Energy informatics is a research field covering the use of information and communication technology to address energy utilization and management challenges. Methods used for "smart" implementations often combine IoT sensors with artificial intelligence and machine learning. Energy Informatics is founded on flow networks that are the major suppliers and consumers of energy. Their efficiency can be improved by collecting and analyzing information. == Application areas == The field among other consider application areas within: Smart Buildings by developing ICT-centred solutions for improving the energy-efficiency of buildings. Smart Cities by investigating the synergies between demand patterns and supply availability of energy flows in cities and communities to improve energy efficiency, increase integration of renewable sources, and provide resilience towards system faults caused by extreme situations, like hurricanes and flooding. Smart Industries including the development of ICT-centred solutions for improving the energy efficiency and predictability of energy intensive industrial processes, without compromising process and product quality. Smart Energy Networks by developing ICT-centred solutions for coordinating the supply and demand in environmentally sustainable energy networks.
Physical access
Physical access is a term in computer security that refers to the ability of people to physically gain access to a computer system. According to Gregory White, "Given physical access to an office, the knowledgeable attacker will quickly be able to find the information needed to gain access to the organization's computer systems and network." == Attacks and countermeasures == === Attacks === Physical access opens up a variety of avenues for hacking. Michael Meyers notes that "the best network software security measures can be rendered useless if you fail to physically protect your systems," since an intruder could simply walk off with a server and crack the password at his leisure. Physical access also allows hardware keyloggers to be installed. An intruder may be able to boot from a CD or other external media and then read unencrypted data on the hard drive. They may also exploit a lack of access control in the boot loader; for instance, pressing F8 while certain versions of Microsoft Windows are booting, specifying 'init=/bin/sh' as a boot parameter to Linux (usually done by editing the command line in GRUB), etc. One could also use a rogue device to access a poorly secured wireless network; if the signal were sufficiently strong, one might not even need to breach the perimeter. === Countermeasures === IT security standards in the United States typically call for physical access to be limited by locked server rooms, sign-in sheets, etc. Physical access systems and IT security systems have historically been administered by separate departments of organizations, but are increasingly being seen as having interdependent functions needing a single, converged security policy. An IT department could, for instance, check security log entries for suspicious logons occurring after business hours, and then use keycard swipe records from a building access control system to narrow down the list of suspects to those who were in the building at that time. Surveillance cameras might also be used to deter or detect unauthorized access.
Jan Leike
Jan Leike (born 1986 or 1987) is an AI alignment researcher who has worked at DeepMind and OpenAI. He joined Anthropic in May 2024. == Education == Jan Leike obtained his undergraduate degree from the University of Freiburg in Germany. After earning a master's degree in computer science, he pursued a PhD in machine learning at the Australian National University under the supervision of Marcus Hutter. == Career == Leike made a six-month postdoctoral fellowship at the Future of Humanity Institute before joining DeepMind to focus on empirical AI safety research, where he collaborated with Shane Legg. === OpenAI === In 2021, Leike joined OpenAI. In June 2023, he and Ilya Sutskever became the co-leaders of the newly introduced "superalignment" project, which aimed to determine how to align future artificial superintelligences within four years to ensure their safety. This project involved automating AI alignment research using relatively advanced AI systems. At the time, Sutskever was OpenAI's Chief Scientist, and Leike was the Head of Alignment. Leike was featured in Time's list of the 100 most influential personalities in AI, both in 2023 and in 2024. In May 2024, Leike announced his resignation from OpenAI, following the departure of Sutskever, Daniel Kokotajlo and several other AI safety employees from the company. Leike wrote that "Over the past years, safety culture and processes have taken a backseat to shiny products", and that he "gradually lost trust" in OpenAI's leadership. In May 2024, Leike joined Anthropic, an AI company founded by former OpenAI employees.
