Search

Fair division of indivisible items is a well-studied topic in Economics and Computer Science. The objective is to allocate items to agents in a fair manner, where each agent has a valuation for each subset of items. Envy-freeness is one of the most widely studied notions of fairness. Since complete envy-free allocations do not always exist when items are indivisible, several relaxations have been considered. Among them, possibly the most compelling one is envy-freeness up to any item (EFX), where no agent envies another agent after the removal of any single item from the other agent's bundle. However, despite significant efforts by many researchers for several years, it is known that a complete EFX allocation always exists only in limited cases. In this paper, we show that a complete EFX allocation always exists when each agent is of one of two given types, where agents of the same type have identical additive valuations. This is the first such existence result for non-identical valuations when there are any number of agents and items and no limit on the number of distinct values an agent can have for individual items. We give a constructive proof, in which we iteratively obtain a Pareto dominating (partial) EFX allocation from an existing partial EFX allocation. [ABSTRACT FROM AUTHOR]

In this paper, we study quantum Ordered Binary Decision Diagrams(OBDD ) model; it is a restricted version of read-once quantum branching programs, with respect to "width" complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique ("reordering") for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function REQ such that the deterministic OBDD complexity of it is at least 2 Ω (n / log n) , and the quantum OBDD complexity of it is at most O (n 2 / log n) . It is the biggest known gap for explicit functions not representable by OBDD s of a linear width. Another function(shifted equality function) allows us to obtain a gap 2 Ω (n) vs O (n 2) . Moreover, we prove the bounded error quantum and probabilistic OBDD width hierarchies for complexity classes of Boolean functions. Additionally, using "reordering" method we extend a hierarchy for read-k-times Ordered Binary Decision Diagrams ( k - OBDD ) of polynomial width, for k = o (n / log 3 n) . We prove a similar hierarchy for bounded error probabilistic k - OBDD s of polynomial, superpolynomial and subexponential width. The extended abstract of this work was presented on International Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June 8 – 12, 2017 Khadiev and Khadieva (2017) [ABSTRACT FROM AUTHOR]

Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation. [ABSTRACT FROM AUTHOR]

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning. [ABSTRACT FROM AUTHOR]

The Robin sequence is a congenital anomaly characterized by a triad of features: micrognathia, glossoptosis, and airway obstruction. This comprehensive historical review maps the evolution of approaches and appliances for its treatment from the past to the current modern possibilities of an interdisciplinary combination of modern engineering, medicine, materials, and computer science combined approach with emphasis on designing appliances inspired by nature and individual human anatomy. Current biomimetic designs are clinically applied, resulting in appliances that are more efficient, comfortable, sustainable, and safer than legacy traditional designs. This review maps the treatment modalities that have been used for patients with a Robin sequence over the years. Early management of the Robin sequence focused primarily on airway maintenance and feeding support, while current management strategies involve both nonsurgical and surgical interventions and biomimetic biocompatible personalized appliances. The goal of this paper was to provide a review of the evolution of management strategies for patients with the Robin sequence that led to the current interdisciplinary biomimetic approaches impacting the future of Robin Sequence treatment with biomimetics at the forefront. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach for solving non-smooth convex optimization problems (NCOPs), which have broad applications in computer science, engineering, and physics. Our approach combines neurodynamic optimization with physics-informed neural networks (PINNs) to provide an efficient and accurate solution. We first use neurodynamic optimization to formulate an initial value problem (IVP) that involves a system of ordinary differential equations for the NCOP. We then introduce a modified PINN as an approximate state solution to the IVP. Finally, we develop a dedicated algorithm to train the model to solve the IVP and minimize the NCOP objective simultaneously. Unlike existing numerical integration methods, a key advantage of our approach is that it does not require the computation of a series of intermediate states to produce a prediction of the NCOP. Our experimental results show that this computational feature results in fewer iterations being required to produce more accurate prediction solutions. Furthermore, our approach is effective in finding feasible solutions that satisfy the NCOP constraint. [ABSTRACT FROM AUTHOR]

