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In the present paper, we propose a new approach on 'distributed systems': the processes are represented through total orders and the communications are characterized by means of biorders. The resulting distributed systems capture situations met in various fields (such as computer science, economics and decision theory). We investigate questions associated to the numerical representability of order structures, relating concepts of economics and computing to each other. The concept of 'quasi-finite partial orders' is introduced as a finite family of chains with a communication between them. The representability of this kind of structure is studied, achieving a construction method for a finite (continuous) Richter–Peleg multi-utility representation. [ABSTRACT FROM AUTHOR]

The concept of book embedding, originating in computer science, has found extensive applications in various problem domains. A book embedding of a graph G involves arranging the vertices of G in an order along a line and assigning the edges to one or more half-planes. The page number of a graph is the smallest possible number of half-planes for any book embedding of the graph. Determining the page number is a key aspect of book embedding and carries significant importance. This paper aims to investigate the non-trivial lower bound of the page number for both a graph G and a random graph G ∈ G (n , p) by incorporating two seemingly unrelated concepts: edge-arboricity and Euler's Formula. Our analysis reveals that for a graph G, which is not a path, p n (G) ≥ ⌈ 1 3 a 1 (G) ⌉ , where a 1 (G) denotes the edge-arboricity of G, and for an outerplanar graph, the lower bound is optimal. For G ∈ G (n , p) , p n (G) ≥ ⌈ 1 6 n p (1 - o (1)) ⌉ with high probability, as long as c n ≤ p ≤ 3 (n - 1) 2 n log n . [ABSTRACT FROM AUTHOR]

In human-human and human-robot interaction, the counterpart influences the human's affective state. Contrary to humans, robots inherently cannot respond empathically, meaning non-beneficial affective reactions cannot be mitigated. Thus, to create a responsible and empathetic human-robot interaction (HRI), involving anthropomorphic service robots, the effect of robot behavior on human affect in HRI must be understood. To contribute to this understanding, we provide the new comprehensive data set AFFECT-HRI, including, for the first time, physiological data labeled with human affect (i.e., emotions and mood) gathered from a conducted HRI study. Within the study, 146 participants interacted with an anthropomorphic service robot in a realistic and complex retail scenario. The participants' questionnaire ratings regarding affect, demographics, and socio-technical ratings are provided in the data set. Five different conditions (i.e., neutral, transparency, liability, moral, and immoral) were considered during the study, eliciting different affective reactions and allowing interdisciplinary investigations (e.g., computer science, law, and psychology). Each condition includes three scenes: a consultation regarding products, a request for sensitive personal information, and a handover. [ABSTRACT FROM AUTHOR]

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]

Digital data transmission and storage are indispensable components for our contemporary information-driven society. As data volumes keep increasing, the need for efficient coding schemes grows. This paper gives a novel coding scheme for generating sixteen codes from generators with quaternary codes, thereby offering a significant increase in data representation capacity, new possibilities for data encoding and decoding, and an overall improvement in the efficiency of the encoding scheme. This provides up a new perspective for numerous fields that can profit from the proposed scheme, including coding theory, computer science, modeling, and experimental design. We focus on only experimental design and modeling, due to space limits and their importance in designing and modeling real-world and scientific phenomena. The main findings indicate that the new generated sixteen codes can be used as optimal experimental designs with good space-filling behavior when designing large-scale experiments, and as efficient datasets that perform well in fitting various functions even with a small number of points and without modeling assumptions. The relationships between the codewords of the generated sixteen codes and the codewords of the generator quaternary codes are investigated. These relationships provide a benchmark by which to select the generator most efficiently, resulting in codewords that are efficient from multiple angles. Furthermore, the space-filling behavior and isomorphism examination of the sixteen codes generated as designs are deeply theoretically studied. [ABSTRACT FROM AUTHOR]

Trust can be considered as a multidisciplinary concept, which is strongly related to the context and it falls in different fields such as Philosophy, Psychology or Computer Science. Trust is fundamental in every relationship, because without it, an entity will not interact with other entities. This aspect is very important especially in the Internet of Things (IoT), where many entities produced by different vendors and created for different purposes have to interact among them through the internet often under uncertainty. Trust can overcome this uncertainty, creating a strong basis to ease the process of interaction among these entities. We believe that considering trust in the IoT is fundamental, and in order to implement it in any IoT entity, it is fundamental to consider it through the whole System Development Life Cycle. In this paper, we propose an analysis of different works that consider trust for the IoT. We will focus especially on the analysis of frameworks that have been developed in order to include trust in the IoT. We will make a classification of them providing a set of parameters that we believe are fundamental in order to properly consider trust in the IoT. Thus, we will identify important aspects to be taken into consideration when developing frameworks that implement trust in the IoT, finding gaps and proposing possible solutions. [ABSTRACT FROM AUTHOR]

