf", " mkocp", " mkofm", " mkpic", " mkt1font", " mktexfmt", " mktexlsr", " mktexmf", " mktexpk", " mktextfm", " mmafm", " mmpfb", " mpost", " mptopdf", " mtxrun", " mtxrunjit", " multibibliography", " musixflx", " musixtex", " odvicopy", " odvitype", " ofm2opl", " omfonts", " opl2ofm", " ot2kpx", " otangle", " otfinfo", " otftotfm", " otp2ocp", " outocp", " ovf2ovp", " ovp2ovf", " patgen", " pbibtex", " pdf180", " pdf270", " pdf90", " pdfannotextractor", " pdfatfi", " pdfbook", " pdfbook2", " pdfcrop", " pdfflip", " pdfjam", " pdfjam-pocketmod", " pdfjam-slides3up", " pdfjam-slides6up", " pdfjoin", " pdflatexpicscale", " pdfnup", " pdfpun", " pdftex", " pdftosrc", " pdfxup", " pdvitomp", " pdvitype", " pedigree", " perltex", " pfarrei", " pkfix", " pkfix-helper", " pktogf", " pktype", " pltotf", " pmpost", " pmxchords", " pn2pdf", " pooltype", " ppltotf", " prepmx", " ps2eps", " ps2frag", " ps4pdf", " pslatex", " pst2pdf", " ptex", " ptex2pdf", " ptftopl", " purifyeps", " pygmentex", " pythontex", " repstopdf", " rpdfcrop", " rubibtex", " rubikrotation", " rumakeindex", " rungs", " simpdftex", " sjisconv", " sjislatex", " sjispdflatex", " splitindex", " srcredact", " sty2dtx", " svn-multi", " synctex", " t1dotlessj", " t1lint", " t1rawafm", " t1reencode", " t1testpage", " t4ht", " tangle", " tex", " tex4ebook", " tex4ht", " texconfig", " texconfig-dialog", " texconfig-sys", " texcount", " texdef", " texdiff", " texdirflatten", " texdoc", " texdoctk", " texexec", " texfot", " texhash", " texlinks", " texliveonfly", " texloganalyser", " texlua", " texluac", " texmfstart", " texosquery", " texosquery-jre5", " texosquery-jre8", " tftopl", " thumbpdf", " tie", " tlmgr", " tlmgr.ln", " tpic2pdftex", " ttf2afm", " ttf2kotexfont", " ttf2pk", " ttf2tfm", " ttftotype42", " typeoutfileinfo", " ulqda", " upbibtex", " updmap", " updmap-sys", " updmap-user", " updvitomp", " updvitype", " upmendex", " upmpost", " uppltotf", " uptex", " uptftopl", " urlbst", " vftovp", " vlna", " vpe", " vpl2ovp", " vpl2vpl", " vptovf", " weave", " wofm2opl", " wopl2ofm", " wovf2ovp", " wovp2ovf", " xdvipdfmx", " xetex", " xhlatex", " yplan", "tig", " tig", "timewarrior", " timew", "tintin++", " tt++", "tinyscheme", " tinyscheme", "tmate", " tmate", "tmux", " tmux", "toilet", " toilet", "tor", " tor", " tor-gencert", " tor-resolve", " torify", "torsocks", " torsocks", "tracepath", " tracepath", " traceroute", "transmission", " transmission-create", " transmission-daemon", " transmission-edit", " transmission-remote", " transmission-show", "tree", " tree", "tsocks", " tsocks", "tty-clock", " tty-clock", "tty-solitaire", " ttysolitaire", "ttyrec", " ttyplay", " ttyrec", " ttytime", "units", " units", " units_cur", "unrar", " unrar", "unzip", " funzip", " unzip", " unzipsfx", " zipgrep", " zipinfo", "utfdecode", " utfdecode", "util-linux", " addpart", " blkdiscard", " blkid", " blkzone", " blockdev", " cal", " chcpu", " chrt", " col", " colcrt", " colrm", " ctrlaltdel", " delpart", " dmesg", " fallocate", " fdformat", " fincore", " findfs", " flock", " fsck.cramfs", " fsck.minix", " fsfreeze", " getopt", " hexdump", " hwclock", " ionice", " isosize", " ldattach", " linux32", " linux64", " look", " losetup", " lscpu", " lsipc", " lsns", " mcookie", " mesg", " mkfs", " mkfs.bfs", " mkfs.cramfs", " mkfs.minix", " mkswap", " more", " namei", " nologin", " nsenter", " partx", " prlimit", " raw", " readprofile", " rename", " renice", " resizepart", " rev", " rtcwake", " script", " scriptreplay", " setarch", " setsid", " setterm", " swaplabel", " taskset", " ul", " uname26", " unshare", " wdctl", " whereis", " wipefs", " zramctl", "valac", " vala", " vala-0.38", " vala-gen-introspect", " vala-gen-introspect-0.38", " valac", " valac-0.38", " vapicheck", " vapicheck-0.38", " vapigen", " vapigen-0.38", "valadoc", " valadoc", " valadoc-0.38", "valgrind", " callgrind_annotate", " callgrind_control", " cg_annotate", " cg_diff", " cg_merge", " ms_print", " valgrind", " valgrind-di-server", " valgrind-listener", " vgdb", "vifm", " vifm", " vifm-convert-dircolors", " vifm-pause", " vifm-screen-split", "vim", " vi", " view", " vim", " vimdiff", " vimtutor", " xxd", "vim-python", " vi", " view", " vim", " vimdiff", " vimtutor", " xxd", "vorbis-tools", " oggdec", " oggenc", " ogginfo", " vcut", " vorbiscomment", "vttest", " vttest", "vtutils", " vtquery", " vtshowkeys", " vtsize", " vttitle", "w3m", " w3m", " w3mman", "wcalc", " wcalc", "weechat", " weechat", "wget", " wget", "wol", " wol", " wol-bootptab", " wol-dhcpdconf", "x264", " x264", "x265", " x265", "xapian-tools", " copydatabase", " quest", " simpleexpand", " simpleindex", " simplesearch", " xapian-check", " xapian-compact", " xapian-delve", " xapian-metadata", " xapian-progsrv", " xapian-replicate", " xapian-replicate-server", " xapian-tcpsrv", "xmlsec", " xmlsec1", " xmlsec1-config", "xmlstarlet", " xml", "xorriso", " osirrox", " xorrecord", " xorriso", " xorriso-tcltk", " xorrisofs", "xsltproc", " xsltproc", "xz-utils", " lzcat", " lzcmp", " lzdiff", " lzegrep", " lzfgrep", " lzgrep", " lzless", " lzma", " lzmadec", " lzmainfo", " lzmore", " unlzma", " unxz", " xz", " xzcat", " xzcmp", " xzdec", " xzdiff", " xzegrep", " xzfgrep", " xzgrep", " xzless", " xzmore", "yasm", " vsyasm", " yasm", " ytasm", "zbar", " zbarimg", "zile", " zile", "zip", " zip", " zipcloak", " zipnote", " zipsplit", "zsh", " zsh", "zstd", " unzstd", " zstd", " zstdcat", " zstdgrep", " zstdless", " zstdmt", "ack-grep", " ack", "apksigner", " apksigner", "asciinema", " asciinema", "autoconf", " autoconf", " autoheader", " autom4te", " autoreconf", " autoscan", " autoupdate", " ifnames", "automake", " aclocal", " aclocal-1.15", " automake", " automake-1.15", "byobu", " byobu", " byobu-config", " byobu-ctrl-a", " byobu-disable", " byobu-disable-prompt", " byobu-enable", " byobu-enable-prompt", " byobu-export", " byobu-janitor", " byobu-keybindings", " byobu-launch", " byobu-launcher", " byobu-launcher-install", " byobu-launcher-uninstall", " byobu-layout", " byobu-prompt", " byobu-quiet", " byobu-reconnect-sockets", " byobu-screen", " byobu-select-backend", " byobu-select-profile", " byobu-select-session", " byobu-shell", " byobu-silent", " byobu-status", " byobu-status-detail", " byobu-tmux", " byobu-ugraph", " byobu-ulevel", " col1", " ctail", " manifest", " purge-old-kernels", " vigpg", " wifi-status", "colordiff", " colordiff", "cowsay", " cowsay", "debootstrap", " debootstrap", "dx", " dx", "ecj", " ecj", "getmail", " getmail", " getmail_fetch", " getmail_maildir", " getmail_mbox", "luarocks", " luarocks", " luarocks-5.3", " luarocks-admin", " luarocks-admin-5.3", "neofetch", " neofetch", "parallel", " env_parallel", " env_parallel.ash", " env_parallel.bash", " env_parallel.csh", " env_parallel.dash", " env_parallel.fish", " env_parallel.ksh", " env_parallel.pdksh", " env_parallel.sh", " env_parallel.tcsh", " env_parallel.zsh", " niceload", " parallel", " parcat", " sem", " sql", "pass", " pass", "pastebinit", " pastebinit", "pathpicker", " fpp", "ranger", " ranger", " rifle", "ruby-ri", " ri", "screenfetch", " screenfetch", "stow", " chkstow", " stow", "termux-am", " am", "termux-apt-repo", " termux-apt-repo", "termux-create-package", " termux-create-package", "termux-tools", " chsh", " dalvikvm", " df", " getprop", " ip", " logcat", " login", " ping", " ping6", " pkg", " pm", " settings", " su", " termux-fix-shebang", " termux-info", " termux-open", " termux-open-url", " termux-reload-settings", " termux-setup-storage", " termux-wake-lock", " termux-wake-unlock", " xdg-open", "tsu", " tsu", "vcsh", " vcsh", "yarn", " yarn", }; Desktop version Sign out
