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The author discusses computational methods utilized in the peer review process. It examines the availability of data in peer review, and experimentation in peer review design. It also discusses author identity bias, overall subjectivity in peer review, and author incentives.

The author discusses collusion rings in computer science research, and his experiences with and knowledge of attempts to undermine peer-review of computer science conference publications. According to the author, collusion rings go beyond the computer architecture field. The author describes the peer-review process, how a collusion ring works, and outlines how the computer research field should respond.

The article discusses the lack of efficiency in how research data within the Computer Science discipline are shared. The author uses the acronym FAIR -- findable, accessible, interoperable, and reusable -- to capture how data should be made available at conferences and in journals.

Byline: Just Earth News ACM, the Association for Computing Machinery, has named Avi Wigderson as recipient of the 2023 ACM A.M. Turing Award for foundational contributions to the theory of [...]

The article presents tips on academic publishing for computer scientists. Specific guides to publication for the NeurIPS and VLDB conference series are offered and "maximalist" and "perfectionist" strategies for publication are compared with respect to top-tier journals and the most prestigious conferences.

The Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of popular lossless compression programs (such as bzip2) as well as recent powerful compressed indexes (such as the r-index7), central in modern bioinformatics. The compressibility of BWT is quantified by the number r of equal-letter runs in the output. Despite the practical significance of BWT, no nontrivial upper bound on r is known. By contrast, the sizes of nearly all other known compression methods have been shown to be either always within a polylog n factor (where n is the length of the text) from z, the size of Lempel--Ziv (LZ77) parsing of the text, or much larger in the worst case (by an nε factor for ε > 0). In this paper, we show that r = O (z log² n) holds for every text. This result has numerous implications for text indexing and data compression; in particular: (1) it proves that many results related to BWT automatically apply to methods based on LZ77, for example, it is possible to obtain functionality of the suffix tree in O (z polylog n) space; (2) it shows that many text processing tasks can be solved in the optimal time assuming the text is compressible using LZ77 by a sufficiently large polylog n factor; and (3) it implies the first nontrivial relation between the number of runs in the BWT of the text and of its reverse. In addition, we provide an O (z polylog n)-time algorithm converting the LZ77 parsing into the run-length compressed BWT. To achieve this, we develop several new data structures and techniques of independent interest. In particular, we define compressed string synchronizing sets (generalizing the recently introduced powerful technique of string synchronizing sets11) and show how to efficiently construct them. Next, we propose a new variant of wavelet trees for sequences of long strings, establish a nontrivial bound on their size, and describe efficient construction algorithms. Finally, we develop new indexes that can be constructed directly from the LZ77 parsing and efficiently support pattern matching queries on text substrings. [ABSTRACT FROM AUTHOR]

2023 JUN 24 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Investigators publish new report on cancer. According to news reporting from the [...]

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points S and a radius parameter r > 0, the r-near neighbor (r-NN) problem asks for a data structure that, given any query point q, returns a point p within distance at most r from q. In this paper, we study the r-NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance r from the query should have the same probability to be returned. The problem is of special interest in high dimensions, where Locality Sensitive Hashing (LSH), the theoretically leading approach to similarity search, does not provide any fairness guarantee. In this work, we show that LSH-based algorithms can be made fair, without a significant loss in efficiency. We propose several efficient data structures for the exact and approximate variants of the fair NN problem. Our approach works more generally for sampling uniformly from a subcollection of sets of a given collection and can be used in a few other applications. We also carried out an experimental evaluation that highlights the inherent unfairness of existing NN data structures. [ABSTRACT FROM AUTHOR]

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The article discusses the relationship between artificial intelligence and computer science and the history and development of each discipline between 1950 and 1970. Information is presented on the research behind artificial intelligence and the beginning of graduate education programs in the United States in both artificial intelligence and computer science.

The author writes about the importance of effectively communicating the broad and impactful aspects of computer science, moving beyond technical jargon to engage both educated non-experts and the general public. This involves creating concise "elevator speeches" and utilizing opportunities to bridge the gap in understanding terms like "artificial intelligence" (AI) and "machine learning." He feels that public engagement, such as interacting with journalists, giving interviews, and writing opinion articles, is crucial to demonstrate how computer science can address complex societal issues as well as secure support for the field, and that active participation in public engagement enhances the field's visibility, attracts diverse students, and safeguards its future, particularly in addressing concerns about AI ethics and workforce effects.

