Actifsource is a domain-specific modeling workbench. It is realized as plug-in for the software development environment Eclipse. Actifsource supports the creation of multiple domain models which can be linked together. It comes with a UML-like graphical editor to create domain-specific languages and a general graphical editor to edit structures in the created languages. It supports code generation using user-defined generic code templates which are directly linked to the domain models. Code generation is integrated into Eclipse's incremental build process. == Interoperability == Actifsource can use models from other modelling tools by importing and exporting the ecore format which is defined by the Eclipse Modeling Framework. == Licensing policy == There are two versions of actifsource available: The free community edition which can be used freely for non-commercial projects and the enterprise edition which contains additional features. The enterprise edition comes with customer support and maintenance for a limited period of time. This package allows the customers to upgrade to new versions and maintenance releases during their support period.
Ghana Post GPS
GhanaPostGPS is a web and smartphone application, sponsored by the government of Ghana and developed by Vokacom, to provide a digital addresses and postal codes for every 5 squared meter location in Ghana. The digital address is a composite of the postcode (region, district & area code) plus a unique address. GhanaPostGPS is the first digital addressing system created by the government of Ghana. GhanaPost GPS is a mandatory requirement for obtaining the National Identification Card and other services.
Mivar-based approach
The Mivar-based approach is a mathematical tool for designing artificial intelligence (AI) systems. Mivar (Multidimensional Informational Variable Adaptive Reality) was developed by combining production and Petri nets. The Mivar-based approach was developed for semantic analysis and adequate representation of humanitarian epistemological and axiological principles in the process of developing artificial intelligence. The Mivar-based approach incorporates computer science, informatics and discrete mathematics, databases, expert systems, graph theory, matrices and inference systems. The Mivar-based approach involves two technologies: Information accumulation is a method of creating global evolutionary data-and-rules bases with variable structure. It works on the basis of adaptive, discrete, mivar-oriented information space, unified data and rules representation, based on three main concepts: “object, property, relation”. Information accumulation is designed to store any information with possible evolutionary structure and without limitations concerning the amount of information and forms of its presentation. Data processing is a method of creating a logical inference system or automated algorithm construction from modules, services or procedures on the basis of a trained mivar network of rules with linear computational complexity. Mivar data processing includes logical inference, computational procedures and services. Mivar networks allow us to develop cause-effect dependencies (“If-then”) and create an automated, trained, logical reasoning system. Representatives of Russian association for artificial intelligence (RAAI) – for example, V. I. Gorodecki, doctor of technical science, professor at SPIIRAS and V. N. Vagin, doctor of technical science, professor at MPEI declared that the term is incorrect and suggested that the author should use standard terminology. == History == While working in the Russian Ministry of Defense, O. O. Varlamov started developing the theory of “rapid logical inference” in 1985. He was analyzing Petri nets and productions to construct algorithms. Generally, mivar-based theory represents an attempt to combine entity-relationship models and their problem instance – semantic networks and Petri networks. The abbreviation MIVAR was introduced as a technical term by O. O. Varlamov, Doctor of Technical Science, professor at Bauman MSTU in 1993 to designate a “semantic unit” in the process of mathematical modeling. The term has been established and used in all of his further works. The first experimental systems operating according to mivar-based principles were developed in 2000. Applied mivar systems were introduced in 2015. == Mivar == Mivar is the smallest structural element of discrete information space. == Object-property-relation == Object-Property-Relation (VSO) is a graph, the nodes of which are concepts and arcs are connections between concepts. Mivar space represents a set of axes, a set of elements, a set of points of space and a set of values of points. A = { a n } , n = 1 , … , N , {\displaystyle A=\{a_{n}\},n=1,\ldots ,N,} where: A {\displaystyle A} is a set of mivar space axis names; N {\displaystyle N} is a number of mivar space axes. Then: ∀ a n ∃ F n = { f n i n } , n = 1 , … , N , i n = 1 , … , I n , {\displaystyle \forall a_{n}\exists F_{n}=\{f_{{ni}_{n}}\},n=1,\ldots ,N,i_{n}=1,\ldots ,I_{n},} where: F n {\displaystyle F_{n}} is a set of axis a n {\displaystyle a_{n}} elements; i n {\displaystyle i_{n}} is a set F n {\displaystyle F_{n}} element identifier; I n = | F n | . {\displaystyle I_{n}=|F_{n}|.