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Supper (Spotify)

Supper is a web-based application on the Spotify digital music streaming platform. The Supper app was born from a group of friends who had backgrounds in the music and gastronomy industries. Digital music solutions company Artisan Council later executed it. The app now sits in the top 40 applications on Spotify. == About == The Supper Spotify application matches recipes for all occasions and skill levels with a playlist for both preparation and presentation, as envisioned by the chefs themselves. Supper is credited with being one of the first apps to pair music with food. Playing on the social nature of music and food culture, users can seamlessly experience both for the first time with real time music streaming. == Supper.mx == In May 2014 Supper was launched outside of the Spotify streaming platform. Though still in partnership with Spotify, supper.mx allows users to view Supper's music + food collaborations on mobile, tablet and desktop, without the need to download Spotify directly. == Curators == All of the recipes and playlists featured on the Supper app come straight from a growing network of tastemakers, including chefs, musicians and institutions around the world. Each month the recipes and playlists are updated in conjunction with current holidays, events and seasons. === Launch === Launching in October 2013 the first edition of Supper featured content from a range of eating institutions and culture makers from the US and Australia. Brooklyn Bowl (Brooklyn) Roberta's Pizza (Brooklyn) Fancy Hanks (Melbourne) The Foresters/Queenies Upstairs (Sydney) Hipstamatic Panama House (Bondi) Sweetwater Inn (Melbourne) Soul Clap (Syd record label) Yellow Birds (Melbourne) === November 2013 === Yardbird (Hong Kong) Sonoma Bakery (Sydney) Do or Dine (Brooklyn) Cameo Gallery (Brooklyn) Hypertrak (Blog) Blue Smoke (NYC) The Crepes of Wrath (Blog) Willin Low // Wild Rocket - Wild Oats - Relish === December 2013 === The Copper Mill (Sydney) Thug Kitchen Mamak (Sydney) Tutu's (Brooklyn) Chin Chin (Melbourne) Flat Iron Steak (London) Greasy Spoon (Copenhagen) === January 2014 === Mexicali Taco & Co. (LA) Church & State (LA) Salts Cure (LA) Nopa (SF) L & E Oyster (LA) 4100 bar (LA) Golden Gopher (LA) The Pie Hole (LA) State Bird Provisions (SF) === Momofuku === In February 2014 Supper teamed up with restaurant heavy weights Momofuku. The recipes featured came from their iconic New York, Toronto and Sydney restaurants. Head office also got involved with an instructional from Brand Director Sue Chan on how to paint Momofuku vibes on to any party. === SXSW === March sees the Supper team migrate to Austin, Texas for SXSW, bringing together the best eateries the city has to offer as well as the music that has influenced them. Restaurants and eateries on board in 2014 included: The Backspace Kelis Swifts Attic Uchi Jackalope Paul Qui/East Side King Thai Kun Wonderland Hole in the Wall Justine's Brasserie The Liberty === Kelis === In April 2014 Kelis presented 5 of her recipes paired with a personal playlist for Supper. Kelis shared her recipes for apple farro, jerk ribs, New York vanilla bean cheesecake and Jerk Ribs. The Kelis/Supper collaboration coincided with the release of Kelis' 2014 album titled 'Food'. === Roberta's Pizza === In May 2014 Bushwick's Roberta's Pizza was guest curator on the Supper app and website. Included in their selections were restaurants and bars from across New York including Bun-ker Vietnamese, Old Stanley's Bar, St. Anselm, Chuko, Frank's Cocktail Lounge, Junior's Cheesecake, Xi'an Famous Foods, Xe Lua, 124 Old Rabbit and Yuji Ramen.
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Data exhaust

Data exhaust (also exhaust data) is the trail of data generated as a by-product of users' online activity, behaviour, and transactions, rather than data they deliberately create or submit. It forms part of a broader category of unconventional data that also includes geospatial, network, and time-series data, and may be useful for predictive analytics. Data exhaust can take the form of cookies, temporary files, log files, clickstream records and stored preferences. Actions such as visiting a web page, following a link, or dwelling on an element may all generate exhaust data that is recorded without the user's active awareness. Unlike primary content — which the user intentionally creates — exhaust data is a passive side effect of interaction. A bank, for example, might treat the amounts and parties involved in a transaction as primary data, while secondary data could include whether the transaction was carried out at a cash machine rather than a branch. == Uses == Data exhaust collected by companies is often information that is not immediately useful in isolation, but can be aggregated and analysed to improve products, personalise content, identify trends, and support quality control. Companies may also store exhaust data for future analysis or sell it to third parties. Shoshana Zuboff has described this practice as a core mechanism of what she terms surveillance capitalism, in which behavioural data generated by users is converted into predictive products. Kosciejew notes that large quantities of often raw data are collected in this way, much of which is never analysed. == Medical exhaust data == Many medical devices — including pacemakers, dialysis machines and surgical cameras — generate exhaust data as a by-product of their operation. The majority of this data is never captured or analysed, and is typically discarded once a procedure ends or a device completes its routine monitoring cycle. The potential use of data generated by implanted devices such as pacemakers raises additional legal and ethical questions around ownership and consent. Using electronic health records for research also creates challenges because of the volume of data involved, creating a need for automated algorithms to process it. == Privacy and regulation == The collection and distribution of data exhaust is not in itself illegal in most jurisdictions, but its use raises questions of privacy and informed consent. Steps commonly taken to address these concerns include data anonymisation, offering users an opt-out from the sale of their data, and publishing explicit privacy policies that disclose what data is collected and how it is used.
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Social recruiting

Social recruiting (social hiring or social media recruitment) is recruiting candidates by using social platforms as talent databases or for advertising. Social recruiting uses social media profiles, blogs, and other Internet sites to find information on candidates. It also uses social media to advertise jobs either through HR vendors or through crowdsourcing where job seekers and others share job openings within their online social networks. Social recruiting's effectiveness and return on investment have been difficult to determine, since applicants do not usually apply through the social channels which first attracted them. In May 2013, Maximum Employment Marketing Group released the Social Recruitment Monitor, which ranks the reach, engagement, and interactivity of employers' social recruiting efforts around the world. == Social recruitment software == The social recruitment software market (a form of e-recruitment) is often included in the wider talent management software sector. Bersin & Associates valued the wider talent management market at over $2bn in 2007. Social recruitment increasingly sits at an intersection of a number of fast-moving areas including social networking, recruitment and now cloud computing. Additionally, mobile recruiting has become another hot topic, especially with the rise in tablet and smartphone usage. In 2012, there was a rise of tech companies using social recruiting applications to find and screen applicants. As more companies saw value in filling jobs by putting them on the social platforms where millions of people spend at least 37 minutes daily, there developed a much larger focus on social recruiting among the talent acquisition community. By mid-2013, many major enterprise companies such as Pepsi, Gap, AIG, and Oracle had begun effectively utilizing social recruiting software, making it clear that large corporations were open to automating or streamlining (and ultimately investing in) their social recruiting processes.
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Private message

