Tellent Recruitee is a cloud-based applicant tracking system (ATS) for talent acquisition owned by Tellent. It is used by internal HR teams for processes including job postings, candidate sourcing, reporting, and applicant tracking. == History == Perry Oostdam and Pawel Smoczyk founded Recruitee after working on a mobile gaming startup. The Recruitee was launched in August 2015. In September 2015, it received a seed funding round with participation from investors Robert Pijselman and Luc Brandts. Merger In February 2021, Recruitee and the Finnish HR software provider Sympa merged their operations, backed by the growth equity firm Providence Strategic Growth (PSG). Acquisition In 2022, the group acquired the French company Javelo and the German company kiwiHR. The parent company was subsequently renamed as Tellent while Recruitee renamed as Tellent Recruitee and continues to operate as a product unit within the Tellent group. == Platform == Tellent Recruitee is a customizable recruitment software. It functions as an ATS and talent acquisition platform and includes tools to create and publish job listings, source candidates, manage recruitment agencies, and track applicants through customizable pipelines. The interface allows drag-and-drop organization of candidates. The platform also includes features for team collaboration, such as shared notes, task assignments, and candidate evaluations. It also has integrated scheduling tools and automated email communication. Tellent Recruitee also provides analytics and reports on hiring and career site metrics. The software allows for customization of career site pages and application forms. It supports integrations with other HR and productivity software, such as WhatsApp, and has various AI functionalities to support with manual recruitment tasks.
Boundary vector field
The boundary vector field (BVF) is an external force for parametric active contours (i.e. Snakes). In the fields of computer vision and image processing, parametric active contours are widely used for segmentation and object extraction. The active contours move progressively towards its target based on the external forces. There are a number of shortcomings in using the traditional external forces, including the capture range problem, the concave object extraction problem, and high computational requirements. The BVF is generated by an interpolation scheme which reduces the computational requirement significantly, and at the same time, improves the capture range and concave object extraction capability. The BVF is also tested in moving object tracking and is proven to provide fast detection method for real time video applications.
Noise-based logic
Noise-based logic (NBL) is a class of multivalued deterministic logic schemes, developed in the twenty-first century, where the logic values and bits are represented by different realizations of a stochastic process. The concept of noise-based logic and its name was created by Laszlo B. Kish. In its foundation paper it is noted that the idea was inspired by the stochasticity of brain signals and by the unconventional noise-based communication schemes, such as the Kish cypher. == The noise-based logic space and hyperspace == The logic values are represented by multi-dimensional "vectors" (orthogonal functions) and their superposition, where the orthogonal basis vectors are independent noises. By the proper combination (products or set-theoretical products) of basis-noises, which are called noise-bit, a logic hyperspace can be constructed with D(N) = 2N number of dimensions, where N is the number of noise-bits. Thus N noise-bits in a single wire correspond to a system of 2N classical bits that can express 22N different logic values. Independent realizations of a stochastic process of zero mean have zero cross-correlation with each other and with other stochastic processes of zero mean. Thus the basis noise vectors are orthogonal not only to each other but they and all the noise-based logic states (superpositions) are orthogonal also to any background noises in the hardware. Therefore, the noise-based logic concept is robust against background noises, which is a property that can potentially offer a high energy-efficiency. == The types of signals used in noise-based logic == In the paper, where noise-based logic was first introduced, generic stochastic-processes with zero mean were proposed and a system of orthogonal sinusoidal signals were also proposed as a deterministic-signal version of the logic system. The mathematical analysis about statistical errors and signal energy was limited to the cases of Gaussian noises and superpositions as logic signals in the basic logic space and their products and superpositions of their products in the logic hyperspace (see also. In the subsequent brain logic scheme, the logic signals were (similarly to neural signals) unipolar spike sequences generated by a Poisson process, and set-theoretical