Использование компьютерных методов и систем в изучении права, интеллектуальном анализе и моделировании правовой деятельности: систематический обзор

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

Seeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the author's latest research in the field that have not appeared in other books.

It is an honour to present the program for the second ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2017), held in Oslo, Norway from March 7 to March 11, 2017. CHIIR offers the opportunity to present and discuss research on user-centered approachesto information retrieval, information access and information interaction, including studies of interactive systems, searcher behaviour, novel interaction paradigms, interface design, new evaluation methods, and a range of related areas. CHIIR is an interdisciplinary event in which researchers, students and practitioners working across several areas, including information retrieval, information science and human computer interaction, come together to think creatively about the challenges and opportunities afforded by digital information systems. Welcome to all the CHIIR-ists joining us this year! The CHIIR 2017 program consists of Full Papers (10 pages), Perspectives Papers (10 pages) and Short Papers (4 pages). We received a total of 112 submissions: 48 Full Papers, 9 Perspectives Papers and 55 Short Papers. Of these, we accepted 19 Full Papers (40%), 5 Perspectives Papers (56%) and 36 Short Papers (65%). We also accepted one Full Paper as a Short Paper. We were pleased to receive so many strong Perspectives Papers, which are meant to present novel ideas or insights, and we hope these will inspire some great discussions. Each Full and Perspectives paper is allocated a 30-minute presentation slot in the program. Short papers are presented as posters during a special evening session, beginning with a short boaster presentation. In addition to contributed papers, the CHIIR program features three keynote talks by prominent academic and industry researchers, representing the breadth of interests in the CHIIR community: Norbert Fuhr (University of Duisburg-Essen, Germany), Ann Blandford (University College London, UK) and Håkon Wium Lie (Opera Software, Norway). In addition to these speakers and the main conference program, CHIIR features a Doctoral Consortium, two Tutorials, and three Workshops. We would like to acknowledge the excellent work of the Organizing Committee, including the Doctoral Consortium Chair, Katriina Byström (Oslo and Akershus University College of Applied Sciences, Norway); the Workshops and Tutorials Chair, Preben Hansen (Stockholm University, Sweden), and the Proceedings Chair, Sampath Jayarathna (California State Polytechnic University Pomona, USA). The paper reviewing and selection process was enabled by an outstanding 12 member Senior Program Committee and a diverse and international general Program Committee consisting of 77 members. Our thanks and the credits for creating a high quality technical program go to them.

Clustering of data points in metric space is among the most fundamental problems in computer science with plenty of applications in data mining, information retrieval and machine learning. Many of these applications deal with large datasets, and hence researchers focused on designing algorithms for these problems in large scale settings such as the streaming setting. One of the sweet versions of clustering problems is balanced clustering (or more generally capacitated clustering), where we do not desire to have some giant and several small clusters. Despite the importance of the context, the best known streaming algorithm for capacitated clustering is far from optimal. The state-of-the-art streaming algorithm for capacitated clustering gives an O(1)-approximate solution, requires three passes and only handles insertions (Bateni et al. NeurIPS'14).

The C* Conference on Computer Science and Software Engineering (C3S2E) was established in 2008. C3S2E 2010 is the third annual meeting in the C3S2E conference series and is held in Montréal, Québec, Canada, during May 19-20, 2010. The conference aims to address the need of the academic community in computer science and software engineering. It brings together academics, government and industry professionals to discuss recent progress and challenges in a broad range of areas in the field of computer science and software engineering such as algorithms and theory, artificial intelligence, bioinformatics, computer architecture, computer graphics, computer security, computer vision, data mining, databases, image processing, information retrieval, machine learning, as well as software engineering. C3S2E 2010 also serves as a platform for theoreticians and practitioners to exchange their original research ideas on academic or application aspects of computing and engineering of software systems, present their new findings on theoretical or practical aspects of computer science and software engineering, share their experiences on integrating new technologies into products and applications, discuss their work on applying computing and software engineering to real-life situations, describe their development and operations of challenging computing related systems, and identify unsolved challenges. Although it is the third time C3S2E is held, this comprehensive conference has grown from a Canadian conference into an international conference. For C3S2E 2010, we continue to keep our tradition for inviting international experts in various areas of computer science and software engineering to join our team of international program committee. As a result, our Program Committee consists of professionals from different parts of the world including Australia, Canada, Germany, Hong Kong, Italy, Japan, Portugal, South Korea, Turkey, UAE, and UK. This committee has done an excellent job in finishing the single-blind review and on-line double-blind debate processes in record time. On average, about 90% of reviews were submitted by the due date and about 3.67 reviews were received for each paper. In terms of submissions, we have attracted many high-quality papers submitted by authors globally. The paper selection process was thorough and competitive. On average, each paper was refereed by at least three reviewers, and about 18% of submissions were accepted as full research papers. This year, we have an intensive program that spans over two days. In addition to research papers and student posters, we also include position papers, which allow authors to give their opinions and vision on exploring new concepts and research directions.

