Hasil untuk "Electronic computers. Computer science"

Meenakshi Sudarvizhi Seenipeyathevar, Prasath Palaniappan, Vijayakumar Arumugam et al.

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the radial Modeling technique. The primary hurdle that is being addressed is the accurate prediction of wear behavior in spatially graded magnesium matrix composites, while simultaneously avoiding extensive experimental testing. Under varying degrees of applied loads (4.4 to 39 N), sliding speeds (0.45 to 4.5 m/s), and sliding distances (500 to 4500 m), the wear performance was experimentally assessed. Results demonstrate a hardness increment of 26.26% in the outer region compared to the inner region, while resistance to wear was enhanced by 19.8% in the outer zone due to the grading of ceramic fibers. A limited experimental dataset consisting of wear measurements from the inner, middle, and outer zones of the composite was utilized in developing and validating four machine‐learning models for wear rate prediction. The tree‐based ensemble methods significantly outperformed deep‐learning strategies, with the LightGBM model providing the best prediction performance across all zones and achieving optimization with a maximum tree depth of 5, 480 leaves, and a feature fraction of 0.05. Moreover, zone‐specific XGBoost models were also developed, employing customized learning rates and minimal loss reduction parameters in order to elevate prediction accuracy. The proposed machine‐learning framework thus provides a pathway for rapid and reliable wear rate estimation for ceramic fiber‐reinforced magnesium composites, significantly lessening experimental burden. Results highlight that recycled magnesium waste, when combined with ceramic reinforcement, can be effectively employed to produce sustainable and economically viable materials with improved wear resistance, particularly for automotive and industrial applications.

Florian Soyka, Peter Nickel, Francisco Rebelo et al.

Biprateep Dey, David Zhao, Brett H Andrews et al.

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all x , and when needed, to reshape the densities of y toward ‘instance-wise’ calibration. This paper introduces the local amortized diagnostics and reshaping of conditional densities (LADaR) framework and proposes a new computationally efficient algorithm ( Cal-PIT ) that produces interpretable local diagnostics and provides a mechanism for adjusting conditional density estimates (CDEs). Cal-PIT learns a single interpretable local probability–probability map from calibration data that identifies where and how the initial model is miscalibrated across feature space, which can be used to morph CDEs such that they are well-calibrated. We illustrate the LADaR framework on synthetic examples, including probabilistic forecasting from image sequences, akin to predicting storm wind speed from satellite imagery. Our main science application involves estimating the probability density functions of galaxy distances given photometric data, where Cal-PIT achieves better instance-wise calibration than all 11 other literature methods in a benchmark data challenge, demonstrating its utility for next-generation cosmological analyzes ^9 .

Yu Wang, Shu Xu, Zenghui Ding et al.

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. The semantic correlation among entity features plays a crucial role in determining the effectiveness of link-prediction models. Notably, the human brain can often infer information using a limited set of salient features. Methods: Inspired by this cognitive principle, this paper proposes a lightweight Bi-level routing attention mechanism specifically designed for link-prediction tasks. This proposed module explores a theoretically grounded and lightweight structural design aimed at enhancing the semantic recognition capability of language models without altering their core parameters. The proposed module enhances the model’s ability to attend to feature regions with high semantic relevance. With only a marginal increase of approximately one million parameters, the mechanism effectively captures the most semantically informative features. Result: It replaces the original feature-extraction module within the KGML framework and is evaluated on the publicly available WN18RR and FB15K-237 dataset. Conclusions: Experimental results demonstrate consistent improvements in standard evaluation metrics, including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@10, thereby confirming the effectiveness of the proposed approach.

Somporn Sahachaiseree, Takashi Oguchi

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximators (FAs) have been predominantly employed in previous studies proposing RL‐based controllers, leaving a significant interpretability issue due to their black‐box nature. In this work, the use of the linear FA for a value‐based RL agent in traffic signal control problems is investigated along with the least‐squares Q‐learning method, abbreviated as LSTDQ. The interpretable linear FA was found to be adequate for the RL agent to learn an optimal policy. This leads to the proposal to replace a non‐linear ANN FA with the linear FA counterpart, resolving the interpretability issue. Moreover, the LSTDQ learning method shows superior behaviour convergence compared to a gradient descent method. In a low‐intensity arrival pattern scenario, the control by the RL agent cuts about half of the average delay resulting from the pretimed control. Owing to the conciseness of the linear FA, a direct interpretation analysis of the converged linear‐FA parameters is presented. Lastly, two online relearning tests of the agents under non‐stationary arrivals are conducted to demonstrate the online performance of LSTDQ. In conclusion, the linear‐FA specification and the LSTDQ method are together proposed to be used for its control algorithm interpretability property, superior convergence quality, and lack of hyperparameters.