vinsguru / Pdf UtilPDF Compare Utility
kb47 / Pdf PopplerConvert PDF files into images using Poppler with promises. It achieves 10x faster performance compared to other PDF converters.
marcdacz / Compare PdfStandalone node module that compares pdfs
jnweiger / Pdfcomparecompare two PDF files, write a resulting PDF with highlighted changes
HyGear / Diff DwgSimple Python script to compare two PDF drawings and highlight the differences.
ShiningRush / PdfComponentComparisonThe repository is used for comparing different pdf handle component such as Aspose.Pdf , Spire.Pdf and iText so on,you also can consider it as a demo repository that show how to use those component.
projectworldsofficial / Jwellary Shop PHP MYSQL Free Download Online Jewellery Shopping System Php Project with source code. Online Jewellery Shop is basically used to build an application program which help people to find and buy latest design of jewellery with different categories like Gold Silver, Diamond . It is useful in the way that it makes an easier way to buy products online. Today most of the jewellery shop is useful for shopping site. The admin have lots of paper work and they are using desktop, spread sheet like MS Excel application to manage data in soft copy about user record. In this proposed jewellery System it will run in server and user can handle whole the registration activities. It has facilities to generate various types of reports (like pdf, excel) which are required by the management during event organizing. This application maintains the centralized database so that any changes done at a location reflects immediately. This is an online tool so more than one user can login into system and use the tool simultaneously. The aim of this application is to reduce the manual effort needed to manage transactions and historical data used in various gods owns. Also this application provides an interface to users to view the details about events. System Actors The application is implemented in PHP and consists of two main components: i. Admin and ii. Customer side Admin side consists of the features such as Creating Username & Password, Input Items, Modify Items, Delete items, Query Sale Data, Query Database’s data, and Logout. Customer?user side consists of the features such as Select Products, Search Products, Buy Items, Continue Shopping, View Cart, Checkout, Sign-in, Creating an Account, Bill/Ship Information, Confirm, Send Order, and Delete Order. There are also the future works for this application.There are mainly three such objectives which are as follows: • To shop in the comfort of your home, without having to step out of the door. • To be able to easily save money and compare prices from website to website. The software specifications are • Operating System: Window 7 and above • HTML, CSS, Bootstrap (Front end) • MY SQL(Back end) • PHP(Server side programming) • XAMPP Server Installation Steps 1. Download zip file and Unzip file on your local server. 2. Put this file inside “c:/wamp/www/” . 3. Database Configuration Open phpmyadmin Create Database named db name. Import database db name.sql from downloaded folder(inside database) 4. Open Your browser put inside “http://localhost/folder name/“ Download
