AI Chat Deep

AI Chat Deep — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Are You Dead?

  Are You Dead? (Chinese: 死了么; pinyin: Sǐleme), also known by its English name Demumu, is a Chinese application designed for young people living alone. It requires setting up one emergency contact and sends automatic notifications if the user has not checked in via the app for consecutive days. The app was released on the App Store on 10 June 2025. In early January 2026, the application gained popularity due to its name and the issue of safety for people living alone, and ranked high on the list of paid applications in the Chinese region of the Apple App Store before being removed. The app's rise in popularity sparked discussions about taboos about death in China. == History == Are You Dead? was founded and operated independently by three people born in the 1990s, and developed in a way that involved remote collaboration in their spare time. According to the New Yellow River report, Guo, the product manager, said that the application was designed for young people and that the inspiration came from the discussion of netizens on social platforms about "an app that everyone must have and will definitely download" that he observed two or three years ago. The name was also "not their original creation". After realizing its potential demand and social significance, the team successfully registered the name and completed the product development in about a month. Regarding the development entity, the New Yellow River cited information from the Apple App Store that the application was developed by Yuejing (Zhengzhou) Technology Service Co., Ltd. According to Tianyancha information, the company was established in March 2025 with a registered capital of 100,000 yuan. === Rise in popularity === The app has been generating buzz on social media since 9 January 2026, due to its name and the topic of safety for people living alone. Around 10 January, it topped the Apple paid app chart. As of 10:00 a.m. on January 11, it ranked first in the App Store paid app chart. It also ranked highly in the utility app chart; it ranked first or second in the paid utility app charts in the United States, Singapore and Hong Kong, and first or fourth in Australia and Spain. The app was subsequently removed from the Apple App Store in China. In terms of functionality and usage, First Financial praised the product for its "simple interface and single function," but pointed out that the interface lacks a display of consecutive check-in days, and there is also the possibility that users may forget to check in, leading to the mistaken issuance of reminders. In addition, since the application mainly relies on email reminders and lacks SMS or telephone notifications, it does not conform to Chinese social habits; the untimely notifications also make the application more like a "death notification" tool, losing its early warning significance for emergency rescue. Hu Xijin, former editor-in-chief of the Global Times, commented on the application on Weibo that it is "really good and can help many lonely elderly people." The Beijing News Quick Review pointed out that the role of technical tools is limited and needs to be connected with real support such as community patrols and liaison mechanisms. Due to the price increase, there have also been questions about the motivation for the price increase. The app's rise in popularity sparked discussions about taboos about death in China. Regarding the popularity of the application, both Southern Metropolis Daily and The Beijing News commented that it reflects the public issue of the risks of living alone and reflects the general anxiety of the living alone group about dying alone. Shangguan News further pointed out that although such technology products provide a certain "low-cost sense of security", their "cold notifications" may not only cause false alarms, but also highlight the embarrassing reality that "there is no one to fill in the emergency contact". It also emphasized that algorithms or applications cannot bring true happiness and called on society to reconstruct a support network full of humanistic care while relying on technology. The name of the application has also sparked controversy. Most netizens believe that the name "Are You Dead?" is unlucky and makes it awkward to share the application. They suggest changing it to a milder name such as "Are You Alive?". Hu Xijin also said that the name change could "give the elderly who use it more psychological comfort" and "believe that the application will become more popular after the name change". Some people also believe that this straightforward name just points out the real dilemma faced by people living alone and has a special meaning. BBC News commented that the name "Are You Dead" is playing a word game with Ele.me (Chinese: 饿了么; pinyin: Èleme) and the pronunciation is also similar. Legal professionals believe that its name is highly similar to Ele.me and may cause confusion. They also raised the possibility of trademark infringement and unfair competition. However, the developers said that the application is developed for young people and death is not a sensitive topic. They will "consider launching a new application that is more suitable for middle-aged and elderly people". They have not yet received any name change requests from relevant departments. On the evening of 13 January 2026, the Are You Dead? team announced that it would change its name to the English brand name Demumu in the upcoming new version. On 11 January, the development team also issued a statement through its official Weibo account, stating that it would study the renaming suggestion and plan to enrich the SMS reminder function, consider adding the message function and explore the direction of age-friendly products; it also stated that it would launch an 8 yuan paid plan to cover the costs of SMS, servers, etc., and welcomed investors to discuss cooperation. In terms of financing and valuation, it plans to sell 10% of the company's shares for 1 million yuan and proposed a valuation of 10 million yuan. On the evening of January 15, the application was removed from the app store in mainland China. == Functions == The application does not require users to enter phone numbers or other information to register. After filling in their name and setting an emergency contact, users can click the sign-in button every day. If they fail to sign in for two consecutive days, the system will send an email reminder to the emergency contact the next day. In addition, users can also bind a smart bracelet to monitor physiological signs, pre-designate a hearse driver and funeral music, and trigger the "one-click body collection" function when no pulse is detected. The application was initially available for free download, but a one yuan paid download option was introduced at the end of 2025. In January 2026, the application team issued a statement saying that an 8 yuan paid option would be launched based on the costs of SMS, servers, etc.

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  CARE Principles for Indigenous Data Governance

  The CARE Principles for Indigenous Data Governance are a set of principles intended to guide open data projects in engaging Indigenous Peoples rights and interests. CARE was created in 2019 by the International Indigenous Data Sovereignty Interest Group, a group that is a part of the Research Data Alliance. It outlines collective rights related to open data in the context of the United Nations Declaration on the Rights of Indigenous Peoples and Indigenous data sovereignty. CARE is an acronym which stands for Collective Benefit, Authority to Control, Responsibility, Ethics. The CARE Principles are 'people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination', and intended as a complement to the data-oriented perspective of other standards such as FAIR data (findable, accessible, interoperable, reusable). The CARE principles have been embedded into the Beta version of Standardised Data on Initiatives (STARDIT). CARE principles were the basis of a submission to the UN's Global Digital Compact.

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  Hilscher netx network controller

  The netX network controller family (based on ASICs), developed by Hilscher Gesellschaft für Systemautomation mbH, is a solution for implementing all proven Fieldbus and Real-Time Ethernet systems. It was the first Multi-Protocol ASIC which combines Real-Time-Ethernet and Fieldbus System in one solution. The Multiprotocol functionality is done over a flexible cpu sub system called XC. Through exchanging some microcode the XC is able to realize beside others a PROFINET IRT Switch, EtherCAT Slave, Ethernet Powerlink HUB, PROFIBUS, CAN bus, CC-Link Industrial Networks Interface. == The Hilscher netX family == === Multiplex Matrix IOs (MMIO) === The Multiplex Matrix is a set of PINs which could be configured freely with peripheral functions. Options are CAN, UART, SPI, I2C, GPIOs, PIOs and SYNC Trigger. === GPIOs === The GPIOs from Hilscher are able to generate Interrupts, could count level or flags, or could be connected to a timer unit to auto generate a PWM. The Resolution of the PWM is normally 10ns. In some netX ASICS is a dedicated Motion unit with a resolution if 1ns is available.