} F n {\displaystyle F_{n}} sets form multidimensional space: M = F 1 × F 2 × ⋯ × F n . {\displaystyle M=F_{1}\times F_{2}\times \cdots \times F_{n}.} m = ( i 1 , i 2 , … , i N ) , {\displaystyle m=(i_{1},i_{2},\ldots ,i_{N}),} where: m ∈ M {\displaystyle m\in M} ; m {\displaystyle m} is a point of multidimensional space; ( i 1 , i 2 , … , i N ) {\displaystyle (i_{1},i_{2},\ldots ,i_{N})} are coordinates of point m {\displaystyle m} . There is a set of values of multidimensional space points of M {\displaystyle M} : C M = { c i 1 , i 2 , … , i N ∣ i 1 = 1 , … , I 1 , i 2 = 1 , … , I 2 , … , i n = 1 , … , I N } , {\displaystyle C_{M}=\{c_{i_{1},i_{2},\ldots ,i_{N}}\mid i_{1}=1,\ldots ,I_{1},i_{2}=1,\ldots ,I_{2},\ldots ,i_{n}=1,\ldots ,I_{N}\},} where: c i 1 , i 2 , … , i N {\displaystyle c_{i_{1},i_{2},\ldots ,i_{N}}} is a value of the point of multidimensional space M {\displaystyle M} is a value of the point of multidimensional space ( i 1 , i 2 , … , i N ) {\displaystyle (i_{1},i_{2},\ldots ,i_{N})} . For every point of space M {\displaystyle M} there is a single value from C M {\displaystyle C_{M}} set or there is no such value. Thus, C M {\displaystyle C_{M}} is a set of data model state changes represented in multidimensional space. To implement a transition between multidimensional space and set of points values the relation μ {\displaystyle \mu } has been introduced: C x = μ ( M x ) , {\displaystyle C_{x}=\mu (M_{x}),} where: M x ⊆ M ; {\displaystyle M_{x}\subseteq M;} M x = F 1 x × F 2 x × ⋯ × F N x . {\displaystyle M_{x}=F_{1x}\times F_{2x}\times \cdots \times F_{Nx}.} To describe a data model in mivar information space it is necessary to identify three axes: The axis of relations « O {\displaystyle O} »; The axis of attributes (properties) « S {\displaystyle S} »; The axis of elements (objects) of subject domain « V {\displaystyle V} ». These sets are independent. The mivar space can be represented by the following tuple: ⟨ V , S , O ⟩ {\displaystyle \langle V,S,O\rangle } Thus, mivar is described by « V S O {\displaystyle VSO} » formula, in which « V {\displaystyle V} » denotes an object or a thing, « S {\displaystyle S} » denotes properties, « O {\displaystyle O} » variety of relations between other objects of a particular subject domain. The category “Relations” can describe dependencies of any complexity level: formulae, logical transitions, text expressions, functions, services, computational procedures and even neural networks. A wide range of capabilities complicates description of modeling interconnections, but can take into consideration all the factors. Mivar computations use mathematical logic. In a simplified form they can be represented as implication in the form of an "if…, then …” formula. The result of mivar modeling can be represented in the form of a bipartite graph binding two sets of objects: source objects and resultant objects. == Mivar network == Mivar network is a method for representing objects of the subject domain and their processing rules in the form of a bipartite directed graph consisting of objects and rules. A Mivar network is a bipartite graph that can be described in the form of a two-dimensional matrix, in that records information about the subject domain of the current task. Generally, mivar networks provide formalization and representation of human knowledge in the form of a connected multidimensional space. That is, a mivar network is a method of representing a piece of mivar space information in the form of a bipartite, directed graph. The mivar space information is formed by objects and connections, which in total represent the data model of the subject domain. Connections include rules for objects processing. Thus, a mivar network of a subject domain is a part of the mivar space knowledge for that domain. The graph can consist of objects-variables and rules-procedures. First, two lists are made that form two nonintersecting partitions: the list of objects and the list of rules. Objects are denoted by circles. Each rule in a mivar network is an extension of productions, hyper-rules with multi-activators or computational procedures. It is proved that from the perspective of further processing, these formalisms are identical and in fact are nodes of the bipartite graph, denoted by rectangles. === Multi-dimensional