Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty" may be provided externally or discovered as part of the training process. This is intended to attain good performance more quickly, or to converge to a better local optimum if the global optimum is not found. == Approach == Most generally, curriculum learning is the technique of successively increasing the difficulty of examples in the training set that is presented to a model over multiple training iterations. This can produce better results than exposing the model to the full training set immediately under some circumstances; most typically, when the model is able to learn general principles from easier examples, and then gradually incorporate more complex and nuanced information as harder examples are introduced, such as edge cases. This has been shown to work in many domains, most likely as a form of regularization. There are several major variations in how the technique is applied: A concept of "difficulty" must be defined. This may come from human annotation or an external heuristic; for example in language modeling, shorter sentences might be classified as easier than longer ones. Another approach is to use the performance of another model, with examples accurately predicted by that model being classified as easier (providing a connection to boosting). Difficulty can be increased steadily or in distinct epochs, and in a deterministic schedule or according to a probability distribution. This may also be moderated by a requirement for diversity at each stage, in cases where easier examples are likely to be disproportionately similar to each other. Applications must also decide the schedule for increasing the difficulty. Simple approaches may use a fixed schedule, such as training on easy examples for half of the available iterations and then all examples for the second half. Other approaches use self-paced learning to increase the difficulty in proportion to the performance of the model on the current set. Since curriculum learning only concerns the selection and ordering of training data, it can be combined with many other techniques in machine learning. The success of the method assumes that a model trained for an easier version of the problem can generalize to harder versions, so it can be seen as a form of transfer learning. Some authors also consider curriculum learning to include other forms of progressively increasing complexity, such as increasing the number of model parameters. It is frequently combined with reinforcement learning, such as learning a simplified version of a game first. Some domains have shown success with anti-curriculum learning: training on the most difficult examples first. One example is the ACCAN method for speech recognition, which trains on the examples with the lowest signal-to-noise ratio first. == History == The term "curriculum learning" was introduced by Yoshua Bengio et al in 2009, with reference to the psychological technique of shaping in animals and structured education for humans: beginning with the simplest concepts and then building on them. The authors also note that the application of this technique in machine learning has its roots in the early study of neural networks such as Jeffrey Elman's 1993 paper Learning and development in neural networks: the importance of starting small. Bengio et al showed good results for problems in image classification, such as identifying geometric shapes with progressively more complex forms, and language modeling, such as training with a gradually expanding vocabulary. They conclude that, for curriculum strategies, "their beneficial effect is most pronounced on the test set", suggesting good generalization. The technique has since been applied to many other domains: Natural language processing: Part-of-speech tagging Intent detection Sentiment analysis Machine translation Speech recognition Language model pre-training Image recognition: Facial recognition Object detection Reinforcement learning: Game-playing Graph learning Matrix factorization
Avid Symphony