Simple SummaryDistinguishing between benign vs. malignant bone lesions is often difficult on imaging. Many bone lesions are infrequent or rarely seen, and often only specialist radiologists have sufficient expertise to provide an accurate diagnosis. In addition, some benign bone tumours may exhibit potentially aggressive features that mimic malignant bone tumours, making the diagnosis even more difficult. The rapid development of artificial intelligence (AI) techniques has led to remarkable progress in image-recognition tasks, including the classification and characterization of various tumours. This study will review the most recent evidence for AI techniques in differentiating bone lesions on various imaging modalities using a systematic approach. Potential clinical applications of AI techniques for clinical diagnosis and management of bone lesions will also be discussed.An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Bio-inspired computing algorithms are inspired by nature that can provide flexible, dynamic, and effective solutions to hard and complex real-life problems. Many bio-inspired algorithms such as ant colony algorithm, firefly algorithm, whale optimization algorithm, grey-wolf algorithm, etc. proved their efficiency in solving various nonlinear, high-dimensional, and complex problems. On the other hand, artificial intelligence techniques can help to build intelligent models to implement the bio-inspired computing algorithms successfully and provide solutions to real-life problems that occurred due to natural disasters. Natural disasters like hurricanes, earthquakes, floods, tsunami, landslides, forest fires, etc. are the inspiration for solving hard and complex problems in computer science since they exhibit robust phenomena. It is necessary for the government agencies to know the accurate measurements of the damage that occurred due to natural disasters. However, these disasters are also called “ACTS OF GOD” because they cannot be found in fixed locations and time. So, here the predictive analysis is necessary to save the economy as well as human and animal lives. The combination of bio-inspired computing algorithms and artificial intelligence techniques will provide flexible solutions in those situations to predict the damage that occurred due to natural disasters. In this study, we proposed a bio-inspired computing-based hybrid approach, which is a combination of the modified version of popular bio-inspired algorithm whale optimization algorithm (MWOA) with significant artificial intelligent technique multi-layer perceptron (MLP) neural network to predict the damage of buildings that occurred due to the natural disasters using the satellite images.

Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.

