Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis. == History == The program STUDENT, written in 1964 by Daniel Bobrow for his PhD dissertation at MIT, is one of the earliest known attempts at NLU by a computer. Eight years after John McCarthy coined the term artificial intelligence, Bobrow's dissertation (titled Natural Language Input for a Computer Problem Solving System) showed how a computer could understand simple natural language input to solve algebra word problems. A year later, in 1965, Joseph Weizenbaum at MIT wrote ELIZA, an interactive program that carried on a dialogue in English on any topic, the most popular being psychotherapy. ELIZA worked by simple parsing and substitution of key words into canned phrases and Weizenbaum sidestepped the problem of giving the program a database of real-world knowledge or a rich lexicon. Yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask.com. In 1969, Roger Schank at Stanford University introduced the conceptual dependency theory for NLU. This model, partially influenced by the work of Sydney Lamb, was extensively used by Schank's students at Yale University, such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner. In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input. Instead of phrase structure rules ATNs used an equivalent set of finite-state automata that were called recursively. ATNs and their more general format called "generalized ATNs" continued to be used for a number of years. In 1971, Terry Winograd finished writing SHRDLU for his PhD thesis at MIT. SHRDLU could understand simple English sentences in a restricted world of children's blocks to direct a robotic arm to move items. The successful demonstration of SHRDLU provided significant momentum for continued research in the field. Winograd continued to be a major influence in the field with the publication of his book Language as a Cognitive Process. At Stanford, Winograd would later advise Larry Page, who co-founded Google. In the 1970s and 1980s, the natural language processing group at SRI International continued research and development in the field. A number of commercial efforts based on the research were undertaken, e.g., in 1982 Gary Hendrix formed Symantec Corporation originally as a company for developing a natural language interface for database queries on personal computers. However, with the advent of mouse-driven graphical user interfaces, Symantec changed direction. A number of other commercial efforts were started around the same time, e.g., Larry R. Harris at the Artificial Intelligence Corporation and Roger Schank and his students at Cognitive Systems Corp. In 1983, Michael Dyer developed the BORIS system at Yale which bore similarities to the work of Roger Schank and W. G. Lehnert. The third millennium saw the introduction of systems using machine learning for text classification, such as the IBM Watson. However, experts debate how much "understanding" such systems demonstrate: e.g., according to John Searle, Watson did not even understand the questions. John Ball, cognitive scientist and inventor of the Patom Theory, supports this assessment. Natural language processing has made inroads for applications to support human productivity in service and e-commerce, but this has largely been made possible by narrowing the scope of the application. There are thousands of ways to request something in a human language that still defies conventional natural language processing. According to Wibe Wagemans, "To have a meaningful conversation with machines is only possible when we match every word to the correct meaning based on the meanings of the other words in the sentence – just like a 3-year-old does without guesswork." == Scope and context == The umbrella term "natural language understanding" can be applied to a diverse set of computer applications, ranging from small, relatively simple tasks such as short commands issued to robots, to highly complex endeavors such as the full comprehension of newspaper articles or poetry passages. Many real-world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require an in-depth understanding of the text, but needs to deal with a much larger vocabulary and more diverse syntax than the management of simple queries to database tables with fixed schemata. Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability. For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. Vulcan later became the dBase system whose easy-to-use syntax effectively launched the personal computer database industry. Systems with an easy-to-use or English-like syntax are, however, quite distinct from systems that use a rich lexicon and include an internal representation (often as first order logic) of the semantics of natural language sentences. Hence the breadth and depth of "understanding" aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The "breadth" of a system is measured by the sizes of its vocabulary and grammar. The "depth" is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding, but they still have limited application. Systems that attempt to understand the contents of a document such as a news release beyond simple keyword matching and to judge its suitability for a user are broader and require significant complexity, but they are still somewhat shallow. Systems that are both very broad and very deep are beyond the current state of the art. == Components and architecture == Regardless of the approach used, most NLU systems share some common components. The system needs a lexicon of the language and a parser and grammar rules to break sentences into an internal representation. The construction of a rich lexicon with a suitable ontology requires significant effort, e.g., the Wordnet lexicon required many person-years of effort. The system also needs theory from semantics to guide the comprehension. The interpretation capabilities of a language-understanding system depend on the semantic theory it uses. Competing semantic theories of language have specific trade-offs in their suitability as the basis of computer-automated semantic interpretation. These range from naive semantics or stochastic semantic analysis to the use of pragmatics to derive meaning from context. Semantic parsers convert natural-language texts into formal meaning representations. Advanced applications of NLU also attempt to incorporate logical inference within their framework. This is generally achieved by mapping the derived meaning into a set of assertions in predicate logic, then using logical deduction to arrive at conclusions. Therefore, systems based on functional languages such as Lisp need to include a subsystem to represent logical assertions, while logic-oriented systems such as those using the language Prolog generally rely on an extension of the built-in logical representation framework. The management of context in NLU can present special challenges. A large variety of examples and counter examples have resulted in multiple approaches to the formal modeling of context, each with specific strengths and weaknesses.