In computer networking, a private message (PM), or direct message (DM), refers to a private communication, often text-based, sent or received by a user of a private communication channel on any given platform. Unlike public posts, PMs are only viewable by the participants. Long a function present on IRCs and Internet forums, private channels for PMs have also been prevalent features on instant messaging (IM) and on social media networks. It may be either synchronous (e.g. on an IM) or asynchronous (e.g. on an Internet forum). The term private message (PM) originated as a feature on internet forums, while the term direct message (DM) originated as a feature on Twitter. Due to the popularity of the latter service, DM has since been appropriated by other platforms, such as Instagram, and is often genericized in popular usage. == Overview == There are two main types of private messages, and one obscure type: One type includes those found on IRCs and Internet forums, as well as on social media services like Twitter, Facebook, and Instagram, where the focus is public posting, PMs allow users to communicate privately without leaving the platform. The second type are those relayed through instant messaging platforms such as WhatsApp and Snapchat, where users join the networks primarily to exchange PMs. A third type, peer-to-peer messaging, occurs when users create and own the infrastructure used to transmit and store the messages; while features vary depending on application, they give the user full control over the data they transmit. An example of software that enables this kind of messaging is Classified-ads. Besides serving as a tool to connect privately with friends and family, PMs have gained momentum in the workplace. Working professionals use PMs to reach coworkers in other spaces and increase efficiency during meetings. Although useful, using PMs in the workplace may blur the boundary between work and private lives. Some common forms of private messaging today include Facebook messaging (sometimes referred to as "inboxing"), Twitter direct messaging, and Instagram direct messaging. These forms of private messaging provide a private space on a usually public site. For instance, most activity on Twitter is public, but Twitter DMs provide a private space for communication between two users. This differs from mediums like email, texting, and Snapchat, where most or all activity is always private. Modern forms of private messaging may include multimedia messages, such as pictures or videos. == History == Email was first developed to send messages between different computers on ARPANET in 1971. Access to ARPANET was primarily limited to universities and other research institutions. Starting in 1983 or 1984, FidoNet allowed home computer users to send and receive email via bulletin board systems. Information services such as CompuServe, America Online, and Prodigy also helped to popularizes online messaging. The advent of the public World Wide Web in 1993 increased access to email via internet service providers, and later via webmail. Instant messaging systems became popular in the mid 1990s, as Internet access improved and personal computers became more common. The introduction of Skype in 2003 popularized Internet-based voice and video messaging. Direct messaging is now a feature of all major social networking services. == Privacy concerns == In January 2014, Matthew Campbell and Michael Hurley filed a class-action lawsuit against Facebook for breaching the Electronic Communications Privacy Act. They alleged that private messages which contained URLs were being read and used to generate profit, through data mining and user profiling, and that it was misleading for Facebook to refer to the functionality as "private" with the implication that the communication was "free from surveillance". In 2012, some Facebook users misinterpreted a redesign of the Facebook wall as publicly sharing private messages from 2008–2009. These were found to be public wall posts from those years, made at a time when it was not possible to like or comment on a wall post, making the notes look like private messages.
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Stop Motion Studio

Stop Motion Studio is a stop motion animation software developed by Cateater LLC. It is available as both an app for iOS and Android and as a software for Windows and Mac. Two versions of the software exist, the standard Stop Motion Studio for free, and the paid Stop Motion Studio Pro, which contains extra, more advanced features. The software is commonly used in brickfilming.
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Social profiling

Social profiling is the process of constructing a social media user's profile using their social data. In general, profiling refers to the data science process of generating a person's profile with computerized algorithms and technology. There are various platforms for sharing this information with the proliferation of growing popular social networks, including but not limited to LinkedIn, Google+, Facebook and Twitter. == Social profile and social data == A person's social data refers to the personal data that they generate either online or offline (for more information, see social data revolution). A large amount of these data, including one's language, location and interest, is shared through social media and social network. Users join multiple social media platforms and their profiles across these platforms can be linked using different methods to obtain their interests, locations, content, and friend list. Altogether, this information can be used to construct a person's social profile. Meeting the user's satisfaction level for information collection is becoming more challenging. This is because of too much "noise" generated, which affects the process of information collection due to explosively increasing online data. Social profiling is an emerging approach to overcome the challenges faced in meeting user's demands by introducing the concept of personalized search while keeping in consideration user profiles generated using social network data. A study reviews and classifies research inferring users social profile attributes from social media data as individual and group profiling. The existing techniques along with utilized data sources, the limitations, and challenges were highlighted. The prominent approaches adopted include machine learning, ontology, and fuzzy logic. Social media data from Twitter and Facebook have been used by most of the studies to infer the social attributes of users. The literature showed that user social attributes, including age, gender, home location, wellness, emotion, opinion, relation, influence are still need to be explored. === Personalized meta-search engines === The ever-increasing online content has resulted in the lack of proficiency of centralized search engine's results. It can no longer satisfy user's demand for information. A possible solution that would increase coverage of search results would be meta-search engines, an approach that collects information from numerous centralized search engines. A new problem thus emerges, that is too much data and too much noise is generated in the collection process. Therefore, a new technique called personalized meta-search engines was developed. It makes use of a user's profile (largely social profile) to filter the search results. A user's profile can be a combination of a number of things, including but not limited to, "a user's manual selected interests, user's search history", and personal social network data. == Social media profiling == According to Samuel D. Warren II and Louis Brandeis (1890), disclosure of private information and the misuse of it can hurt people's feelings and cause considerable damage in people's lives. Social networks provide people access to intimate online interactions; therefore, information access control, information transactions, privacy issues, connections and relationships on social media have become important research fields and are subjects of concern to the public. Ricard Fogues and other co-authors state that "any privacy mechanism has at its base an access control", that dictate "how permissions are given, what elements can be private, how access rules are defined, and so on". Current access control for social media accounts tend to still be very simplistic: there is very limited diversity in the category of relationships on for social network accounts. User's relationships to others are, on most platforms, only categorized as "friend" or "non-friend" and people may leak important information to "friends" inside their social circle but not necessarily users to they consciously want to share the information to. The below section is concerned with social media profiling and what profiling information on social media accounts can achieve. === Privacy leaks === A lot of information is voluntarily shared on online social networks, such as photos and updates on life activities (new job, hobbies, etc.). People rest assured that different social network accounts on different platforms will not be linked as long as they do not grant permission to these links. However, according to Diane Gan, information gathered online enables "target subjects to be identified on other social networking sites such as Foursquare, Instagram, LinkedIn, Facebook and Google+, where more personal information was leaked". The majority of social networking platforms use the "opt out approach" for their features. If users wish to protect their privacy, it is user's own responsibility to check and change the privacy settings as a number of them are set to default option. A major social network platforms have developed geo-tag functions and are in popular usage. This is concerning because 39% of users have experienced profiling hacking; 78% burglars have used major social media networks and Google Street-view to select their victims; and an astonishing 54% of burglars attempted to break into empty houses when people posted their status updates and geo-locations. === Facebook === Formation and maintenance of social media accounts and their relationships with other accounts are associated with various social outcomes. In 2015, for many firms, customer relationship management is essential and is partially done through Facebook. Before the emergence and prevalence of social media, customer identification was primarily based upon information that a firm could directly acquire: for example, it may be through a customer's purchasing process or voluntary act of completing a survey/loyalty program. However, the rise of social media has greatly reduced the approach of building a customer's profile/model based on available data. Marketers now increasingly seek customer information through Facebook; this may include a variety of information users disclose to all users or partial users on Facebook: name, gender, date of birth, e-mail address, sexual orientation, marital status, interests, hobbies, favorite sports team(s), favorite athlete(s), or favorite music, and more importantly, Facebook connections. However, due to the privacy policy design, acquiring true information on Facebook is no trivial task. Often, Facebook users either refuse to disclose true information (sometimes using pseudonyms) or setting information to be only visible to friends, Facebook users who "LIKE" your page are also hard to identify. To do online profiling of users and cluster users, marketers and companies can and will access the following kinds of data: gender, the IP address and city of each user through the Facebook Insight page, who "LIKED" a certain user, a page list of all the pages that a person "LIKED" (transaction data), other people that a user follow (even if it exceeds the first 500, which we usually can not see) and all the publicly shared data. === Twitter === First launched on the Internet in March 2006, Twitter is a platform on which users can connect and communicate with any other user in just 280 characters. Like Facebook, Twitter is also a crucial tunnel for users to leak important information, often unconsciously, but able to be accessed and collected by others. According to Rachel Nuwer, in a sample of 10.8 million tweets by more than 5,000 users, their posted and publicly shared information are enough to reveal a user's income range. A postdoctoral researcher from the University of Pennsylvania, Daniel Preoţiuc-Pietro and his colleagues were able to categorize 90% of users into corresponding income groups. Their existing collected data, after being fed into a machine-learning model, generated reliable predictions on the characteristics of each income group. The mobile app called Streamd.in displays live tweets on Google Maps by using geo-location details attached to the tweet, and traces the user's movement in the real world. === Profiling photos on social network === The advent and universality of social media networks have boosted the role of images and visual information dissemination. Many types of visual information on social media transmit messages from the author, location information and other personal information. For example, a user may post a photo of themselves in which landmarks are visible, which can enable other users to determine where they are. In a study done by Cristina Segalin, Dong Seon Cheng and Marco Cristani, they found that profiling user posts' photos can reveal personal traits such as personality and mood. In the study, convolutional neural networks (CNNs) is introduced. It builds on the main characteristics of computational
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Link encryption