The article analyses the state of the most popular and least popular professions and specialties in Ukraine. It's found that there are no specialties in the field of land management among them. It is also noted that at the legislative level the specialties "Land Management and Cadastre" and "Evaluation of land and real estate" were replaced by the specialty "Geodesy and land management", which belongs to the field of knowledge "Architecture and construction" (technical sciences). It is substantiated that the profession of land manager should relate to the field of knowledge of "Social and behavioural sciences". It's based on the provisions of the Law "On Land Management" and the study of world experience of the essence of the concepts of land management and land management planning (both have technical, socio-economic, legal and environmental focus). It is proved that the profession of land manager needs to be reprofiled. In this regard, there is a need to identify new specialties and specializations and add them into the state classifier DK 003: 2010. It's important to expand the training of specialists within: 1) specialty 193 "Geodesy and land management" specializations "Land Management and Land Use Administration" and "Cadastral Registrar"; 2) specialty 101 "Ecology" specialization "Land management and ecologization of land use", 3) specialty 051 "Economics" specialization "Economics of land management and land use". It is proposed to add them to the classifier. Keywords. Profession "land manager", labour market, perspective specialties and specializations in land management, new qualifications. Formulation of the problem. The development of society at all times was associated with the arrangement of lands, which today remains the main means for the existence of mankind and a source of social wealth. The need to establish the limits disturb landowners from ancient times. This function is performed by land surveyors. When the first representatives of this profession appeared, it is certainly not known. But the land surveyor is now a profession demanded both in the village and in the city. In our country, the profession of land surveyor always respects honour because it is devoted to the invaluable treasures of Ukraine - land, which has always attached special significance. According to the research conducted in Ukraine, soon, the 10 most demanded professions include: programmers (C++, Java), food industry technologists, construction engineers, architects, designers of offices and interiors, personnel managers, recruiters, energy engineers, journalists, technologists of light industry products, financiers (specialization "banking") [1]. In addition, prospective include: hotel business managers, accountants, auditors, lawyers, marketers. According to the Ministry of Education and Science of Ukraine, according to the results of the 2017 admission, the most popular specialties among the entrants for the Bachelor in Ukraine were Philology, Law, and Management. 85 thousand, 74 thousand and 59 thousand applications have been filed for the stated above specialties respectively. In addition, among the leaders are "Medicine", "Computer Science", "Tourism", "Secondary Education", "Psychology", "Economics". These specialties were desirable to enter by 40 to 46 thousand of entrants. The last step in the TOP-10 was "Software Engineering" with the result of 33 thousand applications (Fig. 1) [9]. Despite the stated above facts, it has been revealed that such specialties as "Law", "Management", "Tourism" and "Economics" remain among the most popular among entrants even though they are not in demand in the labour market. At the same time, the high levels in the ranking are occupied by important for the economy specialties, such as "Computer Science" and "Software Engineering". The least popular specialties are (fig. 2) "Theology" (185 applications excluding spiritual higher educational institutions) and "Hydropower" (193 applications for entry). However, specialties for the preparation of a specialist in land management are absent among the most and least popular. Therefore, with a caution we have to perceive the situation. The list of required occupations in the labour market is changing. Focus magazine has ranked the professions that will be promising in 5-10 years [10]. Before allocating the most promising specialties, the publication together with experts identified the areas of the economy that will actively develop in the near future. So, at first there was a list of 40 spheres of the economy, which, according to Focus, will successfully develop in the next 5-10 years. Experts evaluated the prospects for the development of these industries on a 10-point scale, giving each sector a mark from 1 to 10 points. At the same time, each expert could add to the list the industry that was not among the 40 listed, but whose potential he considers rather large. According to the obtained data, the rating of 15 most promising industries has been designed, and include: information technologies, telecommunications and communications, agriculture , pharmaceutical production and pharmacy business, banking, frying and processing industry, transport services, financial services, legal services, household services, metallurgy, alcohol production, energy, livestock , utilities. So, the 20 most promising professions of the next decade will be the following: the seller, the IT specialist, the automation engineer, the marketer, the municipal services engineer, the aeroengineer , the technologist in the production and processing of food products, the technologist of consumer services, logistics, the lawyer (economic and land law ), power engineer veterinarian, pharmacologist, financier, communication engineer, practical psychologist, geneticist, biochemist, robotic technician, nanoengineer. In addition, agrarian countries are actively developing organic and farm farming. This will require the involvement of a large number of employees who are able to work on the ground - and thus, forms a serious niche. In general, the labour market trends in the coming years can be reduced to nine main directions [5]: engineers, IT specialists, nanotechnology specialists, marketers, service providers, logistics, environmentalists, chemists, and physicians. The Resolution of the Cabinet of Ministers of Ukraine dated December 30, 2015, No. 1187, approved new Licensing conditions for the conduct of educational activities [7] concerning the further conduct of educational and scientific work in educational institutions. By the order of the Ministry of Education and Science dated November 6, 2015, No. 1151 "On the peculiarities of the introduction of the list of branches of knowledge and specialties [6], which are the training of applicants for higher education, approved by the Cabinet of Ministers of Ukraine from April 29, 2015, No. 266" [8] approved Table of correspondence List of directions for training of specialists in higher educational establishments for the educational-qualifying level of bachelor (List 1), List of specialties, which was carried out training of specialists in higher education institutions for educational and qualification levels of a specialist and a master's degree (List 2). In the stated above Resolution, it is defined that instead of the specialties "Land Management and Cadastre" and "Land and Real Estate Valuation" – the specialty "Geodesy and Land Management" is established. The latter belongs to the field of knowledge "Architecture and Construction", which belongs to the technical sciences. At the same time, the Law of Ukraine "On Land Management" stipulates that land management is a set of socio-economic and environmental measures aimed at regulating land relations and rational organization of the territory of administrative-territorial units, economic entities carried out under the influence of social and productive relations and development of productive forces [2]. The same law ( Article 1 ) stipulates that activities in the field of land management are the scientific, technical, production and management activities of state authorities, local