Alibaba Cloud
Alibaba Cloud, also known as Aliyun (Chinese: 阿里云; pinyin: Ālǐyún; lit. 'Ali Cloud'), is a cloud computing company, a subsidiary of Alibaba Group. Alibaba Cloud provides cloud computing services to online businesses and Alibaba's own e-commerce ecosystem. Its international operations are registered and headquartered in Singapore. Alibaba Cloud offers cloud services that are available on a pay-as-you-go basis, and include elastic compute, data storage, relational databases, big-data processing, DDoS protection and content delivery networks (CDN). It is the largest cloud computing company in China, and in Asia Pacific according to Gartner. Alibaba Cloud operates data centers in 29 regions and 87 availability zones around the globe. As of June 2017, Alibaba Cloud is placed in the Visionaries' quadrant of Gartner's Magic Quadrant for cloud infrastructure as a service, worldwide. == History == Alibaba Cloud was founded in September 2009, and R&D centers and operation centers were opened in Hangzhou, Beijing, and Silicon Valley. === 2010–2013 === In November 2010, the company supported the first Single's Day (11.11) Taobao shopping festival, with 2.4 billion PageViews (PV) in 24 hours. Two years later, in November 2012, it became the first Chinese cloud service provider to pass ISO27001:2005 (Information Security Management System). In January 2013, Alibaba Cloud merged with HiChina (founded by Xiangning Zhang) for the www.net.cn business as one of the largest acquisitions in the company's history at the time. In August of that year, ApsaraDB architecture supported 5000 physical machines in a single cluster. === 2014–2017 === The company's Hong Kong data center went online in May 2014, and in December of that year, Alibaba Cloud defended a 14-hour-long DDoS attack, peaking at 453.8 Gbit/s. In July 2015, the Alibaba Group invested US$1 billion in Alibaba Cloud. A month later, Alibaba Cloud's first Singapore data center opened, and Singapore was announced as Alibaba Cloud's overseas headquarters. Two US data centers went online in October 2015, and that same month MaxCompute took the lead in the Sort Benchmark, sorting 100 TB data in 377s compared with Apache Spark's previous record of 1406s. The Alibaba Cloud Computing Conference was also held in October 2015 in Hangzhou and attracted over 20,000 developers. A month later, in November, the company supported the 11.11 shopping festival with a record of $14.2 billion transactions in 24 hours. Alibaba Cloud partnered with SK Holdings C&C in April 2016 to provide cloud services to Korean and Chinese companies. A month later, the company formalized a joint venture with SoftBank to launch cloud services in Japan that utilize technologies and solutions from Alibaba Cloud. In June 2016, Alibaba Cloud expanded its data center operations in Singapore with the establishment of a second availability zone. Alibaba Cloud also achieved two new certifications overseas: Singapore Multi-Tier Cloud Security (MTCS) standard Level 3, and the Payment Card Industry Three-Domain Secure (PCI 3DS). The company partnered with Vodafone Germany in November 2016 for Data Center operations and to provide cloud services to German and European companies. Alibaba became the official cloud services provider of the Olympics in January 