Link encryption is an approach to communications security that encrypts and decrypts all network traffic at each network routing point (e.g. network switch, or node through which it passes) until arrival at its final destination. This repeated decryption and encryption is necessary to allow the routing information contained in each transmission to be read and employed further to direct the transmission toward its destination, before which it is re-encrypted. This contrasts with end-to-end encryption where internal information, but not the header/routing information, is encrypted by the sender at the point of origin and only decrypted by the intended recipient. Link encryption offers two main advantages: encryption is automatic so there is less opportunity for human error. if the communications link operates continuously and carries an unvarying level of traffic, link encryption defeats traffic analysis. On the other hand, end-to-end encryption ensures only the intended recipient has access to the plaintext. Link encryption can be used with end-to-end systems by superencrypting the messages. Bulk encryption refers to encrypting a large number of circuits at once, after they have been multiplexed.
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Computer network engineering

Computer network engineering is a technology discipline within engineering that deals with the design, implementation, and management of computer networks. These systems contain both physical components, such as routers, switches, cables, and some logical elements, such as protocols and network services. Computer network engineers attempt to ensure that the data is transmitted efficiently, securely, and reliably over both local area networks (LANs) and wide area networks (WANs), as well as across the Internet. Computer networks often play a large role in modern industries ranging from telecommunications to cloud computing, enabling processes such as email and file sharing, as well as complex real-time services like video conferencing and online gaming. == Background == The evolution of network engineering is marked by significant milestones that have greatly impacted communication methods. These milestones particularly highlight the progress made in developing communication protocols that are vital to contemporary networking. This discipline originated in the 1960s with projects like ARPANET, which initiated important advancements in reliable data transmission. The advent of protocols such as TCP/IP revolutionized networking by enabling interoperability among various systems, which, in turn, fueled the rapid growth of the Internet. Key developments include the standardization of protocols and the shift towards increasingly complex layered architectures. These advancements have profoundly changed the way devices interact across global networks. == Network infrastructure design == The foundation of computer network engineering lies in the design of the network infrastructure. This involves planning both the physical layout of the network and its logical topology to ensure optimal data flow, reliability, and scalability. === Physical infrastructure === The physical infrastructure consists of the hardware used to transmit data, which is represented by the first layer of the OSI model. ==== Cabling ==== Copper cables such as ethernet over twisted pair are commonly used for short-distance connections, especially in local area networks (LANs), while fiber optic cables are favored for long-distance communication due to their high-speed transmission capabilities and lower susceptibility to interference. Fiber optics play a significant role in the backbone of large-scale networks, such as those used in data centers and internet service provider (ISP) infrastructures. ==== Wireless networks ==== In addition to wired connections, wireless networks have become a common component of physical infrastructure. These networks facilitate communication between devices without the need for physical cables, providing flexibility and mobility. Wireless technologies use a range of transmission methods, including radio frequency (RF) waves, infrared signals, and laser-based communication, allowing devices to connect to the network. Wi-Fi based on IEEE 802.11 standards is the most widely used wireless technology in local area networks and relies on RF waves to transmit data between devices and access points. Wireless networks operate across various frequency bands, including 2.4 GHz and 5 GHz, each offering unique ranges and data rates; the 2.4 GHz band provides broader coverage, while the 5 GHz band supports faster data rates with reduced interference, ideal for densely populated environments. Beyond Wi-Fi, other wireless transmission methods, such as infrared and laser-based communication, are used in specific contexts, like short-range, line-of-sight links or secure point-to-point communication. In mobile networks, cellular technologies like 3G, 4G, and 5G enable wide-area wireless connectivity. 3G introduced faster data rates for mobile browsing, while 4G significantly improved speed and capacity, supporting advanced applications like video streaming. The latest evolution, 5G, operates across a range of frequencies, including millimeter-wave bands, and provides high data rates, low latency, and support for more device connectivity, useful for applications like the Internet of Things (IoT) and autonomous systems. Together, these wireless technologies allow networks to meet a variety of connectivity needs across local and wide areas. ==== Network devices ==== Routers and switches help direct data traffic and assist in maintaining network security; network engineers configure these devices to optimize traffic flow and prevent network congestion. In wireless networks, wireless access points (WAP) allow devices to connect to the network. To expand coverage, multiple access points can be placed to create a wireless infrastructure. Beyond Wi-Fi, cellular network components like base stations and repeaters support connectivity in wide-area networks, while network controllers and firewalls manage traffic and enforce security policies. Together, these devices enable a secure, flexible, and scalable network architecture suitable for both local and wide-area coverage. === Logical topology === Beyond the physical infrastructure, a network must be organized logically, which defines how data is routed between devices. Various topologies, such as star, mesh, and hierarchical designs, are employed depending on the network’s requirements. In a star topology, for example, all devices are connected to a central hub that directs traffic. This configuration is relatively easy to manage and troubleshoot but can create a single point of failure. In contrast, a mesh topology, where each device is interconnected with several others, offers high redundancy and reliability but requires a more complex design and larger hardware investment. Large networks, especially those in enterprises, often employ a hierarchical model, dividing the network into core, distribution, and access layers to enhance scalability and performance. == Network protocols and communication standards == Communication protocols dictate how data in a network is transmitted, routed, and delivered. Depending on the goals of the specific network, protocols are selected to ensure that the network functions efficiently and securely. The Transmission Control Protocol/Internet Protocol (TCP/IP) suite is fundamental to modern computer networks, including the Internet. It defines how data is divided into packets, addressed, routed, and reassembled. The Internet Protocol (IP) is critical for routing packets between different networks. In addition to traditional protocols, advanced protocols such as Multiprotocol Label Switching (MPLS) and Segment Routing (SR) enhance traffic management and routing efficiency. For intra-domain routing, protocols like Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) provide dynamic routing capabilities. On the local area network (LAN) level, protocols like Virtual Extensible LAN (VXLAN) and Network Virtualization using Generic Routing Encapsulation (NVGRE) facilitate the creation of virtual networks. Furthermore, Internet Protocol Security (IPsec) and Transport Layer Security (TLS) secure communication channels, ensuring data integrity and confidentiality. For real-time applications, protocols such as Real-time Transport Protocol (RTP) and WebRTC provide low-latency communication, making them suitable for video conferencing and streaming services. Additionally, protocols like QUIC enhance web performance and security by establishing secure connections with reduced latency. == Network security == As networks have become essential for business operations and personal communication, the demand for robust security measures has increased. Network security is a critical component of computer network engineering, concentrating on the protection of networks against unauthorized access, data breaches, and various cyber threats. Engineers are responsible for designing and implementing security measures that ensure the integrity and confidentiality of data transmitted across networks. Firewalls serve as barriers between trusted internal networks and external environments, such as the Internet. Network engineers configure firewalls, including next-generation firewalls (NGFW), which incorporate advanced features such as deep packet inspection and application awareness, thereby enabling more refined control over network traffic and protection against sophisticated attacks. In addition to firewalls, engineers use encryption protocols, including Internet Protocol Security (IPsec) and Transport Layer Security (TLS), to secure data in transit. These protocols provide a means of safeguarding sensitive information from interception and tampering. For secure remote access, Virtual Private Networks (VPNs) are deployed, using technologies to create encrypted tunnels for data transmission over public networks. These VPNs are often used for maintaining security when remote users access corporate networks but are also used ion other settings. To enhance threat detection and r
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The Future of Work and Death