The article focuses on how revisiting key algorithms from computing history can unlock the hidden efficiency in computer science problems. The author discusses the shortest-path problem that has been long-standing, explores the use of the Bellman-Ford algorithm, and examines the work of Aaron Bernstein, Danupon Nanongkai, and Christian Wulff-Nilsen.

Keywords: Computer Science; Computers; Health and Medicine; Manual Communication; Rehabilitation; Rehabilitation of Hearing Impaired; Sign Language; Therapy EN Computer Science Computers Health and Medicine Manual Communication Rehabilitation Rehabilitation of Hearing Impaired Sign Language Therapy 2634 2634 1 09/11/23 20230915 NES 230915 2023 SEP 15 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Investigators discuss new findings in computer science. This paper contributes to the literature by introducing a comprehensive survey of 17 Arabic sign language datasets and by developing a well-organized framework that is used to build a sign language dataset. [Extracted from the article]

On this basis,it gives the back-ground and principle of backdoor attack,distinguishes the similar attack paradigms such as adversarial attack and data poisoning attack,then continues to elaborate on the attack principle and outstanding features of the classic methods of backdoor attack to date. According to the news editors, the research concluded: "Then,this paper surveys the state-of-the-art works of backdoor attack against various typical applications and popular deep learning paradigms,which further reveal the threat of backdoor attack towards deep learning models. [Extracted from the article]

The article examines alleged trends in European computer science research. The relative rates of publication and citation of computer science research articles across Europe, North America, and Asia are compared along with relative rates of collaborative and independent research and alleged asymmetry of benefits derived from collaborative research is described.

2022 OCT 29 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- A new study on scientific programming is now available. According to news [...]

An introduction is presented that discusses an article published about the topic of multi-prover interactive proofs (MIP) as related to quantum interactive proofs and their relevance to computer science.

NTT Research, a division of NTT, has named Dr. Sanjam Garg a Senior Scientist in its Cryptography & Information Security (CIS) Lab. According to a media release, Dr. Garg is [...]

In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. Keywords: Artificial Intelligence; Cyborgs; Emerging Technologies; Machine Learning; Networks; Neural Networks EN Artificial Intelligence Cyborgs Emerging Technologies Machine Learning Networks Neural Networks 6779 6779 1 06/05/23 20230609 NES 230609 2023 JUN 9 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Current study results on artificial intelligence have been published. [Extracted from the article]

A systematic review conducted by researchers from the School of Computer Science and Engineering at Vellore Institute of Technology in Chennai, India, explores the use of machine learning and deep learning prediction models for sepsis and neonatal sepsis. Sepsis and neonatal sepsis are significant challenges in global healthcare, as they can lead to life-threatening organ dysfunction. Early identification of sepsis is difficult, but the implementation of machine learning and deep learning technologies shows promise in predicting sepsis at an early stage and reducing mortality rates. The review analyzes various studies and concludes that prediction models can support researchers and clinicians in making better decisions and administering antibiotics earlier. [Extracted from the article]

2024 MAY 4 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- New study results on heart attack have been published. According to news [...]

A recent report from the University of South Africa examines the research on article processing charges (APCs) in the field of biotechnology. The study analyzes various aspects of APC research, including document types, source titles, affiliations, and open access types. The research found that there has been significant attention given to APCs, with researchers from various subject categories contributing to the field. Interestingly, researchers from outside the field, such as biomedicine and computer science, have also made significant contributions, reflecting interdisciplinary engagement. [Extracted from the article]

2024 MAR 14 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Researchers detail new data in breast cancer. According to news reporting from the Department [...]

2022 AUG 6 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Investigators publish new report on security and communication networks. According to news [...]