2017. A month later, in February, the company became a founding Member of the EU Cloud Code of Conduct. In June 2017, Alibaba Cloud was placed in the Visionaries quadrant of Gartner's Magic Quadrant for Cloud Infrastructure as a Service, Worldwide. Alibaba Cloud partnered with Malaysia's Fusionex in September 2017 to provide cloud solutions in Southeast Asia, and the Malaysia data center commenced operations in October. That same month, the company partnered with Elastic and launched a new service called Alibaba Cloud Elasticsearch. Alibaba Cloud India data center commenced operations in December 2017. In addition, Alibaba Cloud received the C5 standard certification from the German Federal Office for Information Security (BSI) for its data centers in Germany and Singapore. === 2018–2021 === In February 2018, Alibaba Cloud's Indonesia data center commenced operations. The company's first data center opening in the Philippines in June 2021. Alibaba Cloud unveiled the ARM-based Yitian 710 chip, designed in-house, for use in its data centers in October 2021. On November 24, 2021, the bug Log4Shell was disclosed to Apache by Chen Zhaojun of Alibaba Cloud's Security Team. On December 22, 2021, the Chinese Ministry of Industry and Information Technology suspended a partnership with Alibaba Cloud for "failure in reporting cybersecurity vulnerabilities" related to the Log4Shell bug. === 2022 === In September 2022, Alibaba Cloud announced a $1 billion pledge to upgrade its global partner ecosystem. == Data center regions == Alibaba Cloud has 25 regional data centres globally. The Data Center in Germany is operated by Vodafone Germany (Frankfurt) and certified with C5. == Products == Alibaba Cloud provides cloud computing IaaS, PaaS, DBaaS and SaaS, including services such as e-commerce, big data, Database, IoT, Object storage (OSS), Kubernetes and data customization which can be managed from Alibaba web page or using aliyun command line tool. AnalyticDB was first released in May 2018, and the latest version 3.0 was released in 2019. On April 26, 2019, TPC published TPC-DS benchmark result of AnalyticDB. In 2019, a paper about the system design of AnalyticDB was published in VLDB conference 2019. == Academic partners == List of academic alliances: Shanghai Jiao Tong University Universiti Tunku Abdul Rahman (UTAR) University of Malaya Hong Kong Shue Yan University Macao University of Science and Technology Singapore University of Social Sciences (SUSS) Télécom Paris SUPINFO International University Université de technologie sino-européenne de l'université de Shanghai Gadjah Mada University Universitas Prasetiya Mulya Bina Nusantara University Krida Wacana Christian University Hong Kong Institute of Vocational Education Nanyang Polytechnic Republic Polytechnic Sekolah Tinggi Teknologi Informasi NIIT Usman Institute of Technology AISSMS Institute of Information Technology == Controversy == On October 26, 2016, Zhang Kai, CEO of ITHome issued an announcement stating he could no longer tolerate Alibaba Cloud's overselling and service interruption issues, and had migrated the hosting entirely to Baidu Cloud. Alibaba Cloud subsequently issued an apology letter, but indirectly mentioned that website performance should consider system architecture and avoid single-point design.