governments, legal entities and individuals, which is carried out under land management. Land-use documentation ( land management documentation ) is the text and graphic materials approved in accordance with the established procedure, which regulate the use and protection of lands of state, communal and private property, as well as survey and land survey materials, author's supervision of project implementation, etc. Measures on land management – provided by documentation on land management on the rational use and protection of land, the formation and organization of the territory of the object of land management, taking into account their purpose, restrictions on the use and restrictions (encumbrances) on the rights of other persons (land servitudes), preservation and increase of fertility soils. This is evidence that the profession of land surveyor is more likely to belong to the field of knowledge of "Social and behavioural sciences" than to "Architecture and construction". Architecture (Greek αρχιτεκτονικη - construction) is both a science and the art of designing buildings, as well as a system of buildings and structures that form a spatial environment for the lives and activities of people in accordance with the laws of beauty. Land management, as stated in the Law of Ukraine "On Land Management" – a set of socio-economic and environmental measures aimed at regulating land relations and rational organization of the territory of administrative-territorial entities and economic entities on the ground. As the research of A.M. Tretiak and V.M. Tretiak shows, the concept and essence of land management has both a technical direction and socio-economic, legal and environmental. Land managers have worked and work in the structural subdivisions of the State Committee for Land Resources of Ukraine, the State Land Agency of Ukraine and the State Audit Office of Ukraine, state authorities and local self-government bodies, and ministries (where there are departments of land resources); in public and private design and valuation companies, real estate firms, agroholdings, agricultural enterprises; territorial communities, scientific institutions, institutions of legal direction. civic organizations. The land manager learns not only his professional knowledge, but also legal and natural sciences. For a profession it is necessary to have mathematical abilities, engineering approach, spatial imagination, ability to understand the legislation. The profession is extremely interesting and creative, which requires the ability to make informed decisions, communicate with people, requires care, clarity and responsibility. To become a successful specialist in this field, you need to be persistent, self-confident, active, have a well-developed intuition and logical thinking. According to the classification of professions, the professional activity of the land manager relates to professions such as "human-nature". To date, the land manager must have a higher education in the field of "Architecture and Construction" from the specialty "Geodesy and Land Management" , according to the educational qualification level: a junior specialist, a bachelor's or a master's degree. Currently, land managers use modern equipment in their work: electronic instruments; new technologies of geographic information systems. Significant problems arise when making managerial decisions due to the imperfection of the regulatory framework - when three different land managers can have three different views on the same issue. At the same time, for professionals it is extremely important to come to a joint decision. But how can the problems of legislation be explained to an ordinary peasant? It's a shame when you can not help a person in solving his question, and even more - when you do not know how to explain the impossibility of solving a particular problem. The disadvantage of the profession is also that there is little free time for personal life, family and wage mismatch in modern times. The advantages include: work is interesting, lots of communication, new knowledge in land law, and more. At the same time, we consider it necessary to stay on the proposals of A. Martyn outlined in the scientific paper "Directions of adaptation of the content of land management education to the needs of the national and world economy" [3], where he publishes "bad news" for land management education – whether we are ready for them? He states: "Most of our graduates will not develop landuse documentation at all; the number of jobs in the industry is quite limited; the overwhelming part of the "modern" Ukrainian land management is a terrible mix of fairly archaic rules, a crazy bureaucracy and meaningless rituals, in order to master the development of primitive land management documentation, it takes 2-3 weeks of practical training, but not 5 years at the university, and a significant part of cadastral registrar are lawyers. Some of these reservations agreeable. Therefore, his thoughts emphasize once again the need for serious changes in the training of land management specialists. Thus, all of the above stipulates the need for changes in the classification of occupations DK 003: 2010 and the definition of new specialties and specialization training of land management specialists. In our opinion, it is expedient to extend the training of specialists in land surveying in the following areas: 1) specialty 193 "Geodesy and land management" of the specializations "Land Management and Land Use Administration" and "Cadastral Registrar" ; 2) specialty 101 "Ecology" specialization "Land management and ecologization of land use" ; 3) specialty 051 "Economics" specialization "Economics of land management and land use" . At the same time, we consider it expedient to add the following qualifications to the classifier of professions DK 003: 2010: in section 8 "Main specialists – heads of scientific research units and subdivisions on scientific and technical preparation of production and other managers" – Chief land surveyor; in section 14 "Managers of enterprises, institutions, organizations and their subdivisions" – Manager of land management, Manager in the field of operations with land, Manager in the field of research in the land market; in section 25 "Agronomists, hydraulic engineering, forest managers and professionals of related professions" – Land manager; in section 33 "Assistant veterinarians, junior specialists in agronomy, forestry, water management and nature conservation" – Land Resources Inspector; in section 34 "Other technical specialists in the field of management" – State Inspector for the use and protection of land; in section 249 "Professionals not included in other classification groups". Land manager-ecologist, Land manager-designer. Conclusions. Summing up, we note that the assignment of the specialty "Geodesy and land management" to the field of knowledge "Architecture and Construction" (engineering science) does not comply with the provisions of the Law "On Land Management", the study of world experience of the essence of the concepts of land management, which determined that they are inherent in technical, as well as socio-economic, legal and environmental trends. In this connection it is proved that the profession of land manager belongs to the field of knowledge of "Social and behavioral sciences" . In view of this, the need for re-engineering the directions of land management training is urgent. Therefore, we must work ahead and make changes to the classification of professions DK 003: 2010 regarding the definition of new specialties and specialization training of specialists in land management. We consider it expedient to expand the training of specialists within: 1) specialty 193 "Geodesy and land management" of the specialties "Land Management and Land Use Administration" and "Cadastral Registrar" ; 2) specialty 101 "Ecology" specialization "Land management and ecologization of land use" ; 3) specialty 051 "Economics" specialization "Economics of land management and land use" (qualification "Land Management Manager"). It is proposed to make changes to the list of qualifications.