Purpose: For decades, troubled information systems (IS)/information technology (IT) projects have continued to lose billions of dollars all over the world. The purpose of this paper is to look for the root causes of this widespread and important phenomenon. Design/methodology/approach: This paper begins with a concise survey of empirical research and shows that a significant proportion of project failures are derived from the behavior of managers rather than from objective or technical obstacles. Although researchers have made significant efforts to improve the professional conduct of managers, the situation ameliorated only slowly. This raises the issue of a possible hidden cause lying behind the perceived determinants of failure; the authors, therefore, look for the root-cause of this broad noteworthy phenomenon and apply the techniques typical of "root-cause analysis". Findings: IS/IT managers handling troubled projects worldwide have nothing in common except for a similar cultural basis. As students, they learned the fundamental concepts of informatics, and it is, therefore, natural to hypothesize a specific cause and effect relationship between these lessons and the ineffective behaviors of managers. A careful analysis shows that computing theories are narrow, self-referential and abstract, and this negatively affects the conduct of managers, who have broad, practical responsibilities. Practical implications: The cultural causes of runaway projects require advanced theoretical research on the foundations of computer science to create a comprehensive and consistent construct that can support the manifold duties of IS/IT project leaders. A future theoretical structure will underpin innovative courses in universities and professional organizations, making leaders more aware of their roles and more capable of managing them. Originality/value: In general, when a problem is traced to its origin, it can be eradicated. This work offers evidence that the root causes of troubled projects lie in the management culture, and therefore, the professional preparation of managers needs to be improved so to remove the present difficulties in IS/IT projects. [ABSTRACT FROM AUTHOR]

An interview with Association for Computing Machinery (ACM) fellow Dana Stewart Scott who discusses various topics including his career involving the fields of computing science, automata theory, and the theory of programming languages. Scott's receipt of the ACM's A.M. Turing Award with colleague Michael Rabin in 1976 is mentioned, along with Scott's education, his involvement with mathematical logic, and his work at the University of Chicago and the University of California, Berkeley.

Keywords: Computer Science; Computers; Health and Medicine; Networks; Neural Networks; Poisoning EN Computer Science Computers Health and Medicine Networks Neural Networks Poisoning 4253 4253 1 05/22/23 20230526 NES 230526 2023 MAY 26 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Data detailed on computer science have been presented. This paper summarizes the existing backdoor defense technologies from the perspective of attack and defense confrontation, which include poisoned sample identifying, poisoned model identifying, poisoned test sample filtering, and others. [Extracted from the article]

An introduction to an article published in the journal about the computing topic of switched linear systems, particularly a method for computing their topological entropy.

Byline: Akanksha Sondhi Gaur This algorithm seamlessly integrates with a single camera and onboard vehicle systems, providing real-time alerts for approaching vehicles. ICA Group Engineers at the University of Malaga's [...]

A recent study conducted at the School of Computer Science and Engineering in Shaanxi, China, focused on the development of a medical dialogue generation system. The researchers aimed to improve the efficiency of clinical consultations and reduce the burden on doctors by creating a model that can automatically generate responses. The study utilized external knowledge and large-scale pre-training models to enhance the accuracy of the dialogue generation. Experimental data showed that the proposed model outperformed traditional methods in terms of response effectiveness and accuracy. For more information, the full research article can be accessed through the provided link. [Extracted from the article]

A recent study conducted by researchers at Beijing Jiaotong University in China explores the impact of teleworkers' psychology on their job performance and how they relieve negative emotions through enterprise social media (ESM). The study surveyed 835 teleworkers from various industries in China and found that family-to-work conflict and social isolation contribute to emotional exhaustion, which in turn negatively affects job performance. However, the study also found that the use of ESM can reduce family-to-work conflict and social isolation, thereby alleviating emotional exhaustion. The research provides practical suggestions for managers and teleworkers to improve job performance while working from home. [Extracted from the article]

The article provides a perspective on the use of algorithms in maintaining maximum flow through computing networks. Offers a critique of various max-flow theorems and algorithms. Discusses prior research into max-flow applications. Mentions the hope for future breakthroughs in developing scalable max-flow algorithms.

According to their explanation, the majority of current Lifelong Learning (LL) research revolves around a single AI agent learning tasks sequentially, a method that inherently presents slow progress. In their paper, Itti and Ge introduce their creation, SKILL (Shared Knowledge Lifelong Learning), a powerful tool designed to enable AI systems to learn and master 102 diverse tasks. [Extracted from the article]

Keywords: Data Modeling; Epidemic; Epidemiology; Information Technology; Risk and Prevention EN Data Modeling Epidemic Epidemiology Information Technology Risk and Prevention 1337 1337 1 07/03/23 20230707 NES 230707 2023 JUL 9 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- A new study on data modeling is now available. According to the news editors, the research concluded: "Conclusions show that existing vocabularies and ontologies provide significant coverage of the concepts underlying epidemic use cases, but there remain gaps in the coverage. This paper surveys vocabularies and ontologies relevant to the task of achieving epidemic-related data interoperability. [Extracted from the article]

The authors present their thoughts about the significance of computer ethics and the construction and support of ethical computing cultures in an era of rapid technological innovations. The article revisits the "Menlo Report" from 2012, calls for codes of data science ethics, and research governance.

