Neurocomputing is a peer-reviewed scientific journal covering research on artificial intelligence, machine learning, and neural computation. It was established in 1989 and is published by Elsevier. The editor-in-chief is Zidong Wang (Brunel University London). Independent scientometric studies noted that despite being one of the most productive journals in the field, it has kept its reputation across the years intact and plays an important role in leading the research in the area. The journal is abstracted and indexed in Scopus and Science Citation Index Expanded. According to the Journal Citation Reports, its 2023 impact factor is 5.5.
Tapingo
Tapingo was an American mobile commerce application that offers advance ordering for pickup and food delivery services for college campuses. The company was acquired by Grubhub in September 2018 for approximately $150 million. Following the acquisition, Tapingo’s campus-ordering functionality was integrated into the Grubhub app (Grubhub Campus Dining) and the Tapingo service was discontinued during 2019. Tapingo is differentiated from other on-demand delivery/logistics companies, such as Waiter.com, Postmates, or DoorDash, by focusing its efforts on serving the college market. Through Tapingo, users can browse menus, place orders, pay for the meal and schedule the pickup or have it delivered. On certain campuses, students are able to use their university's meal dollars to pay for food. In the spring of 2012, Tapingo first launched its services on five campuses (Santa Clara University, Loyola Marymount University, Biola University, the University of Maine, and California Lutheran University), and has since expanded to more than 200 college campuses across the U.S. and Canada, serving 100 markets. To date, Tapingo has received venture funding from Carmel Ventures, Khosla Ventures, Kinzon Capital, DCM Ventures and Qualcomm Ventures. In fall 2015, Tapingo announced expansion plans through major partnership deals with national brands like Chipotle Mexican Grill and 7-Eleven, regional restaurants such as Taco Bueno, and global foodservice provider Aramark.
IRCF360
Infrared Control Freak 360 (IRCF360) is a 360-degree proximity sensor and a motion sensing devices, developed by ROBOTmaker. The sensor is in BETA developers release as a low cost (software configurable) sensor for use within research, technical and hobby projects. == Overview == The 360-degree sensor was originally designed as a short range micro robot proximity sensor and mainly intended for Swarm robotics, Ant robotics, Swarm intelligence, autonomous Qaudcopter, Drone, UAV, multi-robot simulations e.g. Jasmine Project where 360 proximity sensing is required to avoid collision with other robots and for simple IR inter-robot communications. To overcome certain limitation with Infra-red (IR) proximity sensing (e.g. detection of dark surfaces) the sensing module includes ambient light sensing and basic tactile sensing functionality during forward movement sensing/probing providing photovore and photophobe robot swarm behaviours and characteristics. A project named Sensorium Project was started aimed at broadening the Sensors audience beyond its typical robot sensor usage. To demonstrate the sensor's functionality, opensource Java based Integrated Development Environments (IDE) are used, such as Arduino and Processing (programming language).
Planner (programming language)
Planner (often seen in publications as "PLANNER" although it is not an acronym) is a programming language designed by Carl Hewitt at MIT, and first published in 1969. First, subsets such as Micro-Planner and Pico-Planner were implemented, and then essentially the whole language was implemented as Popler by Julian Davies at the University of Edinburgh in the POP-2 programming language. Derivations such as QA4, Conniver, QLISP and Ether (see scientific community metaphor) were important tools in artificial intelligence research in the 1970s, which influenced commercial developments such as Knowledge Engineering Environment (KEE) and Automated Reasoning Tool (ART). == Procedural approach versus logical approach == The two major paradigms for constructing semantic software systems were procedural and logical. The procedural paradigm was epitomized by Lisp which featured recursive procedures that operated on list structures. The logical paradigm was epitomized by uniform proof procedure resolution-based derivation (proof) finders. According to the logical paradigm it was “cheating” to incorporate procedural knowledge. == Procedural embedding of knowledge == Planner was invented for the purposes of the procedural embedding of knowledge and was a rejection of the resolution uniform proof procedure paradigm, which Converted everything to clausal form. Converting all information to clausal form is problematic because it hides the underlying structure of the information. Then used resolution to attempt to obtain a proof by contradiction by adding the clausal form of the negation of the theorem to be proved. Using only resolution as the rule of inference is problematical because it hides the underlying structure of proofs. Also, using proof by contradiction is problematical because the axiomatizations of all practical domains of knowledge are inconsistent in practice. Planner was a kind of hybrid between the procedural and logical paradigms because it combined programmability with logical reasoning. Planner featured a procedural interpretation of logical sentences where an implication of the form (P implies Q) can be procedurally interpreted in the following ways using pattern-directed invocation: Forward chaining (antecedently): If assert P, assert Q If assert not Q, assert not P Backward chaining (consequently) If goal Q, goal P If goal not P, goal not Q In this respect, the development of Planner was influenced by natural deductive logical systems (especially the one by Frederic Fitch [1952]). == Micro-planner implementation == A subset called Micro-Planner was implemented by Gerry Sussman, Eugene Charniak and Terry Winograd and was used in Winograd's natural-language understanding program SHRDLU, Eugene Charniak's story understanding work, Thorne McCarty's work on legal reasoning, and some other projects. This generated a great deal of excitement in the field of AI. It also generated controversy because it proposed an alternative to the logic approach that had been one of the mainstay paradigms for AI. At SRI International, Jeff Rulifson, Jan Derksen, and Richard Waldinger developed QA4 which built on the constructs in Planner and introduced a context mechanism to provide modularity for expressions in the database. Earl Sacerdoti and Rene Reboh developed QLISP, an extension of QA4 embedded in INTERLISP, providing Planner-like reasoning embedded in a procedural language and developed in its rich programming environment. QLISP was used by Richard Waldinger and Karl Levitt for program verification, by Earl Sacerdoti for planning and execution monitoring, by Jean-Claude Latombe for computer-aided design, by Nachum Dershowitz for program synthesis, by Richard Fikes for deductive retrieval, and by Steven Coles for an early expert system that guided use of an econometric model. Computers were expensive. They had only a single slow processor and their memories were very small by comparison with today. So Planner adopted some efficiency expedients including the following: Backtracking was adopted to economize on the use of time and storage by working on and storing only one possibility at a time in exploring alternatives. A unique name assumption was adopted to save space and time by assuming that different names referred to different objects. For example, names like Peking (previous PRC capital name) and Beijing (current PRC capital transliteration) were assumed to refer to different objects. A closed-world assumption could be implemented by conditionally testing whether an attempt to prove a goal exhaustively failed. Later this capability was given the misleading name "negation as failure" because for a goal G it was possible to say: "if attempting to achieve G exhaustively fails then assert (Not G)." == The genesis of Prolog == Gerry Sussman, Eugene Charniak, Seymour Papert and Terry Winograd visited the University of Edinburgh in 1971, spreading the news about Micro-Planner and SHRDLU and casting doubt on the resolution uniform proof procedure approach that had been the mainstay of the Edinburgh Logicists. At the University of Edinburgh, Bruce Anderson implemented a subset of Micro-Planner called PICO-PLANNER, and Julian Davies (1973) implemented essentially all of Planner. According to Donald MacKenzie, Pat Hayes recalled the impact of a visit from Papert to Edinburgh, which had become the "heart of artificial intelligence's Logicland," according to Papert's MIT colleague, Carl Hewitt. Papert eloquently voiced his critique of the resolution approach dominant at Edinburgh "…and at least one person upped sticks and left because of Papert." The above developments generated tension among the Logicists at Edinburgh. These tensions were exacerbated when the UK Science Research Council commissioned Sir James Lighthill to write a report on the AI research situation in the UK. The resulting report [Lighthill 1973; McCarthy 1973] was highly critical although SHRDLU was favorably mentioned. Pat Hayes visited Stanford where he learned about Planner. When he returned to Edinburgh, he tried to influence his friend Bob Kowalski to take Planner into account in their joint work on automated theorem proving. "Resolution theorem-proving was demoted from a hot topic to a relic of the misguided past. Bob Kowalski doggedly stuck to his faith in the potential of resolution theorem proving. He carefully studied Planner.”. Kowalski [1988] states "I can recall trying to convince Hewitt that Planner was similar to SL-resolution." But Planner was invented for the purposes of the procedural embedding of knowledge and was a rejection of the resolution uniform proof procedure paradigm. Colmerauer and Roussel recalled their reaction to learning about Planner in the following way: "While attending an IJCAI convention in September ‘71 with Jean Trudel, we met Robert Kowalski again and heard a lecture by Terry Winograd on natural language processing. The fact that he did not use a unified formalism left us puzzled. It was at this time that we learned of the existence of Carl Hewitt’s programming language, Planner. The lack of formalization of this language, our ignorance of Lisp and, above all, the fact that we were absolutely devoted to logic meant that this work had little influence on our later research." In the fall of 1972, Philippe Roussel implemented a language called Prolog (an abbreviation for PROgrammation en LOGique – French for "programming in logic"). Prolog programs are generically of the following form (which is a special case of the backward-chaining in Planner): When goal Q, goal P1 and ... and goal Pn Prolog duplicated the following aspects of Micro-Planner: Pattern directed invocation of procedures from goals (i.e. backward chaining) An indexed data base of pattern-directed procedures and ground sentences. Giving up on the completeness paradigm that had characterized previous work on theorem proving and replacing it with the programming language procedural embedding of knowledge paradigm. Prolog also duplicated the following capabilities of Micro-Planner which