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  Stegomalware

  Stegomalware is a form of malicious software that leverages steganography techniques to conceal its code, configuration data, or command-and-control (C&C) communications within seemingly benign digital media such as images, audio files, videos, documents, or network traffic. It typically embeds encrypted or obfuscated payloads into digital media and only extracts and executes them at runtime, which makes traditional signature-based and sandbox-based detection significantly more difficult. Stegomalware has been observed in attacks ranging from advanced persistent threats (APTs) to financially motivated cybercrime, and is now the subject of dedicated academic surveys, research projects, and international law-enforcement initiatives. The key distinction between stegomalware and traditional obfuscated malware lies in the encoding location. After obfuscation, malicious code remains present within the executable and can theoretically be discovered through static analysis. In contrast, stegomalware hides the payload entirely within a cover medium (image, audio, etc.), remaining invisible until the malware dynamically extracts and executes it at runtime. == History == The term stegomalware was formally introduced by researchers Águila, Laskov, and others in the context of mobile malware and presented at the Inscrypt (Information Security and Cryptology) conference in 2014. This marked the first academic formalization of the concept, though earlier work had already identified that botnets and mobile malware could use steganography and covert channels for command-and-control communication over probabilistically unobservable channels. Since its introduction, stegomalware has evolved from a theoretical concern to a documented threat. In 2011, the APT operation known as "Operation Shady RAT" became one of the first documented cases of stegomalware in the wild, using digital images to hide Internet Protocol addresses and command-and-control server addresses. The same year, the Duqu malware (targeting industrial manufacturers) embedded victim data into JPEG image files before exfiltration, making the data transfer virtually undetectable to network-level security tools. From 2014 onwards, stegomalware became more prevalent in organized cybercrime and advanced persistent threat campaigns. Notable examples include Zeus/Zbot, which masked configuration data in images; Gatak/Stegoloader, which hid shellcode in PNG files; TeslaCrypt, which embedded C&C commands in JPEGs; and Cerber, which concealed ransomware payloads within images. By the 2010s, stegomalware had become established as a preferred evasion technique for espionage, financial theft, and ransomware distribution campaigns. Recent surveys (2020–2025) document that stegomalware has increasingly been exploited by adversaries targeting banks, enterprises, government agencies, educational institutions, and internet users via malvertising campaigns. The technique is now considered a sophisticated method of attack worthy of dedicated international law-enforcement attention. == Technical Characteristics and Definitions == Stegomalware operates through a three-component architecture: Stegotext (R): An innocent-looking digital asset (image, audio file, etc.) into which the malicious payload is embedded. Secret key (sk): A key used by the embedding and extraction algorithms, typically hardcoded into the malware. Payload (p): The actual malicious code, configuration data, or C&C commands hidden within the stegotext. The malware extracts the payload at runtime using the secret key and either executes it directly or uses it to download additional stages of the attack. Stegomalware can be classified into several types based on deployment method: Type 0 (Autonomous): Both the stegotext and extraction algorithm are embedded within the malware application itself. The malicious payload is extracted and executed locally without external communication. Type I (Update): The stegotext and secret key are downloaded from a remote server at runtime; only the extraction algorithm is included in the malware. This variant is more flexible, allowing attackers to push updated payloads. Type II (External Algorithm): Neither the stegotext nor the extraction algorithm are distributed with the malware; both are fetched from an attacker-controlled infrastructure, providing maximum flexibility and evasion. == Steganography techniques == === Spatial domain methods === Stegomalware predominantly uses steganographic methods designed for images, as images are the most common cover medium in the wild. The most basic spatial domain technique is Least Significant Bit (LSB) substitution, which replaces the least significant bits of pixel color values with payload bits. While simple and easy to implement, LSB is also relatively easy to detect through statistical analysis. More sophisticated spatial domain techniques include: HUGO (High Undetectable steGO) (2010): Minimizes detectable distortion by distributing the payload across multiple pixels, achieving embedding capacity with reduced statistical footprint. WOW (Wavelet Obtained Weights) (2012): Embeds data preferentially in textured regions of images where modifications are less perceptually noticeable. UNIWARD (Universal Wavelet Relative Distortion) (2014): Uses a universal distortion function applicable to multiple image formats, balancing payload capacity with undetectability. HILL (2014): Applies high-pass and low-pass filters to identify robust embedding regions. MiPOD (Minimizing the Power of Optimal Detector) (2016): Designed to minimize the power of theoretical optimal steganalysis detectors. === Transform domain methods === Transform domain techniques convert images into the frequency domain (e.g., using DCT or DWT) before embedding, allowing for more robust hiding in JPEG and other compressed formats: Embedding in DCT coefficients (used in JPEG compression) Embedding in DWT coefficients (used in lossless formats) Spread spectrum techniques, which distribute the payload across many frequency components Transform domain methods are generally more resistant to noise, compression, and image transformations than spatial methods. === Generative adversarial network (GAN) methods === Recent advances in machine learning have introduced GAN-based steganography, where a generative model produces stego images that minimize detectable artifacts: SGAN (Steganographic GAN) (2017): First GAN applied to steganography, using a generator, discriminator, and steganalysis network. ASDL-GAN (2017): Performs automatic steganographic distortion learning at the pixel level. SteganoGAN (2019): Improves upon earlier GAN models, achieving higher embedding capacity and robustness. HiGAN (Hiding Images GAN) (2020): Enables hiding one image within another while maintaining visual plausibility. GAN-based approaches are more resilient to standard steganalysis attacks but remain an emerging threat requiring further research. == Notable malware campaigns == Stegomalware has been documented in numerous high-profile cyber attacks and campaigns. Notable examples include: Operation Shady RAT (2011): Used digital images to hide command-and-control server addresses in targeted espionage. Duqu (2011): Embedded victim data into JPEG files to exfiltrate industrial control system information. Zeus/Zbot (2014): Masked banking configuration data inside JPEG files exploited via malvertising. Gatak/Stegoloader (2015): Hid shellcode in PNG files for software licensing attacks and bot command execution. TeslaCrypt (2015): Embedded C&C commands and ransomware keys in JPEG images. Cerber (2016): Concealed executable ransomware code in JPEG files distributed via phishing. DNSChanger (2016): Embedded malicious code in PNG files for DNS hijacking campaigns. Sundown Exploit Kit (2017): Distributed exploit code in PNG files via malvertising. AdGholas (2017): Used JPEG steganography to distribute ransomware via malvertising. Synccrypt (2017): Hidden ransomware components in JPEG-steganographic encrypted archives. ZeroT/PlugX (2017): Hid Remote Access Trojan payloads in BMP files for espionage. Loki Bot (2018): Concealed malware installers in JPEG and video files. Waterbug (APT28) (2019): Injected malicious DLLs into WAV audio files. Shlayer (macOS adware) (2019): Hid malicious URLs in JPEG files via malvertising. === Attack vectors === The most common attack vectors for stegomalware include: Phishing emails with malicious attachments or links Malvertising campaigns using malicious banner advertisements Exploit kits through compromised or malicious websites Legitimate application vulnerabilities (e.g., watering-hole attacks) Fake software distribution (cracked software, keygen tools) === Exploitation stages === Stegomalware typically serves one or more roles in attack lifecycles: Payload delivery: Stego images contain full executable code or shellcode. C&C communication: Hidden data contains server addresses or command instructio

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  Secure element

  A secure element (SE) is a secure operating system (OS) in a tamper-resistant processor chip or secure component. It can protect assets (root of trust, sensitive data, keys, certificates, applications) against high-level software and hardware attacks. Applications that process this sensitive data on an SE are isolated and so operate within a controlled environment not affected by software (including possible malware) found elsewhere on the OS. The hardware and embedded software meet the requirements of the Security IC Platform Protection Profile [PP 0084] including resistance to physical tampering scenarios described within it. More than 96 billion secure elements were produced and shipped between 2010 and 2021. SEs exist in various form factors, as devices such as smart cards, UICCs, or smart microSD cards, or embedded, or integrated, as parts of larger devices. SEs are an evolution of the chips in earlier smart cards, which have been adapted to suit the needs of numerous use cases, such as smartphones, tablets, set-top boxes, wearables, connected cars, and other internet of things (IoT) devices. The technology is widely used by technology firms such as Oracle, Apple and Samsung. SEs provide secure isolation, storage and processing for applications (called applets) they host while being isolated from the external world (e.g. rich OS and application processor when embedded in a smartphone) and from other applications running on the SE. Java Card and MULTOS are the most deployed standardized multi-application operating systems currently used to develop applications running on SEs. Since 1999, GlobalPlatform has been the body responsible for standardizing secure element technologies to support a dynamic model of application management in a multi-actor model. GlobalPlatform also runs Functional and Security Certification programmes for secure elements, and hosts a list of Functional Certified and Security Certified products. GlobalPlatform technology is also embedded in other standards such as ETSI SCP (now SET) since release 7. A Common Criteria Secure Element Protection Profile has been released targeting EAL4+ level with ALC_DVS.2 and AVA_VAN.5 extension to standardize the security features of a secure element across markets.