Data analysis
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a variety of unstructured data. All of the above are varieties of data analysis. == Data analysis process == Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Statistician John Tukey, defined data analysis in 1961, as:"Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." There are several phases, and they are iterative, in that feedback from later phases may result in additional work in earlier phases. === Data requirements === The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). === Data collection === Data may be collected from a variety of sources. A list of data sources are available for study & research. The requirements may be communicated by analysts to custodians of the data; such as, Information Technology personnel within an organization. Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation. === Data processing === Data integration is a precursor to data analysis: Data, when initially obtained, must be processed or organized for analysis. For instance, this may involve placing data into rows and columns in a table format (known as structured data) for further analysis, often through the use of spreadsheet (e.g. Excel) or statistical software. === Data cleaning === Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that the data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable. Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. Quantitative data methods for outlier detection can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Text data spell checkers can be used to lessen the amount of mistyped words. However, it is harder to tell if the words are contextually (i.e., semantically and idiomatically) correct. === Exploratory data analysis === Once the datasets are cleaned, they can then begin to be analyzed using exploratory data analysis. The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned above. Descriptive statistics, such as the average, median, and standard deviation, are often used to broadly characterize the data. Data visualization is also used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights about messages within the data. === Modeling and algorithms === Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. === Communication === Once data is analyzed, it may be presented in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience. Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data. == Quantitative messages == Stephen Few described eight types of quantitative messages that users may attempt to communicate from a set of data, including the associated graphs. Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend. Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by salespersons (the category, with each salesperson a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the salespersons. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market. Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference amount. Frequency distribution:
Social computing
Social computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or fostering existing social conventions and social contexts through the use of software and technology. Blogs, email, instant messaging, social network services, wikis, social bookmarking and other instances of what is often called social software illustrate ideas from social computing. The rise in social computing is attributed to the prevalence of personal devices and increased overall computing power. This enables a growing number of users to participate in sharing content and interact with another. == Definitions == Humans—and human behavior—are profoundly social. Humans tend to orient to one another and develop abilities to interact with each other and other species. This ranges from expression and gesture through spoken, written, and body language. Humans are influenced by the behavior of those around them and can rely on social context and cues to make decisions. An example of a behavior relying on social contexts is applauding at the end of the play. This is based on the context that the show ended, and other audience members are applauding. Social information provides a basis for inferences, planning, and coordinating activity. == Examples == Common tools include blogs, email, instant messaging, social networking sites, wikis, and social bookmarking platforms. These technologies enable users to generate content, share knowledge, and interact in real time. == Applications == The rise of social computing has highlighted opportunities for businesses. Businesses are interacting on social computing platforms and investing in facilities to support and research social computing.Business models can leverage the massive customer bases that accumulate through social computing channels. Some organizations have started their own blogs and networks (McAfee, 2006, Joe, 2005). Organizations from diverse industry sectors such as Google, Cisco, and Fox, have sought to acquire or invest in successful social computing enterprises. A business blog can serve as a source of information and promotion for the company. This allows the company to share content about the company and their initiatives. Businesses have also interacted with social computing to market themselves and interact with customers. A notable example is Wendy's with their X (formerly Twitter) account. The account was primarily used to promote business promotions and interact with users in a playful or meaningful way. E-commerce web sites have allowed users to leave reviews and feedback on purchases which has improved online shopping experience for sellers and consumers.As another example of social computing’s business applications, many e-commerce Web sites have adopted online product/vendor feedback/reputation systems. Such systems provide an asynchronous platform for the consumer community to share experiences collectively and influence their purchasing behavior. They also provide a