Avid Symphony is non-linear editing software aimed at professionals in the film and television industry. It is available for Microsoft Windows PCs and Apple Macintosh platforms. Symphony is Avid's high end SD/HD finishing platform for long form work, such as documentary and episodic TV. Its interface is based on the same look and feature set as the Media Composer and Xpress systems, but contains the highest level of features and resolution including secondary color correction, uncompressed HD, and higher real-time performance. == Release history == Symphony is the software component of a tightly integrated package that includes specific hardware audio/video interfaces, storage, and the computer, also sold by Avid. Its release history is therefore tightly related to the release of new Avid interface hardware: Symphony was introduced to the market in 1998. It was based on Avid's Meridien hardware, supporting SD only, and was available first only for the PC and later for the Macintosh platforms. Its last release was 5.0.5 which supported Windows 2000 and Mac OS X v10.2. The next major upgrade was Symphony Nitris in 2005, with a redesigned software and integration with the Nitris DNA hardware (PCI-X). It supported 8 bit and 10 bit SD and HD resolutions in both compressed and uncompressed forms, the MXF format and DNxHD codec, and ran only on Windows PC platforms. Symphony Nitris DX, released in 2008, added support for a range of HD codecs, including HDV, XDCAM-HD, DVCPRO HD, and AVC-I, and brought back Mac OS support for OS X 10.5, as well as Windows Vista. Since the introduction of Symphony 6, it can be used in software-only mode (where a Nitris or Nitris DX BOB used to be required), and at the same time, like Media Composer, Symphony was opened up with "Open I/O", allowing users to have Symphony use their third party hardware from companies like AJA, Matrox, BlueFish, Blackmagic Design and MOTU. The last remaining features that differentiate it from Media Composer are Advanced Color Correction (channels, secondary color correction,), Relational Color Correction (corrections based on common clip name, tape name, program track) and Universal HD Mastering (only with Nitris DX hardware). The latter allows cross-conversions of 23.976p or 24p projects sequences to most any other format during Digital Cut. In 2013, Avid announced it would no longer offer Symphony a standalone product. Starting version 7, Symphony will be sold as an option to Media Composer. This optional package (sold at a premium) will contain all the traditional Symphony-only features to any Media Composer install. == Use in movies == The Celibacy, Director: Horacio Bocaranda Avid Media Composer 6 and Avid Symphony 6 Nitris DX American Hardcore, Director: Paul Rachman Avid Xpress Pro and Symphony Summercamp!, Director: Spike Lee Avid Xpress Pro and Symphony When the Levees Broke Avid Media Composer and Symphony Nitris Superman Returns Edited with Mac-based Film Composer XL, but HD screenings prepped with Symphony
Exposure Notification
The (Google/Apple) Exposure Notification System (GAEN) is a framework and protocol specification developed by Apple Inc. and Google to facilitate digital contact tracing during the COVID-19 pandemic. When used by health authorities, it augments more traditional contact tracing techniques by automatically logging close approaches among notification system users using Android or iOS smartphones. Exposure Notification is a decentralized reporting protocol built on a combination of Bluetooth Low Energy technology and privacy-preserving cryptography. It is an opt-in feature within COVID-19 apps developed and published by authorized health authorities. Unveiled on April 10, 2020, it was made available on iOS on May 20, 2020, as part of the iOS 13.5 update and on December 14, 2020, as part of the iOS 12.5 update for older iPhones. On Android, it was added to devices via a Google Play Services update, supporting all versions since Android Marshmallow. The Apple/Google protocol is similar to the Decentralized Privacy-Preserving Proximity Tracing (DP-3T) protocol created by the European DP-3T consortium and the Temporary Contact Number (TCN) protocol by Covid Watch, but is implemented at the operating system level, which allows for more efficient operation as a background process. Since May 2020, a variant of the DP-3T protocol is supported by the Exposure Notification Interface. Other protocols are constrained in operation because they are not privileged over normal apps. This leads to issues, particularly on iOS devices where digital contact tracing apps running in the background experience significantly degraded performance. The joint approach is also designed to maintain interoperability between Android and iOS devices, which constitute nearly all of the market. The ACLU stated the approach "appears to mitigate the worst privacy and centralization risks, but there is still room for improvement". In late April, Google and Apple shifted the emphasis of the naming of the system, describing it as an "exposure notification service", rather than "contact tracing" system. == Technical specification == Digital contact tracing protocols typically have two major responsibilities: encounter logging and infection reporting. Exposure Notification only involves encounter logging which is a decentralized