This document is a foreword for a special issue of the journal Microsystem Technologies. The special issue features selected papers from the 11th International Conference on Computing, Communication and Sensor Network (CCSN) held in September 2022 at Utkal University in Bhubaneswar, Odisha, India. The conference covered various interdisciplinary areas such as computing, communication, sensor networks, and circuit designs. The papers included in this special issue have undergone a rigorous review process and were selected for publication. The foreword expresses the hope that readers will find the papers informative. [Extracted from the article]

Research institutions are under siege from cybercriminals and other digital assailants. How do you make sure you don’t let them in? [ABSTRACT FROM AUTHOR]

Some researchers see superhuman qualities in artificial intelligence. All scientists need to be alert to the risks this creates. [ABSTRACT FROM AUTHOR]

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers. Mass spectrometry-based proteomics faces the challenge of processing vast data amounts. Here, the authors introduce AlphaPept, an open-source, Python-based framework that offers high speed analysis and easy integration for large-scale proteome analysis. [ABSTRACT FROM AUTHOR]

Large language models are transforming scientific writing and publishing. But the productivity boost that these tools bring could have a downside. [ABSTRACT FROM AUTHOR]

The 33rd International Workshop On Combinatorial Algorithms (IWOCA 2022) was held at the University of Trier in Germany. This workshop covers a wide range of topics related to combinatorial algorithms. The special issue of the journal Algorithmica contains extended versions of selected papers from IWOCA 2022, which were nominated by the Program Committee and underwent a rigorous reviewing process. The special issue includes nine papers on various topics such as perfect matchings, algorithmic questions, and winner determination algorithms. One paper was chosen as the Best Paper of IWOCA 2022 and another as the Best Student Paper. The special issue is recommended for readers interested in exploring more papers from IWOCA 2022. [Extracted from the article]

The approximate uniform sampling of graph realizations with a given degree sequence is an everyday task in several social science, computer science, engineering etc. projects. One approach is using Markov chains. The best available current result about the well-studied switch Markov chain is that it is rapidly mixing on P-stable degree sequences (see DOI:10.1016/j.ejc.2021.103421). The switch Markov chain does not change any degree sequence. However, there are cases where degree intervals are specified rather than a single degree sequence. (A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed.) Rechner, Strowick, and Müller–Hannemann introduced in 2018 the notion of degree interval Markov chain which uses three (separately well studied) local operations (switch, hinge-flip and toggle), and employing on degree sequence realizations where any two sequences under scrutiny have very small coordinate-wise distance. Recently, Amanatidis and Kleer published a beautiful paper (DOI:10.4230/LIPIcs.STACS.2023.7), showing that the degree interval Markov chain is rapidly mixing if the sequences are coming from a system of very thin intervals which are centered not far from a regular degree sequence. In this paper, we substantially extend their result, showing that the degree interval Markov chain is rapidly mixing if the intervals are centered at P-stable degree sequences. [ABSTRACT FROM AUTHOR]

Our world is turbulent: ecological, social, political, technological, economic, and competitive business environments change constantly. Consumers have changing preferences, learn, build trust in brands, adopt new products, and are persuaded by advertising. Firms innovate and engage in and respond to competition. Exogenous events, such as changes in economic conditions and regulations, as well as human crises, also cause major shifts in markets. This special issue focuses on novel Marketing data and modern methodologies from different fields (e.g., Operations Research (OR), Statistics, Econometrics, and Computer Science), which help firms understand, utilize, and respond to market dynamics more efficiently. Here we propose a framework comprising analytical methods and data for dynamic markets that is useful for structuring research in this domain. Next, we summarize the history of the Marketing/OR interface. We highlight studies at the Marketing/OR interface from the last decade focusing specifically on dynamic markets and use our proposed framework to identify trends and gaps in the extant literature. After that, we present and summarize the papers of the current special issue and their contributions to the field against the backdrop of our framework and the trends in the literature. Finally, we conclude and discuss which future Marketing/OR research could tackle important issues in dynamic markets. [ABSTRACT FROM AUTHOR]