vehicle for eliciting feedback information valuable to the vendors and e-commerce site operators.Consumers can use the feedback systems to make a more educated choice on a purchase by comparing reviews between products or vendors. Sellers can track consumer behaviors and trends regarding a product and adjust their supply according to the demand. == Challenges and criticism == Social computing raises several concerns related to privacy, data security, and algorithmic bias. The widespread collection and analysis of user-generated data can lead to ethical dilemmas, especially when users are unaware of how their information is used. Critics also highlight issues of digital labor, surveillance, and the spread of misinformation, which can influence public opinion and social dynamics. === Term appearance === The term appeared in the mid 1990s after technology advancements and development of the web. In 1994, the concept of social computing was first proposed by Schuler. He thought, "Social computing is a computing application, with software as the medium or focus of social relationships." === Premise === The premise of social computing is that it is possible to design digital systems that support useful functionality by making socially produced information available to their users. This information may be provided directly, as when systems show the number of users who have rated a review as helpful or not. Or the information may be provided after being filtered and aggregated, as is done when systems recommend a product based on what else people with similar purchase history have purchased. Alternatively, the information may be provided indirectly, as is the case with Google's page rank algorithms which orders search results based on the number of pages that (recursively) point to them. In all of these cases, information that is produced by a group of people is used to provide or enhance the functioning of a system. Social computing is concerned with systems of this sort and the mechanisms and principles that underlie them. Social computing can be defined as follows: "Social Computing" refers to systems that support the gathering, representation, processing, use, and dissemination of information that is distributed across social collectivities such as teams, communities, organizations, and markets. Moreover, the information is not "anonymous" but is significantly precise because it is linked to people, who are in turn linked to other people. More recent definitions, however, have foregone the restrictions regarding anonymity of information, acknowledging the continued spread and increasing pervasiveness of social computing. As an example, Hemmatazad, N. (2014) defined social computing as "the use of computational devices to facilitate or augment the social interactions of their users, or to evaluate those interactions in an effort to obtain new information." Social computing has to do with supporting "computations" that are carried out by groups of people, an idea that has been popularized in James Surowiecki's book, The Wisdom of Crowds. Examples of social computing in this sense include collaborative filtering, online auctions, reputation systems, computational social choice, tagging, and verification games. The social information processing page focuses on this sense of social computing. == History == === Technology infrastructure === Users were able to interact more with websites after the development of Web 2.0. This was an advancement from Web 1.0. Comode G. and Krishnamurthy B. (2008) note that "content creators were few in Web 1.0 with the vast majority of users simply acting as consumers of content." Web 2.0 provided functionalities that allowed for low-cost web-hosting services and introduced features with browser windows that used basic information structure and expanded it to as many devices as possible using HTTP, or Hypertext Transfer Protocol. Sometimes referred to as "Enterprise 2.0", a term derived from Web 2.0, social software for enterprise generally refers to the use of social computing in corporate intranets and in other medium- and large-scale business environments. It consisted of a class of tools that allowed for networking and social changes to businesses at the time. It was a layering of the business tools on Web 2.0 and brought forth several applications and collaborative software with specific uses. FinanceElectronic negotiation, which first came up in 1969 and was adapted over time to suit financial markets networking needs, represents an important and desirable coordination mechanism for electronic markets. Negotiation between agents (software agents as well as humans) allows cooperative and competitive sharing of information to determine a proper price. Recent research and practice has also shown that electronic negotiation is beneficial for the coordination of complex interactions among organizations. Electronic negotiation has recently emerged as a very dynamic, interdisciplinary research area covering aspects from disciplines such as Economics, Information Systems, Computer Science, Communication Theory, Sociology and Psychology.Social computing has become more widely known because of its relationship to a number of recent trends. These include the growing popularity of social software and Web 3.0, increased academic interest in social network analysis, the rise of open source as a viable method of production, and a growing conviction that all of this can have a profound impact on daily life. A February 13, 2006 paper by market research company Forrester Research suggested that: === Developments === PLATO was one of the earliest examples of social computing in a live production environment with initially hundreds and soon thousands of users. The PLATO computer system was developed by the University of Illinois at Urbana Champaign in 1960s. In the 70s, the system supported social software applications for multi-us
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.