Simple Knowledge Organization System
Simple Knowledge Organization System (SKOS) is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. SKOS is part of the Semantic Web family of standards built upon RDF and RDFS, and its main objective is to enable easy publication and use of such vocabularies as linked data. == History == === DESIRE II project (1997–2000) === The most direct ancestor to SKOS was the RDF Thesaurus work undertaken in the second phase of the EU DESIRE project . Motivated by the need to improve the user interface and usability of multi-service browsing and searching, a basic RDF vocabulary for Thesauri was produced. As noted later in the SWAD-Europe workplan, the DESIRE work was adopted and further developed in the SOSIG and LIMBER projects. A version of the DESIRE/SOSIG implementation was described in W3C's QL'98 workshop, motivating early work on RDF rule and query languages: A Query and Inference Service for RDF. === LIMBER (1999–2001) === SKOS built upon the output of the Language Independent Metadata Browsing of European Resources (LIMBER) project funded by the European Community, and part of the Information Society Technologies programme. In the LIMBER project CCLRC further developed an RDF thesaurus interchange format which was demonstrated on the European Language Social Science Thesaurus (ELSST) at the UK Data Archive as a multilingual version of the English language Humanities and Social Science Electronic Thesaurus (HASSET) which was planned to be used by the Council of European Social Science Data Archives CESSDA. === SWAD-Europe (2002–2004) === SKOS as a distinct initiative began in the SWAD-Europe project, bringing together partners from both DESIRE, SOSIG (ILRT) and LIMBER (CCLRC) who had worked with earlier versions of the schema. It was developed in the Thesaurus Activity Work Package, in the Semantic Web Advanced Development for Europe (SWAD-Europe) project. SWAD-Europe was funded by the European Community, and part of the Information Society Technologies programme. The project was designed to support W3C's Semantic Web Activity through research, demonstrators and outreach efforts conducted by the five project partners, ERCIM, the ILRT at Bristol University, HP Labs, CCLRC and Stilo. The first release of SKOS Core and SKOS Mapping were published at the end of 2003, along with other deliverables on RDF encoding of multilingual thesauri and thesaurus mapping. === Semantic web activity (2004–2005) === Following the termination of SWAD-Europe, SKOS effort was supported by the W3C Semantic Web Activity in the framework of the Best Practice and Deployment Working Group. During this period, focus was put both on consolidation of SKOS Core, and development of practical guidelines for porting and publishing thesauri for the Semantic Web. === Development as W3C Recommendation (2006–2009) === The SKOS main published documents — the SKOS Core Guide, the SKOS Core Vocabulary Specification, and the Quick Guide to Publishing a Thesaurus on the Semantic Web — were developed through the W3C Working Draft process. Principal editors of SKOS were Alistair Miles, initially Dan Brickley, and Sean Bechhofer. The Semantic Web Deployment Working Group, chartered for two years (May 2006 – April 2008), put in its charter to push SKOS forward on the W3C Recommendation track. The roadmap projected SKOS as a Candidate Recommendation by the end of 2007, and as a Proposed Recommendation in the first quarter of 2008. The main issues to solve were determining its precise scope of use, and its articulation with other RDF languages and standards used in libraries (such as Dublin Core). === Formal release (2009) === On August 18, 2009, W3C released the new standard that builds a bridge between the world of knowledge organization systems – including thesauri, classifications, subject headings, taxonomies, and folksonomies – and the linked data community, bringing benefits to both. Libraries, museums, newspapers, government portals, enterprises, social networking applications, and other communities that manage large collections of books, historical artifacts, news reports, business glossaries, blog entries, and other items can now use SKOS to leverage the power of linked data. === Historical view of components === SKOS was originally designed as a modular and extensible family of languages, organized as SKOS Core, SKOS Mapping, and SKOS Extensions, and a Metamodel. The entire specification is now complete within the namespace http://www.w3.org/2004/02/skos/core#. == Overview == In addition to the reference itself, the SKOS Primer (a W3C Working Group Note) summarizes the Simple Knowledge Organization System. The SKOS defines the classes and properties sufficient to represent the common features found in a standard thesaurus. It is based on a concept-centric view of the vocabulary, where primitive objects are not terms, but abstract notions represented by terms. Each SKOS concept is defined as an RDF resource. Each concept can have RDF properties attached, including: one or more preferred index terms (at most one in each natural language) alternative terms or synonyms definitions and notes, with specification of their language Concepts can be organized in hierarchies using broader-narrower relationships, or linked by non-hierarchical (associative) relationships. Concepts can be gathered in concept schemes, to provide consistent and structured sets of concepts, representing whole or part of a controlled vocabulary. === Element categories === The principal element categories of SKOS are concepts, labels, notations, documentation, semantic relations, mapping properties, and collections. The associated elements are listed in the table below. === Concepts === The SKOS vocabulary is based on concepts. Concepts are the units of thought—ideas, meanings, or objects and events (instances or categories)—which underlie many knowledge organization systems. As such, concepts exist in the mind as abstract entities which are independent of the terms used to label them. In SKOS, a Concept (based on the OWL Class) is used to represent items in a knowledge organization system (terms, ideas, meanings, etc.) or such a system's conceptual or organizational structure. A ConceptScheme is analogous to a vocabulary, thesaurus, or other way of organizing concepts. SKOS does not constrain a concept to be within a particular