Inverting a matrix is time-consuming, and many works focus on accelerating the inversion of a single large matrix by GPU. However, the problem of parallelizing the inversion of a large number of small matrices has received little attention. These problems are widely applied in computer science, including accelerating cryptographic algorithms and image processing algorithms. In this paper, we propose a Revised In-Place Inversion algorithm for inverting a large number of small matrices on the CUDA platform, which adopts a more refined parallelization scheme and outperforms other algorithms, achieving a speedup of up to 20.9572 times over the batch matrix inverse kernel in CUBLAS. Additionally, we found that there is an upper bound on the input data size for each GPU device, and the performance will degrade if the input data size is too large. Therefore, we propose the Saturation Size Curve based on this finding to divide matrices into batches and improve the algorithm performance. Experimental results show that this strategy increases the algorithm's performance by 1.75 times and effectively alleviates the problem of performance degradation. [ABSTRACT FROM AUTHOR]

Image recognition based on computers is something human beings have been working on for many years. It is one of the most difficult tasks in the field of computer science, and improvements to this system are made when we speak. In this paper, we propose a methodology to automatically propose an appropriate title and add a specific sound to the image. Two models have been extensively trained and combined to achieve this effect. Sounds are recommended based on the image scene and the headings are generated using a combination of natural language processing and state-of-the-art computer vision models. A Top 5 accuracy of 67% and a Top 1 accuracy of 53% have been achieved. It is also worth mentioning that this is also the first model of its kind to make this forecast. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a subfield of computer science that aims to implement computer systems that perform tasks that generally require human learning, reasoning, and perceptual abilities. AI is widely used in the medical field. The interpretation of medical images requires considerable effort, time, and skill. AI-aided interpretations, such as automated abnormal lesion detection and image classification, are promising areas of AI. However, when images with different characteristics are extracted, depending on the manufacturer and imaging environment, a so-called domain shift problem occurs in which the developed AI has a poor versatility. Domain adaptation is used to address this problem. Domain adaptation is a tool that generates a newly converted image which is suitable for other domains. It has also shown promise in reducing the differences in appearance among the images collected from different devices. Domain adaptation is expected to improve the reading accuracy of AI for heterogeneous image distributions in gastrointestinal (GI) endoscopy and medical image analyses. In this paper, we review the history and basic characteristics of domain shift and domain adaptation. We also address their use in gastrointestinal endoscopy and the medical field more generally through published examples, perspectives, and future directions. [ABSTRACT FROM AUTHOR]

Digital false information is a global problem and the European Union (EU) has taken profound actions to counter it. However, from an academic perspective the United States has attracted particular attention. This article aims at mapping the current state of academic inquiry into false information at scale in the EU across fields. Systematic filtering of academic contributions resulted in the identification of 93 papers. We found that Italy is the most frequently studied country, and the country of affiliation for most contributing authors. The fields that are best represented are computer science and information studies, followed by social science, communication, and media studies. Based on the review, we call for (1) a greater focus on cross-platform studies; (2) resampling of similar events, such as elections, to detect reoccurring patterns; and (3) longitudinal studies across events to detect similarities, for instance, in who spreads misinformation. [ABSTRACT FROM AUTHOR]

Students in their first year of computer science (CS1) at universities typically struggle to grasp fundamental programming concepts. This paper discusses research carried out using a Java-based visual execution environment (VEE) to introduce fundamental programming concepts to CS1 students. The VEE guides beginner programmers through the fundamentals of programming, utilizing visual metaphors to explain and direct interactive tasks implemented in Java. The study's goal was to determine if the use of the VEE in the instruction of a group of 63 CS1 students from four different groups enrolled in two academic institutions (based in Madrid, Spain and Galway, Ireland) results in an improvement in their grasp of fundamental programming concepts. The programming concepts covered included those typically found in an introductory programming course, e.g., input and output, conditionals, loops, functions, arrays, recursion, and files. A secondary goal of this research was to examine if the use of the VEE enhances students' understanding of particular concepts more than others, i.e., whether there exists a topic-dependent benefit to the use of the VEE. The results of the study found that use of the VEE in the instruction of these students resulted in a significant improvement in their grasp of fundamental programming concepts compared with a control group who received instruction without the use of the VEE. The study also found a pronounced improvement in the students' grasp of particular concepts (e.g., operators, conditionals, and loops), suggesting the presence of a topic-dependent benefit to the use of the VEE. [ABSTRACT FROM AUTHOR]