Hildon
Hildon is an application framework originally developed for mobile devices (PDAs, mobile phones, etc.) running the Linux operating system as well as the Symbian operating system. The Symbian variant of Hildon was discontinued with the cancellation of Series 90. It was developed by Nokia for the Maemo operating system. It focuses on providing a finger-friendly interface. It is primarily a set of GTK extensions that provide mobile-device–oriented functionality, but also provides a desktop environment that includes a task navigator for opening and switching between programs, a control panel for user settings, and status bar, task bar and home applets. It is standard on the Maemo platform used by the Nokia Internet Tablets and the Nokia N900 smartphone. Hildon has also been selected as the framework for Ubuntu Mobile and Embedded Edition. Hildon was an early instance of a software platform for generic computing in a tablet device intended for internet consumption. But Nokia didn't commit to it as their only platform for their future mobile devices and the project competed against other in-house platforms. The strategic advantage of a modern platform was not exploited, being displaced by the Series 60, though its development is continued by the Maemo Leste project. == Components == The Hildon framework includes components that effectively provide a desktop environment. === Hildon Application Manager === Hildon Application Manager is the Hildon graphical package manager, it uses the Debian package management tools APT (Advanced Packaging Tool and dpkg) and provides a graphical interface for installing, updating and removing packages. It is a limited package manager, designed specifically for end-users, in that it doesn't directly offer the user access to system files and libraries. With the Diablo release of Maemo, Hildon Application Manager now supports "Seamless Software Update" (SSU), which implements a variety of features to allow system upgrades to be easily performed through it. === Hildon Control Panel === Hildon Control Panel is the user settings interface for Hildon. It provides simple access to control panels used to change system settings. === Hildon Desktop === Hildon Desktop is the primary UI component of Hildon, so makes up the bulk of what a user will see as "Hildon". It controls application launching and switching, general system control, and provides interfaces for task bar (application menu and task switcher), status bar (brightness and volume control), and home (internet radio and web search) applets. === Hildon Library === The Hildon library, originally developed by Nokia but since Maemo 5, developed by Igalia and Lanedo (who developed MaemoGTK+, the Maemo version of GTK+). It is a set of mobile specific GTK+ widgets for applications in Maemo. Up to Maemo 4, these widgets were designed for stylus usage. However, in Maemo 5, most widgets were deprecated and new widgets for direct finger manipulation were introduced, including a kinetic panning container.
European Grid Infrastructure
EGI (originally an initialism for European Grid Infrastructure) is a federation of computing and storage resource providers that deliver advanced computing and data analytics services for research and innovation. The Federation is governed by its participants represented in the EGI Council and coordinated by the EGI Foundation. As of 2024, the EGI Federation supports 160 scientific communities worldwide and over 95,000 users in their intensive data analysis. The most significant scientific communities supported by EGI in 2022 were Medical and Health Sciences, High Energy Physics, and Engineering and Technology. The EGI Federation provideds services through over 150 data centres, of which 25 are cloud sites, in 43 countries and 64 Research Infrastructures (4 of which are members of the Federation). == Name == Originally, EGI stood for European Grid Infrastructure. This reflected its focus on providing access to high-throughput computing resources across Europe using Grid computing techniques. However, as EGI's service offerings expanded beyond traditional grid computing, particularly with the incorporation of federated cloud services, the original meaning of the acronym became less accurate. To emphasise the broader scope of EGI's services and avoid any confusion associated with the outdated term "grid," it is recommended to refer to EGI simply as EGI. == Structure == === EGI Federation === The EGI Federation delivers a scalable digital research infrastructure (e-infrastructure), empowering tens of thousands of researchers across diverse scientific disciplines. Through the EGI Federation, researchers gain access to advanced computing and data analytics capabilities, including large-scale data analysis, while benefiting from the collaborative efforts of hundreds of service providers from both public and private sectors, consolidating resources from Europe and beyond. Overall, the EGI Federation offers a range of services, encompassing distributed high-throughput computing and cloud computing, storage and data management capabilities, co-development of new solutions, expert support, and comprehensive training opportunities. This ecosystem propels collaboration, scientific progress and innovation. === EGI Foundation === The EGI Foundation is the coordinating body of the EGI Federation. It was established in 2010 with headquarters in Amsterdam, Netherlands. The Foundation coordinates the research and innovation efforts of its members, spanning technical areas critical to data-intensive science, including large-scale data processing and analysis, distributed Artificial