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  Payment tokenization

  Payment tokenization is a data security process that replaces sensitive payment information, such as credit card numbers, with a unique identifier or "token." This token can be used in place of actual data during transactions but has no exploitable value if breached, thereby reducing the risk of data theft and fraud. == Overview == Payment tokenization is generally categorized into two types: security tokens and payment tokens. Security tokens, also known as post-authorization tokens, are used to replace sensitive information like Primary Account Numbers (PANs), such as credit card numbers either after a payment is authorized or for storing data securely (data-at-rest), such as in merchant databases. These models have been in use since the mid-2000s, following the introduction of the Payment Card Industry Data Security Standard in 2004, which established standards for safeguarding cardholder data. The Payment Card Industry Security Standards Council's 2011 Tokenization Guidelines and the proposed American National Standards Institute X9 standards emphasize using tokens primarily to secure sensitive information, not as replacements for payment credentials processed over financial networks. Traditionally, merchants stored PANs to support backend operations such as settlements, reconciliations, chargebacks, loyalty programs, and customer service. However, with the adoption of security tokenization, merchants can substitute PANs with tokens in their systems. This not only reduces their exposure to fraud but also helps minimize the scope and cost of PCI-DSS compliance, offering a more secure and efficient way to manage cardholder data. == Applications == Payment tokenization is widely used by mobile wallets such as Apple Pay, Google Pay, and Samsung Pay use tokenization to safely store card data on devices. E-commerce platforms rely on it to securely retain customer payment details for recurring purchases. At the physical point of sale, EMV-enabled systems use tokenization to protect card information during in-store transactions. Also, subscription billing services implement tokenization to manage and safeguard payment credentials for ongoing charges.

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  Smart-ID

  Smart-ID is an electronic authentication tool developed by SK ID Solutions, an Estonian company. Users can log in to various electronic services and sign documents with an electronic signature. Smart-ID meets the European Union's eIDAS Regulation and the European Central Bank's standards for a secure authentication solution. Smart-ID is a Qualified Signature Creator Device (QSCD) that can issue a Qualified Electronic Signature (QES). The Smart-ID app is compatible with both iOS and Android devices and does not require a SIM card. By 2021, the Smart-ID application was launched in the Huawei AppGallery. As of May 2023, Smart-ID has 3,298,969 active users across the Baltic States (Latvia, Lithuania, and Estonia). Every month, the Smart-ID processes 79 million transactions. In March 2023, Smart-ID users made an exceptional 85 million transactions. == History == In November 2016, SK ID Solutions debuted the Smart-ID tool for the first time at its annual conference. In February 2017, eKool, Starman, and Tallinn Kaubamaja Grupp were the first to implement Smart-ID authentication in their e-services. In March 2017, Smart-ID was added as an authentication option to SEB bank and Swedbank's online banking in all three Baltic States. Dokobit, previously known as DigiDoc, began offering its clients the ability to use e-services using Smart-ID in April 2017. More than 100 service providers had implemented Smart-ID as an authentication solution for their services by November 2019. At its annual conference on November 8, 2018, SK ID Solutions revealed that Smart-ID had been certified as compatible with the QSCD[8] level, the highest level of qualified electronic signature in the European Union, following a rigorous certification process. As a result, the Smart-QES-level ID's electronic signature, the digital counterpart of a handwritten signature, is now available to all users who have registered with the tool. This signature is accepted by all European Union member states. On August 26, 2019, Estonian Information Systems Supervisory Authority experts reviewed Smart-ID (ISSA). Based on the methods provided in the eIDAS Regulation, the expert committee concluded that Smart-ID offers a high level of electronic identification assurance. SK ID Solutions and RIA struck an agreement in September 2019 that allows Smart-ID to authenticate Estonian state e-services via RIA's central authentication service, which is used by over 60 public authorities. Smart-ID accounts created three years ago have expired in January 2020. Therefore, renewing them and performing mandatory updates was necessary. In February 2020, SK ID Solutions announced that Smart-ID could be used to give digital signatures in the national digital signature software DigiDoc4, which up until this moment was only possible with ID cards via Mobile-ID. Users must have at least version 4.2.4.71 or later of the DigiDoc4 software installed on their computers to use this feature. Since February 2020, Smart-ID accounts can now be created with biometric information from an ID card or passport, but only by users who have previously used a Smart-ID account. Since October 2022, 13–17 years old minors in Lithuania are able to create a Smart-ID account using biometric information too. A parent or legal guardian must approve the registration. SK ID Solutions collaborated on the new solution with iProov from the United Kingdom and InnoValor from the Netherlands. TÜV Informationstechnik GmbH, a German certification company, assessed it. Since May 2023, Smart-ID can be used to submit company's annual reports in Estonia and digitally sign anything in the e-business register using your PIN2. == Overview == The Smart-ID app is available for download on Google Play and Apple's App Store. Android 4.4 and iOS 11 are the oldest supported operating system versions for Smart-ID. Smart-ID works on the premise of two-factor authentication, combining an intelligent device (something the user owns) with PINs (something the user knows). A new user must first authenticate themselves with an ID card or a mobile phone number and then confirm a PIN1 and PIN2 code, either manually or automatically produced. The first PIN is used to authenticate a person's identity when accessing e-banking or e-services, while the second PIN is used to support electronic signatures and authenticate transactions (e.g., transfers). The PIN1 code must be four digits long, while the PIN2 code must be five digits long. To log in to an e-service, the user must use Smart-ID as the authentication method and enter their unique Smart-ID user ID. A notification will open on the user's smart device where the software is installed and display a verification code. If the code matches the code presented to the user by the e-service, then the user can confirm the match by entering their PIN1 code. The user must verify the action with their PIN2 code when giving digital signatures. A Smart-ID account is valid for three years. The report can be updated, changed, and deleted at any given time, free of charge. Smart-ID is available in five languages: Estonian, Latvian, Lithuanian, Russian, and English. An international survey conducted in 2021 revealed that Smart-ID is the most reliable authentication solution in Baltic countries. In January 2023, the number of times Smart-ID was used to access State Authentication Service (TARA) in Estonia has surpassed those of Mobile-ID and ID-cards for the first time since July 2022. == Security == Smart-ID is based on Cybernetica's SplitKey authentication and digital signature platform technology, for which the company has filed a patent application. Public key cryptography, digital signature methods, and critical public infrastructures are all used in the technology. The user's PIN is not saved on the device and is only needed to decrypt the private key in the Smart-ID app. When the user inputs the PIN, the private key is cracked, and the answer is transmitted to the Smart-ID server, where a portion of the key given by the app is joined with the server's encrypted key. The app will block the user from accessing it for three hours if they input the incorrect PIN three times in a row. If this happens once again, the app will lock for 24 hours. If this happens a third time, the account will be permanently disabled. PINs cannot be changed or recovered once an account has been created. The user must create a new account if the account is permanently blocked. Smart-ID uses the Apple and Google messaging networks to notify the app when new data is saved on its servers. == Phishing == In February 2019, unknown criminals attempted to create Smart-ID accounts with stolen IDs obtained via phishing customers' text messages and website addresses, according to a monthly report by the Estonian Information System Manager in April 2019. The Latvian Information Technology Security Incident Assessment Body Cert was also notified of these intrusions on March 1. Fraudsters sent emails to potential victims pretending to be bank representatives. The mails linked users to a phishing page after redirecting them to a phony bank login page. Victims were asked to log in using their identification information and PIN1 code. The fraudsters then began the process of generating a new Smart-ID account. As a result, the victim had to input a PIN2 number, which permitted the fraudster to finish setting up a new tab with the victim's personal information. Fraudsters in Estonia were able to log in to multiple e-services utilizing Smart-ID using a Smart-ID account and the victim's data. On behalf of the victims, fraudsters also employed online banking services. Later, the Estonian Information System Manager identified several victims, some of whom had also experienced financial losses. The Estonian Information System Manager requested a full report on the event from SK ID Solutions. The organization opted not to criticize the corporation after receiving the information, although it did propose that the procedure of creating Smart-ID accounts be reviewed. According to the Estonian Banking Association, Estonian banks have not discontinued using Smart-ID and do not think it is required. Smart-ID was exposed to a thorough review process in September 2019 to determine this authentication instrument's level of security. Reviewers discovered no flaws, and SK ID Solutions and the Estonian Information System Manager signed a contract. Estonia later introduced Smart-ID and other authentication mechanisms to the central public services portal.