Decision problems in physics have been an active field of research for quite a few decades resulting in some interesting findings in recent years. However, such research investigations are based on a priori knowledge of theoretical computer science and the technical jargon of set theory. Here, I discuss a particular, but a significant, instance of how decision problems in physics can be realised without such specific prerequisites. I expose a hitherto unnoticed contradiction, that can be posed as as decision problem, concerning the oil drop experiment, thereby resolve it by refining the notion of 'existence' in physics. This consequently leads to the undecidability of the charge spectral gap through the notion of 'undecidable charges' which is in tandem with the completeness condition of a theory as was stated by Einstein, Podolsky and Rosen in their seminal work. Decision problems can now be realised in connection with basic physics, in general, rather than quantum physics, in particular, as per some recent claims. [ABSTRACT FROM AUTHOR]

Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner. [ABSTRACT FROM AUTHOR]

One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research. [ABSTRACT FROM AUTHOR]

From small steps to great leaps, metaphors of spatial mobility abound to describe discovery processes. Here, we ground these ideas in formal terms by systematically studying mobility patterns in the scientific knowledge landscape. We use low-dimensional embedding techniques to create a knowledge space made up of 1.5 million articles from the fields of physics, computer science, and mathematics. By analyzing the publication histories of individual researchers, we discover patterns of scientific mobility that closely resemble physical mobility. In aggregate, the trajectories form mobility flows that can be described by a gravity model, with jumps more likely to occur in areas of high density and less likely to occur over longer distances. We identify two types of researchers from their individual mobility patterns: interdisciplinary explorers who pioneer new fields, and exploiters who are more likely to stay within their specific areas of expertise. Our results suggest that spatial mobility analysis is a valuable tool for understanding the evolution of science. [ABSTRACT FROM AUTHOR]

To explain why, in scientific problem solving, a diagram can be "worth ten thousand words," Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be "informationally" equivalent but differ "computationally," just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the "imagery debate" of the 1970s on whether there are image-like mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon's approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. [ABSTRACT FROM AUTHOR]

Let d-claw (or d-star) stand for K 1 , d , the complete bipartite graph with 1 and d ≥ 1 vertices on each part. The d-claw vertex deletion problem, d -claw-vd, asks for a given graph G and an integer k if one can delete at most k vertices from G such that the resulting graph has no d-claw as an induced subgraph. Thus, 1 -claw-vd and 2 -claw-vd are just the famous vertex cover problem and the cluster vertex deletion problem, respectively. In this paper, we strengthen a hardness result recently proved in Jena and Subramani (in: Du, Du, Wu, and Zhu (eds) Theory and applications of models of computation - 17th annual conference, TAMC 2022, Tianjin, China, September 16–18, 2022, Proceedings, 2022), by showing that cluster vertex deletion remains NP -complete even when restricted to planar bipartite graphs of maximum degree 3 and arbitrary large girth. Moreover, for every d ≥ 3 , we show that d -claw-vd is NP -complete even when restricted to planar bipartite graphs of maximum degree d. These hardness results are optimal with respect to degree constraint. By extending the hardness result in Bonomo-Braberman et al (in: Computing and combinatorics - 26th international conference, COCOON 2020, Proceedings, Lecture Notes in Computer Science, vol 12273, Springer, 2020, pp 14–26, 2020), we show that, for every d ≥ 3 , d -claw-vd is NP -complete even when restricted to split graphs without (d + 1) -claws, and split graphs of diameter 2. On the positive side, we prove that d -claw-vd is polynomially solvable on what we call d-block graphs, a class properly contains all block graphs. This result extends the polynomial-time algorithm in Cao et al (Theor Comput Sci, 2018) for 2 -claw-vd on block graphs to d -claw-vd for all d ≥ 2 and improves the polynomial-time algorithm proposed by Bonomo-Brabeman et al. for (unweighted) 3 -claw-vd on block graphs to 3-block graphs. [ABSTRACT FROM AUTHOR]