The Future of Work and Death is a 2016 documentary by Sean Blacknell and Wayne Walsh about the exponential growth of technology. The film showed at several film festivals including Raindance Film Festival, International Film Festival Rotterdam, Academia Film Olomouc and CPH:DOX. In May 2017 it received an official screening at the European Commission. It was distributed by First Run Features and Journeyman Pictures and was released on iTunes, Amazon Prime and On-demand on 9 May 2017. The film was made available on Sundance Now on 27 November 2017. A companion piece to the film, The Cost of Living, a documentary concerning universal basic income in Britain, was released on Amazon Prime on 8 October 2020. == Synopsis == World experts in the fields of futurology, anthropology, neuroscience, and philosophy consider the impact of technological advances on the two 'certainties' of human life; work and death. Charting human developments from Homo habilis, past the Industrial Revolution, to the digital age and beyond, the film looks at the shocking exponential rate at which mankind has managed to create technologies to ease the process of living. As we embark on the next phase of our adaptation, with automation and artificial intelligence signifying the complete move from man to machine, the film asks what the implications are for human fulfilment in an approaching era of job obsolescence and extreme longevity. == Cast == Dudley Sutton – Narrator Aubrey de Grey – Biomedical gerontologist and CSO of the SENS Research Foundation Will Self – Writer, journalist, political commentator and Professor of Contemporary Thought at Brunel University Rudolph E. Tanzi – Professor of Neurology at Harvard University and Director of the Genetics and Aging Research Unit at Massachusetts General Hospital (MGH) Martin Ford – Futurist and author Steve Fuller – Auguste Comte Chair in Social Epistemology at the Department of sociology at University of Warwick Murray Shanahan – Professor of Cognitive Robotics at Imperial College London Gray Scott – Futurist, executive producer of this production Vivek Wadhwa – Entrepreneur, academic and Director of Research at the Center for Entrepreneurship and Research Commercialization at the Pratt School of Engineering, Duke University Zoltan Istvan – Transhumanist and journalist Joanna Cook – Anthropologist, University College London Nicholas Kamara – Physician, Kable Hospital David Pearce – Transhumanist philosopher and co-founder of Humanity+ Peter Cochrane – Futurist and entrepreneur John Harris – Bioethicist, philosopher and Director of the Institute for Science, Ethics and Innovation at the University of Manchester Riva Melissa-Tez – Entrepreneur and transhumanist Ian Pearson – Futurologist Stuart Armstrong – Artificial intelligence researcher at Future of Humanity Institute
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Microsoft Security Development Lifecycle

The Microsoft Security Development Lifecycle (SDL) is the approach Microsoft uses to integrate security into DevOps processes (sometimes called a DevSecOps approach). You can use this SDL guidance and documentation to adapt this approach and practices to your organization. == Overview == The practices outlined in the SDL approach are applicable to all types of software development and across all platforms, ranging from traditional waterfall methodologies to modern DevOps approaches. They can generally be applied to the following: Software – whether you are developing software code for firmware, AI applications, operating systems, drivers, IoT Devices, mobile device apps, web services, plug-ins or applets, hardware microcode, low-code/no-code apps, or other software formats. Note that most practices in the SDL are applicable to secure computer hardware development as well. Platforms – whether the software is running on a ‘serverless’ platform approach, on an on-premises server, a mobile device, a cloud hosted VM, a user endpoint, as part of a Software as a Service (SaaS) application, a cloud edge device, an IoT device, or anywhere else. == Practices == The SDL recommends 10 security practices to incorporate into your development workflows. Applying the 10 security practices of SDL is an ongoing process of improvement so a key recommendation is to begin from some point and keep enhancing as you proceed. This continuous process involves changes to culture, strategy, processes, and technical controls as you embed security skills and practices into DevOps workflows. The 10 SDL practices are: Establish security standards, metrics, and governance Require use of proven security features, languages, and frameworks Perform security design review and threat modeling Define and use cryptography standards Secure the software supply chain Secure the engineering environment Perform security testing Ensure operational platform security Implement security monitoring and response Provide security training == Versions ==
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Factorization of polynomials over finite fields