At the University of Pécs (Hungary) mathematics courses are the basis for programs such as engineering and computer science. An online question bank was developed for university students to practice mathematical problem-solving and to use for exam assignments. Computer-aided test and assessment is widely used to support the teaching and learning of mathematics. In the research some aspects of question design were asked about the new possibilities of randomizing the question:: 1. What type of randomization is useful to introduce, and what are the limits of randomization? 2. Why is it sometimes so dangerous, what are the benefits, and what are the limits of it? 3. What type of math problems can be solved using random variables? 4. How can we ensure equal difficulty for the variation of questions? In our paper, some case studies of the task design are presented. The examples are from courses in mathematics at BSc and MSc levels, as well. [ABSTRACT FROM AUTHOR]

David A. Huffman (1925–1999) is best known in computer science for his work in information theory, particularly Huffman codes, and best known in origami as a pioneer of curved-crease folding. But during his early paper folding in the 1970s, he also designed and folded over a 100 different straight-crease origami tessellations. Unlike most origami tessellations designed in the past 20 years, Huffman's straight-crease tessellations are mostly three-dimensional, rigidly foldable, and have no locking mechanism. In collaboration with Huffman's family, our goal is to document all of his designs by reverse-engineering his models into the corresponding crease patterns, or in some cases, matching his models with his sketches of crease patterns. Here, we describe several of Huffman's origami tessellations that are most interesting historically, mathematically, and artistically. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

In general, models of universe problems in almost all fields, such as engineering, mathematics, medical sciences, computer science, physics, management sciences, artificial intelligence, and operations research, are practically full of complexities and include various types of uncertainties when dealing with them on numerous occasions. Different theories, such as probability, rough sets, fuzzy sets, soft ideals, and so on, have been created to deal with these uncertainties. An algebraic structure, AG-groupoid, is an intermediate structure between two types of structures: commutative semigroup and groupoid. This structure has a very close relationship to a commutative semigroup since a commutative AG-groupoid is always a semigroup. These structures have so many applications in flocks theory, geometry, topology, and many more. We explore several structural properties of an AG-groupoid by using hybrid structures in this paper. The main motivation behind this paper is to present the concepts of hybrid ideals, hybrid bi-ideals and hybrid interior ideals of an AG-groupoid and characterize AG-groupoid in terms of hybrid structures. Also, we show that the hybrid intersection and hybrid product structures will coincide under certain conditions. [ABSTRACT FROM AUTHOR]

This paper's main goal is to introduce left and right exponential trigonometric convex interval-valued mappings and to go over some of their important characteristics. Additionally, we demonstrate the Hermite–Hadamard inequality for interval-valued functions by utilizing fractional integrals with exponential kernels. Moreover, we use the idea of left and right exponential trigonometric convex interval-valued mappings to show various findings for midpoint- and Pachpatte-type inequalities. Additionally, we show that the results provided in this paper are expansions of several of the results already demonstrated in prior publications The suggested research generates variants that are applicable for conducting in-depth analyses of fractal theory, optimization, and research challenges in several practical domains, such as computer science, quantum mechanics, and quantum physics. [ABSTRACT FROM AUTHOR]

Let G be a graph with n vertices and maximum degree d. Fix some minor-closed property P (such as planarity). We say that G is ε -far from P if one has to remove εdn edges to make it have P . The problem of property testing P was introduced in the seminal work of Benjamini, Schramm, and Shapira (Symposium on the Theory of Computing 2008) that gave a tester with query complexity triply exponential in ε - 1 . Levi and Ron [ACM Trans. Algorithms, 11 (2005), 24 2015] have given the best tester to date, with a quasi-polynomial (in ε - 1 ) query complexity. It remained an open problem to show whether there is a property tester whose query complexity is poly(dε - 1 ), even for planarity. In this paper, we resolve this open question. For any minor-closed property, we give a tester with query complexity d. poly(ε- 1 ). The previous line of work on (independent of n, two-sided) testers is primarily combinatorial. Our work, on the other hand, employs techniques from spectral graph theory. This paper is a continuation of recent work of the authors (Foundations of Computer Science 2018) analyzing random walk algorithms that find forbidden minors. [ABSTRACT FROM AUTHOR]