Currently, generative applications are reshaping different fields, such as art, computer vision, speech processing, and natural language. The computer science personalization area is increasingly relevant since large companies such as Spotify, Netflix, TripAdvisor, Amazon, and Google use recommender systems. Then, it is rational to expect that generative learning will increasingly be used to improve current recommender systems. In this paper, a method is proposed to generate synthetic recommender system datasets that can be used to test the recommendation performance and accuracy of a company on different simulated scenarios, such as large increases in their dataset sizes, number of users, or number of items. Specifically, an improvement in the state-of-the-art method is proposed by applying the Wasserstein concept to the generative adversarial network for recommender systems (GANRS) seminal method to generate synthetic datasets. The results show that our proposed method reduces the mode collapse, increases the sizes of the synthetic datasets, improves their ratings distributions, and maintains the potential to choose the desired number of users, number of items, and starting size of the dataset. Both the baseline GANRS and the proposed Wasserstein-based WGANRS deep learning architectures generate fake profiles from dense, short, and continuous embeddings in the latent space instead of the sparse, large, and discrete raw samples that previous GAN models used as a source. To enable reproducibility, the Python and Keras codes are provided in open repositories along with the synthetic datasets generated to test the proposed architecture (https://github.com/jesusbobadilla/ganrs.git). [ABSTRACT FROM AUTHOR]

The theory of Formal Concept Analysis (FCA) finds diverse applications in fields like knowledge extraction, cognitive concept learning and data mining. The construction of a concept lattice significantly influences the effectiveness of formal concept analysis; hence, the development of high-performance algorithms for concept construction is crucial. In this paper, we introduce a novel algorithm called "Bit-Close" for formal concept construction. Bit-Close leverages bit representation and operations, fundamental to computer science, to enhance the In-Close algorithm. Furthermore, we explore the parallel method of Bit-Close. Our experimental results, obtained from multiple public and random datasets, demonstrate that Bit-Close outperforms In-Close by approximately 20% and is significantly better than other competing algorithms. [ABSTRACT FROM AUTHOR]

With the increase interest and number of the users in Social media, the file handling has also increased. To manage the load, cloud servers are being used by the service providers. To identify and cluster file is a difficult task that is important in the domain of computer science. Various traditional approaches for identification exists that uses design features. The problem with these methods is get that they can be easily spoofed. To resolve the issue, in this paper, a hybrid algorithm combining the features of Random Forest with AdaBoost is proposed. The algorithm Internet of Thing (IoT) data file formatting (IDFF) classifies data as (text, image, audio and video) and gives better accuracy. Our proposed research obtains better Accuracy (93%), Precision (95%), Recall (95%), F-Measure (95%), and G-Mean (96%). [ABSTRACT FROM AUTHOR]

Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]

The article discusses a Bayesian measure of association, B-index, and compares it with the other existing measures of agreement, association, and similarity, both chance-corrected and non-corrected: Scott's π, Krippendorff's α, Cohen's κ, Bennett, Alpert & Goldstein's S, Cosine similarity, and the Jaccard similarity coefficient. PageRank adapted to particularities of annotation is also added to this list. Two versions of B-index are considered: with the informative and non-informative priors. An algorithm for calculating B-index written in pseudocode is provided. Particular attention is devoted to the uses of those measures in content analysis, communication studies, computational linguistics, psychology, computer science and network science. Real-world data gathered using an online platform for content analysis allowed comparing the behavior of all eight measures included in the scope of analysis. Three short texts (164 data points/sentences in total) were coded by 66 annotators. The behaviors of B-index with the non-informative prior and Bennett, Alpert & Goldstein's S have some common patterns. [ABSTRACT FROM AUTHOR]

Co-authored publications can bring positive results for those who participate, such as gaining additional expertise, accessing more funding or increasing the publication impact. China, the European Union, and the United States have been collaborating between each other throughout the years in the field of Computer Science. These collaborations varied over time, as well as they impacted the regions in different ways. In this paper, we collected the publications from these territories across 31 years on the topic of Computer Science and studied them focusing on how the regions have approached co-authorship. In particular, we have analyzed the number of collaborations during that period, the impact of those papers measured as the number of citations, and the topics that have been researched. We conclude that China's focus on Computer Science fields has led it to be the most productive region in recent years; plus, it has benefited from the American and European reputation, by increasing its citation rates when collaborating with them. On the other hand, the EU and the US have benefited from Chinese interest in computer science, increasing the number of publications together. [ABSTRACT FROM AUTHOR]