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  Majal (organization)

  Majal is a regional not-for-profit organization focused on "amplifying voices of dissent" throughout the Middle East and North Africa via digital media. Founded in Bahrain, the organization "creates platforms and web applications that promote freedom of expression and social justice." Majal, which relies on open source platforms, like WordPress and Ruby on Rails, was launched in 2006 by Esra'a Al Shafei as a simple group-blogging idea. However, it has changed course to focus on the development of unique applications and tools. == Objectives and means == Majal's content, in addition to its projects and applications, is free open source content to ensure right to access information for everyone. The organization uses a broad spectrum of social media tools, ranging from written blogs, podcasts, vlogs, comics, video animation and pictures to live broadcasting through radio. == Projects and applications == Majal runs various active projects that include Alliance for Kurdish Rights, The Muslim Network for Baháʼí Rights, a discussion tool for Arab LGBT youth and various Mobile apps. == Funding == Majal is funded through private donations and grants from non-governmental organizations, as well as any potential revenues earned through freelance development. Its primary funders are the Shuttleworth Foundation and the Omidyar Network. In 2008, Majal won the Berkman Award from the Berkman Klein Center for Internet & Society at Harvard University in the Human Rights/Global Advocacy category. This $10,000 award was Majal’s first source of funding. This award is presented to “people or institutions that have made a significant contribution to the Internet and its impact on society over the past decade.” In 2009, the March 18 Movement, a project of Majal, received the Think Social Award, which demonstrates how social media can be used to solve the world’s problems. Esra'a Al-Shafei was named a 2009 Echoing Green Fellow for Civil and Human Rights, a seed funding award for young entrepreneurs engaged in social change. Financially, the fellowship consists of a $60,000 stipend paid over two years. Most recently, MEY has received a grant from the Arab Fund for Arts and Culture for its Mideast Tunes website. == Awards == Winner of Human Rights Tulip 2014 Human Rights Tulip - Human rights - Government.nl Ashoka Changemakers Citizen Media competition in 2011 for their CrowdVoice project. Monaco Media Prize 2011 for Majal founder and director Esra'a Al Shafei in 2011. The BOBS Special Topic Human Rights award in 2011 for the Majal website Migrant Rights. ThinkSocial Award in 2009, as powerful model for how social media can be used to address global problems. Echoing Green, 2009 Fellowship. TEDGlobal 2009 Fellowship. Berkman Award for Internet Innovation from Berkman Klein Center for Internet & Society at Harvard Law School in 2008 for the outstanding contributions to the internet and its impact on society. The Global Journal selected Majal as one of the Top 100 NGOs in 2013. 2013-2014 Shuttleworth Foundation Fellowship. == Leadership == Majal team is led primarily by women. The organization was founded by Esra'a Al Shafei, a blogger from Bahrain in 2006. Ahmed Zidan of Egypt has served for over three years as the Editor-in-Chief of Majal Arabic, and is the co-founder of Ahwaa, and is also a podcaster. Other team members include Mona Kareem, Rima Kalush, Abir Ghattas, Namita Malhotra, and Vani Saraswathi. == 2011 Middle East and North Africa protests == Blogs and video played a role in the documentation of protests throughout the Middle East and North Africa during 2010-2011, also known as the Arab Spring. During this period, MEY's project, CrowdVoice (launched in 2010) helped curate and archive the large amounts of videos, images, and eye-witness reports being aggregated and crowdsourced from across the region. As a result, it had been censored temporarily in Yemen and is still censored in Bahrain. == Media coverage == Majal claims to have received various coverage from news agencies, TV satellite channels, radio stations, newspapers, magazines. For instance, Sky News, CNN, New York Times, BBC, The Guardian, NPR, Time, MTV political blog "Act", VH1, Daily Telegraph, Die Zeit, Frankfurter Rundschau FR-online, Toronto Star, TechCrunch, Rolling Stone Middle East, Abu Dhabi TV, Gulf News, Al-Hasnaa' magazine, ReadWriteWeb, Mashable, The Next Web, Radio Sawt Beirut International, Radio Farda among many others.

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  Egocentric vision

  Egocentric vision or first-person vision is a sub-field of computer vision that entails analyzing images and videos captured by a wearable camera, which is typically worn on the head or on the chest and naturally approximates the visual field of the camera wearer. Consequently, visual data capture the part of the scene on which the user focuses to carry out the task at hand and offer a valuable perspective to understand the user's activities and their context in a naturalistic setting. The wearable camera looking forwards is often supplemented with a camera looking inward at the user's eye and able to measure a user's eye gaze, which is useful to reveal attention and to better understand the user's activity and intentions. == History == The idea of using a wearable camera to gather visual data from a first-person perspective dates back to the 70s, when Steve Mann invented "Digital Eye Glass", a device that, when worn, causes the human eye itself to effectively become both an electronic camera and a television display. Subsequently, wearable cameras were used for health-related applications in the context of Humanistic Intelligence and Wearable AI. Egocentric vision is best done from the point-of-eye, but may also be done by way of a neck-worn camera when eyeglasses would be in-the-way. This neck-worn variant was popularized by way of the Microsoft SenseCam in 2006 for experimental health research works. The interest of the computer vision community into the egocentric paradigm has been arising slowly entering the 2010s and it is rapidly growing in recent years, boosted by both the impressive advances in the field of wearable technology and by the increasing number of potential applications. The prototypical first-person vision system described by Kanade and Hebert, in 2012 is composed by three basic components: a localization component able to estimate the surrounding, a recognition component able to identify object and people, and an activity recognition component, able to provide information about the current activity of the user. Together, these three components provide a complete situational awareness of the user, which in turn can be used to provide assistance to the user or to the caregiver. Following this idea, the first computational techniques for egocentric analysis focused on hand-related activity recognition and social interaction analysis. Also, given the unconstrained nature of the video and the huge amount of data generated, temporal segmentation and summarization were among the first problems addressed. After almost ten years of egocentric vision (2007–2017), the field is still undergoing diversification. Emerging research topics include: Social saliency estimation Multi-agent egocentric vision systems Privacy preserving techniques and applications Attention-based activity analysis Social interaction analysis Hand pose analysis Ego graphical User Interfaces (EUI) Understanding social dynamics and attention Revisiting robotic vision and machine vision as egocentric sensing Activity forecasting Gaze prediction == Technical challenges == Today's wearable cameras are small and lightweight digital recording devices that can acquire images and videos automatically, without the user intervention, with different resolutions and frame rates, and from a first-person point of view. Therefore, wearable cameras are naturally primed to gather visual information from our everyday interactions since they offer an intimate perspective of the visual field of the camera wearer. Depending on the frame rate, it is common to distinguish between photo-cameras (also called lifelogging cameras) and video-cameras. The former (e.g., Narrative Clip and Microsoft SenseCam), are commonly worn on the chest, and are characterized by a very low frame rate (up to 2fpm) that allows to capture images over a long period of time without the need of recharging the battery. Consequently, they offer considerable potential for inferring knowledge about e.g. behaviour patterns, habits or lifestyle of the user. However, due to the low frame-rate and the free motion of the camera, temporally adjacent images typically present abrupt appearance changes so that motion features cannot be reliably estimated. The latter (e.g., Google Glass, GoPro), are commonly mounted on the head, and capture conventional video (around 35fps) that allows to capture fine temporal details of interactions. Consequently, they offer potential for in-depth analysis of daily or special activities. However, since the camera is moving with the wearer head, it becomes more difficult to estimate the global motion of the wearer and in the case of abrupt movements, the images can result blurred. In both cases, since the camera is worn in a naturalistic setting, visual data present a huge variability in terms of illumination conditions and object appearance. Moreover, the camera wearer is not visible in the image and what he/she is doing has to be inferred from the information in the visual field of the camera, implying that important information about the wearer, such for instance as pose or facial expression estimation, is not available. == Applications == A collection of studies published in a special theme issue of the American Journal of Preventive Medicine has demonstrated the potential of lifelogs captured through wearable cameras from a number of viewpoints. In particular, it has been shown that used as a tool for understanding and tracking lifestyle behaviour, lifelogs would enable the prevention of noncommunicable diseases associated to unhealthy trends and risky profiles (such as obesity and depression). In addition, used as a tool of re-memory cognitive training, lifelogs would enable the prevention of cognitive and functional decline in elderly people. More recently, egocentric cameras have been used to study human and animal cognition, human-human social interaction, human-robot interaction, human expertise in complex tasks. Other applications include navigation/assistive technologies for the blind, monitoring and assistance of industrial workflows, and augmented reality interfaces.