Stacy A. Doore, Michelle Trim, Joycelyn Streator et al.

Teaching applied ethics in computer science has shifted from a perspective of teaching about professional codes of conduct and an emphasis on risk management towards a broader understanding of the impacts of computing on humanity and the environment and the principles and practices of responsible computing. One of the primary shifts in the approach to teaching computing ethics comes from research in the social sciences and humanities. This position is grounded in the idea that all computing artifacts, projects, tools, and products are situated within a set of ideas, attitudes, goals, and cultural norms. This means that all computing endeavors have embedded within them a set of values. To teach responsible computing always requires us to first recognize that computing happens in a context that is shaped by cultural values, including our own professional culture and values. The purpose of this paper is to highlight current scholarship, principles, and practices in the teaching of responsible computing in undergraduate computer science settings. The paper is organized around four primary sections: 1) a high-level rationale for the adoption of different pedagogical approaches based on program context and course learning goals, 2) a brief survey of responsible computing pedagogical approaches; 3) illustrative examples of how topics within the CS 2023 Social, Ethical, and Professional (SEP) knowledge area can be implemented and assessed across the broad spectrum of undergraduate computing courses; and 4) links to examples of current best practices, tools, and resources for faculty to build responsible computing teaching into their specific instructional settings and CS2023 knowledge areas.

Al Amin Biswas

Nowadays, artificial intelligence (AI) has been utilized in several domains of the healthcare sector. Despite its effectiveness in healthcare settings, its massive adoption remains limited due to the transparency issue, which is considered a significant obstacle. To achieve the trust of end users, it is necessary to explain the AI models' output. Therefore, explainable AI (XAI) has become apparent as a potential solution by providing transparent explanations of the AI models' output. In this review paper, the primary aim is to review articles that are mainly related to machine learning (ML) or deep learning (DL) based human disease diagnoses, and the model's decision-making process is explained by XAI techniques. To do that, two journal databases (Scopus and the IEEE Xplore Digital Library) were thoroughly searched using a few predetermined relevant keywords. The PRISMA guidelines have been followed to determine the papers for the final analysis, where studies that did not meet the requirements were eliminated. Finally, 90 Q1 journal articles are selected for in-depth analysis, covering several XAI techniques. Then, the summarization of the several findings has been presented, and appropriate responses to the proposed research questions have been outlined. In addition, several challenges related to XAI in the case of human disease diagnosis and future research directions in this sector are presented.

K. Subrahmanyam, P. Greenfield, R. Kraut et al.

In recent years, electronic games, home computers, and the Internet have assumed an important place in our lives. This paper presents a review of the research on the impact of home computer use on the development of children and adolescents. Time use data are presented along with a discussion of factors such as age, gender, and ethnicity, which impact the time spent on computers as well as the activities engaged in. Research on the impact of computer use on cognitive skill and academic development, social development and relationships, and perceptions of reality and violent behavior is reviewed. The special role of the Internet in the lives of adolescents is brought out using data from the HomeNet study. The paper concludes with recommendations for future study in order to better understand the growing impact of computers on our youth. D 2001 Elsevier Science Inc. All rights reserved.

Patrick Bassner, Eduard Frankford, Stephan Krusche

Integrating AI-driven tools in higher education is an emerging area with transformative potential. This paper introduces Iris, a chat-based virtual tutor integrated into the interactive learning platform Artemis that offers personalized, context-aware assistance in large-scale educational settings. Iris supports computer science students by guiding them through programming exercises and is designed to act as a tutor in a didactically meaningful way. Its calibrated assistance avoids revealing complete solutions, offering subtle hints or counter-questions to foster independent problem-solving skills. For each question, it issues multiple prompts in a Chain-of-Thought to GPT-3.5-Turbo. The prompts include a tutor role description and examples of meaningful answers through few-shot learning. Iris employs contextual awareness by accessing the problem statement, student code, and automated feedback to provide tailored advice. An empirical evaluation shows that students perceive Iris as effective because it understands their questions, provides relevant support, and contributes to the learning process. While students consider Iris a valuable tool for programming exercises and homework, they also feel confident solving programming tasks in computer-based exams without Iris. The findings underscore students' appreciation for Iris' immediate and personalized support, though students predominantly view it as a complement to, rather than a replacement for, human tutors. Nevertheless, Iris creates a space for students to ask questions without being judged by others.