architecture. The majority of infection reporting is centralized in individual app implementations. To handle encounter logging, the system uses Bluetooth Low Energy to send tracking messages to nearby devices running the protocol to discover encounters with other people. The tracking messages contain unique identifiers that are encrypted with a secret daily key held by the sending device. These identifiers change every 15–20 minutes as well as Bluetooth MAC address in order to prevent tracking of clients by malicious third parties through observing static identifiers over time. The sender's daily encryption keys are generated using a random number generator. Devices record received messages, retaining them locally for 14 days. If a user tests positive for infection, the last 14 days of their daily encryption keys can be uploaded to a central server, where it is then broadcast to all devices on the network. The method through which daily encryption keys are transmitted to the central server and broadcast is defined by individual app developers. The Google-developed reference implementation calls for a health official to request a one-time verification code (VC) from a verification server, which the user enters into the encounter logging app. This causes the app to obtain a cryptographically signed certificate, which is used to authorize the submission of keys to the central reporting server. The received keys are then provided to the protocol, where each client individually searches for matches in their local encounter history. If a match meeting certain risk parameters is found, the app notifies the user of potential exposure to the infection. Google and Apple intend to use the received signal strength (RSSI) of the beacon messages as a source to infer proximity. RSSI and other signal metadata will also be encrypted to resist deanonymization attacks. === Version 1.0 === To generate encounter identifiers, first a persistent 32-byte private Tracing Key ( t k {\displaystyle tk} ) is generated by a client. From this a 16 byte Daily Tracing Key is derived using the algorithm d t k i = H K D F ( t k , N U L L , 'CT-DTK' | | D i , 16 ) {\displaystyle dtk_{i}=HKDF(tk,NULL,{\text{'CT-DTK'}}||D_{i},16)} , where H K D F ( Key, Salt, Data, OutputLength ) {\displaystyle HKDF({\text{Key, Salt, Data, OutputLength}})} is a HKDF function using SHA-256, and D i {\displaystyle D_{i}} is the day number for the 24-hour window the broadcast is in starting from Unix Epoch Time. These generated keys are later sent to the central reporting server should a user become infected. From the daily tracing key a 16-byte temporary Rolling Proximity Identifier is generated every 10 minutes with the algorithm R P I i , j = Truncate ( H M A C ( d t k i , 'CT-RPI' | | T I N j ) , 16 ) {\displaystyle RPI_{i,j}={\text{Truncate}}(HMAC(dtk_{i},{\text{'CT-RPI'}}||TIN_{j}),16)} , where H M A C ( Key, Data ) {\displaystyle HMAC({\text{Key, Data}})} is a HMAC function using SHA-256, and T I N j {\displaystyle TIN_{j}} is the time interval number, representing a unique index for every 10 minute period in a 24-hour day. The Truncate function returns the first 16 bytes of the HMAC value. When two clients come within proximity of each other they exchange and locally store the current R P I i , j {\displaystyle RPI_{i,j}} as the encounter identifier. Once a registered health authority has confirmed the infection of a user, the user's Daily Tracing Key for the past 14 days is uploaded to the central reporting server. Clients then download this report and individually recalculate every Rolling Proximity Identifier used in the report period, matching it against the user's local encounter log. If a matching entry is found, then contact has been established and the app presents a notification to the user warning them of potential infection. === Version 1.1 === Unlike version 1.0 of the protocol, version 1.1 does not use a persistent tracing key, rather every day a new random 16-byte Temporary Exposure Key ( t e k i {\displaystyle tek_{i}} ) is generated. This is analogous to the daily tracing key from version 1.0. Here i {\displaystyle i} denotes the time is discretized in 10 minute intervals starting from Unix Epoch Time. From this two 128-bit keys are calculated, the Rolling Proximity Identifier Key ( R P I K i {\displaystyle RPIK_{i}} ) and the Associated Encrypted Metadata Key ( A E M K i {\displaystyle AEMK_{i}} ). R P I K i {\displaystyle RPIK_{i}} is calculated with the algorithm R P I K i = H K D F ( t e k i , N U L L , 'EN-RPIK' , 16 ) {\displaystyle RPIK_{i}=HKDF(tek_{i},NULL,{\text{'EN-RPIK'}},16)} , and A E M K i {\displaystyle AEMK_{i}} using the algorithm A E M K i = H K D