The present study uses bio-bibliometrics to examine the contributions made by Prof. K.R. Venugopal, the Vice-Chancellor of Bangalore University. He was the Principal of the University Visvesvaraya College of Engineering (UVCE) for 15 years and Chairman of the Department of Electronics and Communications, Computer Science Engineering, and Information Science Engineering at UVCE. He has an illustrious, distinguished, and brilliant academic career with eleven degrees, including two Ph.Ds. He was a post-doctoral research scholar and visiting professor at the University of Southern California, USA. It is essential to study his contributions to understand the magnitudes and contributions in a particular domain. Data has been collected based on his curriculum vitae, available on the website of Bangalore University. Further, the paper highlights Prof. K.R. Venugopal's author productivity, year-wise distribution, collaboration, communications channels, highly cited documents and research papers, and most downloaded books. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to reflect on the implications for higher education of a rapidly changing global context, where technologies play a role in the propagation of unsustainable patterns of change. Drawing from research on science and technology studies as well as existing work on (higher) education for sustainability, it seeks to expose the multidimensional nature of these patterns to raise a series of questions for educators, particularly those in engineering, computer science and other technical disciplines, and inform structured curricular and organisational interventions. Design/methodology/approach: Theoretical perspectives on unsustainable patterns of technological change are explored with the objective of informing how the mitigation of their evolution could be addressed in educational settings. Key themes in the sustainability and higher education literature are then identified with the purpose of informing curricular and organisational intervention. Findings: Countering unsustainable patterns of technological change requires the development of key sustainability competencies within more technical disciplines, informed by concepts in science and technology studies. This requires change at curricular, organisational and institutional levels to enable spaces for inter- and transdisciplinary learning to be created, so that skills may be developed. Originality/value: This paper reflects on unsustainable patterns of technological change, identifying challenges for educators, in particular those in more technical disciplines. [ABSTRACT FROM AUTHOR]

The emergence of new technologies such as AI, machine learning, and so on drives the discussion over human consciousness among researchers from various disciplines, particularly those from philosophy of mind and computer science. This is true since certain academic theories argue that new technologies like those are much more efficient at simulating human tasks. This allows the discussion concerning human consciousness to acquire attention among researchers interested in learning more about human consciousness. As a result, this study provides a bibliometric analysis of human consciousness research from 2000 to 2022 to provide researchers and academics worldwide with an overview of the outcomes and trends in human consciousness research. A textual query on two databases; Scopus (1413 articles), and PubMed (258 papers) using the term "human consciousness" was conducted on 16 August 2022 by collecting 1671 academic works from 2000 to 2022 relating to human consciousness research for in-depth study. Bibliometric analyses were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to show the current of human consciousness research. This bibliometric study examined the yearly scientific publishing growth, the most productive authors, most often term that has been utilised, most renowned journal name, and which nation has the maximum cooperation with other country. According to the findings of the study, there is a significant inconsistency in worldwide patterns in yearly scientific output, with the number of publications growing and declining. Among all nations, United States of America (USA) published the most papers around human consciousness study. According to the research, it has shown that the most relevant contributors is Jun'ichi Takeno (Scopus), and Brian L Edlow (PubMed). Moreover, the most famous journal in human consciousness research includes Journal of Consciousness Studies (Scopus), and Frontiers in Psychology (PubMed). The significance of this study may aid new researchers in this area by offering information on important publications and authors to refer while undertaking research on this issue. Furthermore, this research helps other researchers understand current trends in this field of study. As an outcome, the objective for conducting this study is to give bibliometric analysis and to fulfill research gaps in bibliometric studies of human consciousness by giving information in the form of an overview, and suggestions. [ABSTRACT FROM AUTHOR]