Researchers say their artificial-intelligence system could help to democratize medicine. [ABSTRACT FROM AUTHOR]

A branch of computer science known as genetic programming has been given a boost with the application of large language models that are trained on the combined intuition of the world’s programmers.Autonomous mathematical discovery enabled by LLMs. [ABSTRACT FROM AUTHOR]

Researchers call for more funding to study the boundary between conscious and unconscious systems.Coalition of researchers calls for more funding to study the boundary between conscious and unconscious systems. [ABSTRACT FROM AUTHOR]

Within the field of social reintegration and re-education, this paper presents an educational experience carried out at the Iquitos Penitentiary Center, Lima, Peru, with the aim of providing an introduction to informatics to 25 inmates who volunteered to take part in the project. Twenty students and a teacher from the Scientific University of the South also in Peru, were responsible for initiating the transmission of knowledge from the university to inmates, with the collaboration and participation of the penitentiary coordinator. The main objectives of the case study were to validate both the suitability of the CS unplugged proposal and the adaptability of the L2T2L pedagogic strategy to the transmission of knowledge to adults, specifically penitentiary inmates. This strategy had been originally designed to transmit informatics knowledge from university to primary school. The validity and effectiveness of the experience was assessed using surveys. Results confirm that inmates achieved a good level of understanding when endeavoring to resolve most of the CS unplugged assignments designed for them. It was also seen that L2T2L is adaptable and valid for different scenarios other than those for which it was initially designed. Indeed, it was proven to be valid for transmitting knowledge to the prison population. Finally, it should be pointed out that the experience is easily replicable and that it brings an opportunity to introduce informatics into education programs in prisons, something which can contribute enormously to social reintegration and re-education, facilitating the subsequent reentry of inmates into the community once their period of imprisonment has ended. [ABSTRACT FROM AUTHOR]

This paper presents a preliminary comparison of citation impact between two types of Best Paper Awards recipients: pre-and post-publication peer-selected best papers. Two hundred and ninety-nine pairs of pre-and post-publication peer-selected best papers from 15 highly prestigious computer science conferences spanning more than two decades were gathered for empirical analysis. Each pair was published in the same proceedings, and their citation information was collected within a 10-year period after their formal publication. A series of pairwise comparisons were performed in order to unbiasedly uncover their differences in citation counts (i.e., a good proxy for citation impact), as well as in citation speed, citation diversity, and citation location distributions (i.e., three characteristic properties of citation impact). The empirical results demonstrate that in computer science, the post-publication peer-selected best papers receive significantly more citations than their pre-publication counterparts, with an estimated probability of up to 0.79 (95% CI: 0.74 ~ 0.83). Meanwhile, they tend to accumulate their citations more quickly in the later (versus earlier) stages—they can scarcely be fast-aging papers, and their accumulated citations tend to cover a broader scope of academic disciplines. Overall, however, the pre-and post-publication peer-selected best papers have very similar citation location distributions. Both of them are most frequently cited in the Background sections, followed by the Methods sections, and finally the Results sections. In addition, the citation impact superiority of the post-publication peer-selected best papers remains consistent across different fields of research and over a long period of time, from 1997 to 2022. [ABSTRACT FROM AUTHOR]

Ilya Sutskever has played a key part in developing the conversational AI systems that are starting to change society. [ABSTRACT FROM AUTHOR]

The poster child for generative AI software is a startling human mimic. It represents a potential new era in research, but brings risks. [ABSTRACT FROM AUTHOR]

Automation of chemistry research has focused on developing robots to execute jobs. Artificial-intelligence technology has now been used not only to control robots, but also to plan their tasks on the basis of simple human prompts.AI agent uses simple human prompts to plan chemistry research. [ABSTRACT FROM AUTHOR]