F ( t e k i , N U L L , 'EN-AEMK' , 16 ) {\displaystyle AEMK_{i}=HKDF(tek_{i},NULL,{\text{'EN-AEMK'}},16)} . From these values a temporary Rolling Proximity Identifier ( R P I i , j {\displaystyle RPI_{i,j}} ) is generated every time the BLE MAC address changes, roughly every 15–20 minutes. The following algorithm is used: R P I i , j = A E S 128 ( R P I K i , 'EN-RPI' | | 0 x 000000000000 | | E N I N j ) {\displaystyle RPI_{i,j}=AES128(RPIK_{i},{\text{'EN-RPI'}}||{\mathtt {0x000000000000}}||ENIN_{j})} , where A E S 128 ( Key, Data ) {\displaystyle AES128({\text{Key, Data}})} is an AES cryptography function with a 128-bit key, the data is one 16-byte block, j {\displaystyle j} denotes the Unix Epoch Time at the moment the roll occurs, and E N I N j {\displaystyle ENIN_{j}} is the corresponding 10-minute interval number. Next, additional Associated Encrypted Metadata is encrypted. What the metadata represents is not specified, likely to allow the later expansion of the protocol. The following algorithm is used: Associated Encrypted Metadata i , j = A E S 128 _ C T R ( A E M K i , R P I i , j , Metadata ) {\displaystyle {\text{Associated Encrypted Metadata}}_{i,j}=AES128\_CTR(AEMK_{i},RPI_{i,j},{\text{Metadata}})} , where A E S 128 _ C T R ( Key, IV, Data ) {\displaystyle AES128\_CTR({\text{Key, IV, Data}})} denotes AES encryption with a 128-bit key in CTR mode. The Rolling Proximity Identifier and the Associated Encrypted Metadata are then combined and broadcast using BLE. Clients exchange and log these payloads. Once a registered health authority has confirmed the infection of a user, the user's Temporary Exposure Keys t e k i {\displaystyle tek_{i}} and their respective interval numbers i {\displaystyle i} for the past 14 days are uploaded to the central reporting server. Clients then download this report and individually recalculate every Rolling Proximity Identifier starting from interval number i {\displaystyle i} ,
Cinema 4D
Cinema 4D is a 3D software suite developed by the German company Maxon. == Overview == As of R21, only a single version of Cinema 4D is available. It replaces all previous variants, including BodyPaint 3D, and includes all features of the past 'Studio' variant. With R21, all binaries were unified. There is no technical difference between commercial, educational, or demo versions. The difference is now only in licensing. 2014 saw the release of Cinema 4D Lite, which came packaged with Adobe After Effects Creative Cloud 2014. "Lite" acts as an introductory version, with many features withheld. This is part of a partnership between the two companies, where a Maxon-produced plug-in, called Cineware, allows any variant to create a seamless workflow with After Effects. The "Lite" variant is dependent on After Effects CC, needing the latter application running to launch, and is only sold as a package component included with After Effects CC through Adobe. Initially, Cinema 4D was developed for Amiga computers in the early 1990s, and the first three versions of the program were available exclusively for that platform. With v4, however, Maxon began to develop the application for Windows and Macintosh computers as well, citing the wish to reach a wider audience and the growing instability of the Amiga market following Commodore's bankruptcy. It was also released for BeOS. On Linux, Cinema 4D is available as a commandline rendering version. == Modules and older variants == From R12 to R20, Cinema 4D was available in four variants. A core Cinema 4D 'Prime' application, a 'Broadcast' version with additional motion-graphics features, 'Visualize,' which adds functions for architectural design and 'Studio,' which includes all modules. From Release 8 until Release 11.5, Cinema 4D had a modular approach to the application, with the ability to expand upon the core application with various modules. This ended with Release 12, though the functionality of these modules remains in the different flavors of Cinema 4D (Prime, Broadcast, Visualize, Studio) The old modules were: Advanced Render (global illumination/HDRI, caustics, ambient occlusion and sky simulation) BodyPaint 3D (direct painting on UVW meshes; now included in the core. In essence Cinema 4D Core/Prime and the BodyPaint 3D products are identical. The only difference between the two is the splash screen that is shown at startup and the default user interface.) Dynamics (for simulating soft body and rigid body dynamics) Hair (simulates hair, fur, grass, etc.) MOCCA (character animation and cloth simulation) MoGraph (Motion Graphics procedural modelling and animation toolset) NET Render (to render animations over a TCP/IP network in render farms) PyroCluster (simulation of