were pragmatically useful for the computers of the era because they saved space and time: Backtracking control structure Unique Name Assumption by which different names are assumed to refer to distinct entities, e.g., Peking and Beijing are assumed to be different. Reification of Failure. The way that Planner established that something was provable was to successfully attempt it as a goal and the way that it establish that something was unprovable was to attempt it as a goal and explicitly fail. Of course the other possibility is that the attempt to prove the goal runs forever and never returns any value. Planner also had a (not expression) construct which succeeded if expression failed, which gave rise to the “Negation as Failure” terminology in Planner. Use of the Unique Name Assumption and Negation as Failure became more questionable when attention turned to Open Systems. The following capabiliti
Layer (deep learning)
A layer in a deep learning model is a structure or network topology in the model's architecture, which takes information from the previous layers and then passes it to the next layer. == Layer types == The first type of layer is the Dense layer, also called the fully-connected layer, and is used for abstract representations of input data. In this layer, neurons connect to every neuron in the preceding layer. In multilayer perceptron networks, these layers are stacked together. The Convolutional layer is typically used for image analysis tasks. In this layer, the network detects edges, textures, and patterns. The outputs from this layer are then fed into a fully-connected layer for further processing. See also: CNN model. The Pooling layer is used to reduce the size of data input. The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. The Normalization layer adjusts the output data from previous layers to achieve a regular distribution. This results in improved scalability and model training. A Hidden layer is any of the layers in a Neural Network that aren't the input or output layers. == Differences with layers of the neocortex == There is an intrinsic difference between deep learning layering and neocortical layering: deep learning layering depends on network topology, while neocortical layering depends on intra-layers homogeneity.
Feng Office Community Edition
Feng Office Community Edition (formerly OpenGoo) is an open-source collaboration platform developed and supported by Feng Office and the OpenGoo community. It is a fully featured online office suite with a similar set of features as other online office suites, like Google Workspace, Microsoft 365, Zimbra, LibreOffice Online and Zoho Office Suite. The application can be downloaded and installed on a server. Feng Office could also be categorized as collaborative software and as personal information manager software. == Features == Feng Office Community Edition main features include project management, document management, contact management, e-mail and time management. Text documents and presentations can be created and edited online. Files can be uploaded, organized and shared, independent of file formats. Organization of the information in Feng Office Community Edition is done using workspaces and tags. The application presents the information stored using different interfaces such as lists, dashboards and calendar views. == Licensing == Feng Office Community Edition is distributed under the GNU Affero General Public License, version 3 only. == Technology used == Feng Office uses PHP, JavaScript, AJAX (ExtJS) and MySQL technology. Several open source projects served as a basis for development. ActiveCollab's last open sourced release was used as the initial code base. It includes CKEditor for online document editing. == System requirements == The server could run on any operating system. The system needs the following packages: Apache HTTP Server 2.0+ PHP 5.0+ MySQL 4.1+ (InnoDB support recommended) On the client side, the user is only required to use a modern Web browser. == History == OpenGoo started as a degree project at the faculty of Engineering of the University of the Republic, Uruguay. The project was presented and championed by Software Engineer Conrado Viña. Software Engineers Marcos Saiz and Ignacio de Soto developed the first prototype as their thesis. Professors Eduardo Fernández and Tomas Laurenzo served as tutors. Conrado, Ignacio and Marcos founded the OpenGoo community and remain active members and core developers. The thesis was approved with the highest score. In 2008, Viña joined the Uruguayan software development company Moove It. Currently there is a second project for OpenGoo at the same university being developed by students Fernando Rodríguez, Ignacio Vázquez and Juan Pedro del Campo. Their project aims to build an open source Web-based spreadsheet. In December 2009 the OpenGoo name was changed to Feng Office Community Edition.
Paradigms of AI Programming
Paradigms of AI Programming: Case Studies in Common Lisp (ISBN 1-55860-191-0) is a well-known programming book by Peter Norvig about artificial intelligence programming using Common Lisp. == History == The Lisp programming language has survived since 1958 as a primary language for artificial intelligence research. This text was published in 1992 as the Common Lisp standard was becoming widely adopted. Norvig introduces Lisp programming in the context of classic AI programs, including General Problem Solver (GPS) from 1959, ELIZA: Dialog with a Machine, from 1966, and STUDENT: Solving Algebra Word Problems, from 1964. The book covers more recent AI programming techniques, including Logic Programming, Object-Oriented Programming, Knowledge Representation, Symbolic Mathematics and Expert Systems.