Intelligence/Machine Learning, federated Identity and access management and the application of digital twins for research. The day-to-day running of the EGI Foundation is supervised by the Executive Board. The board’s members work closely with the EGI Director on operational, technical and financial issues. The Executive Board’s members are appointed by the EGI Council for a two-year term. === EGI Council === The EGI Council is responsible for defining the strategic direction of the EGI Federation. The Council acts as the senior decision-making and supervisory authority of the EGI Foundation, with a mandate to define the strategic direction of the entire EGI ecosystem. === EGI Services === EGI offers a suite of services to support data-intensive research. These services include compute resources, orchestration tools, storage and data management solutions, training programmes, security and identity services, and applications. Compute resources encompass cloud compute, cloud container compute, high-throughput compute, and software distribution. Orchestration tools include the Workload Manager and infrastructure manager. Storage and data management solutions include online storage, data transfer, and DataHub. Training programmes cover FitSM, ISO 27001, and general training infrastructure. EGI Check-in and Secrets Store are key security and identity services, while applications such as Notebooks and Replay enhance research productivity. In addition to services for Research, EGI also provides services for Federation and Business. Services for Federation are designed to help resource providers and user communities collaborate and share resources. EGI also offers a range of services to support businesses in their digital transformation. Through the EGI Digital Innovation Hub (EGI DIH), companies can access advanced computing resources, networking, funding and training opportunities, collaborate with research institutions, and test solutions before investing. == History == In 2002, the first large-scale experimental facility was successfully demonstrated by the DataGrid project under the lead of CERN with tens of technical architects from the major High Energy Physics institutes in the world. For the first time, distributed computing was applied to data-intensive processing. It aimed at developing a large-scale computational grid to facilitate distributed data-intensive scientific computing across High Energy Physics, Earth Observation, and Biology science applications. On 28 February 2003, the first software release of LCG-MW was published. gLite, the Lightweight Middleware for Grid Computing and LCG, Large Hadron Collider Computing Grid, are the cornerstone of the Worldwide LHC Computing Grid, which expanded over time towards the EGI Federation. 2004 marks the year of the first pilot infrastructure, seeing the participation of CERN and data centres in the United Kingdom, Spain, Germany, the Netherlands, France, Canada, Russia, Bulgaria, the Asia-Pacific region and Switzerland. Over the years, the infrastructure has grown into a federation of 128 data centres and 25 cloud providers serving more than 95,000 users worldwide. In 2004, the first data processing tasks started being formally recorded in a central accounting system. The EGI Accounting Portal provides the accounting data for Compute, Storage and Data services gathered from the data centres of the EGI Federation. A few years later, in 2010, EGI was established as the coordinating body of the EGI Federation to build an integrated pan-European infrastructure to support European research communities primarily. In the same year, EGI launched the flagship project EGI Inspire. That project brought together European organisations to establish a sustainable European Grid Infrastructure for large-scale data analysis. The success of the project was due to the adoption of a distributed computing model to solve big data problems. Moreover, EGI-Inspire harmonised operational policies across its federation of affiliated data centres and cloud service providers worldwide, integrating e-infrastructures from 57 countries. The EGI Federation was the first to apply federation to cloud provisioning, opening a new avenue in large-scale interactive data analysis. In 2015, within EGI Engage, opening a new avenue in large-scale interactive data analysis. The EGI Federated Cloud is an IaaS-type cloud, incorporating academic and private clouds and virtualised resources built using open standards. Its development is driven by the needs of the scientific community, resulting in a novel research e-infrastructure that relies on well-established federated operational services, making EGI a dependable resource for scientific endeavours. In 2015, EGI, EUDAT, GÉANT, LIBER and OpenAIRE published a position paper on a 'European Open Science Cloud for Research'. With the EOSC-hub project in 2016, EGI started contributing in practice to shaping the services for the EOSC. The work continued with a series of projects, like EOSC Enhance, EOSC Life and EOSC Synergy. With EGI-ACE and its contribution to EOSC Future, EGI has continued developing the EOSC Core. In early 2024, EGI started providing services to the EOSC EU Node, and with EOSC Beyond it will provide new EOSC Core capabilities and pilot additional national and thematic nodes. In October 2024, EUDAT, GÉANT, OpenAIRE, PRACE and EGI signed a Memorandum of Understanding establishing the European e-Infrastructures Assembly. This collaboration will bolster the position and promote the services of e-Infrastructures, empowering researchers across Europe to drive innovation and advance scientific discovery.