smoke and fire effects) Prime (the core application) Broadcast (adds MoGraph2) Visualize (adds Virtual Walkthrough, Advanced Render, Sky, Sketch and Toon, data exchange, camera matching) Studio (the complete package) == Version history == == Use in industry == A number of films and related works have been modeled and rendered in Cinema 4D, including: == Cinebench == Cinebench is a cross-platform test suite which tests a computer's hardware capabilities. It can be used as a test for Cinema 4D's 3D modeling, animation, motion graphic and rendering performance on multiple CPU cores. The program "target[s] a certain niche and [is] better suited for high-end desktop and workstation platforms". Cinebench is commonly used to demonstrate hardware capabilities at tech shows to show a CPU performance, especially by tech YouTubers and review sites.
BeHafizh
BeHafizh is a mobile application to assist in the effort to memorize Qur'anic verses. The software runs on the Android operating system. This application was made by a team from Gadjah Mada University (UGM) consisting of Farid Amin Ridwanto, Rian Adam Rajagede and Alfian Try Putranto in order to participate in the National Student Musabaqoh Tilawatil Quran (MTQ) held at University of Indonesia (UI) on 1- August 8, 2015. This application then won a gold medal in the branch of Computer Application Design in the competition. == Features == === Audio Player === Audio player, paragraph can be played repeatedly, with pause, and can be done on a certain range of Quranic verses. === Memorization Test === Memorization testing continues users to improve their memorization. Memorization Recorders improves user's ability to recite Quran. === Colour indicators === === Achievements === === Reminders ===
Language identification
In natural language processing, language identification or language guessing is the problem of determining which natural language a given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods. == Overview == === Logical approach === A common non-statistical intuitive approach (though highly uncertain) is to look for common letter combinations, or distinctive diacritics or punctuation. === Statistical approach === There are several statistical approaches to language identification. An older statistical method by Grefenstette was based on the frequency of short n-grams, which are often function morphemes. For example, "ing" is more common in English than in French, while the sequence "que" is more common in French. Given a new page found on the Web, one counts the number of occurrences of each such short sequence and picks the language whose frequency table it matches the most. One technique is to compare the compressibility of the text to the compressibility of texts in a set of known languages. This approach is known as mutual information based distance measure. The same technique can also be used to empirically construct family trees of languages which closely correspond to the trees constructed using historical methods. Mutual information based distance measure is essentially equivalent to more conventional model-based methods and is not generally considered to be either novel or better than simpler techniques. Another technique, as described by Cavnar and Trenkle (1994) and Dunning (1994) is to create a language n-gram model from a "training text" for each of the languages. These models can be based on characters (Cavnar and Trenkle) or encoded bytes (Dunning); in the latter, language identification and character encoding detection are integrated. Then, for any piece of text needing to be identified, a similar model is made, and that model is compared to each stored language model. The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result. Also problematic for any approach are pieces of input text that are composed of several languages, as is common on the Web. As of 2025, a commonly used baseline method is via the fastText library, which has comparable classification accuracy as deep learning techniques, but much faster. == Identifying similar languages == One of the great bottlenecks of language identification systems is to distinguish between closely related languages. Similar languages like Bulgarian and Macedonian or Indonesian and Malay present significant lexical and structural overlap, making it challenging for systems to discriminate between them. In 2014 the DSL shared task has been organized providing a dataset (Tan et al., 2014) containing 13 different languages (and language varieties) in six language groups: Group A (Bosnian, Croatian, Serbian), Group B (Indonesian, Malaysian), Group C (Czech, Slovak), Group D (Brazilian Portuguese, European Portuguese), Group E (Peninsular Spanish, Argentine Spanish), Group F (American English, British English). The best system reached performance of over 95% results (Goutte et al., 2014). Results of the DSL shared task are described in Zampieri et al. 2014. == Software == Apache OpenNLP includes char n-gram based statistical detector and comes with a model that can distinguish 103 languages Apache Tika contains a language detector for 18 languages