Geodesics are of fundamental interest in mathematics, physics, computer science, and many other subjects. The so-called leapfrog algorithm was proposed in [L. Noakes, J. Aust. Math. Soc., 65 (1998), pp. 37--50] (but not named there as such) to find geodesics joining two given points x0 and x1 on a path-connected complete Riemannian manifold. The basic idea is to choose some junctions between x0 and x1 that can be joined by geodesics locally and then adjust these junctions. It was proved that the sequence of piecewise geodesics\{γk} k≥1 generated by this algorithm converges to a geodesic joining x0 and x1. The present paper investigates leapfrog's convergence rate τi,n of ith junction depending on the manifold M. A relationship is found with the maximal root λn of a polynomial of degree n-3, where n (n >3) is the number of geodesic segments. That is, the minimal τi,n is upper bounded by λn(1+c+), where c+ is a sufficiently small positive constant depending on the curvature of the manifold M. Moreover, we show that λn increases as n increases. These results are illustrated by implementing leapfrog on two Riemannian manifolds: the unit 2-sphere and the manifold of all 2 × 2 symmetric positive definite matrices. [ABSTRACT FROM AUTHOR]

Business Information Systems (BIS) is a professional discipline that combines Computer Science, Management, and Business Administration. In this article the researchers evaluate the evolution of BIS curriculum in terms of its past, present and future, considering its historical background, the existing strengths and weaknesses, and how it can be developed to meet market needs. The study reveals the skills required by a BIS graduate and highlights how the existing/existed BIS curricula have made their graduates weaker when compared with Computer Science and Business Administration specialists. The research further compares the existential crisis of BIS with Library and Information Science (LIS) discipline, which have also faced a similar controversial crisis concerning their disciplinary boundaries, whether it belong to Social and Behavioral Sciences or Computer Science and Engineering and yet succeeded in its growth and contributions. This research concludes that the BIS curriculum is still budding and needs to be re-evaluated and updated regularly to meet the changing demands of the market highlighting the need for collaboration between academia and industry to ensure that BIS graduates are equipped with the skills that employers require. The paper finally proposes a balanced model of BIS curriculum that incorporates the latest technology updates. The proposed curriculum model aims to address the current gaps in BIS education and meets the changing demands of the market. [ABSTRACT FROM AUTHOR]

In this paper, we formalize precisely the sense in which the application of a cellular automaton to partial configurations is a natural extension of its local transition function through the categorical notion of Kan extension. In fact, the two possible ways to do such an extension and the ingredients involved in their definition are related through Kan extensions in many ways. These relations provide additional links between computer science and category theory, and also give a new point of view on the famous Curtis–Hedlund theorem of cellular automata from the extended topological point of view provided by category theory. These links also allow to relatively easily generalize concepts pioneered by cellular automata to arbitrary kinds of possibly evolving spaces. No prior knowledge of category theory is assumed for the most part. [ABSTRACT FROM AUTHOR]

Entropy and extropy are emerging concepts in machine learning and computer science. Within the past decade, statisticians have created estimators for these measures. However, associated variability metrics, specifically varentropy and varextropy, have received comparably less attention. This paper presents a novel methodology for computing varentropy and varextropy, drawing inspiration from Bayesian nonparametric methods. We implement this approach using a computational algorithm in R and demonstrate its effectiveness across various examples. Furthermore, these new estimators are applied to test uniformity in data. [ABSTRACT FROM AUTHOR]

Regularization regression learning scheme finds its powerful applications in many areas, such as Computer science, manufacturing engineering, economic decision making, etc. In this paper, the indefinite kernel based coefficient dependent regularization algorithm for block-wise streaming data is proposed, and learning performance of this algorithm is studied by bounding the learning error. Our learning scheme works in an online and weighted average manner. The total error is decomposed into weighted average of local variance error and weighted average of local bias error. By the kernel decompose and integral operator method, satisfied error bound and learning rates are derived. Our error analysis shows that mild growth of the sizes of data block and the underregularization strategy can guarantee the convergence of the learning scheme. [ABSTRACT FROM AUTHOR]