scheme, nor does it provide any way to declare a complete scheme—there is no way to say the scheme consists only of certain members. A topConcept is (one of) the upper concept(s) in a hierarchical scheme. === Labels and notations === Each SKOS label is a string of Unicode characters, optionally with language tags, that are associated with a concept. The prefLabel is the preferred human-readable string (maximum one per language tag), while altLabel can be used for alternative strings, and hiddenLabel can be used for strings that are useful to associate, but not meant for humans to read. A SKOS notation is similar to a label, but this literal string has a datatype, like integer, float, or date; the datatype can even be made up (see 6.5.1 Notations, Typed Literals and Datatypes in the SKOS Reference). The notation is useful for classification codes and other strings not recognizable as words. === Documentation === The Documentation or Note properties provide basic information about SKOS concepts. All the properties are considered a type of skos:note; they just provide more specific kinds of information. The property definition, for example, should contain a full description of the subject resource. More specific note types can be defined in a SKOS extension, if desired. A query for skos:note ? will obtain all the notes about , including definitions, examples, and scope, history and change, and editorial documentation. Any of these SKOS Documentation properties can refer to several object types: a literal (e.g., a string); a resource node that has its own properties; or a reference to another document, for example using a URI. This enables the documentation to have its own metadata, like creator and creation date. Specific guidance on SKOS documentation properties can be found in the SKOS Primer Documentary Notes. === Semantic relations === SKOS semantic relations are intended to provide ways to declare relationships between concepts within a concept scheme. While there are no restrictions precluding their use with two concepts from separate schemes, this is discouraged because it is likely to overstate what can be known about the two schemes, and perhaps link them inappropriately. The property related simply makes an association relationship between two concepts; no hierarchy or generality relation is implied. The properties broader and narrower are used to assert a direct hierarchical link between two concepts. The meaning may be unexpected; the relat
OpenL Tablets
OpenL Tablets is a business rule management system (BRMS) and a business rules engine (BRE) based on table representation of rules. Engine implements optimized sequential algorithm. OpenL includes such table types as decision table, decision tree, spreadsheet-like calculator. == History == The OpenL Tablets project was started as an in-house development project in 2003 and later in 2006 was uploaded to SourceForge. Initially it was an open-source business rule engine for Java. Starting from version 5 it became a BRMS. == Technology == OpenL Tablets engine is specially designed for business rules and uses table rules presentation. Table format enforces rules to be structured and format itself is close to tables found in various business documents. OpenL Tablets is based on OpenL framework for creating custom languages running on Java VM. The engine is designed to allow pluggable language implementations. Currently, it uses 2 languages: table structure for rules format and java-like for code snippets in rules. Java-like language is Java 5.0 implementation with Business User Extensions. OpenL Tablets rules are mixture of declarative programming for rules logic and imperative programming for workflow control. Table formats are flexible enough to match the semantics of the problem domain. Tests, traces, benchmarks are integral part of the engine. It also provides powerful type definition capabilities to handle rules domain model inside rules files. The project is written in Java, but can be used at any platform using Service-oriented architecture approach, e.g. via web service. === Patents === The OpenL Tablets engine has patent pending validation feature. There are usages of OpenL Tablets which may be patented. == BRMS == OpenL Tablets includes several productivity tools and applications addressing BRMS related capabilities. They include web application to edit rules called OpenL WebStudio, web application to deploy rules as web services, Rules Repository to store and manage rules, Eclipse plug-ins to work with rules projects. == Related systems == CLIPS: public domain software tool for building expert systems. ILOG rules: a business rule management system. JBoss Drools: a business rule management system (BRMS). JESS: a rule engine for the Java platform - it is a superset of CLIPS programming language. Prolog: a general purpose logic programming language. DTRules: a Decision Table-based, open-sourced rule engine for Java.
Transportation Economic Development Impact System
Transportation Economic Development Impact System (TREDIS) is an economic analysis system sold by consulting firm Economic Development Research Group that is used in planning major transportation investments in the US and Canada. The role of economic impact analysis and TREDIS in the transportation planning process is explained in guidebooks of the US Department of Transportation and the American Association of State Highway and Transportation Officials. TREDIS has been most commonly used for assessing the expected economic impacts of statewide highway programs, regional multi-modal plans and public transport investment. Its history and theoretical foundation are explained in peer reviewed journal articles. == How It Works == TREDIS has a series of modules that calculate different forms of impacts and benefits. One module is an accounting framework that calculates user benefits, including impacts on cargo transportation and commuting costs, based on transportation forecasting results. A second module calculates wider economic development benefits, including impacts on business productivity, economic development and multiplier effects from the input-output analysis. It applies an economic model to estimate impacts on jobs, income, gross regional product and business output, by sector of the economy. A third module applies cost-benefit analysis from alternative perspectives.