Facebook Messenger
Messenger (formerly known as Facebook Messenger) is an American proprietary instant messaging service developed by Meta Platforms, the company that operates Facebook. Originally developed as Facebook Chat in 2008, the client application of Messenger is currently available on iOS and Android mobile platforms, Windows and macOS desktop platforms, through the Messenger.com web application, and on the standalone Meta Portal hardware. Messenger is used to send messages and exchange photos, videos, stickers, audio, and files, and also react to other users' messages and interact with bots. The service also supports voice and video calling. The standalone apps support using multiple accounts, conversations with end-to-end encryption, and playing games. There are also group chats where you can connect with multiple people at once in a private space such as Panama Chat. With a monthly userbase of over 1 billion people, it is among the largest social media platforms. == History == Following tests of a new instant messaging platform on Facebook in March 2008, the feature, then-titled "Facebook Chat", was gradually released to users in April 2008. Facebook revamped its messaging platform in November 2010, and subsequently acquired group messaging service Beluga in March 2011, which the company used to launch its standalone iOS and Android mobile apps on August 9, 2011. Facebook later launched a BlackBerry version in October 2011. An app for Windows Phone, though lacking features including voice messaging and chat heads, was released in March 2014. In April 2014, Facebook announced that the messaging feature would be removed from the main Facebook app and users will be required to download the separate Messenger app. An iPad-optimized version of the iOS app was released in July 2014. On April 8, 2015, Facebook launched a website interface for Messenger. A Tizen app was released on July 13, 2015. Facebook launched Messenger for Windows 10 in April 2016. In October 2016, Facebook released Messenger Lite, a stripped-down version of Messenger with a reduced feature set. The app is aimed primarily at old Android phones and regions where high-speed Internet is not widely available. In April 2017, Messenger Lite was expanded to 132 more countries. In May 2017, Facebook revamped the design for Messenger on Android and iOS, bringing a new home screen with tabs and categorization of content and interactive media, red dots indicating new activity, and relocated sections. Facebook announced a Messenger program for Windows 7 in a limited beta test in November 2011. The following month, Israeli blog TechIT leaked a download link for the program, with Facebook subsequently confirming and officially releasing the program. The program was eventually discontinued in March 2014. A Firefox web browser add-on was released in December 2012, but was also discontinued in March 2014. In December 2017, Facebook announced Messenger Kids, a new app aimed for persons under 13 years of age. The app comes with some differences compared to the standard version. In 2019, Messenger announced to be the 2nd most downloaded mobile app of the decade, from 2011 to 2019. In December 2019, Messenger dropped support for users to sign in using only a mobile number, meaning that users must sign in to a Facebook account in order to use the service. In March 2020, Facebook started to ship its dedicated Messenger for macOS app through the Mac App Store. The app is currently live in regions including France, Australia, Mexico, Poland, and many others. In April 2020, Facebook began rolling out a new feature called Messenger Rooms, a video chat feature that allows users to chat with up to 50 people at a time. The feature rivals Zoom, an application that gained a lot of popularity during the COVID-19 pandemic. Privacy concerns arose since the feature uses the same data collection policies as mainstream Facebook. In July 2020, Facebook added a new feature in Messenger that lets iOS users to use Apple's Face ID or Touch ID to lock their chats. The feature is called App Lock and is a part of several changes in Messenger regarding privacy and security. The option to view only "Unread Threads" was removed from the inbox, requiring the account holder to scroll through