unifications (superpositions) and intersections (products) of different spike sequences. Later, in the instantaneous noise-based logic schemes and computation works, random telegraph waves (periodic time, bipolar, with fixed absolute value of amplitude) were also utilized as one of the simplest stochastic processes available for NBL. With choosing unit amplitude and symmetric probabilities, the resulting random-telegraph wave has 0.5 probability to be in the +1 or in the −1 state which is held over the whole clock period. == The noise-based logic gates == Noise-based logic gates can be classified according to the method the input identifies the logic value at the input. The first gates analyzed the statistical correlations between the input signal and the reference noises. The advantage of these is the robustness against background noise. The disadvantage is the slow speed and higher hardware complexity. The instantaneous logic gates are fast, they have low complexity but they are not robust against background noises. With either neural spike type signals or with bipolar random-telegraph waves of unity absolute amplitude, and randomness only in the sign of the amplitude offer very simple instantaneous logic gates. Then linear or analog devices unnecessary and the scheme can operate in the digital domain. However, whenever instantaneous logic must be interfaced with classical logic schemes, the interface must use correlator-based logic gates for an error-free signal. == Universality of noise-based logic == All the noise-based logic schemes listed above have been proven universal. The papers typically produce the NOT and the AND gates to prove universality, because having both of them is a satisfactory condition for the universality of a Boolean logic. == Computation by noise-based logic == The string verification work over a slow communication channel shows a powerful computing application where the methods is inherently based on calculating the hash function. The scheme is based on random telegraph waves and it is mentioned in the paper that the authors intuitively conclude that the intelligence of the brain is using similar operations to make a reasonably good decision based on a limited amount of information. The superposition of the first D(N) = 2N integer numbers can be produced with only 2N operations, which the authors call "Achilles ankle operation" in the paper. == Computer chip realization of noise-based logic == Preliminary schemes have already been published to utilize noise-based logic in practical computers. However, it is obvious from these papers that this young field has yet a long way to go before it will be seen in everyday applications.
Eden: It's an Endless World!
Eden: It's an Endless World!, also known simply as Eden (stylized in all caps), is a Japanese science fiction manga series written and illustrated by Hiroki Endo. It was serialized in Kodansha's seinen manga magazine Monthly Afternoon from September 1997 to June 2008, with its chapters collected in 18 tankōbon volumes. == Premise == The story is set in the near future, following the "closure virus" pandemic has killed 15 percent of the world's population, crippled or disfigured many more, with catastrophic effect on global politics. Its themes and many character names are taken from Gnostic mythology. == Plot == The series begins with a long introduction, with the characters Ennoia and Hannah living a peaceful life on a remote and isolated island called Eden, with researcher Lane Morris, who is their guardian and a victim of the pandemic. The events that led to this situation are revealed in flashbacks, leading up to the return of Ennoia's father, along with the forces of the Propater Federation. Following this, the story moves forwards twenty years, and focuses on Ennoia's son, Elijah, the main character, and his own conflict with the powerful and monopolistic Propater federation to save his sister, Mana Ballard, kidnapped by Propater when he was very young. She is being held to threaten Ennoia Ballard, father of the two characters, who has become a powerful drug lord in South America, feared and despised by many, including, to an extent, his own family. During a terrorist attack, Elijah, aged 15, is separated from his mother and his sister is kidnapped, along with his mother Hannah and now has to handle things on his own. Eden is about his coming-of-age as a man and trying to survive both bodily and morally in world that is too complex for mere "black and white". He encounters many other characters, both allies and enemies, all sharing the same struggle to survive in a post-apocalyptic dystopian world. Many stories are included of the people Elijah meets, telling their past or following life, sometimes volumes later, furthering understanding of the characters and giving increased depth to the world of the book as a whole. Later in the series, the story once again moves forwards in time, jumping four