Linear equations are commonly used in various fields of science such as engineering, physics, computer science, technology, business, and economics. A recent and promising extension of fuzzy set theory is represented by hesitant fuzzy sets (HFSs). The promising characteristic of HFSs is that they permit to model a type of imprecise human knowledge that cannot be captured by classical fuzzy sets. As no research has been performed in the field of hesitant fuzzy equations, in this paper, hesitant parametric fuzzy set (HPFS) is introduced and operations on this set are investigated. Also, fully hesitant parametric fuzzy equation (FHPFE) in form of a x = b is solved for the first time in which a and b are known HPFS and x is an unknown HPFS. Then, the existence of different approximate solutions of FHPFE is investigated and proved. Finally, some numerical examples are presented that verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Protecting electronic documents, especially those containing sensitive data, is a major challenge in an open web. The data security industry has long struggled to manage the security of e-books, data shows that information security issues can cause significant economic losses after three years Although cryptographic methods have been proposed as a solution in of these challenges Focusing on speed and efficiency The shortcomings of traditional encryption methods have been thoroughly examined Although a number of network management techniques assure retention privacy and integrity though, when it comes to encryption. Digital signature algorithms, although effective in detecting unauthorized changes and limiting the scope for copyright protection, do not ensure the confidentiality of shared electronic documents When addressing research gaps addressing this issue, the paper proposes a security framework for electronic documents that combines three important security mechanisms: encryption, digital signatures, and watermark algorithms Dhishu By matching their strengths, constraints are overcome. The combination of encryption and digital signatures is explored as a promising approach to protecting electronic documents, ensuring authenticity and confidentiality Importantly, the need to explore security mechanisms such as digital is highlighted emphasis on handwriting, encryption, and watermarking systems in depth. [ABSTRACT FROM AUTHOR]

A control flow graph (CFG) is a type of directed graph that shows the execution paths of the programs. It is a mathematical structure that is actively used in software testing. It can be constructed from the source or the executable of the program. Construction of the CFG from the executable is called CFG reconstruction. CFG reconstruction is used in many areas of computer science, like reverse engineering, security analysis, and worst-case execution time analysis. CFG reconstruction can be performed using a static, dynamic, or hybrid approach. This paper introduces a new CFG reconstruction tool named Turna that uses a hybrid approach. Turna works on programs that are compiled for RISC-V architecture. One of the main phases of CFG reconstruction is basic block detection. Therefore, together with Turna, a new rule set and an algorithm for basic block detection from RISC-V executables are also introduced. The CFG reconstruction process and the outputs of Turna are shared and discussed. [ABSTRACT FROM AUTHOR]

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme. [ABSTRACT FROM AUTHOR]

In this paper we consider coloring problems on graphs and other combinatorial structures on standard Borel spaces. Our goal is to obtain sufficient conditions under which such colorings can be made well-behaved in the sense of topology or measure. To this end, we show that such well-behaved colorings can be produced using certain powerful techniques from finite combinatorics and computer science. First, we prove that efficient distributed coloring algorithms (on finite graphs) yield well-behaved colorings of Borel graphs of bounded degree; roughly speaking, deterministic algorithms produce Borel colorings, while randomized algorithms give measurable and Baire-measurable colorings. Second, we establish measurable and Baire-measurable versions of the Symmetric Lovász Local Lemma (under the assumption p (d + 1) 8 ⩽ 2 − 15 , which is stronger than the standard LLL assumption p (d + 1) ⩽ e − 1 but still sufficient for many applications). From these general results, we derive a number of consequences in descriptive combinatorics and ergodic theory. [ABSTRACT FROM AUTHOR]