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  Forking lemma

  The forking lemma is any of a number of related lemmas in cryptography research. The lemma states that if an adversary (typically a probabilistic Turing machine), on inputs drawn from some distribution, produces an output that has some property with non-negligible probability, then with non-negligible probability, if the adversary is re-run on new inputs but with the same random tape, its second output will also have the property. This concept was first used by David Pointcheval and Jacques Stern in "Security proofs for signature schemes," published in the proceedings of Eurocrypt 1996. In their paper, the forking lemma is specified in terms of an adversary that attacks a digital signature scheme instantiated in the random oracle model. They show that if an adversary can forge a signature with non-negligible probability, then there is a non-negligible probability that the same adversary with the same random tape can create a second forgery in an attack with a different random oracle. The forking lemma was later generalized by Mihir Bellare and Gregory Neven. The forking lemma has been used and further generalized to prove the security of a variety of digital signature schemes and other random-oracle based cryptographic constructions. == Statement of the lemma == The generalized version of the lemma is stated as follows. Let A be a probabilistic algorithm, with inputs (x, h1, ..., hq; r) that outputs a pair (J, y), where r refers to the random tape of A (that is, the random choices A will make). Suppose further that IG is a probability distribution from which x is drawn, and that H is a set of size h from which each of the hi values are drawn according to the uniform distribution. Let acc be the probability that on inputs distributed as described, the J output by A is greater than or equal to 1. We can then define a "forking algorithm" FA that proceeds as follows, on input x: Pick a random tape r for A. Pick h1, ..., hq uniformly from H. Run A on input (x, h1, ..., hq; r) to produce (J, y). If J = 0, then return (0, 0, 0). Pick h'J, ..., h'q uniformly from H. Run A on input (x, h1, ..., hJ−1, h'J, ..., h'q; r) to produce (J', y'). If J' = J and hJ ≠ h'J then return (1, y, y'), otherwise, return (0, 0, 0). Let frk be the probability that FA outputs a triple starting with 1, given an input x chosen randomly from IG. Then frk ≥ acc ⋅ ( acc q − 1 h ) . {\displaystyle {\text{frk}}\geq {\text{acc}}\cdot \left({\frac {\text{acc}}{q}}-{\frac {1}{h}}\right).} === Intuition === The idea here is to think of A as running two times in related executions, where the process "forks" at a certain point, when some but not all of the input has been examined. In the alternate version, the remaining inputs are re-generated but are generated in the normal way. The point at which the process forks may be something we only want to decide later, possibly based on the behavior of A the first time around: this is why the lemma statement chooses the branching point (J) based on the output of A. The requirement that hJ ≠ h'J is a technical one required by many uses of the lemma. (Note that since both hJ and h'J are chosen randomly from H, then if h is large, as is usually the case, the probability of the two values not being distinct is extremely small.) === Example === For example, let A be an algorithm for breaking a digital signature scheme in the random oracle model. Then x would be the public parameters (including the public key) A is attacking, and hi would be the output of the random oracle on its ith distinct input. The forking lemma is of use when it would be possible, given two different random signatures of the same message, to solve some underlying hard problem. An adversary that forges once, however, gives rise to one that forges twice on the same message with non-negligible probability through the forking lemma. When A attempts to forge on a message m, we consider the output of A to be (J, y) where y is the forgery, and J is such that m was the Jth unique query to the random oracle (it may be assumed that A will query m at some point, if A is to be successful with non-negligible probability). (If A outputs an incorrect forgery, we consider the output to be (0, y).) By the forking lemma, the probability (frk) of obtaining two good forgeries y and y' on the same message but with different random oracle outputs (that is, with hJ ≠ h'J) is non-negligible when acc is also non-negligible. This allows us to prove that if the underlying hard problem is indeed hard, then no adversary can forge signatures. This is the essence of the proof given by Pointcheval and Stern for a modified ElGamal signature scheme against an adaptive adversary. == Known issues with application of forking lemma == The reduction provided by the forking lemma is not tight. Pointcheval and Stern proposed security arguments for Digital Signatures and Blind Signature using Forking Lemma. Claus P. Schnorr provided an attack on blind Schnorr signatures schemes, with more than p o l y l o g ( n ) {\displaystyle polylog(n)} concurrent executions (the case studied and proven secure by Pointcheval and Stern). A polynomial-time attack, for Ω ( n ) {\displaystyle \Omega (n)} concurrent executions, was shown in 2020 by Benhamouda, Lepoint, Raykova, and Orrù. Schnorr also suggested enhancements for securing blind signatures schemes based on discrete logarithm problem.

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  Format-preserving encryption