more years ahead. The Closure Virus, the cause of the original pandemic, mutates, this time assimilating non-organic matter as well as organic, known as "colloid" (or "Disclosure Virus"). The story rejoins Elijah, now 19 years old, as well as many other old characters, and some new, as the world begins to deal with this new threat that is swallowing many cities in the world, leaving lakes and craters, and many people. It is later discovered that the several colloids in the world, are linked with a net of underground auto-built "cables," and that the colloid itself, stores all the memories of the people it swallows. == Characters == Elijah Ballard (エリヤ・バラード, Eriya Barādo) Elijah is introduced while on the run from Propater. He becomes involved in his father's criminal activities, and undergoes a coming of age into adulthood. Ennoia Ballard (エンノイア・バラード, Ennoia Barādo) Elijah's father. Hannah Mayall (ハナ・メイオール, Hana Meiōru) Elijah's mother. Mana Ballard (マナ・バラード, Mana Barādo) Elijah's sister, who remains in Propater hands whilst her mother is rescued. Elijah's fight to free her is a focus of the later parts of the story. Nazarbaiev Khan (ナザルバイエフ・カーン, Nazarubaiefu Kān) Colonel Khan is an old soldier from Azerbaijan. He leads the Nomad group (including Kenji and Sophia) fleeing Propater at the start of the series. Khan became Kenji's mentor after killing his brother, and the two share a slightly strained, but at the same time, trusting, relationship. Sophia Theódores (ソフィア・テオドレス, Sofia Teodoresu) A powerful Greek computer hacker, and full-body cyborg. Maya (マーヤ, Māya) A nearly godlike AI, which seems to roughly correspond to the savior of Gnostic mythology. Kenji Asai (ケンジ・アサイ) The brother of a low-level yakuza boss. Helena Montoya (ヘレナ・モントーヤ, Herena Montōya) A prostitute now working in a brothel. Has a complex relationship with Elijah and acts as a surrogate big sister. == Media == === Manga === Eden: It's an Endless World! was written and illustrated by Hiroki Endo. The series ran in Kodansha's Monthly Afternoon magazine from September 25, 1997, to June 25, 2008. Kodansha collected its chapters into 18 tankōbon volumes, released from April 21, 1998, to July 23, 2008. In July 2005, Dark Horse Comics announced in San Diego Comic-Con that it has licensed Eden for North American distribution, with publication to begin in November of that year. As of March 2014, 14 volumes were released in total. ==== Volumes ==== == Reception == Eden was named Wizard magazine's best manga of 2007. In his review of another work by Hiroki Endo titled Hiroki Endo's Tanpenshu, David F. Smith of Newtype USA has called Eden one of the best manga American money can buy.
SciGraph
SciGraph was a search engine tool developed by Springer Nature, the former URL was https://scigraph.springernature.com/explorer. The technology, which was considered a Linked Open Data (LOD) platform, collects information that covers the research landscape, which includes research projects, publications, conferences, funding agencies, and others. Key features of the platform include the detailed semantic description of the relationship of information and the visualization of the scholarly domain. It was launched in 2017 and retired in 2023. == Development == The development of SciGraph began with an initiative to create a platform that will host Springer Nature's entire publication archive, which cover texts published as early as 1815. The number of these resources is reported to be about 13 million. The technology behind the platform was built on earlier Springer Nature projects developed for the purpose of collecting information on the research landscape. The first SciGraph data set was published in February 2017. The platform was launched in March 2017 and significantly expanded with the addition of publications of key partners. The datasets span a broad range of topics, which include computer science, medicine, life sciences, chemistry, engineering, and astronomy, among others. The developers also plan to include citations, patents, and clinical trials in the future. == Technology == SciGraph constitutes 1.5 to 2 billion triples where a triple is formatted as "subject-predicate-object" and could link any subject or concept through a predicate (verb) to another object, demonstrating the type of relationship that exists between them. Its graph structure is used by other academic search engines such as Semantic Scholar. SciGraph collects data from Springer Nature and its partners from the scholarly domain as well as funders, research projects, conferences, affiliations, and publications. The collected information serves as rich semantic description of how information is related and it also provides a visualization of the scholarly domain. The platform has been considered the only large-scale dataset that reconciles authors' affiliations through the disambiguation and linking with external authoritative datasets according to institutions.