binary matrices === Mivar networks can be implemented on single computing systems or service-oriented architectures. Certain constraints restrict their application, in particular, the dimension of matrix of linear matrix method for determining logical inference path on the adaptive rule networks. The matrix dimension constraint is due to the fact that implementation requires sending a general matrix to multiple processors. Since every matrix value is initially represented in symbol form, the amount of sent data is crucial when obtaining, for example, 10000 rules/variables. Classical mivar-based method requires storing three values in each matrix cell: 0 – no value; x – input variable for the rule; y – output variable for the rule. The analysis of possibility of firing a rule is separated from determining output variables according to stages after firing the rule. Consequently, it is possible to use different matrices for “search for fired rules” and “setting values for output variables”. This allowsthe use of multidimensional binary m
Rumelhart Prize
The David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition was founded in 2001 in honor of the cognitive scientist David Rumelhart to introduce the equivalent of a Nobel Prize for cognitive science. It is awarded annually to "an individual or collaborative team making a significant contemporary contribution to the theoretical foundations of human cognition". The annual award is presented at the Cognitive Science Society meeting, where the recipient gives a lecture and receives a check for $100,000. At the conclusion of the ceremony, the next year's award winner is announced. The award is funded by the Robert J. Glushko and Pamela Samuelson Foundation. The Rumelhart Prize committee is independent of the Cognitive Science Society. However, the society provides a large and interested audience for the awards. == Selection Committee == As of 2022, the selection committee for the prize consisted of: Richard Cooper (chair) Dedre Gentner Robert J. Glushko Tania Lombrozo Steven T. Piantadosi Jesse Snedeker == Recipients ==
Algorithmic accountability
Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes. Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes. This means they ought to evaluate only relevant characteristics of the input data, avoiding distinctions based on attributes that are generally inappropriate in social contexts, such as an individual's ethnicity in legal judgments. However, adherence to this principle is not always guaranteed, and there are instances where individuals may be adversely affected by algorithmic decisions. Responsibility for any harm resulting from a machine's decision may lie with the algorithm itself or with the individuals who designed it, particularly if the decision resulted from bias or flawed data analysis inherent in the algorithm's design. == Algorithm usage == Algorithms are widely utilized across various sectors of society that incorporate computational techniques in their control systems. These applications span numerous industries, including but not limited to medical, transportation, and payment services. In these contexts, algorithms perform functions such as: Approving or denying credit card applications; Approving or denying immigrant visas; Determining which taxpayers will be audited on their income taxes; Managing systems that control self-driving cars on a highway; Scoring individuals as potential criminals for use in legal proceedings; Search engines that match and rank database and internet search results; Recommendation systems that filter which news, entertainment, or purchase items are featured in a feed; Market-making algorithms that match sellers and buyers, such as in transportation (ride-hailing) or financial platforms. However, the implementation of these algorithms can be complex and opaque. Generally, algorithms function as "black boxes," meaning that the specific processes an input undergoes during execution are often not transparent, with users typically only seeing the resulting output. This lack of transparency raises concerns about potential biases within the algorithms, as the parameters influencing decision-making may not be well understood. The outputs generated can lead to perceptions of bias, especially if individuals in similar circumstances receive different results. According to Nicholas Diakopoulos: But these algorithms can make mistakes. They have biases. Yet they sit in opaque black boxes, their inner workings, their inner “thoughts” hidden behind layers of complexity. We need to get inside that black box, to understand how they may be exerting power on us, and to understand where they might be making unjust mistakes == Wisconsin Supreme Court case == Algorithms are prevalent across