In mathematics and computer algebra the factorization of a polynomial consists of decomposing it into a product of irreducible factors. This decomposition is theoretically possible and is unique for polynomials with coefficients in any field, but rather strong restrictions on the field of the coefficients are needed to allow the computation of the factorization by means of an algorithm. In practice, algorithms have been designed only for polynomials with coefficients in a finite field, in the field of rationals or in a finitely generated field extension of one of them. All factorization algorithms, including the case of multivariate polynomials over the rational numbers, reduce the problem to this case; see polynomial factorization. It is also used for various applications of finite fields, such as coding theory (cyclic redundancy codes and BCH codes), cryptography (public key cryptography by the means of elliptic curves), and computational number theory. As the reduction of the factorization of multivariate polynomials to that of univariate polynomials does not have any specificity in the case of coefficients in a finite field, only polynomials with one variable are considered in this article. == Background == === Finite field === The theory of finite fields, whose origins can be traced back to the works of Gauss and Galois, has played a part in various branches of mathematics. Due to the applicability of the concept in other topics of mathematics and sciences like computer science there has been a resurgence of interest in finite fields and this is partly due to important applications in coding theory and cryptography. Applications of finite fields introduce some of these developments in cryptography, computer algebra and coding theory. A finite field or Galois field is a field with a finite order (number of elements). The order of a finite field is always a prime or a power of prime. For each prime power q = pr, there exists exactly one finite field with q elements, up to isomorphism. This field is denoted GF(q) or Fq. If p is prime, GF(p) is the prime field of order p; it is the field of residue classes modulo p, and its p elements are denoted 0, 1, ..., p−1. Thus a = b in GF(p) means the same as a ≡ b (mod p). === Irreducible polynomials === Let F be a finite field. As for general fields, a non-constant polynomial f in F[x] is said to be irreducible over F if it is not the product of two polynomials of positive degree. A polynomial of positive degree that is not irreducible over F is called reducible over F. Irreducible polynomials allow us to construct the finite fields of non-prime order. In fact, for a prime power q, let Fq be the finite field with q elements, unique up to isomorphism. A polynomial f of degree n greater than one, which is irreducible over Fq, defines a field extension of degree n which is isomorphic to the field with qn elements: the elements of this extension are the polynomials of degree lower than n; addition, subtraction and multiplication by an element of Fq are those of the polynomials; the product of two elements is the remainder of the division by f of their product as polynomials; the inverse of an element may be computed by the extended GCD algorithm (see Arithmetic of algebraic extensions). It follows that, to compute in a finite field of non prime order, one needs to generate an irreducible polynomial. For this, the common method is to take a polynomial at random and test it for irreducibility. For sake of efficiency of the multiplication in the field, it is usual to search for polynomials of the shape xn + ax + b. Irreducible polynomials over finite fields are also useful for pseudorandom number generators using feedback shift registers and discrete logarithm over F2n. The number of irreducible monic polynomials of degree n over Fq is the number of aperiodic necklaces, given by Moreau's necklace-counting function Mq(n). The closely related necklace function Nq(n) counts monic polynomials of degree n which are primary (a power of an irreducible); or alternatively irreducible polynomials of all degrees d which divide n. === Example === The polynomial P = x4 + 1 is irreducible over Q but not over any finite field. On any field extension of F2, P = (x + 1)4. On every other finite field, at least one of −1, 2 and −2 is a square, because the product of two non-squares is a square and so we have If − 1 = a 2 , {\displaystyle -1=a^{2},} then P = ( x 2 + a ) ( x 2 − a ) . {\displaystyle P=(x^{2}+a)(x^{2}-a).} If 2 = b 2 , {\displaystyle 2=b^{2},} then P = ( x 2 + b x + 1 ) ( x 2 − b x + 1 ) . {\displaystyle P=(x^{2}+bx+1)(x^{2}-bx+1).} If − 2 = c 2 , {\displaystyle -2=c^{2},} then P = ( x 2 + c x − 1 ) ( x 2 − c x − 1 ) . {\displaystyle P=(x^{2}+cx-1)(x^{2}-cx-1).} === Complexity === Polynomial factoring algorithms use basic polynomial operations such as products, divisions, gcd, powers of one polynomial modulo another, etc. A multiplication of two polynomials of degree at most n can be done in O(n2) operations in Fq using "classical" arithmetic, or in O(nlog(n) log(log(n)) ) operations in Fq using "fast" arithmetic. A Euclidean division (division with remainder) can be performed within the same time bounds. The cost of a polynomial greatest common divisor between two polynomials of degree at most n can be taken as O(n2) operations in Fq using classical methods, or as O(nlog2(n) log(log(n)) ) operations in Fq using fast methods. For polynomials h, g of degree at most n, the exponentiation hq mod g can be done with O(log(q)) polynomial products, using exponentiation by squaring method, that is O(n2log(q)) operations in Fq using classical methods, or O(nlog(q)log(n) log(log(n))) operations in Fq using fast methods. In the algorithms that follow, the complexities are expressed in terms of number of arithmetic operations in Fq, using classical algorithms for the arithmetic of polynomials. == Factoring algorithms == Many algorithms for factoring polynomials over finite fields include the following three stages: Square-free factorization Distinct-degree factorization Equal-degree factorization An important exception is Berlekamp's algorithm, which combines stages 2 and 3. === Berlekamp's algorithm === Berlekamp's algorithm is historically important as being the first factorization algorithm which works well in practice. However, it contains a loop on the elements of the ground field, which implies that it is practicable only over small finite fields. For a fixed ground field, its time complexity is polynomial, but, for general ground fields, the complexity is exponential in the size of the ground field. === Square-free factorization === The algorithm determines a square-free factorization for polynomials whose coefficients come from the finite field Fq of order q = pm with p a prime. This algorithm firstly determines the derivative and then computes the gcd of the polynomial and its derivative. If it is not one then the gcd is again divided into the original polynomial, provided that the derivative is not zero (a case that exists for non-constant polynomials defined over finite fields). This algorithm uses the fact that, if the derivative of a polynomial is zero, then it is a polynomial in xp, which is, if the coefficients belong to Fp, the pth power of the polynomial obtained by substituting x by x1/p. If the coefficients do not belong to Fp, the pth root of a polynomial with zero derivative is obtained by the same substitution on x, completed by applying the inverse of the Frobenius automorphism to the coefficients. This algorithm works also over a field of characteristic zero, with the only difference that it never enters in the blocks of instructions where pth roots are computed. However, in this case, Yun's algorithm is much more efficient because it computes the greatest common divisors of polynomials of lower degrees. A consequence is that, when factoring a polynomial over the integers, the algorithm which follows is not used: one first computes the square-free factorization over the integers, and to factor the resulting polynomials, one chooses a p such that they remain square-free modulo p. Algorithm: SFF (Square-Free Factorization) Input: A monic polynomial f in Fq[x] where q = pm Output: Square-free factorization of f R ← 1 # Make w be the product (without multiplicity) of all factors of f that have # multiplicity not divisible by p c ← gcd(f, f′) w ← f/c # Step 1: Identify all factors in w i ← 1 while w ≠ 1 do y ← gcd(w, c) fac ← w / y R ← R · faci w ← y; c ← c / y; i ← i + 1 end while # c is now the product (with multiplicity) of the remaining factors of f # Step 2: Identify all remaining factors using recursion # Note that these are the factors of f that have multiplicity divisible by p if c ≠ 1 then c ← c1/p R ← R·SFF(c)p end if Output(R) The idea is to identify the product of all irreducible factors of f with the same multiplicity. This is done in two steps. The first step uses the formal d
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Business continuity and disaster recovery auditing