the entire inbox to be certain every unread message has been seen. On October 13, 2020, the Messenger application introduced cross-app messaging with Instagram, which was launched in September 2021. In addition to the integrated messaging, the application announced the introduction of a new logo, which should be an amalgamation of the Messenger and Instagram logo. The desktop app of Messenger was shut down on December 15, 2025. Messaging services were moved to the Facebook website or Messenger's site for those without an account on the former. The Messenger site was discontinued on April 16, 2026. Messaging services were moved to the Facebook website on the morning of April 17, 2026 without an Messenger account on the former to use Facebook account. == Features == The following is a table of features available in Messenger, as well as their geographical coverage and what devices they are available on. In addition there is a vanishing message feature. In addition there is an audio recording feature which allows audio recordings of up to one minute which may or may not be vanishing: === Messenger Rooms === It is a video conferencing feature of Messenger. It allows users to add up to 50 people at a time. Messenger Rooms does not require a Facebook account. Messenger Rooms competes with other services such as Zoom. Back in 2014, Facebook introduced an unrelated, stand-alone application named Rooms, letting users create places for users with similar interests, with users being anonymous to others. This was shut down in December 2015. In April 2020, during the COVID-19 pandemic, Facebook revealed video conferencing features for Messenger called Messenger Rooms. This was seen as a response to the popularity of other video conferencing platforms such as Zoom and Skype in the midst of the COVID-19 pandemic. Messenger Rooms allows users to add up to 50 people per room, without restrictions on time. It does not require a Facebook account or a separate app from Messenger. When used, it only prompts the user for basic information. Users can add 360° virtual backgrounds, mood lighting, and other AR effects as well as share screens. To prevent unwanted participants from joining, users can lock rooms and remove participants. Some have voiced concerns in regards to Messenger Room's privacy and how its parent, Facebook, handles data. Messenger Rooms, unlike some of its competitors, does not use end-to-end encryption. In addition, there have been concerns over how Messenger Rooms collects user data. == Monetization == In January 2017, Facebook announced that it was testing showing advertisements in Messenger's home feed. At the time, the testing was limited to a "small number of users in Australia and Thailand", with the ad format being swipe-based carousel ads. In July, the company announced that they were expanding the testing to a global audience. Stan Chudnovsky, head of Messenger, told VentureBeat that "We'll start slow ... When the average user can be sure to see them we truly don't know because we're just going to be very data-driven and user feedback-driven on making that decision". Facebook told TechCrunch that the advertisements' placement in the inbox depends on factors such as thread count, phone screen size, and pixel density. In a TechCrunch editorial by Devin Coldewey, he described the ads as "huge" in the space they occupy, "intolerable" in the way they appear in the user interface, and "irrelevant" due to the lack of context. Coldewey finished by writing "Advertising is how things get paid for on the internet, including TechCrunch, so I'm not an advocate of eliminating it or blocking it altogether. But bad advertising experiences can spoil a perfectly good app like (for the purposes of argument) Messenger. Messaging is a personal, purposeful use case and these ads are a bad way to monetize it." == Reception == In November 2014, the Electronic Frontier Foundation (EFF) listed Messenger (Facebook chat) on its Secure Messaging Scorecard. It received a score of 2 out of 7 points on the scorecard. It received points for having communications encrypted in transit and for having recently completed an independent security audit. It missed points because the communications were not encrypted with keys the provider didn't have access to, users could not verify contacts' identities, past messages were not secure if the encryption keys were stolen, the source code was not open to independent review, and the security design was not properly documented. As stated by Facebook in its Help Center, there is no way to log out of the Messenger application. Instead, users can choose between different availability statuses, including "Appear as inactive", "S