Computer audition
Computer audition (CA) or machine listening is the general field of study of algorithms and systems for audio interpretation by machines. Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems — "software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents." Inspired by models of human audition, CA deals with questions of representation, transduction, grouping, use of musical knowledge and general sound semantics for the purpose of performing intelligent operations on audio and music signals by the computer. Technically this requires a combination of methods from the fields of signal processing, auditory modelling, music perception and cognition, pattern recognition, and machine learning, as well as more traditional methods of artificial intelligence for musical knowledge representation. == Applications == Like computer vision versus image processing, computer audition versus audio engineering deals with understanding of audio rather than processing. It also differs from problems of speech understanding by machine since it deals with general audio signals, such as natural sounds and musical recordings. Applications of computer audition are widely varying, and include search for sounds, genre recognition, acoustic monitoring, music transcription, score following, audio texture, music improvisation, emotion in audio and so on. == Related disciplines == Computer Audition overlaps with the following disciplines: Music information retrieval: methods for search and analysis of similarity between music signals. Auditory scene analysis: understanding and description of audio sources and events. Computational musicology and mathematical music theory: use of algorithms that employ musical knowledge for analysis of music data. Computer music: use of computers in creative musical applications. Machine musicianship: audition driven interactive music systems. == Areas of study == Since audio signals are interpreted by the human ear–brain system, that complex perceptual mechanism should be simulated somehow in software for "machine listening". In other words, to perform on par with humans, the computer should hear and understand audio content much as humans do. Analyzing audio accurately involves several fields: electrical engineering (spectrum analysis, filtering, and audio transforms); artificial intelligence (machine learning and sound classification); psychoacoustics (sound perception); cognitive sciences (neuroscience and artificial intelligence); acoustics (physics of sound production); and music (harmony, rhythm, and timbre). Furthermore, audio transformations such as pitch shifting, time stretching, and sound object filtering, should be perceptually and musically meaningful. For best results, these transformations require perceptual understanding of spectral models, high-level feature extraction, and sound analysis/synthesis. Finally, structuring and coding the content of an audio file (sound and metadata) could benefit from efficient compression schemes, which discard inaudible information in the sound. Computational models of music and sound perception and cognition can lead to a more meaningful representation, a more intuitive digital manipulation and generation of sound and music in musical human-machine interfaces. The study of CA could be roughly divided into the following sub-problems: Representation: signal and symbolic. This aspect deals with time-frequency representations, both in terms of notes and spectral models, including pattern playback and audio texture. Feature extraction: sound descriptors, segmentation, onset, pitch and envelope detection, chroma, and auditory representations. Musical knowledge structures: analysis of tonality, rhythm, and harmonies. Sound similarity: methods for comparison between sounds, sound identification, novelty detection, segmentation, and clustering. Sequence modeling: matching and alignment between signals and note sequences. Source separation: methods of grouping of simultaneous sounds, such as multiple pitch detection and time-frequency clustering methods. Auditory cognition: modeling of emotions, anticipation and familiarity, auditory surprise, and analysis of musical structure. Multi-modal analysis: finding correspondences between textual, visual, and audio signals. === Representation issues === Computer audition deals with audio signals that can be represented in a variety of fashions, from direct encoding of digital audio in two or more channels to symbolically represented synthesis instructions. Audio signals are usually represented in terms of analogue or digital recordings. Digital recordings are samples of acoustic waveform or parameters of audio compression algorithms. One of the unique properties of musical signals is that they often combine different types of representations, such as graphical scores and sequences of performance actions that are encoded as MIDI files. Since audio signals usually comprise multiple sound sources, then unlike speech signals that can be efficiently described in terms of specific models (such as source-filter model), it is hard to devise a parametric representation for general audio. Parametric audio representations usually use filter banks or sinusoidal models to capture multiple sound parameters, sometimes increasing the representation size in order to capture internal structure in the signal. Additional types of data that are relevant for computer audition are textual descriptions of audio contents, such as annotations, reviews, and visual information in the case of audio-visual recordings. === Features === Description of contents of general audio signals usually requires extraction of features that capture specific aspects of the audio signal. Generally speaking, one could divide the features into signal or mathematical descriptors such as energy, description of spectral shape etc., statistical characterization such as change or novelty detection, special representations that are better adapted to the nature of musical signals or the auditory system, such as logarithmic growth of sensitivity (bandwidth) in frequency or octave invariance (chroma). Since parametric models in audio usually require very many parameters, the features are used to summarize properties of multiple parameters in a more compact or salient representation. === Musical knowledge === Finding specific musical