various fields and significantly influence decisions that affect the population at large. Their underlying structures and parameters often remain unknown to those impacted by their outcomes. A notable case illustrating this issue is a recent ruling by the Wisconsin Supreme Court concerning "risk assessment" algorithms used in criminal justice. The court determined that scores generated by such algorithms, which analyze multiple parameters from individuals, should not be used as a determining factor for arresting an accused individual. Furthermore, the court mandated that all reports submitted to judges must include information regarding the accuracy of the algorithm used to compute these scores. This ruling is regarded as a noteworthy development in how society should manage software that makes consequential decisions, highlighting the importance of reliability, particularly in complex settings like the legal system. The use of algorithms in these contexts necessitates a high degree of impartiality in processing input data. However, experts note that there is still considerable work to be done to ensure the accuracy of algorithmic results. Questions about the transparency of data processing continue to arise, which raises issues regarding the appropriateness of the algorithms and the intentions of their designers. == Controversies == A notable instance of potential algorithmic bias is highlighted in an article by The Washington Post regarding the ride-hailing service Uber. An analysis of collected data revealed that estimated waiting times for users varied based on the neighborhoods in which they resided. Key factors influencing these discrepancies included the predominant ethnicity and average income of the area. Specifically, neighborhoods with a majority white population and higher economic status tended to have shorter waiting times, while those with more diverse ethnic compositions and lower average incomes experienced longer waits. It’s important to clarify that this observation reflects a correlation identified in the data, rather than a definitive cause-and-effect relationship. No value judgments are made regarding the behavior of the Uber app in these cases. In TechCrunch website, Hemant Taneja wrote: Concern about “black box” algorithms that govern our lives has been spreading. New York University’s Information Law Institute hosted a conference on algorithmic accountability, noting: “Scholars, stakeholders, and policymakers question the adequacy of existing mechanisms governing algorithmic decision-making and grapple with new challenges presented by the rise of algorithmic power in terms of transparency, fairness, and equal treatment.” Yale Law School’s Information Society Project is studying this, too. “Algorithmic modeling may be biased or limited, and the uses of algorithms are still opaque in many critical sectors,” the group concluded. == Possible solutions == Discussions among experts have sought viable solutions to understand the operations of algorithms, often referred to as "black boxes." It is generally proposed that companies responsible for developing and implementing these algorithms should ensure their reliability by disclosing the internal processes of their systems. Hemant Taneja, writing for TechCrunch, emphasizes that major technology companies, such as Google, Amazon, and Uber, must actively incorporate algorithmic accountability into their operations. He suggests that these companies should transparently monitor their own systems to avoid stringent regulatory measures. One potential approach is the introduction of regulations in the tech sector to enforce oversight of algorithmic processes. However, such regulations could significantly impact software developers and the industry as a whole. It may be more beneficial for companies to voluntarily disclose the details of their algorithms and decision-making parameters, which could enhance the trustworthiness of their solutions. Another avenue discussed is the possibility of self-regulation by the companies that create these algorithms, allowing them to take proactive steps in ensuring accountability and transparency in their operations. In TechCrunch website, Hemant Taneja wrote: There’s another benefit — perhaps a huge one — to software-defined regulation. It will also show us a path to a more efficient government. The world’s legal logic and regulations can be coded into software and smart sensors can offer real-time monitoring of everything from air and water quality, traffic flows and queues at the DMV. Regulators define the rules, technologist create the software to implement them and then AI and ML help refine iterations of policies going forward. This should lead to much more efficient, effective governments at the local, national and global levels.