Motivation Accurately predicting the likelihood of interaction between two objects (compound–protein sequence, user–item, author–paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects. Results We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exploited via Contrastive Multiview Coding to learn embeddings that maximize the mutual information across congruent data views. CSI assigns a key and multiple views to each data point, where data partitions under a particular key form congruent views of the data. We showcase the effectiveness of CSI by applying it to the compound–protein sequence interaction prediction problem, a pressing problem whose solution promises to expedite drug delivery (drug–protein interaction prediction), metabolic engineering, and synthetic biology (compound–enzyme interaction prediction) applications. Comparing CSI with a baseline model that does not utilize data stratification and contrastive learning, and show gains in average precision ranging from 13.7% to 39% using compounds and sequences as keys across multiple drug–target and enzymatic datasets, and gains ranging from 16.9% to 63% using reaction features as keys across enzymatic datasets. Availability and implementation Code and dataset available at https://github.com/HassounLab/CSI. [ABSTRACT FROM AUTHOR]

In this review paper, several research studies were surveyed to assist future researchers to identify available techniques in the field of infectious disease modeling across complex networks. Infectious disease modelling is becoming increasingly important because of the microbes and viruses that threaten people’s lives and societies in all respects. It has long been a focus of research in many domains, including mathematical biology, physics, computer science, engineering, economics, and the social sciences, to properly represent and analyze spreading processes. This survey first presents a brief overview of previous literature and some graphs and equations to clarify the modeling in complex networks, the detection of societies and their medical information, the identification of nodes, the method of communication with individuals and their spread, the analysis of their transmission through complex networks, and the detection of mathematical methods over the past century. Secondly, the types of epidemiological models and complex networks and the extent of their impact on humans are presented. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of a STEM scholarship program which utilized a holistic approach to providing a multi-dimensional student support system. The program has been successful in encouraging and supporting women in Applied Mathematics and Computer Science by offering a diverse suite of extracurricular opportunities, actively engaging them in organized events, research projects, and participation in STEM communities, and helping them achieve higher GPAs and shorter times to graduation. The supported women also benefitted from close mentoring relationships with the faculty mentors. The program emphasized the development of empowering settings for women’s engagement and achievement, which act to sustain and expand interest in mathematics and computing, and thereby help them to see themselves as future professionals in the field. [ABSTRACT FROM AUTHOR]

The design and evaluation of accessibility technology is a core component of the computer science landscape, aiming to ensure that digital innovations are accessible to all. One of the most prominent and long-lasting areas of accessibility research focuses on motor impairments--deficiencies that affect the ability to move, manipulate objects, and interact with the physical world. In this survey article, we present an extensive overview of the past two decades of research into accessibility for people with motor impairments. Following a structured selection process, we analyzed the study details as reported in 177 relevant papers. Based on this analysis, we critically assess user representation, measurement instruments, and existing barriers that exist in accessibility research. Finally, we discuss future directions for accessibility research within the computer science domain. [ABSTRACT FROM AUTHOR]

This paper attempts to scrutinise the publication productivity of Prof. Sunilkumar S. Manvi, Dean of Engineering and Technology at REVA University, Bengaluru, by data obtained from Scopus, Web of Science, and Google Scholar databases. In a total of 24 productive years (1999-2022), he has 292 publications (September 2022), and the highest number of documents was published in the year 2011 with 33 publications. His publications were analysed by types of document, year-wise distribution, distribution of articles in different domains, citation productivity, authorship pattern, measure of collaborations, collaboration indices, most prolific journals with number publication, highly cited articles, and citations during the study period of 1999-2022. As per collaboration indices, his highest Collaborative Coefficient is 0.67, MCC is 1.00 in the year 1999 and 2000 respectively. The highest Degree of Collaboration is 1.00, Collaborative Index is 3.11 in the year 2007. He published 98 documents during the period of 2009-2013. His major contributions to the field of Computer Science and Engineering are numerous. [ABSTRACT FROM AUTHOR]