User modeling
User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing. == Background == A user model is the collection and categorization of personal data associated with a specific user. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling. Therefore, it is the basis for any adaptive changes to the system's behavior. Which data is included in the model depends on the purpose of the application. It can include personal information such as users' names and ages, their interests, their skills and knowledge, their goals and plans, their preferences and their dislikes or data about their behavior and their interactions with the system. There are different design patterns for user models, though often a mixture of them is used. Static user models Static user models are the most basic kinds of user models. Once the main data is gathered they are normally not changed again, they are static. Shifts in users' preferences are not registered and no learning algorithms are used to alter the model. Dynamic user models Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models Stereotype based user models are based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. The application therefore can make assumptions about a user even though there might be no data about that specific area, because demographic studies have shown that other users in this stereotype have the same characteristics. Thus, stereotype based user models mainly rely on statistics and do not take into account that personal attributes might not match the stereotype. However, they allow predictions about a user even if there is rather little information about him or her. Highly adaptive user models Highly adaptive user models try to represent one particular user and therefore allow a very high adaptivity of the system. In contrast to stereotype based user models they do not rely on demographic statistics but aim to find a specific solution for each user. Although users can take great benefit from this high adaptivity, this kind of model needs to gather a lot of information first. == Data gathering == Information about users can be gathered in several ways. There are three main methods: Asking for specific facts while (first) interacting with the system Mostly this kind of data gathering is linked with the registration process. While registering users are asked for specific facts, their likes and dislikes and their needs. Often the given answers can be altered afterwards. Learning users' preferences by observing and interpreting their interactions with the system In this case users are not asked directly for their personal data and preferences, but this information is derived from their behavior while interacting with the system. The ways they choose to accomplish a tasks, the combination of things they takes interest in, these observations allow inferences about a specific user. The application dynamically learns from observing these interactions. Different machine learning algorithms may be used to accomplish this task. A hybrid approach which asks for explicit feedback and alters the user model by adaptive learning This approach is a mixture of the ones above. Users have to answer specific questions and give explicit feedback. Furthermore, their interactions with the system are observed and the derived information are used to automatically adjust the user models. Though the first method is a good way to quickly collect main data it lacks the ability to automatically adapt to shifts in users' interests. It depends on the users' readiness to give information and it is unlikely that they are going to edit their answers once the registration process is finished. Therefore, there is a high likelihood that the user models are not up to date. However, this first method allows the users to have full control over the collected data about them. It is their decision which information they are willing to provide. This possibility is missing in the second method. Adaptive changes in a system that learns users' preferences and needs only by interpreting their behavior might appear a bit opaque to the users, because they cannot fully understand and reconstruct why the system behaves the way it does. Moreover, the system is forced to collect a certain amount of data before it is able to predict the users' needs with the required accuracy. Therefore, it takes a certain learning time before a user can benefit from adaptive changes. However, afterwards these automatically adjusted user models allow a quite accurate adaptivity of the system. The hybrid approach tries to combine the advantages of both methods. Through collecting data by directly asking its users it gathers a first stock of information which can be used for adaptive changes. By learning from the users' interactions it can adjust the user models and reach more accuracy. Yet, the designer of the system has to decide, which of these information should have which amount of influence and what to do with learned data that contradicts some of the information given by a user. == System adaptation == Once a system has gathered information about a user it can evaluate that data by preset analytical algorithm and then start to adapt to the user's needs. These adaptations may concern every aspect of the system's behavior and depend on the system's purpose. Information and functions can be presented according to the user's interests, knowledge or goals by displaying only relevant features, hiding information the user does not need, making proposals what to do next and so on. One has to distinguish between adaptive and adaptable systems. In an adaptable system the user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options. Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system itself, based on the built user model. Thus, an adaptive system needs ways to interpret information about the user in order to make these adaptations. One way to accomplish this task is implementing rule-based filtering. In this case a set of IF... THEN... rules is established that covers the knowledge base of the system. The IF-conditions can check for specific user-information and if they match the THEN-branch is performed which is responsible for the adaptive changes. Another approach is based on collaborative filtering. In this case information about a user is compared to that of other users of the same systems. Thus, if characteristics of the current user match those of another, the system can make assumptions about the current user by presuming that he or she is likely to have similar characteristics in areas where the model of the current user is lacking data. Based on these assumption the system then can perform adaptive changes. == Usages == Adaptive hypermedia: In an adaptive hypermedia system the displayed content and the offered hyperlinks are chosen on basis of users' specific characteristics, taking their goals, interests, knowledge and abilities into account. Thus, an adaptive hypermedia system aims to reduce the "lost in hyperspace" syndrome by presenting only relevant information. Adaptive educational hypermedia: Being a subdivision of adaptive hypermedia the main focus of adaptive educational hypermedia lies on education, displaying content and hyperlinks corresponding to the user's knowledge on the field of study. Intelligent tutoring system: Unlike adaptive educational hypermedia systems intelligent tutoring systems are stand-alone systems. Their aim is to help students in a specific field of study. To do so, they build up a user model where they store information about abilities, knowledge and needs of the user. The system can now adapt to this user by presenting approp