SurveyLab
SurveyLab is an online system designed for creating and deploying surveys, questionnaires, web forms, tests, and quizzes. The platform functions as a web application, without the need for additional software installation. Founded in 2006, by the Polish company 7 Points, SurveyLab is used by businesses and professional users for market research, human resources assessments, customer feedback, and academic research. == History == SurveyLab was launched in 2006 under the name MySurveyLab, developed by the Warsaw-based company 7 Points. Early media coverage described the system as supporting online survey creation, real-time reporting, group collaboration and question logic, and noted that the platform was opened to custom feature development. MySurveyLab featured multi-user accounts, SSL-secured surveys, and support for right-to-left languages. Further 2010s updates improved reporting capabilities, expanded question types, and integration options. In 2020, the platform was rebranded to SurveyLab. By the early 2020s, the software supported integrations with external tools including Zapier, and offered additional analytics features. In 2025, 7 Points reported that SurveyLab had over 85,000 registered users and had processed over 7 million surveys. == Functionalities == SurveyLab is a web-based platform used for creating online surveys, questionnaires, and forms. Independent reviewers and software directories describe it as a tool used for market research, customer feedback management, and human resources-related assessments, including employee feedback surveys. According to the creators at 7 Points, SurveyLab supports customer satisfaction measurement, survey analysis, and 360-degree feedback evaluations. The platform allows users to create surveys with no limits on the number of questions or responses. Independent reviews describe SurveyLab as offering multiple-choice, matrix, rating-scale, and open-ended questions. According to 7 Points, the platform manages market-research workflows, including Net Promoter Score, Customer Satisfaction, and Customer Effort Score questions. The tool can also re-use previous answers in later questions, and create A/B survey variants. SurveyLab can integrate with external services and applications through APIs and third-party connectors. According to its developers, the platform can connect with customer service tools, as well as CRM, marketing automation, e-commerce, and data-storage tools An industry review cited workflow integrations with CINT, Slack, Salesforce, and Zendesk Other integrations included Aquera (SSO), Sona Systems (internet research), and Synerise (customer data management). == Data collection and aggregation == Independent descriptions note that SurveyLab can combine results from emails, SMS, website widgets and pop-ups, QR codes, and social media. Its surveys are also accessible through mobile apps on iOS and Android, used for online and offline data collection in the field. Developers state that the tool supports exporting data as CSV, Excel, and SPSS, with independent reviews also mentioning PDF and PowerPoint. SurveyLab can automate response collection through a multi-channel survey distribution and reporting. It includes data trends, offline responses, and reminders to non-respondents. According to its documentation, newer versions include AI-based tools that detect and analyze sentiment, and a survey builder generating questionnaires based on user prompts. === Data security and compliance === According to 7 Points, SurveyLab provides password-protected surveys, token-based access, IP-address filtering, and two-factor authentication for user accounts, and it complies with the General Data Protection Regulation. == Awards and accolades == In 2017, SurveyLab was listed in Capterra’s Top 20 Survey Software ranking, among 20 highest-scoring survey tools based on market presence and user base. In 2018, a software review platform FinancesOnline awarded SurveyLab the Rising Star Award and the Great User Experience Award, distinctions given to products that demonstrate positive user satisfaction and strong usability characteristics.
They're Made Out of Meat
"They're Made Out of Meat" is a short story by American writer Terry Bisson. It was originally published in OMNI. It consists entirely of dialogue between two characters. Bisson's website hosts a theatrical adaptation. A film adaptation won the Grand Prize at the Seattle Science Fiction Museum's 2006 film festival. The story was collected in the 1993 anthology Bears Discover Fire and Other Stories, and has circulated widely on the Internet, which Bisson found "flattering". It has been quoted in cognitive, cosmological, and philosophical scholarship. == Plot == The two characters are intelligent beings capable of traveling faster than light, on a mission to "contact, welcome and log in any and all sentient races or multibeings in this quadrant of the Universe." Bisson's stage directions represent them as "two lights moving like fireflies among the stars" on a projection screen. One of them tells the incredulous other about the recent discovery of carbon-based lifeforms "made up entirely of meat". After conversing briefly about it, they both deem such beings and communication with them too bizarre and agree to "erase the records and forget the whole thing", marking the Solar System "unoccupied". == Film adaptations == === They're Made out of Meat (2005) === In 2005, Stephen O'Regan wrote and directed a live film adaptation starring Tom Noonan and Ben Bailey. The film was made as a final project for the New York Film Academy. The main action takes place inside a diner full of teenagers in Staten Island, New York. The music for the film was scored by Bob Reynolds. === They're Made out of Meat (2010) === Jeff Frumess and Trevor Scott produced a version in 2010. They added the character of a homeless conspiracy theorist with an original score by musician Sam Belkin. The film was shot at Hartsdale station in Westchester County, New York. === Meat (2021) === Masha Maksimova developed a version in Cinemiracle format, a triple split-screen process, as a student project at the Berlin University of Applied Sciences in the communication design course. The dialogue is conducted by two telepathic humanoid aliens and the thoughts are visualised by found-footage collages.