Although today's advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel. In addition, storage, reduction, retrieval, processing, and presentation of information are significant challenges. These problems are most severe in critical care environments such as intensive care units (ICUs) and operating room (ORs) where many events are life-threatening and thus require immediate attention and the execution of definitive corrective actions. This article focuses on intelligent monitoring and control (IMC), or the use of artificial intelligence (AI) techniques to alleviate some of the common information management problems encountered in health-care environments. This article presents the findings of a survey of over 30 IMC projects. A major finding of the survey is that although significant advances have been made in introducing AI technology in critical care, successful examples of fielded systems are still few and far between. Widespread acceptance of these systems in critical care environments depends on a number of factors, including fruitful collaborations between clinicians and computer scientists, emphasis on evaluation studies, and easy access to clinical information.

Diffractive optical neural networks (DONNs) are emerging as high‐throughput and energy‐efficient hardware platforms to perform all‐optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs are largely image classification tasks, which undermine the prospect of developing and utilizing such hardware for other ML applications. Herein, the deployment of an all‐optical reconfigurable DONNs system for scientific computing is demonstrated numerically and experimentally, including guiding two‐dimensional quantum material synthesis, predicting the properties of two‐dimensional quantum materials and small molecular cancer drugs, predicting the device response of nanopatterned integrated photonic power splitters, and the dynamic stabilization of an inverted pendulum with reinforcement learning. Despite a large variety of input data structures, a universal feature engineering approach is developed to convert categorical input features to images that can be processed in the DONNs system. The results open up new opportunities for employing DONNs systems for a broad range of ML applications.

Numerical Methods provides a clear and concise exploration of standard numerical analysis topics, as well as nontraditional ones, including mathematical modeling, Monte Carlo methods, Markov chains, and fractals. Filled with appealing examples that will motivate students, the textbook considers modern application areas, such as information retrieval and animation, and classical topics from physics and engineering. Exercises use MATLAB and promote understanding of computational results. The book gives instructors the flexibility to emphasize different aspects--design, analysis, or computer implementation--of numerical algorithms, depending on the background and interests of students. Designed for upper-division undergraduates in mathematics or computer science classes, the textbook assumes that students have prior knowledge of linear algebra and calculus, although these topics are reviewed in the text. Short discussions of the history of numerical methods are interspersed throughout the chapters. The book also includes polynomial interpolation at Chebyshev points, use of the MATLAB package Chebfun, and a section on the fast Fourier transform. Supplementary materials are available online. Clear and concise exposition of standard numerical analysis topics Explores nontraditional topics, such as mathematical modeling and Monte Carlo methods Covers modern applications, including information retrieval and animation, and classical applications from physics and engineering Promotes understanding of computational results through MATLAB exercises Provides flexibility so instructors can emphasize mathematical or applied/computational aspects of numerical methods or a combination Includes recent results on polynomial interpolation at Chebyshev points and use of the MATLAB package Chebfun Short discussions of the history of numerical methods interspersed throughout Supplementary materials available online