Cluster analysis is one of the most fundamental methods in data mining, and it has been widely used in economics, social sciences and computer science. However, with the rapid development of Internet technology, the volume of data required for various web applications has grown rapidly, making the traditional clustering analysis methods face technical challenges. How to obtain useful information in a large amount of data quickly and efficiently is an urgent problem in many industrial fields. With the continuous development of cloud computing technology, large amounts of data can be performed quickly and efficiently. Hadoop is an open source distributed cloud computing platform with HDFS (Digital File System) and MapReduce as its core. HDFS provides massive data storage, while MapReduce uses the MapReduce programming model to achieve parallel processing. Compared with the traditional parallel programming model, it contains basic functions such as data partitioning, task scheduling, and parallel processing, making it possible for users to develop distributed applications on their own without understanding the basics of distributed basics, thus facilitating the design of parallel programs. K-means algorithm is a typical clustering analysis method, which is widely used in industry, but the number of iterations will increase significantly due to the growth of data volume, thus reducing the efficiency of computation. In order to better apply to the cluster analysis of large-scale data, this paper firstly implements a parallelization algorithm based on MapReduce on Hadoop platform using the basic idea of MapReduce and improves the K-means algorithm for the problems of blindness and easy to fall into local optimum when selecting randomly in clusters. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as the data are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we propose a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance as indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications. [ABSTRACT FROM AUTHOR]

The goal of this paper is to estimate object's 6D pose based on the texture-less dataset. The pose of each projection view is obtained by rendering the 3D model of each object, and then the orientation feature of the object is implicitly represented by the latent space obtained from the RGB image. The 3D rotation of the object is estimated by establishing the codebook based on a template matching architecture. To build the latent space from the RGB images, this paper proposes a network based on a variant Adversarial Autoencoder (Makhzani et al. in Computer Science, 2015). To train the network, we use the dataset without pose annotation, and the encoder and decoder do not have a structural symmetry. The encoder is inspired by the existing model (Yang et al. in proceedings of IJCAI, 2018), (Yang et al. in proceedings 11 of CVPR, 2019) that incorporates the function of feature extraction from two different streams. Based on this network, the latent feature vector that implicitly represents the orientation of the object is obtained from the RGB image. Experimental results show that the method in this paper can realize the 6D pose estimation of the object and the result accuracy is better than the advanced method (Sundermeyer et al. in proceedings of ECCV, 2018). [ABSTRACT FROM AUTHOR]

Portfolio optimization has been studied extensively by researchers in computer science and finance, with new and novel work frequently published. Traditional methods, such as quadratic programming, are not computationally effective for solving complex portfolio models. For example, portfolio models with constraints that introduce nonlinearity and non-convexity (such as boundary constraints and cardinality constraints) are NP-Hard. As a result, researchers often use meta-heuristic approaches to approximate optimal solutions in an efficient manner. This paper conducts a comprehensive review of over 140 papers that have applied evolutionary and swarm intelligence algorithms to the portfolio optimization problem. These papers are categorized by the type of portfolio optimization problem considered, i.e., unconstrained or constrained, and are further categorized by single-objective and multi-objective approaches. Furthermore, the various portfolio models used, as well as the constraints, objectives, and properties in which they differ, are also discussed in a detailed analysis. Based on the findings of the reviewed work, guidance for future research in portfolio optimization is given. Possible areas for future work include dynamic portfolio optimization, predictive pricing, the further investigation of multi-objective approaches. [ABSTRACT FROM AUTHOR]

The security that resides in the public-key cryptosystems relies on the presumed computational hardness of mathematical problems behind the systems themselves (e.g. the semiprime factorization problem in the RSA cryptosystem), that is because there is not known any polynomial time (classical) algorithm to solve them. The paper focuses on the computing paradigm of virus machines within the area of Unconventional Computing and Natural Computing. Virus machines, which incorporate concepts of virology and computer science, are considered as number computing devices with the environment. The paper designs a virus machine that solves a generalization of the semiprime factorization problem and verifies it formally. [ABSTRACT FROM AUTHOR]

In just a year, ChatGPT has permeated scientific research. Seven scientists reveal what they have learnt about how the chatbot should — and shouldn't — be used. [ABSTRACT FROM AUTHOR]

This article argues that existing approaches to programming ethical AI fail to resolve a serious moral-semantic trilemma, generating interpretations of ethical requirements that are either too semantically strict, too semantically flexible, or overly unpredictable. This paper then illustrates the trilemma utilizing a recently proposed 'general ethical dilemma analyzer,' GenEth. Finally, it uses empirical evidence to argue that human beings resolve the semantic trilemma using general cognitive and motivational processes involving 'mental time-travel,' whereby we simulate different possible pasts and futures. I demonstrate how mental time-travel psychology leads us to resolve the semantic trilemma through a six-step process of interpersonal negotiation and renegotiation, and then conclude by showing how comparative advantages in processing power would plausibly cause AI to use similar processes to solve the semantic trilemma more reliably than we do, leading AI to make better moral-semantic choices than humans do by our very own lights. [ABSTRACT FROM AUTHOR]