LRNavin / AutoCommentsDescription: We want to create a deep Neural Network that can automatically generate comments for code snippets passed to it. The motivation behind this is that in software development and maintenance, developers spend around 59% of their time on program comprehension activities. Having comments that are generated automatically will hopefully cut this time down. In order to do this we will combine the recent paper Code2Vec: Learning Distributed Representations of Code by Alon et al. with the paper Deep Code Comment Generation in order to make a better performing model using the newer Code2Vec encoding that was not used in the Deep Code Comment Generation paper. Dataset: The dataset that we will use is the same dataset used by the Deep Code Comment Generation paper, this is a dataset of more than 500,000 code snippets including comments. This also gives us a baseline against which to compare. Papers: Deep Code: https://xin-xia.github.io/publication/icpc182.pdf Code2Vec: https://arxiv.org/abs/1803.09473
ssibb / PDF Diff ViewerPDF Diff Viewer, a side-by-side, visual highlight, sync-scroll, PDF comparer, written in Python. Open source, mostly powered by PyMuPDF and Tkinter. Optional support for git diff, for a better comparison algorithm.
Aryia-Behroziuan / Other SourcesAsada, M.; Hosoda, K.; Kuniyoshi, Y.; Ishiguro, H.; Inui, T.; Yoshikawa, Y.; Ogino, M.; Yoshida, C. (2009). "Cognitive developmental robotics: a survey". IEEE Transactions on Autonomous Mental Development. 1 (1): 12–34. doi:10.1109/tamd.2009.2021702. S2CID 10168773. "ACM Computing Classification System: Artificial intelligence". ACM. 1998. Archived from the original on 12 October 2007. Retrieved 30 August 2007. Goodman, Joanna (2016). Robots in Law: How Artificial Intelligence is Transforming Legal Services (1st ed.). Ark Group. ISBN 978-1-78358-264-8. Archived from the original on 8 November 2016. Retrieved 7 November 2016. Albus, J. S. (2002). "4-D/RCS: A Reference Model Architecture for Intelligent Unmanned Ground Vehicles" (PDF). In Gerhart, G.; Gunderson, R.; Shoemaker, C. (eds.). Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology. Unmanned Ground Vehicle Technology IV. 3693. pp. 11–20. Bibcode:2002SPIE.4715..303A. 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Darwin among the Machines. Allan Lane Science. ISBN 978-0-7382-0030-9. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Edelman, Gerald (23 November 2007). "Gerald Edelman – Neural Darwinism and Brain-based Devices". Talking Robots. Archived from the original on 8 October 2009. Edelson, Edward (1991). The Nervous System. New York: Chelsea House. ISBN 978-0-7910-0464-7. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Fearn, Nicholas (2007). The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers. New York: Grove Press. ISBN 978-0-8021-1839-4. Gladwell, Malcolm (2005). Blink. New York: Little, Brown and Co. ISBN 978-0-316-17232-5. Gödel, Kurt (1951). Some basic theorems on the foundations of mathematics and their implications. Gibbs Lecture. In Feferman, Solomon, ed. (1995). Kurt Gödel: Collected Works, Vol. III: Unpublished Essays and Lectures. Oxford University Press. pp. 304–23. 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If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)
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