  In cryptography, format-preserving encryption (FPE), refers to encrypting in such a way that the output (the ciphertext) is in the same format as the input (the plaintext). The meaning of "format" varies. Typically only finite sets of characters are used; numeric, alphabetic or alphanumeric. For example: Encrypting a 16-digit credit card number so that the ciphertext is another 16-digit number. Encrypting an English word so that the ciphertext is another English word. Encrypting an n-bit number so that the ciphertext is another n-bit number (this is the definition of an n-bit block cipher). For such finite domains, and for the purposes of the discussion below, the cipher is equivalent to a permutation of N integers {0, ... , N−1} where N is the size of the domain. == Motivation == === Restricted field lengths or formats === One motivation for using FPE comes from the problems associated with integrating encryption into existing applications, with well-defined data models. A typical example would be a credit card number, such as 1234567812345670 (16 bytes long, digits only). Adding encryption to such applications might be challenging if data models are to be changed, as it usually involves changing field length limits or data types. For example, output from a typical block cipher would turn credit card number into a hexadecimal (e.g.0x96a45cbcf9c2a9425cde9e274948cb67, 34 bytes, hexadecimal digits) or Base64 value (e.g. lqRcvPnCqUJc3p4nSUjLZw==, 24 bytes, alphanumeric and special characters), which will break any existing applications expecting the credit card number to be a 16-digit number. Apart from simple formatting problems, using AES-128-CBC, this credit card number might get encrypted to the hexadecimal value 0xde015724b081ea7003de4593d792fd8b695b39e095c98f3a220ff43522a2df02. In addition to the problems caused by creating invalid characters and increasing the size of the data, data encrypted using the CBC mode of an encryption algorithm also changes its value when it is decrypted and encrypted again. This happens because the random seed value that is used to initialize the encryption algorithm and is included as part of the encrypted value is different for each encryption operation. Because of this, it is impossible to use data that has been encrypted with the CBC mode as a unique key to identify a row in a database. FPE attempts to simplify the transition process by preserving the formatting and length of the original data, allowing a drop-in replacement of plaintext values with their ciphertexts in legacy applications. == Comparison to truly random permutations == Although a truly random permutation is the ideal FPE cipher, for large domains it is infeasible to pre-generate and remember a truly random permutation. So the problem of FPE is to generate a pseudorandom permutation from a secret key, in such a way that the computation time for a single value is small (ideally constant, but most importantly smaller than O(N)). == Comparison to block ciphers == An n-bit block cipher technically is a FPE on the set {0, ..., 2n-1}. If an FPE is needed on one of these standard sized sets (for example, n = 64 for DES and n = 128 for AES) a block cipher of the right size can be used. However, in typical usage, a block cipher is used in a mode of operation that allows it to encrypt arbitrarily long messages, and with an initialization vector as discussed above. In this mode, a block cipher is not an FPE. == Definition of security == In cryptographic literature (see most of the references below), the measure of a "good" FPE is whether an attacker can distinguish the FPE from a truly random permutation. Various types of attackers are postulated, depending on whether they have access to oracles or known ciphertext/plaintext pairs. == Algorithms == In most of the approaches listed here, a well-understood block cipher (such as AES) is used as a primitive to take the place of an ideal random function. This has the advantage that incorporation of a secret key into the algorithm is easy. Where AES is mentioned in the following discussion, any other good block cipher would work as well. === The FPE constructions of Black and Rogaway === Implementing FPE with security provably related to that of the underlying block cipher was first undertaken in a paper by cryptographers John Black and Phillip Rogaway, which described three ways to do this. They proved that each of these techniques is as secure as the block cipher that is used to construct it. This means that if the AES algorithm is used to create an FPE algorithm, then the resulting FPE algorithm is as secure as AES because an adversary capable of defeating the FPE algorithm can also defeat the AES algorithm. Therefore, if AES is secure, then the FPE algorithms constructed from it are also secure. In all of the following, E denotes the AES encryption operation that is used to construct an FPE algorithm and F denotes the FPE encryption operation. ==== FPE from a prefix cipher ==== One simple way to create an FPE algorithm on {0, ..., N-1} is to assign a pseudorandom weight to each integer, then sort by weight. The weights are defined by applying an existing block cipher to each integer. Black and Rogaway call this technique a "prefix cipher" and showed it was provably as good as the block cipher used. Thus, to create an FPE on the domain {0,1,2,3}, given a key K apply AES(K) to each integer, giving, for example, weight(0) = 0x56c644080098fc5570f2b329323dbf62 weight(1) = 0x08ee98c0d05e3dad3eb3d6236f23e7b7 weight(2) = 0x47d2e1bf72264fa01fb274465e56ba20 weight(3) = 0x077de40941c93774857961a8a772650d Sorting [0,1,2,3] by weight gives [3,1,2,0], so the cipher is F(0) = 3 F(1) = 1 F(2) = 2 F(3) = 0 This method is only useful for small values of N. For larger values, the size of the lookup table and the required number of encryptions to initialize the table gets too big to be practical. ==== FPE from cycle walking ==== If there is a set M of allowed values within the domain of a pseudorandom permutation P (for example P can be a block cipher like AES), an FPE algorithm can be created from the block cipher by repeatedly applying the block cipher until the result is one of the allowed values (within M). CycleWalkingFPE(x) { if P(x) is an element of M then return P(x) else return CycleWalkingFPE(P(x)) } The recursion is guaranteed to terminate. (Because P is one-to-one and the domain is finite, repeated application of P forms a cycle, so starting with a point in M the cycle will eventually terminate in M.) This has the advantage that the elements of M do not have to be mapped to a consecutive sequence {0,...,N-1} of integers. It has the disadvantage, when M is much smaller than P's domain, that too many iterations might be required for each operation. If P is a block cipher of a fixed size, such as AES, this is a severe restriction on the sizes of M for which this method is efficient. For example, an application may want to encrypt 100-bit values with AES in a way that creates another 100-bit value. With this technique, AES-128-ECB encryption can be applied until it reaches a value which has all of its 28 highest bits set to 0, which will take an average of 228 iterations to happen. ==== FPE from a Feistel network ==== It is also possible to make a FPE algorithm using a Feistel network. A Feistel network needs a source of pseudo-random values for the sub-keys for each round, and the output of the AES algorithm can be used as these pseudo-random values. When this is done, the resulting Feistel construction is good if enough rounds are used. One way to implement an FPE algorithm using AES and a Feistel network is to use as many bits of AES output as are needed to equal the length of the left or right halves of the Feistel network. If a 24-bit value is needed as a sub-key, for example, it is possible to use the lowest 24 bits of the output of AES for this value. This may not result in the output of the Feistel network preserving the format of the input, but it is possible to iterate the Feistel network in the same way that the cycle-walking technique does to ensure that format can be preserved. Because it is possible to adjust the size of the inputs to a Feistel network, it is possible to make it very likely that this iteration ends very quickly on average. In the case of credit card numbers, for example, there are 1015 possible 16-digit credit card numbers (accounting for the redundant check digit), and because the 1015 ≈ 249.8, using a 50-bit wide Feistel network along with cycle walking will create an FPE algorithm that encrypts fairly quickly on average. === The Thorp shuffle === A Thorp shuffle is like an idealized card-shuffle, or equivalently a maximally-unbalanced Feistel cipher where one side is a single bit. It is easier to prove security for unbalanced Feistel ciphers than for balanced ones. === VIL mode === For domain sizes that are a power of two, and an existing block cipher with a smaller bl

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  TRAME

  TRAME (TRAnsmission of MEssages) was the name of the second computer network in the world similar to the internet to be used in an electric utility. Like the internet, the base technology was packet switching; it was developed by the electric utility ENHER in Barcelona. It was deployed by the same utility, first in Catalonia and Aragón, Spain, and later in other places. Its development started in 1974 and the first routers, called nodes at that time, were deployed by 1978. The network was in operation until 2016 (38 years) with successive technological software and hardware updates. == Beginnings == In 1974, packet switching was a technology known only in research circles. The concept began in 1968 in association with the United States' Advanced Research Projects Agency (ARPA) research project ARPANET. The idea of applying the packet switching concept to electric utilities control communication networks first appeared in 1974 when the Swedish power utility Vattenfall started to create its TIDAS packet-switching network and was followed by the Spanish electric utility ENHER, which aimed to telecontrol and automate its high-voltage power grid. For this purpose, ENHER created a specific team of people to develop both the packet-switching network and the supervisory control and data acquisition (SCADA) system, also called the telecontrol system. By 1978 the first four TRAME routers were available and by 1980, eight of them were deployed and operating. The printed circuit boards (PCBs) controlling the communication lines were connected to a shared memory PCB allowing them to exchange data and messages. The project was developed together with its main initial application, the Telecontrol or SCADA system SICL (Sistema Integral de Control Local) with which initially they shared a very similar hardware. The maximum link capacity was 9600 bit/s, which in 1980 was the maximum possible on a 4 kHz wide voice channel at the time. These channels were the basic unit of the then-analog communication systems in use. By that time power utilities used either telephone calls or low speed (below 1200bit/s) dedicated links for telecontrol, typically shared among ten high-voltage electrical substations. == Services == The basic service provided by the TRAME network was SCADA or Telecontrol to automate the high-voltage power grid, thus improving operational efficiency, which was until then operated manually with telephone communication between human operators. Each TRAME router was associated with one or more remote terminal units (RTUs) of the SICL telecontrol system. It also had connected screens, and later PCs, located in electrical substations to interchange messages between them and with the Control Center located in the well-known Casa Fuster in Barcelona. It was a kind of predecessor to today's e-mail. Later, in the 1990s, other protocols (X.25, IP) were developed to include corporate information technology (IT) terminals, company physical surveillance systems and other services. Additionally, applications and terminals were developed for the transmission of voice and video over the TRAME network. == Protocols == The TRAME routing system, like that of the original ARPANET, was based on the Bellman-Ford algorithm but with "split-horizon" as in the Swedish TIDAS network, but with an original improvement. This protocol allows optimal paths to be found in meshed networks for each packet to be transmitted, allowing the shared use of the same network by multiple services. In contrast, traditional circuit-switched technology used to establish dedicated circuits for each service or communication. The addressing of routers and terminals used a proprietary system with a 16-bit address; it would be the equivalent of the well-known IP (Internet Protocol) version 4 (IPv4), still in use on the internet today, which uses 32-bit addresses. It is necessary to take into account that in 1978, the IPv4 protocol did not yet exist since the IPv4 version used on the internet did not appear until 1981, and in fact, did not reach the general public until much later. The line protocols were also proprietary and were called UCL (Unidad de Control de Línea, 'line control unit'), which linked the routers together, and UTR (Unión TRAME-Remotas), the access protocol. They were designed to offer the highest quality of service required by the telecontrol/SCADA function in terms of data integrity and availability set by the International Electrotechnical Commission (IEC) IEC-870-5-1 and ANSI C37.1. standards, and because the protocol used at the time in corporate computer networks, HDLC (high-level data link control), did not offer enough quality for critical industrial applications. Later on, other protocols like X.25 and IP were also made compatible with the aforementioned TRAME protocols. In 2000, the UTR protocol was replaced by the international standard IEC 60870- 5-101/104. Initially network flow control was based on the management of eight data priorities in head-of-the-line (HOL) waiting queues. Later and after some experimentation, a flow control method based on a bit indicating route congestion and management of the gap between packets when accessing the network was adopted. This required measuring the capacity of the route bottleneck. An end-to-end protocol was also added for some flows requiring order preservation like X.25. == Evolution == To last for 38 years, the technology had to endure intense evolution. There were essentially four TRAME generations which are summarized in the table. A description of the four generations of TRAME is provided below. === TRAME 1 === The project began in 1974 and in 1978 a first network with four routers was already installed and in operation at the electric utility ENHER. In 1980, the network had eight nodes in operation (see Figure I). The hardware was based on the Zilog Z80 processor and had a multiprocessor structure with 16 processors sharing a common memory. The software was developed at ENHER's headquarters located in the well-known Casa Fuster, Passeig de Gràcia, 132, Barcelona, using the Z80 assembly language. Beyond 1980 the software began to be written in C programming language and an HP64000 Logic Development System emulator was used for the purpose. The hardware was produced by ISEL, an INI (Instituto Nacional de Indústria) company. The routing system was a variant of Bellman-Ford with split-horizon. It was an improvement of the original ARPA network routing system consisting of an original update procedure which allowed for a faster reaction to changes. The distance function was the number of packets in the output waiting queues plus one. The line protocols (UCL for internal lines linking routers and UTR for accessing the network) were designed to meet the stringent requirements set for telecontrol (SCADA) of high-voltage power networks (IEC-870-5-1 and ANSI C37.1 standards). At the OSI transport layer, windows with a width of 1 to 8, depending on the required service, residing in the terminals were used. Initially, addresses were only 14 bits long to address both the routers (called nodes by then) and the devices connected to them. They were made up of two fields, an 8-bit field to address the router and a 6-bit sub-address to address the terminals connected to it. The node address was assigned to the nodes and not to the ends of the links as in the internet. The basic advantages of TRAME over other technologies used in electric utilities at the time were in part due to the packet technology itself: ability to manage any network topology, automatic adaptability to topological and traffic changes, integration of different link technologies (digital or analog) and capacities in a single network, open and decentralized intercommunicability between users and devices, simultaneous communication with several users and locations from a single physical connection, and integrated network supervision. In fact, the network was provided from its inception with a supervision center consisting of a computer and a synoptic board located at the company's headquarters (see Figure II). But other advantages were due to the specific design of TRAME: high data integrity, priority support for packets, and ease of including special protocols such as the many SCADA protocols in use at that time. All of the above resulted in improved quality of service, especially with respect to data availability and data integrity, and in the integration of services in a single network. Part of the evolution of its deployment can be seen in Figures II to IV. === TRAME 2 === In 1990, TRAME 2 was fully deployed and TRAME 1 was replaced. The processor of the new hardware was Intel 80286 and the hardware structure and external appearance of the routers was very similar to that of TRAME 1. The software was written in C and the above-mentioned emulator continued to be used. Improvements over TRAME 1 were the introduction of the standardized X.25 access protocol