In recent decades, Functionalism has become a standard approach towards philosophy of mind and cognitive science, Functionalism is inspired by computer science and against prior theories about mind. Functionalism takes mind as a processor and body as a provider of raw data for this processor. Mind with the help of mental algorithms translates perceptional data to symbolic representations as concepts of objects and events. According to some philosophers, this leads to problems like the problem of meaning. In this paper, with an analytical approach, Embodiment is proposed as a solution to these problems. According to Embodied Mind, regarding the body as a mere machine is the root of all problems in Functionalism. Embodied mind is consisting of three pars: 1. Perception-Motor system which presents meaningful perception of objects and events to the mind. 2. Conceptualization system which unites information provided by perceptionmotor system. 3. Metaphors, processes in mind, which enable us to think and talk about everyday and mathematical concepts and meta-language. These are processes, mainly unconscious, in mind for understanding concepts. Embodied Mind rejects the idea of body as mere machine and mind as a brain processes. It also rejects symbolic representations as concepts. Concepts are simulators of objects and events, rooting in interaction between body and the world. Human as an Embodied Being exist in the world and interact with it and this is what makes sense of concepts in mind. [ABSTRACT FROM AUTHOR]

Metaphors in gesture and speech play a pivotal role in the way that programming concepts are presented in the classroom. However, little is known about the function of teachers' metaphors in practice. This study aims to explore teachers' use of metaphors in gesture and speech in a lecture on programming. Based on video observations of three upper secondary teachers, we employ Metaphor Identification Procedure (MIP) and Metaphor Identification for Gesture Guidelines (MIG-G) as methodological tools for identifying metaphoric speech and gestures related to programming concepts. The results of the study reveal that the gestures of the three teachers mainly function in two ways: (1) to add spatial properties to a programming concept and (2) to provide additional imagery for a programming concept. Consequently, the gestures identified in this study reduce the communicative burden of teachers' speech. Furthermore, the study reveals that teachers' gestures serve as means for making abstract concepts more tangible. For example, gestures concerning the abstract term "data" can generally be related to an object that could be received or moved. Hence, despite its metaphorical origin, data could be considered a graspable aspect of programming. Furthermore, spatial gestures enable the teachers to communicate programming processes in a tangible way, for example assigning programming processes a forward direction. Theoretical implications, potential implications for teaching and future research are discussed in the paper. [ABSTRACT FROM AUTHOR]

In recent years, massive open online courses (MOOCs) have become quite popular. Such courses are either completely free or cost nominally. Generally, the MOOCs face the challenge of not being recognized as 'regular' courses (i.e., the ones taken at the traditional learning institutions). One of the main reasons for this lack of acceptability is the assessment in an unsupervised environment, which is prone to the problems of test-taker's online lookups or interaction with others for finding the answers to the test questions. A few ways of alleviating this problem include: limiting the time for answering the questions, the avoidance of repetitive questions, and the creation of a large number of questions. This paper presents a tool named QAgen that enables the automatic creation of a large number of questions and answers related to different topics in computer/electrical engineering (ECE), computer science, physics, etc. Specifically, the tool is related to the courses on digital logic design, computer architecture, etc. The generated questions are in a format that is suitable both for learning management system (LMS) based and/or non-LMS-based assessment in conventional courses or MOOCs. The proposed tool is based on open-source software, thus eliminating the need for any commercial software packages. The underlying principles of QAgen are applicable to other engineering/science courses as well, if the assessment methods require the creation of some connected-object diagrams, tables and equations. For assessing the usefulness of QAgen, practice question sets were created for three different courses. The student surveys for these courses indicated that the questions helped students prepare for actual tests/examinations. Especially favored by the students was the availability of correct answers at the end of each practice test/examination. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

Motivated by analogous concepts in theoretical computer science, the concepts of simulation and bisimulation relations have recently been introduced in the study of the geometrictheory of nonlinear control systems. Simulation relations generalize such notions as trajectory propagation and trajectory lifting, while bisimulation relations generalize such notions as system equivalence and state-space reduction. This paper explores previously obtained necessary and sufficient conditions for the existence of (bi)simulation relations and determines to what extent those relations can be maintained if inputs and disturbances are restricted to specified ‘admissible’ classes. We show how the results obtained here for general simulation relations extend prior results obtained for trajectory propagation and trajectory lifting. Also addressed briefly are some sufficient conditions for simulation relations to hold semiglobally in time. [ABSTRACT FROM AUTHOR]