Given organizations' increasing dependency on information technology (IT) to run their operations, business continuity planning (and its subset IT service continuity planning) covers the entire organization, while disaster recovery focuses on IT. Auditing documents covering an organization's business continuity and disaster recovery (BCDR) plans provides a third-party validation to stakeholders that the documentation is complete and does not contain material misrepresentations. == Overview == Often used together, the terms business continuity (BC) and disaster recovery (DR) are very different. BC refers to the ability of a business to continue critical functions and business processes after the occurrence of a disaster, whereas DR refers specifically to the IT functions of the business, albeit a subset of BC. == Metrics == The primary objective is to protect the organization in the event that all or part of its operations and/or computer services are rendered partially or completely unusable. === DR metrics === Minimizing downtime and data loss during disaster recovery is typically measured in terms of two key concepts: Recovery time objective (RTO), time until a system is completely up and running Recovery point objective (RPO), a measure of the ability to recover files by specifying a point in time the backup copy will restore to. == The auditor's role == Role of the Internal Auditor in Auditing a Disaster Recovery Plan (DRP): 1. Governance & Oversight - Confirm roles, responsibilities, and oversight are defined, and DRP aligns with risk appetite and continuity strategy. 2. Risk Assessment & BIA - Verify risk and impact assessments identify critical systems and define RTO/RPO. 3. Plan Design & Documentation - Ensure the DRP is current, complete, and includes key recovery procedures. 4. Testing & Validation - Confirm regular DRP testing occurs and results are used to improve the plan. 5. Backup & Recovery - Assess backup frequency and recovery capabilities against RTO/RPO targets. 6. Communication & Training - Verify staff are trained and communication protocols are in place for crises. 7. Maintenance & Improvement - Ensure the DRP is regularly updated and lessons learned are integrated. == Documentation == === Disaster recovery plan === A disaster recovery plan (DRP) is a documented process or set of procedures to execute an organization's disaster recovery processes and recover and protect a business IT infrastructure in the event of a disaster. It is "a comprehensive statement of consistent actions to be taken before, during and after a disaster". The disaster could be natural, environmental or man-made. Man-made disasters could be intentional (for example, an act of a terrorist) or unintentional (that is, accidental, such as the breakage of a man-made dam or even "fat fingers" - or errant commands entered - on a computer system). ==== Types of plans ==== Although there is no one-size-fits-all plan, there are three basic strategies: prevention, including proper backups, having surge protectors and generators detection, a byproduct of routine inspections, which may discover new (potential) threats correction The latter may include securing proper insurance policies, and holding a "lessons learned" brainstorming session. ==== Best practices ==== To maximize their effectiveness, DRPs are most effective when updated frequently, and should: be an integral part of all business analysis processes, be revisited at every major corporate acquisition, at every new product launch and at every new system development milestone. be thoroughly tested, not just unpracticed bureaucratic documentation Adequate records need to be retained by the organization. The auditor examines records, billings, and contracts to verify that records are being kept. One such record is a current list of the organization's hardware and software vendors. Such list is made and periodically updated to reflect changing business practices and as part of an IT asset management system. Copies of it are stored on and off site and are made available or accessible to those who require them. An auditor tests the procedures used to meet this objective and determine their effectiveness. === Relationship to BCPs === Disaster recovery is a subset of business continuity. Where DRP encompasses the policies, tools and procedures to enable recovery of data following a catastrophic event, BCP involves keeping all aspects of a business functioning regardless of potential disruptive events. As such, a business continuity plan is a comprehensive organizational strategy that includes the DRP as well as threat prevention, detection, recovery, and resumption of operations should a data breach or other disaster event occur. Therefore, BCP consists of five component plans: Business resumption plan Occupant emergency plan Continuity of operations plan Incident management plan Disaster recovery plan The first three components (business resumption, occupant emergency, and continuity of operations plans) do not deal with the IT infrastructure. The incident management plan (IMP) does deal with the IT infrastructure, but since it establishes structure and procedures to address cyber attacks against an organization's IT systems, it generally does not represent an agent for activating the DRP; thus DRP is the only BCP component of active interest to IT. == Testing == The overall categorization of tests are functional- and discussion-based. Types of tests include: tabletop exercises, checklists, simulations, parallel processing (testing recovery site while primary site is in operation), and full interruption (fail over) tests. These apply to both BC and DR. == Benefits == Like every insurance plan, there are benefits that can be obtained from proper business continuity planning, including: Studies have shown a correlation between higher spending on auditing fees and lower rates of Incidents. Minimizing risk of delays Guaranteeing the reliability of standby systems (even automating the failure detection and recovery in certain scenarios) Providing a standard for testing the plan Minimizing decision-making during a disaster Reducing potential legal liabilities Lowering unnecessarily stressful work environment === Planning and testing methodology === According to Geoffrey H. Wold of the Disaster Recovery Journal, the entire process involved in developing a Disaster Recovery Plan consists of 10 steps: Performing a risk assessment: The planning committee prepares a risk analysis and a business impact analysis (BIA) that includes a range of possible disasters. Each functional area of the organization is analyzed to determine potential consequences. Traditionally, fire has posed the greatest threat. A thorough plan provides for "worst case" situations, such as destruction of the main building. Establishing priorities for processing and operations: Critical needs of each department are evaluated and prioritized. Written agreements for alternatives selected are prepared, with details specifying duration, termination conditions, system testing, cost, any special security procedures, procedure for the notification of system changes, hours of operation, the specific hardware and other equipment required for processing, personnel requirements, definition of the circumstances constituting an emergency, process to negotiate service extensions, guarantee of compatibility, availability, non-mainframe resource requirements, priorities, and other contractual issues. Collecting data: This includes various lists (employee backup position listing, critical telephone numbers list, master call list, master vendor list, notification checklist), inventories (communications equipment, documentation, office equipment, forms, insurance policies, workgroup and data center computer hardware, microcomputer hardware and software, office supply, off-site storage location equipment, telephones, etc.), distribution register, software and data files backup/retention schedules, temporary location specifications, any other such lists, materials, inventories, and documentation. Pre-formatted forms are often used to facilitate the data gathering process. Organizing and documenting a written plan Developing testing criteria and procedures: reasons for testing include Determining the feasibility and compatibility of backup facilities and procedures. Identifying areas in the plan that need modification. Providing training to the team managers and team members. Demonstrating the ability of the organization to recover. Providing motivation for maintaining and updating the disaster recovery plan. Testing the plan: An initial "dry run" of the plan is performed by conducting a structured walk-through test. An actual test-run must be performed. Problems are corrected. Initial testing can be plan is done in sections and after normal business hours to minimize disruptions. Subsequent tests occur during normal business hours. === Caveats/controversie
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Feed forward (control)