Modern computer architectures have evolved from uni-processor platforms to multi-processor and multi-core platforms, but traditional real-time distributed middleware such as RT-CORBA has not kept pace with that evolution. To address those issues, this paper describes the design and implementation of MCFlow, a new real-time distributed middleware for dependent task graphs running on multi-core platforms. MCFlow provides the following contributions to the state of the art in real-time middleware: (1) it provides an efficient C++ based component model through which computations can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) it allows optimizations for inter-component communication to avoid data copying without sacrificing the parallel executability of data dependent tasks; (3) it strictly separates timing and functional concerns of an application so that they can evolve and can be configured independently; and (4) it provides a novel event dispatching architecture that uses lock free algorithms to avoid mutex locking and reduce memory contention, CPU context switching, and priority inversion. We also present an empirical evaluation that demonstrates the efficacy of our approach. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 The Design and Implementation of MCFlow: a Real-time Multi-core Aware Middleware for Dependent Task Graphs Huang-Ming Huang, Christopher Gill, Chengyang Lu Department of Computer Science and Engineering, Washington University St. Louis, MO, USA {hh1, cdgill, lu}@cse.wustl.edu Abstract—Modern computer architectures have evolved from uni-processor platforms to multi-processor and multi-core platforms, but traditional real-time distributed middleware such as RT-CORBA has not kept pace with that evolution. To address those issues, this paper describes the design and implementation of MCFlow, a new real-time distributed middleware for dependent task graphs running on multi-core platforms. MCFlow provides the following contributions to the state of the art in real-time middleware: (1) it provides an efficient C++ based component model through which computations can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) it allows optimizations for inter-component communication to avoid data copying without sacrificing the parallel executability of data dependent tasks; (3) it strictly separates timing and functional concerns of an application so that they can evolve and can be configured independently; and (4) it provides a novel event dispatching architecture that uses lock free algorithms to avoid mutex locking and reduce memory contention, CPU context switching, and priority inversion. We also present an empirical evaluation that demonstrates the efficacy of our approach.Modern computer architectures have evolved from uni-processor platforms to multi-processor and multi-core platforms, but traditional real-time distributed middleware such as RT-CORBA has not kept pace with that evolution. To address those issues, this paper describes the design and implementation of MCFlow, a new real-time distributed middleware for dependent task graphs running on multi-core platforms. MCFlow provides the following contributions to the state of the art in real-time middleware: (1) it provides an efficient C++ based component model through which computations can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) it allows optimizations for inter-component communication to avoid data copying without sacrificing the parallel executability of data dependent tasks; (3) it strictly separates timing and functional concerns of an application so that they can evolve and can be configured independently; and (4) it provides a novel event dispatching architecture that uses lock free algorithms to avoid mutex locking and reduce memory contention, CPU context switching, and priority inversion. We also present an empirical evaluation that demonstrates the efficacy of our approach. Keywords-component; middleware

The concept of "quality of life" (QoL) encompasses all aspects of people's standard of living, including economic, social, or health-related factors, as well as their perceptions of their own lives. Although the growing application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in processing and modeling the diverse datasets associated with these domains, there remains a significant challenge in addressing different issues in QoL area of research and in fully harnessing these technologies to improve QoL research outcomes. Despite the technological advancements, current research endeavors often overlook the complex, multifaceted nature of QoL study. This oversight results in fragmented insights and leaves significant areas underexplored. In this work, we conducted a systematic literature review (SLR) to investigate the contribution of AI to QoL studies. For this, we collected 68 research works published between 2008 and 2022. This review covers a range of research questions about the objectives and methods of studies on QoL, the sources and types of data utilized, and the advancements made through the application of natural language processing (NLP), ML, deep learning (DL), statistical models, and semantic approaches. The goal of this review is to tackle the prevalent ambiguity in QoL dimensions, synthesize the research findings, and highlight the contributions, advancements, and most innovative approaches in the field. Moreover, we identify gaps and limitations in the current literature and suggest potential areas for future research, aiming to inspire more cohesive and comprehensive approaches to studying QoL using AI and ML techniques.