Hamidreza Bolhasani, Maryam Mohseni, A. Rahmani

Abstract In machine learning, deep learning is the most popular topic having a wide range of applications such as computer vision, natural language processing, speech recognition, visual object detection, disease prediction, drug discovery, bioinformatics, biomedicine, etc. Of these applications, health care and medical science-related applications are dramatically on the rise. The tremendous big data growth, the Internet of Things (IoT), connected devices, and high-performance computers utilizing GPUs and TPUs are the main reasons why deep learning is so popular. Based on their specific tasks, medical IoT, digital images, electronic health record (EHR) data, genomic data, and central medical databases are the primary data sources for deep learning systems. Several potential issues such as privacy, QoS optimization, and deployment indicate the pivotal part of deep learning. In this paper, deep learning for IoT applications in health care systems is reviewed based on the Systematic Literature Review (SLR). This paper investigates the related researches, selected from among 44 published research papers, conducted within a period of ten years – 2010 to 2020. Firstly, theoretical concepts and ideas of deep learning and technical taxonomy are proposed. Afterwards, major deep learning applications for IoT in health care and medical sciences are presented through analyzing the related works. Later, the main idea, advantages, disadvantages, and limitations of each study are discussed, preceding suggestions for further research.

Rebecca R. Ruckdashel, Ninad Khadse, J. H. Park

Smart textiles have gained great interest from academia and industries alike, spanning interdisciplinary efforts from materials science, electrical engineering, art, design, and computer science. While recent innovation has been promising, unmet needs between the commercial and academic sectors are pronounced in this field, especially for electronic-based textiles, or e-textiles. In this review, we aim to address the gap by (i) holistically investigating e-textiles’ constituents and their evolution, (ii) identifying the needs and roles of each discipline and sector, and (iii) addressing the gaps between them. The components of e-textiles—base fabrics, interconnects, sensors, actuators, computers, and power storage/generation—can be made at multiscale levels of textile, e.g., fiber, yarn, fabric, coatings, and embellishments. The applications, current state, and sustainable future directions for e-textile fields are discussed, which encompasses health monitoring, soft robotics, education, and fashion applications.

GREX-PLUS Science Team, :, Akio K. Inoue et al.

GREX-PLUS (Galaxy Reionization EXplorer and PLanetary Universe Spectrometer) is a mission candidate for a JAXA's strategic L-class mission to be launched in the 2030s. Its primary sciences are two-fold: galaxy formation and evolution and planetary system formation and evolution. The GREX-PLUS spacecraft will carry a 1.2 m primary mirror aperture telescope cooled down to 50 K. The two science instruments will be onboard: a wide-field camera in the 2-8 $μ$m wavelength band and a high resolution spectrometer with a wavelength resolution of 30,000 in the 10-18 $μ$m band. The GREX-PLUS wide-field camera aims to detect the first generation of galaxies at redshift $z>15$. The GREX-PLUS high resolution spectrometer aims to identify the location of the water ``snow line'' in proto-planetary disks. Both instruments will provide unique data sets for a broad range of scientific topics including galaxy mass assembly, origin of supermassive blackholes, infrared background radiation, molecular spectroscopy in the interstellar medium, transit spectroscopy for exoplanet atmosphere, planetary atmosphere in the Solar system, and so on.

Angelo Salatino, Simone Angioni, Francesco Osborne et al.

Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors' expertise. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics we defined, reflecting the diversity of expertise, are significantly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.

Manuela Petrescu, Kuderna Bentasup

The scope of this paper was to find out how the students in Computer Science perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we designed and implemented an experiment in which the same groups of students (86 students) were exposed to different teaching styles (presented by the same teacher at a difference of two weeks between lectures). We tried to minimize external factors' impact by carefully selecting the dates (close ones), having the courses in the same classroom and on the same day of the week, at the same hour, and checking the number and the complexity of the introduced items to be comparable. We asked for students' feedback and we define a set of countable body signs for their involvement in the course. The results were comparable by both metrics (body language) and text analysis results, students prefer a more interactive course, with a relaxing atmosphere, and are keener to learn in these conditions.

SONG Wei, ZHAO Huifen, CAI Wenqin, ZHOU Wanqiang

The Virtual Traffic Light (VTL) in a Vehicle-to-Vehicle (V2V) environment can negotiate the right-of-way allocation through the information directly exchanged between vehicles.When the equipment obtains relevant information, the vehicle can strategically provide information to obtain the priority right of way.To apply to a scene where unmeasurable factors affect the right of way, a virtual traffic light with group strategy protection characteristics is proposed.By abstracting the real information provided by vehicles into a cost allocation and cooperative game, a group strategy protection auction mechanism is designed, and the Shapley value is used to calculate the cost allocation of each vehicle as the payment of vehicles.On this basis, the green light signal is established according to the real evaluation value in the auction results, and the green light signal generated by multiple auctions is integrated through the signal merging algorithm to produce a reasonable right-of-way allocation.The experimental results show that the virtual traffic light has the characteristics of group strategy protection, which can prevent vehicles from forming an alliance of false information to obtain benefits and can also prevent vehicles from obtaining the right-of-way priority through false information.Compared with the virtual traffic light with a fixed threshold of the number of green lights, the virtual traffic lights protected by the group strategy show some improvement in the overall average driving time and the average driving time of high-value vehicles.