A feed forward (sometimes written feedforward) is an element or pathway within a control system that passes a controlling signal from a source in its external environment to a load elsewhere in its external environment. This is often a command signal from an external operator. In control engineering, a feedforward control system is a control system that uses sensors to detect disturbances affecting the system and then applies an additional input to minimize the effect of the disturbance. This requires a mathematical model of the system so that the effect of disturbances can be properly predicted. A control system which has only feed-forward behavior responds to its control signal in a pre-defined way without responding to the way the system reacts; it is in contrast with a system that also has feedback, which adjusts the input to take account of how it affects the system, and how the system itself may vary unpredictably. In a feed-forward system, the control variable adjustment is not error-based. Instead it is based on knowledge about the process in the form of a mathematical model of the process and knowledge about, or measurements of, the process disturbances. Some prerequisites are needed for control scheme to be reliable by pure feed-forward without feedback: the external command or controlling signal must be available, and the effect of the output of the system on the load should be known (that usually means that the load must be predictably unchanging with time). Sometimes pure feed-forward control without feedback is called 'ballistic', because once a control signal has been sent, it cannot be further adjusted; any corrective adjustment must be by way of a new control signal. In contrast, 'cruise control' adjusts the output in response to the load that it encounters, by a feedback mechanism. These systems could relate to control theory, physiology, or computing. == Overview == With feed-forward or feedforward control, the disturbances are measured and accounted for before they have time to affect the system. In the house example, a feed-forward system may measure the fact that the door is opened and automatically turn on the heater before the house can get too cold. The difficulty with feed-forward control is that the effects of the disturbances on the system must be accurately predicted, and there must not be any unmeasured disturbances. For instance, if a window was opened that was not being measured, the feed-forward-controlled thermostat might let the house cool down. The term has specific meaning within the field of CPU-based automatic control. The discipline of feedforward control as it relates to modern, CPU based automatic controls is widely discussed, but is seldom practiced due to the difficulty and expense of developing or providing for the mathematical model required to facilitate this type of control. Open-loop control and feedback control, often based on canned PID control algorithms, are much more widely used. There are three types of control systems: open-loop, feed-forward, and feedback. An example of a pure open-loop control system is manual non-power-assisted steering of a motor car; the steering system does not have access to an auxiliary power source and does not respond to varying resistance to turning of the direction wheels; the driver must make that response without help from the steering system. In comparison, power steering has access to a controlled auxiliary power source, which depends on the engine speed. When the steering wheel is turned, a valve is opened which allows fluid under pressure to turn the wheels. A sensor monitors that pressure so that the valve only opens enough to cause the correct pressure to reach the wheel turning mechanism. This is feed-forward control where the output of the system, the change in direction of travel of the vehicle, plays no part in the system. See Model predictive control. If the driver is included in the system, then they do provide a feedback path by observing the direction of travel and compensating for errors by turning the steering wheel. In that case you have a feedback system, and the block labeled System in Figure(c) is a feed-forward system. In other words, systems of different types can be nested, and the overall system regarded as a black-box. Feedforward control is distinctly different from open-loop control and teleoperator systems. Feedforward control requires a mathematical model of the plant (process and/or machine being controlled) and the plant's relationship to any inputs or feedback the system might receive. Neither open-loop control nor teleoperator systems require the sophistication of a mathematical model of the physical system or plant being controlled. Control based on operator input without integral processing and interpretation through a mathematical model of the system is a teleoperator system and is not considered feedforward control. == History == Historically, the use of the term feedforward is found in works by Harold S. Black in US patent 1686792 (invented 17 March 1923) and D. M. MacKay as early as 1956. While MacKay's work is in the field of biological control theory, he speaks only of feedforward systems. MacKay does not mention feedforward control or allude to the discipline of feedforward controls. MacKay and other early writers who use the term feedforward are generally writing about theories of how human or animal brains work. Black also has US patent 2102671 invented 2 August 1927 on the technique of feedback applied to electronic systems. The discipline of feedforward controls was largely developed by professors and graduate students at Georgia Tech, MIT, Stanford and Carnegie Mellon. Feedforward is not typically hyphenated in scholarly publications. Meckl and Seering of MIT and Book and Dickerson of Georgia Tech began the development of the concepts of Feedforward Control in the mid-1970s. The discipline of Feedforward Controls was well defined in many scholarly papers, articles and books by the late 1980s. == Benefits == The benefits of feedforward control are significant and can often justify the extra cost, time and effort required to implement the technology. Control accuracy can often be improved by as much as an order of magnitude if the mathematical model is of sufficient quality and implementation of the feedforward control law is well thought out. Energy consumption by the feedforward control system and its driver is typically substantially lower than with other controls. Stability is enhanced such that the controlled device can be built of lower cost, lighter weight, springier materials while still being highly accurate and able to operate at high speeds. Other benefits of feedforward control include reduced wear and tear on equipment, lower maintenance costs, higher reliability and a substantial reduction in hysteresis. Feedforward control is often combined with feedback control to optimize performance. == Model == The mathematical model of the plant (machine, process or organism) used by the feedforward control system may be created and input by a control engineer or it may be learned by the control system. Control systems capable of learning and/or adapting their mathematical model have become more practical as microprocessor speeds have increased. The discipline of modern feedforward control was itself made possible by the invention of microprocessors. Feedforward control requires integration of the mathematical model into the control algorithm such that it is used to determine the control actions based on what is known about the state of the system being controlled. In the case of control for a lightweight, flexible robotic arm, this could be as simple as compensating between when the robot arm is carrying a payload and when it is not. The target joint angles are adjusted to place the payload in the desired position based on knowing the deflections in the arm from the mathematical model's interpretation of the disturbance caused by the payload. Systems that plan actions and then pass the plan to a different system for execution do not satisfy the above definition of feedforward control. Unless the system includes a means to detect a disturbance or receive an input and process that input through the mathematical model to determine the required modification to the control action, it is not true feedforward control. === Open system === In control theory, an open system is a feed forward system that does not have any feedback loop to control its output. In contrast, a closed system uses on a feedback loop to control the operation of the system. In an open system, the output of the system is not fed back into the input to the system for control or operation. == Applications == === Physiological feed-forward system === In physiology, feed-forward control is exemplified by the normal anticipatory regulation of heartbeat in advance of actual physical exertion by the central autonomic network. Feed-forward
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Commit (data management)