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  Azure Data Lake

  Azure Data Lake is a scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud. == History == Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live. COSMOS features a SQL-like query engine called SCOPE upon which U-SQL was built. == Storage == Data Lake Storage is a cloud service to store structured, semi-structured or unstructured data produced from applications including social networks, relational data, sensors, videos, web apps, mobile or desktop devices. A single account can store trillions of files where a single file can be greater than a petabyte in size. == Analytics == Data Lake Analytics is a parallel on-demand job service. The parallel processing system is based on Microsoft Dryad. Dryad can represent arbitrary Directed Acyclic Graphs (DAGs) of computation. Data Lake Analytics provides a distributed infrastructure that can dynamically allocate resources so that customers pay for only the services they use. The system uses Apache YARN, the part of Apache Hadoop which governs resource management across clusters. Data Lake Store supports any application that uses the Hadoop Distributed File System (HDFS) interface. == U-SQL == U-SQL is a query language for Data Lake Analytics parallel data transformation and processing programs. It combines SQL and C#: it is and an evolution of the declarative SQL language with native extensibility through user code written in C#. U-SQL uses C# data types and the C# expression language. == Retirement == In 2021, Microsoft announced the 2024 retirement of the original Azure Data Lake Storage, now called "Gen1". The related Azure Data Lake Analytics / U-SQL technologies are also being retired. Azure Data Lake Storage Gen2, an extension of Azure Storage, will continue. The suggested replacement technologies are Azure Synapse Analytics and Apache Spark.

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  Viber

  Rakuten Viber, commonly known as Viber, is a cross-platform voice over IP (VoIP) and instant messaging (IM) software application owned by the Japanese technology company Rakuten Group. The service is available as freeware for Android, iOS, Microsoft Windows, macOS and Linux. Users are registered and identified through a mobile phone number, although the service can also be accessed on desktop platforms without mobile connectivity. In addition to instant messaging, the platform allows users to exchange media such as images, videos and files, and provides a paid international calling service called Viber Out. The software was launched in 2010 by the company Viber Media, founded by Talmon Marco and Igor Magazinnik. Rakuten acquired Viber Media in 2014 and later renamed the company Rakuten Viber. The company is headquartered in Cyprus and maintains offices in London, Manila, Paris, San Francisco, Singapore, Tokyo and Beijing. == History == === Founding (2010) === Viber Media was founded in Tel Aviv, Israel, in 2010 by Talmon Marco and Igor Magazinnik. Marco and Magazinnik are also co-founders of the peer-to-peer media and file-sharing client iMesh. The company was run from Israel and was registered in Cyprus. Sani Maroli and Ofer Smocha soon joined the company as well. Marco said Viber allows instant calling and synchronization with contacts because the ID is the user's cell number. In its early days, Viber relied on a patchwork of outsourcing partners from different countries, commissioning specific solutions from external vendors — including teams based in Cyprus and Belarus. According to the company's statements, development of Viber's core functionality historically originated from its Tel Aviv office — a testament to its roots — even though the legal entity was registered elsewhere. === Early monetisation (2011) === In its first two years of availability, Viber did not generate revenues. It began doing so in 2013, via user payments for Viber Out voice calling and the Viber graphical messaging "sticker market". The company was originally funded by individual investors, described by Marco as "friends and family". They invested $20 million in the company, which had 120 employees as of May 2013. On 24 July 2013, Viber's support system was defaced by the Syrian Electronic Army. According to Viber, no sensitive user information was accessed. By the time Rakuten came forward with its acquisition deal in 2014, Viber had already stopped working with external vendors, choosing instead to consolidate development under its own offices. === Rakuten acquires Viber (2014) === On 13 February 2014, Rakuten announced they had acquired Viber Media for $900 million, and since then Viber has been owned by Rakuten, Inc., an e-commerce conglomerate headquartered in Tokyo. The sale of Viber earned the Shabtai family (Benny, his brother Gilad, and Gilad's son Ofer) some $500 million from their 55.2% stake in the company. At that sale price, the founders each realized over 30 times return on their investments. Later that year, the company established a UK presence with the incorporation of Viber UK Limited in London. Djamel Agaoua became Viber Media CEO in February 2017, replacing co-founder Marco who left in 2015. In July 2017 the corporate name of Viber Media was changed to Rakuten Viber and a new wordmark logo was introduced. Its legal name remains Viber Media, S.à r.l. based in Luxembourg. === Post-acquisition === In August 2015 Viber opened a regional office for Central and Eastern Europe in Sofia to support growth in the region. In 2017, Rakuten Viber and the World Wildlife Fund engaged in a commercial transaction aimed at raising awareness and protecting wildlife. After first using Viber to spread its message in June 2020, the International Federation of the Red Cross launched an official chatbot and community on the messaging app to combat the spread of false information, which they termed an infodemic, about COVID-19. The chatbot is still active as of June 2022, with over 1.4 million subscribers. In 2020, Rakuten Viber and the World Health Organization (the WHO) engaged in a commercial transaction for a chatbot to inform users of issues such as women's health. and an anti-smoking campaign. In the wake of the July–August 2020 Belarusian election protests, to avoid sanctions and harassment from monopolies the company closed its office in Minsk. In 2022, Ofir Eyal became Viber CEO, replacing Djamel Agaoua. Eyal is a Viber veteran; he worked as Vice President of Product in 2014 before his promotion to Chief Operating Officer in 2019. Shortly after the appointment of a new CEO, Viber continued its international expansion. In March 2022, Rakuten announced the opening of a development center in Tbilisi, Georgia, intended to support work on mobile applications and technology projects in the region. In July 2022, Rakuten Viber partnered with Rapyd to launch instant cross-border P2P payments. The company launched payments on the Viber app first in Greece and Germany, and then in other countries. In August, Mineski teamed up with Viber to develop a social minigame platform that can play off Viber's application. In May 2022, Rakuten Viber launched the premium chat service Viber Plus that offers exclusive features, including sticker market privileges, ad-free use, priority Viber support, exclusive badge, unique Viber icon, large file sharing, and more. In 2022, Viber joined the European Union’s Code of Conduct on countering illegal hate speech online. As part of this framework, the company undertook to review reported content and remove material identified as hate speech in accordance with the Code and its platform rules. In January 2024 Rakuten (the company behind Viber) established an office in Kyiv to bring together engineering and marketing departments. Alongside launching its Kyiv office the company joined Diia.City as a resident. Subsequently in October 2024 Rakuten Viber inaugurated an office in Manila to broaden its operations, in the Philippines. The company’s legal entity remains Viber Media S.à r.l., registered in Luxembourg. Viber’s engineering work has been carried out across multiple countries and through external partners, including outsourcing and near-shore vendors. As a result, its development operations are distributed internationally rather than concentrated in a single location. In December 2024, Viber was blocked in Russia. Roskomnadzor announced the nationwide blocking of the messaging app due to non-compliance with local legal requirements. == Security audit == On 4 November 2014, Viber scored 1 out of 7 points on the Electronic Frontier Foundation's "Secure Messaging Scorecard". Viber received a point for encryption during transit but lost points because communications were not encrypted with keys that the provider did not have access to (i.e. the communications were not end-to-end encrypted), users could not verify contacts' identities, past messages were not secure if the encryption keys were stolen (i.e. the service did not provide forward secrecy), the code was not open to independent review (i.e. the code was not open-source), the security design was not properly documented, and there had not been a recent independent security audit. On 14 November 2014, the EFF changed Viber's score to 2 out of 7 after it had received an external security audit from Ernst & Young's Advanced Security Centre. On 19 April 2016, with the announcement of Viber version 6.0, Rakuten added end-to-end encryption to their service. The company said that the encryption protocol had only been audited internally, and promised to commission external audits "in the coming weeks". In May 2016, Viber published an overview of their encryption protocol, saying that it is a custom implementation that "uses the same concepts" as the Signal Protocol. In 2022, Rakuten Viber won a Security Award, by test.de, a tech firm based in Germany where there are over 3 million Viber users. In 2024, Rakuten Viber received SOC certification following an audit conducted by Ernst & Young. The certification relates to the company’s controls for data protection and information security. == Market share == As of December 2016, Viber had 800 million registered users. According to Statista, there are 260 million monthly active users as of January 2019. The Viber messenger is very popular in the Philippines, Greece, Eastern Europe, Russia, the Middle East, and some Asian markets. India was the largest market for Viber as of December 2014 with 33 million registered users, the fifth most popular instant messenger in the country. At the same time there were 30 million users in the United States, 28 million in Russia and 18 million in Brazil. Viber is particularly popular in Eastern Europe, being the most downloaded messaging app on Android in Belarus, Moldova and Ukraine as of 2016. It is also popular in Iraq, Libya and Nepal. Viber is translated in 44 languages and used in more than 190 co