structures is possible by using musical knowledge as well as supervised and unsupervised machine learning methods. Examples of this include detection of tonality according to distribution of frequencies that correspond to patterns of occurrence of notes in musical scales, distribution of note onset times for detection of beat structure, distribution of energies in different frequencies to detect musical chords and so on. === Sound similarity and sequence modeling === Comparison of sounds can be done by comparison of features with or without reference to time. In some cases an overall similarity can be assessed by close values of features between two sounds. In other cases when temporal structure is important, methods of dynamic time warping need to be applied to "correct" for different temporal scales of acoustic events. Finding repetitions and similar sub-sequences of sonic events is important for tasks such as texture synthesis and machine improvisation. === Source separation === Since one of the basic characteristics of general audio is that it comprises multiple simultaneously sounding sources, such as multiple musical instruments, people talking, machine noises or animal vocalization, the ability to identify and separate individual sources is very desirable. Unfortunately, there are no methods that can solve this problem in a robust fashion. Existing methods of source separation rely sometimes on correlation between different audio channels in multi-channel recordings. The ability to separate sources from stereo signals requires different techniques than those usually applied in communications where multiple sensors are available. Other source separation methods rely on training or clustering of features in mono recording, such as tracking harmonically related partials for multiple pitch detection. Some methods, before explicit recognition, rely on revealing structures in data without knowing the structures (like recognizing objects in abstract pictures without attributing them meaningful labels) by finding the least complex data representations, for instance describing audio scenes as generated by a few tone patterns and their trajectories (polyphonic voices) and acoustical contours drawn by a tone (c
Computer-assisted legal research
Computer-assisted legal research (CALR) or computer-based legal research is a mode of legal research that uses databases of court opinions, statutes, court documents, and secondary material. Electronic databases make large bodies of case law easily available. Databases also have additional benefits, such as Boolean searches, evaluating case authority, organizing cases by topic, and providing links to cited material. Databases are available through paid subscription or for free. Subscription-based services include Westlaw, LexisNexis, JustCite, HeinOnline, Bloomberg Law, Lex Intell, VLex and LexEur. As of 2015, the commercial market grossed $8 billion. Free services include OpenJurist, Google Scholar, AltLaw, Ravel Law, WIPO Lex, Law Delta and the databases of the Free Access to Law Movement. == Purposes == Computer-assisted legal research is undertaken by a variety of actors. It is taught as a topic in many law degrees and is used extensively by undergraduate and postgraduate law students in meeting the work requirements of their degree courses. Professors of Law rely on the digitization of primary and secondary sources of law when conducting their research and writing the material that they submit for publication. Professional lawyers rely on computer-assisted legal research in order to properly understand the status of the law and so to act effectively in the best interest of their client. They may also consult the text of case judgements and statutes specifically, as well as wider academic comment, in order to form the basis of (or response to) an appeal. The availability of legal information online differs by type, jurisdiction and subject matter. The types of information available include: Texts of statutes, statutory instruments, civil codes, etc. Explanatory notes and government publications relating to statutes and their operation Texts of governing documents such as constitutions and treaties Case judgements Journals on legal matters or legal theory Dictionaries and legal encyclopedia Legal texts and materials in the form of e-books Current affairs and market information Educational information on the law and its operation == Before the Internet == Prior to the advent and popularization of the World Wide Web, access to digital legal information was largely through the use of CD-ROMs, designed and sold by commercial organizations. Dial-up services were also available from the 1970s. As the use of the Internet spread in the early 1990s, companies such as LexisNexis and Westlaw incorporated Internet connectivity into their software packages. Browser-based legal information started to be published by Legal Information Institutes from 1992. == Publicly available information == The first effort to provide free computer access to legal information was made by two academics, Peter Martin and Tom Bruce, in 1992. Today, the Legal Information Institute freely publishes such resources as the text of the United States Constitution, judgements of the United States Supreme Court, and the text of the United States Code. The Australasian Legal Information Institute (AusLII) was established soon after in 1995. Other legal information institutes, such as those of Great Britain and Ireland (BAILII), Canada (CII) and South Africa (SAfLI) soon followed. LIIs were partially formalized in 2002 following the signing of the Declaration of Free Access to the Law, which has been signed by 54 countries. At the time of writing, the World Legal Information Institute contains in excess of 1800 databases from 123 jurisdictions. Many governments also publish legal information online. For example, UK legislation and statutory instruments have been publicly available online since 2010. Depending on the jurisdiction in question, the decisions of higher appellate courts may also be published online, either by the Legal Information Institute or by the court service directly. Sources of European Union Law are published for free by EUR-Lex in 23 languages, including judgments of the European Courts. Similarly, judgements of the European Court of Human Rights are published on its website.