In computer science and data management, a commit is a behavior that marks the end of a transaction and provides Atomicity, Consistency, Isolation, and Durability (ACID) in transactions. The submission records are stored in the submission log for recovery and consistency in case of failure. In terms of transactions, the opposite of committing is giving up tentative changes to the transaction, which is rolled back. Due to the rise of distributed computing and the need to ensure data consistency across multiple systems, commit protocols have been evolving since their emergence in the 1970s. The main developments include the Two-Phase Commit (2PC) first proposed by Jim Gray, which is the fundamental core of distributed transaction management. Subsequently, the Three-phase Commit (3PC), Hypothesis Commit (PC), Hypothesis Abort (PA), and Optimistic Commit protocols gradually emerged, solving the problems of blocking and fault recovery. Today, new fields such as e-commerce payment and blockchain technology are emerging, and submission protocols play a significant role in various business areas. By effectively handling transactions, resolving faults and recovering problems, the commit protocol becomes crucial in ensuring the reliability and consistency of data management. == History == The concept of Commit originated in the late 1960s and early 1970s, when computer technology was rapidly advancing and data management was becoming an important requirement in business and finance. Enterprises have gradually replaced the traditional paper records with computers, which has fully improved the work efficiency. The reliability and consistency of data have become a necessary requirement. Transaction management at this stage is relatively simple, limited to using a single computer for processing. It merely effectively records the changes in data to ensure that the data remains stable after the transaction is completed or terminated. In the late 1970s, as database systems moved from a single calculator operation to multiple distributed collaborations, ensuring data consistency and reliability became a new challenge. In 1978, computer scientist Jim Gray proposed the famous two-phase Commit Protocol (2PC), which became an effective solution for distributed transaction management, successfully managing data synchronization problems between multiple nodes. However, this commit protocol has some potential transaction blocking problems when nodes fail. In the early 1980s, researchers discovered that although the two-step commit protocol was effective at synchronizing data, there could be long waits and even system crashes, with limitations. To improve this problem, people have begun to explore new and effective methods, including enhancing efficiency by reducing message communication during the protocol process. IBM's R database introduced the Assumed Commit and Assumed abort protocols, which contributed significantly to transaction management efficiency. These two protocols have greatly improved the processing efficiency of distributed transactions by reducing communication overhead and have become an important breakthrough in the technology of transaction commit protocols. By the early 1990s, with the increase in business demands and the complexity of transactions, enterprises required higher efficiency in distributed transaction processing. In order to adapt to the needs of different environments, the scientific community has gradually developed various variants of commit protocols to provide more flexible transaction management options for different needs. For example, the three-phase commit protocol promotes the commit of transactions more effectively and reduces the occurrence of blocking problems by adding a pre-commit protocol and a timeout mechanism. In the 21st century, with the popularization of mobile Internet and wireless technology, the commit protocol has been further developed, and researchers have begun to pay attention to how to reduce the blocking in the transaction process to solve the problem of broadband limitation, battery life and network instability in the mobile environment. The proposal of optimistic commit protocol marks the extension of commit technology from traditional database to the emerging mobile data field. This protocol allows transactions to temporarily use unconfirmed data, improving the user experience in cases of poor network conditions. In recent years, with the rise of blockchain and decentralized technologies, submission protocols and consensus mechanisms have gradually merged. These consensus algorithms play a role in tamper-proofing and preventing malicious attacks on node pairs in a decentralized environment. This enables commit to no longer be confined to the scope of traditional database management, but to become the core technology of trust computing and distributed ledgers, further expanding the application field of commit in the digital age. This integration has brought about extensive application impacts. Each transaction can achieve the effect of tracking global submissions through the verification of the consensus mechanism, becoming an important technical foundation for promoting the circulation of digital assets, the operation of cryptocurrencies and decentralized applications. == Commit Protocol Types == In the world of data management, a transaction is a series of database operations, such as bank transfers and order submission. In order to ensure the accuracy, consistency, and security of the data, transactions are usually completed completely, or cancelled completely, leaving no partially completed results. Commit protocol is the method used to coordinate this process. Different protocols are applicable to different submission scenarios and have their own advantages and disadvantages. There are four major commit protocols. === Two-Phase Commit (2PC) === The two-phase commit protocol is the most classic and broadest approach to distributed transactions, which includes both a preparation phase and a commit phase. This commit protocol is designed to allow the database coordinator to determine if all participating nodes agree. The preparation phase is the phase in which the coordination node sends a ready to commit request to all nodes participating in the transaction. The commit phase is a global commit after all participating nodes are ready, and if no agreement is reached, all nodes roll back the transaction and undo all previous operations. Although the two-phase commit protocol is the easiest to operate and widely used, its obvious drawback is that it can cause transactions to be blocked for a long time when nodes fail, resulting in a decline in system performance and making it difficult to terminate or continue immediately. === Three-Phase Commit (3PC) === The three-phase commit protocol is an improved non-blocking protocol based on 2PC, which is divided into three stages: preparation, pre-commit and commit. Firstly, each node sends a "preparation" request. After confirmation, a "pre-submission" stage is added. At this point, each node has completed most of the preparatory work and is waiting for the final confirmation. Finally, in the formal commit stage, after all nodes send the "commit" request, the transaction is completed and committed. Compared with 2PC, it increases the timeout mechanism, avoids the blocking problem caused by single point of failure, and improves the reliability of the system. The three-phase commit protocol significantly optimizes transaction reliability, but adds additional overhead for message transmission and state maintenance. It is more suitable for distributed application scenarios with high transaction sensitivity and no acceptance of long waiting times. === Presumed Commit (PC) and Presumed Abort (PA) === Presumed Commit (PC) is the default that the transaction will be committed successfully and rollback will be notified unless an anomaly is encountered. This commit reduces the message overhead and logging costs of a normal commits. Presumed Abort (PA) is assumed that the default state of the transaction is a rollback and will only be committed when all nodes have explicitly agreed. This commit is applicable to transactions that are not updated frequently or have a low probability of successful commit. The IBM R Distributed Database management System was the first to propose and practice the PC and PA protocols, handling distributed transaction management very efficiently and becoming a classic case in the field of database transaction management. === Optimistic Commit Protocol === With the rise of the Internet, the previous commit protocols are facing new challenges, especially in mobile scenarios with unstable networks. Excessively long transaction waiting times can affect the user experience. The Optimistic Commit Protocol allows a transaction to temporarily access uncommitted data before committing to avoid wait times. This type of commit is suitable f
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Social search

Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc. Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries. == Research and implementations == Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks. Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology. This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentiment that turn search personal by searching within the users' social circles. In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results. The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before. The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results. In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still is not wildly adopted or has much usage among many users. Later on, Google was criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content. By Google integrating Google+, the company was encouraging users to switch to Google's social networking site in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites. In December 2008, Twitter had re-introduced their people search feature. While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine. In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal was to allow users to prioritize results that were popular with their social circle over the general internet. Facebook's Graph search utilized Facebook's user generated content to target users. Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they would like to keep it in house. In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information. == Social discovery == Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal. == Social search engines == A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query. Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added. An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question. In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aar
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