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  Social news website

  A social news website is a website that features user-posted stories. Such stories are ranked based on popularity, as voted on by other users of the site or by website administrators. Users typically comment online on the news posts and these comments may also be ranked in popularity. Since their emergence with the birth of Web 2.0, social news sites have been used to link many types of information, including news, humor, support, and discussion. All such websites allow the users to submit content and each site differs in how the content is moderated. On the Slashdot and Fark websites, administrators decide which articles are selected for the front page. On Reddit and Digg, the articles that get the most votes from the community of users will make it to the front page. Many social news websites also feature an online comment system, where users discuss the issues raised in an article. Some of these sites have also applied their voting system to the comments, so that the most popular comments are displayed first. Some social news websites also have a social networking service, in that users can set up a user profile and follow other users' online activity on the website. Like many other Web 2.0 tools, social news websites use the collective intelligence of all of the users to operate. Social news websites also "impl[y] the technical, economic, legal, and human enhancement of a universally distributed intelligence that will unleash a positive dynamic of recognition and skills mobilization". Social news websites help participants to share a collective vision and awareness of how their actions are integrated with those of other individuals. Social news websites provide a new and innovative way to participate in a community that is constantly being flooded with new information. These social news websites "include opportunities for peer-to-peer learning, a changed attitude toward intellectual property, the diversification of cultural expression, the development of skills valued in the modern workplace, and a more empowered conception of citizenship". These websites can help to shape and reshape democratic opinions and perspectives. Social news sites may mitigate the gatekeeping of mainstream news sources and allow the public to decide what counts as "news", which may facilitate a more participatory culture. Social news sites may also support democratic participation by allowing users from across geographic and national boundaries to access the same information, respond to fellow users' views and beliefs, and create a virtual sphere for users to contribute within. == Websites == === Active === ==== Fark ==== Fark, which started in 1997, features news on any topic. On Fark, users can submit articles to the administrators of the site. Each day, these administrators pick out 50 articles to display on the front page. ==== Slashdot ==== Slashdot, started in 1997, was one of the first social news websites. It focuses mainly on science and technology-related news. Users can submit stories and the editors pick out the best stories each day for the front page. Users can then post comments on the stories. The influx of web traffic that resulted from Slashdot linking to external websites led to the effect being called the Slashdot effect ==== Digg ==== Digg, started in December 2004, introduced the voting system. This system allows users to "digg" or "bury" articles. "Digging" is the equivalent of voting positively, so that popular articles are displayed first. "Burying" does not lower an article's score. However, if an article is buried enough times, it will be automatically deleted from the site. Digg offers a social networking service, as members can follow other members and build personal profiles with information about their interests. ==== Reddit ==== Reddit, started in June 2005, is a social news website where users can submit articles and comments and vote on these submissions. The submissions are organized into categories called "subreddits". Unlike Digg, with Reddit, users can directly affect an article's score. An "upvote" will increase the score and a "downvote" will decrease it. Articles with the highest scores are displayed on the front page. There is also a page for "controversial" articles, that have an almost equal number of upvotes and downvotes. Free speech debates have arisen due to the shutting down of obscene or potentially illegal "subreddits" (including /r/jailbait, a collection of sexually suggestive underage pictures.) Reddit introduced a system of user-created communities called "subreddits", which are essentially categories for a specific type of news. Comments on the featured posts are shown in a hierarchical fashion also based on votes. Users have the ability to earn "karma" for their participation and time on the website. ==== Hacker News ==== Hacker News, started in February 2007, is a social news site focusing on computer science and entrepreneurship, created by Paul Graham and run by his startup incubator, Y Combinator. === Defunct === ==== Newsvine ==== Newsvine, started in March 2006, was a social news website mostly focused on politics, both international and domestic. The Newsvine home page allowed users to customize "seeds" and story feeds. Users received articles via "The Wire" from sources including The Associated Press or The Huffington Post, and from "The Vine" a stream of content from other Newsvine users. The "Top of the Vine" displayed the most voted and commented on articles of the day, week, month, or year. Additionally, Newsvine allowed members to create their own "Customizable Column", which could highlight a user's content posted, recent comments, and information about the specific Newsvine member. ==== feedalizr ==== feedalizr was a cross-platform, desktop social media aggregator built using Adobe Integrated Runtime that consolidates the updates from social media and social networking websites. Users can then use this application to update those sites from their desktop and view a consolidated stream of information. ==== Voat ==== Voat, launched in April 2014 and discontinued in December of 2020, was also a social news website and is very similar to Reddit visually and functionally. The site's userbase included a large number of alt right users, many of whom migrated to Voat after being banned on Reddit. ==== Prismatic ==== Prismatic combined machine learning, user experience design, and interaction design to create a new way to discover, consume, and share media. Prismatic software used social network aggregation and machine learning algorithms to filter the content that aligns with the interests of a specific user. Prismatic integrated with Facebook, Twitter, and Pocket to gather information about user's interests and suggest the most relevant stories to read. ==== Artifact ==== Artifact was an iOS and Android app that used machine learning to personalize news recommendations to readers, and also had social features such as liking articles, commenting, and reputation scores for users.

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