An introduction to an article published in the journal about the topic of graph isomorphism in computer science is presented.

2023 OCT 14 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Investigators discuss new findings in lung cancer. According to news reporting out [...]

The article discusses an algorithm designed to compute maximum flow and minimum-cost flows. It builds on the works of prior researchers to propose new theorems and provides formulas and graphs to illustrate these theorems and schemes.

The article discusses the community that developed during the decade of the 1970's that fostered the growth of research into artificial intelligence. It details the slow progress of AI researchers in developing the field. It mentions the importance of military backing in aid of AI research, including funding acquired through Advanced Research Projects Agency (ARPA) to places like MIT and Stanford University. It outlines the continued struggle for broader funding. It describes the growth of AI organizations, increased conference attendance, class enrollment in universities, and the growing number of published articles throughout the 1970's as evidence of the growing support for this field in the academic and computer science communities.

The article examines the rise of ubiquitous computing. Describes the proliferation of smart phones, mobile computing devices and immersive technologies that produce "anywhere and everywhere data" and how such devices are becoming increasing entwined. Describes the local, temporal and contextual nature of generated data. Applies the post-cognitive framework from social science to information science in order to describe how information is transformed through interactions between media sources. Discusses how the various devices should complement each other as ubiquitous analytics works to leverage the shared data.

An introduction to an article published in the journal is presented about the topic of pattern matching, particularly the Burrows-Wheeler Transform (BWT).

WORDS: CHRIS DOWS BENEVOLENT BOON OR CATASTROPHIC COMPUTING? In his groundbreaking 1950 paper ‘Computing Machinery and Intelligence’, British mathematician Alan Turing asked a simple question – can machines think? Widely [...]

2023 JUL 22 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- A new study on acute lymphoblastic leukemia is now available. According to [...]

BY JON “MADDOG” HALL Considering FOSS Databases Recently a friend started taking a postgraduate course in software engineering, and part of that course was the topic of databases. My friend [...]

This article investigates the current state of informatics higher education in Europe by utilizing the Informatics Europe Higher Education (IEHE) data portal. First, the article provides a detailed look at the IEHE data portal. Then the article provides a case study comparing informatics higher education at applied sciences universities compared to traditional research universities in five selected European countries using data providing in the portal.

Keywords Reinforcement learning; Android malware detection; Feature selection; RNN; Sequence processing Highlights * This paper applied reinforcement learning algorithms to the feature selection phase of Android malware detection, reducing the burden of feature selection tasks. * This paper adopts Natural Language Processing methods to tackle the feature selection methods. * This paper presented a modifiable framework that can be easily ported to other feature selection tasks for malware detection. Abstract Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. Machine learning, leading to a great evolution in Android malware detection in recent years, is typically applied in the classification phase. Since the correlation between features is ignored in some traditional ranking-based feature selection algorithms, applying wrapper-based feature selection models is a topic worth investigating. Though considering the correlation between features, wrapper-based approaches are time-consuming for exploring all possible valid feature subsets when processing a large number of Android features. To reduce the computational expense of wrapper-based feature selection, a framework named DroidRL is proposed. The framework deploys DDQN algorithm to obtain a subset of features which can be used for effective malware classification. To select a valid subset of features over a larger range, the exploration-exploitation policy is applied in the model training phase. The recurrent neural network (RNN) is used as the decision network of DDQN to give the framework the ability to sequentially select features. Word embedding is applied for feature representation to enhance the framework's ability to find the semantic relevance of features. The framework's feature selection exhibits high performance without any human intervention and can be ported to other feature selection tasks with minor changes. The experiment results show a significant effect when using the Random Forest as DroidRL's classifier, which reaches 95.6% accuracy with only 24 features selected. Author Affiliation: (a) College of Software Engineering, Sichuan University, Chengdu, China (b) School of Cyber Science and Engineering, Sichuan University, Chengdu, China (c) College of Computer Science, Sichuan University, Chengdu, China (d) School of Business, Sichuan University, Chengdu, China * Corresponding author. Article History: Received 21 September 2022; Revised 21 December 2022; Accepted 26 January 2023 Byline: Yinwei Wu (a), Meijin Li (a), Qi Zeng (c), Tao Yang (c), Junfeng Wang (c), Zhiyang Fang [fangzhiyang@scu.edu.cn] (*,b), Luyu Cheng (d)