In the context of linear cryptanalysis of block ciphers, let p 0 (resp. p 1 ) be the probability that a particular linear approximation holds for the right (resp. a wrong) key choice. The standard right key randomisation hypothesis states that p 0 is a constant p ≠ 1 / 2 and the standard wrong key randomisation hypothesis states that p 1 = 1 / 2 . Using these hypotheses, the success probability P S of the attack can be expressed in terms of the data complexity N. The resulting expression for P S is a monotone increasing function of N. Building on earlier work by O'Connor (In: Preneel B (ed) Fast Software Encryption: Second International Workshop. Leuven, Belgium, 14–16 December 1994, Proceedings, volume 1008 of Lecture Notes in Computer Science, pp. 131–136. Springer, 1994) and Daemen and Rijmen (J Math Cryptol 1(3):221–242, 2007), Bogdanov and Tischhauser (In: Moriai S (ed) Fast Software Encryption—20th International Workshop, FSE 2013, Singapore, March 11–13, 2013. Revised Selected Papers, volume 8424 of Lecture Notes in Computer Science, pp. 19–38. Springer, 2013) argued that p 1 should be considered to be a random variable. They postulated the adjusted wrong key randomisation hypothesis which states that p 1 follows a normal distribution. A non-intuitive consequence is that the resulting expression for P S is no longer a monotone increasing function of N. A later work by Blondeau and Nyberg (Des Codes Cryptogr 82(1–2):319–349, 2017) argued that p 0 should also be considered to be a random variable and they postulated the adjusted right key randomisation hypothesis which states that p 0 follows a normal distribution. In this work, we revisit the key randomisation hypotheses. While the argument that p 0 and p 1 should be considered to be random variables is indeed valid, we show that if p 0 and p 1 follow any distributions with supports which are subsets of [0, 1], and E [ p 0 ] = p and E [ p 1 ] = 1 / 2 , then the expression for P S that is obtained is exactly the same as the one obtained using the standard key randomisation hypotheses. Consequently, P S is a monotone increasing function of N even when p 0 and p 1 are considered to be random variables. [ABSTRACT FROM AUTHOR]

This paper articulates an approach to incorporating instructor feedback in design-based research. Throughout the process of designing and implementing curriculum to support middle school students' debugging practices in a summer computer science workshop, our research and practice team utilized instructor-generated conjecture maps as boundary objects, providing insight into the instructors' reflections on their classroom teaching. We develop an analytic tool for categorizing instructors' reflections on their conjecture maps, attending specifically to how instructors push back on design choices, whether by envisioning new mediating processes, introducing new connections, discussing new design features, articulating confusion/uncertainty, and/or presenting hopes and predictions. The tool is then applied to seven instructors' daily reflections over the course of four weeks of instruction, focused on three conjecture maps. Overall, the paper documents a range of tensions that instructors encounter when aiming to provide sustained debugging support to students and introduces a tool for understanding the detailed ways that instructors critique design conjectures. [ABSTRACT FROM AUTHOR]

Binary search trees (BSTs) are one of the most important data structures in the field of computer science. We may easily write a parallel construction program of a BST by extending the sequential algorithm straightly. However, in such conventional approaches, the order of nodes inserted into a BST is determined dynamically, depending on the occasional state of the parallel thread execution. It results in a BST with a different structure (node position) generated on every execution of the parallel program. On the other hand, we have been developing parallel construction schemes of the BST with the same structure as a BST generated by the sequential algorithm. One is the speculatively parallel construction of a BST. And another is the purely (non-speculatively) parallel construction, but it was derived through the concept of thread-level speculation. This paper evaluates the performances of those construction schemes on several types of shared-memory multiprocessors. For the large enough size of BST, our new parallel programs can construct a BST with always the same structure on a little lower or sometimes higher performance than the program that makes a BST with a different structure on every execution. And in contrast with the general expectation that simply enlarging the size of parallel tasks increases misspeculation and damages the performance, we found that it sometimes enhances the performance of speculatively parallel execution. [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]