LIU Peng, LIU Bo, ZHOU Na-qin, PENG Xin-yi, LIN Wei-wei

In the context of data explosion,traditional cloud computing is faced with the dilemma of insufficient local cloud resources and high expansion cost.However,the newly emerging hybrid cloud combining resource-rich public cloud and data-sensitive private cloud has become a research hotspot and application direction at present.As an attractive paradigm,workflow has been increasing in data scale and computing scale.Therefore,workflow scheduling is a key issue in the direction of hybrid cloud research.For this reason,this paper first makes an in-depth investigation and analysis of workflow scheduling technology in hybrid cloud environment,and then classifies and compares workflow scheduling in hybrid cloud environment:for deadline,for cost,for energy-efficient and for multi-objective constraints.On this basis,the future research directions of workflow scheduling in hybrid cloud environment are analyzed and summarized:workflow scheduling based on Serverless platform,workflow scheduling based on edge server network collaboration,cloud native workflow scheduling based on Argo integration,and workflow scheduling based on fog computing fusion.

Emma Riese, Madeleine Lorås, Martin Ukrop et al.

Teaching assistants (TAs) are heavily used in computer science courses as a way to handle high enrollment and still being able to offer students individual tutoring and detailed assessments. TAs are themselves students who take on this additional role in parallel with their own studies at the same institution. Previous research has shown that being a TA can be challenging but has mainly been conducted on TAs from a single institution or within a single course. This paper offers a multi-institutional, multi-national perspective of challenges that TAs in computer science face. This has been done by conducting a thematic analysis of 180 reflective essays written by TAs from three institutions across Europe. The thematic analysis resulted in five main challenges: becoming a professional TA, student focused challenges, assessment, defining and using best practice, and threats to best practice. In addition, these challenges were all identified within the essays from all three institutions, indicating that the identified challenges are not particularly context-dependent. Based on these findings, we also outline implications for educators involved in TA training and coordinators of computer science courses with TAs.

Clarisa V. Albarillo, Emely A. Munar, Maria Concepcion M. Balcita

The main objective of the study is to provide ICT awareness, literacy and skills development to the barangay officials of Agoo, La Union. Specifically, it aimed the following objectives: 1) to determine the profile of the respondents in terms of personal information, educational background and availability of computer unit and background in using computer; 2) to determine the effectiveness of the CILC in terms of services delivered, timeliness of the service, and improvement on the computer and internet knowledge of the trainees; and 3) to determine the level of relevance of the training sessions of the CILC. The study used a descriptive design. Data were gathered by using survey questionnaire and were analyzed by using statistical treatments such as frequency count, percentage and mean. As to the profile of the trainees, the study found that most of the trainees are female (88%); 84% are married, and 56% of them are at the age bracket of 30-39 years old. In terms of educational background, many are high school graduate (n= 17; 68%). In addition, most of them (84%) have background in computer. The result also shows that the CILC is at the high level of effectiveness (4.67) in terms of services delivered and is much relevant (4.45) in terms of its relevance.

Sreekrishnan Venkateswaran

Computer Science education has been evolving over the years to reflect applied realities. Until about a decade ago, theory of computation, algorithm design and system software dominated the curricula. Most courses were considered core and were hence mandatory; the programme structure did not allow much of a choice or variety. This column analyses why this changed Circa 2010 when elective subjects across scores of topics become part of mainstream education to reflect the on-going lateral acceleration of Computer Science. Fundamental discoveries in artificial intelligence, machine learning, virtualization and cloud computing are several decades old. Many core theories in data science are centuries old. Yet their leverage exploded only after Circa 2010, when the stage got set for people-centric problem solving in massive scale. This was due in part to the rush of innovative real-world applications that reached the common man through the ubiquitous smart phone. AI/ML modules arrived in popular programming languages; they could be used to build and train models on powerful - yet affordable - compute on public clouds reachable through high-speed Internet connectivity. Academia responded by adapting Computer Science curricula to align it with the changing technology landscape. The goal of this experiential piece is to trigger a lively discussion on the past and future of Computer Science education.

G. R. D. Prabhu, P. L. Urban

With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.