Keywords Cyberspace security; Intrusion detection; Adversarial training; Machine learning; Generative adversarial networks Abstract Network Intrusion Detection Systems (IDS) have achieved high accuracy by widely applying Machine Learning (ML) models. However, most current ML-based IDSs can not cope with targeted attacks from adversaries because they are commonly trained and tested using fixed data sets. In this paper, we propose an Adversarial Intrusion Detection Training Framework (AIDTF) to improve the robustness of IDSs, which consists of an attacker model a-model, a defender model d-model, and a black-box trainer t-module. Both the a-model and d-model are multilayer perceptrons, and the t-module is the module used to train IDSs. AIDTF improves the accuracy of IDS by using an adversarial training method, which is different from traditional training methods. Taking the distribution of normal samples in the dataset as the distribution that the a-model and d-model need to learn, the goal of the a-model is to generate samples that deceive the d-model, while the goal of the d-model is to determine whether the input samples are real samples, so there is an adversarial relationship between a-model and d-model. Different types of IDSs can be trained by the t-module using the samples generated from the confrontation between the a-model and the d-model, and we call this kind of IDS the Adversarial Training Intrusion Detection System (ATIDS). The main contribution of this paper is to propose a training method that is used to obtain an IDS with high accuracy not only for known test sets but also to identify unknown disguised attack samples. We tested different types of ATIDSs using the current mainstream attack methods, which include the Fast Gradient Method, Fast Gradient Sign Method, Projected Gradient Descent, and Jacobs Saliency Map Algorithm. The experimental results prove that AIDTF outperforms other adversarial training methods with not only higher accuracy for the test set but also up to a 99% recognition rate for the attack samples. Author Affiliation: (a) School of Computer Science and Technology, Dongguan University Of Technology, GuangDong, China (b) School of Cyberspace Security, Dongguan University Of Technology, GuangDong, China * Corresponding author. Article History: Received 11 October 2022; Revised 2 February 2023; Accepted 13 February 2023 Byline: Wen Ding Xiong [xiongwending@hotmail.com] (a), Kai Lun Luo [luokl@dgut.edu.cn] (b), Rui Li [ruili@dgut.edu.cn] (*,b)

The article explores Propositional Satisfiability (SAT), described by the authors as a cornerstone of computational complexity theory. Particular focus is given to how it has become a central target problem for solving hard computational problems in practice. Other topics discussed include how initial progress was made in SAT solving in the early 1990s and details on other eras of practical SAT solving.

In this paper, ASD is diagnosed using brain magnetic resonance imaging (MRI) enabled deep learning with a hybrid optimization algorithm." Keywords: Andhra Pradesh; India; Asia; Autism; Autism Spectrum Disorders; Developmental Disabilities; Developmental Diseases and Conditions; Health and Medicine; Mental Health; Neurology EN Andhra Pradesh India Asia Autism Autism Spectrum Disorders Developmental Disabilities Developmental Diseases and Conditions Health and Medicine Mental Health Neurology 260 260 1 07/17/23 20230721 NES 230721 2023 JUL 17 (NewsRx) -- By a News Reporter-Staff News Editor at Mental Health Weekly Digest -- Investigators publish new report on Developmental Diseases and Conditions - Autism Spectrum Disorders. [Extracted from the article]

Our news editors obtained a quote from the research from University Putra Malaysia: "This paper introduces the BukaGini algorithm, an innovative and robust approach for feature interaction analysis that capitalizes on the Gini impurity index. Keywords: Algorithms; Cybersecurity; Engineering; Selection Algorithm EN Algorithms Cybersecurity Engineering Selection Algorithm 1006 1006 1 07/03/23 20230704 NES 230704 2023 JUL 4 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- A new study on engineering is now available. [Extracted from the article]