The review presents the history of the creation and work of the Scientific Council "Cybernetics" (SCC)—the center for development of cybernetics in the USSR and Russia. The Council's activity is covered as a structure that coordinates scientific and technical activity in cybernetics in the country via a system of sections: about 16 public and professional bodies involving thousands of specialists. Brief information about the sections is given. The chronology of development of the SCC is discussed, in particular, information about its chairmen, starting with the renowned organizer of research and development, Academician and Admiral Axel Ivanovich Berg and the following academicians: B.N. Petrov, O.M. Belotserkovsky, A.P. Ershov, E.P. Velikhov, B.V. Bunkin, and Yu.I. Zhuravlev. The SCC's publishing activity is briefly reviewed. The second function, built into the SCC from its onset, is as a research institute conducting its own research on the most pressing scientific problems insufficiently covered by existing structures. Several main areas of research by the SCC itself and its successor, the Axel Berg Institute of Cybernetics and Educational Computing of the Federal Research Center "Computer Science and Control" of the RAS: coding and transmission of information, economic cybernetics, aircraft control, supercomputers, digital technologies in education, mathematical logic and the theory of algorithms, pattern recognition and image analysis, mathematical methods of image analysis, theoretical physics, biomedical cybernetics, and ultrasound imaging. Another function of the SCC is considered: as an incubator of new academic industrially oriented structures and the fate of one of them—the Institute of Cybernetics Problems—is traced. [ABSTRACT FROM AUTHOR]

What is computational explanation? Many accounts treat it as a kind of causal explanation. I argue against two more specific versions of this view, corresponding to two popular treatments of causal explanation. The first holds that computational explanation is mechanistic, while the second holds that it is interventionist. However, both overlook an important class of computational explanations, which I call limitative explanations. Limitative explanations explain why certain problems cannot be solved computationally, either in principle or in practice. I argue that limitative explanations are not plausibly understood in either mechanistic or interventionist terms. One upshot of this argument is that there are causal and non-causal kinds of computational explanation. I close the paper by suggesting that both are grounded in the notion of computational implementation. [ABSTRACT FROM AUTHOR]

Immersive virtual reality (VR) environments create a very strong sense of presence and immersion. Nowadays, especially when student isolation and online autonomous learning is required, such sensations can provide higher satisfaction and learning rates than conventional teaching. However, up until the present, learning outcomes with VR tools have yet to prove their advantageous aspects over conventional teaching. The project presents a VR serious game for teaching concepts associated with computer hardware assembly. These concepts are often included in any undergraduate's introduction to Computer Science. The learning outcomes are evaluated using a pre-test of previous knowledge, a satisfaction/usability test, and a post-test on knowledge acquisition, structured with questions on different knowledge areas. The results of the VR serious game are compared with another two learning methodologies adapted to online learning: (1) an online conventional lecture; and (2) playing the same serious game on a desktop PC. An extensive sample of students (n = 77) was formed for this purpose. The results showed the strong potential of VR serious games to improve student well-being during spells of confinement, due to higher learning satisfaction. Besides, ease of usability and the use of in-game tutorials are directly related with game-user satisfaction and performance. The main novelty of this research is related to academic performance. Although a very limited effect was noted for learning theoretical knowledge with the VR application in comparison with the other methodologies, this effect was significantly improved through visual knowledge, understanding and making connections between different concepts. It can therefore be concluded that the proposed VR serious game has the potential to increase student learning and therefore student satisfaction, by imparting a deeper understanding of the subject matter to students. [ABSTRACT FROM AUTHOR]

Programming is an interdisciplinary practice with applications in both mathematics and computer science. Mathematics concerns rigor, abstraction, and generalization. Computer science predominantly concerns efficiency, concreteness, and physicality. This makes programming a medium for problem solving that mediates between mathematics and computer science in intriguing ways. Behind programming practices is computational thinking (CT), a mode of thinking involved in formulating and solving problems so that the solutions could be represented and carried out by computing means. In this paper, CT is seen as a boundary object connecting mathematics and computer science in a school problem-solving context. In particular, we examine and analyse middle school students' work upon engaging in mathematical problem solving-in a programming environment, taking CT as a boundary object embedded in the block-based programming environment, Scratch. The analysis is guided by observing boundary crossing features of CT in the students' artefacts produced in Scratch while solving mathematical problems related to symmetry and arithmetic sequence. The findings of this study open up new dimensions to explore CT as a boundary object in integrated STEM pedagogy. [ABSTRACT FROM AUTHOR]