Hasil untuk "Electronic computers. Computer science"

M. Ackerman

Wenbin Ye, YanZhou Duan, Jun Yuan et al.

Abstract In the field of engineering, the utilization of surrogate models to replace computationally intensive simulation software has become a widely adopted approach. However, when addressing complex engineering problems, the costs of simulations can escalate significantly, making it challenging for simulation data to fulfill the training requirements of surrogate models. Recognizing that designers accumulate valuable design knowledge throughout the design process, this knowledge inherently governs the mapping rules between design parameters and performance metrics. This paper introduces a novel method for constructing surrogate models by integrating limited simulation data with engineering knowledge through Bayesian neural networks (B-DaKnow). In B-DaKnow, neural networks employ variational inference and automatic differentiation to amalgamate simulation data and engineering knowledge while optimizing weights and biases via evolutionary algorithms. The proposed methodology is validated using ten benchmark functions and three engineering cases. The experimental results demonstrate that: (1) the incorporation of diverse engineering knowledge enhances prediction accuracy in B-DaKnow to varying degrees; (2) in tackling complex engineering challenges, B-DaKnow exhibits superior performance compared to alternative algorithms; (3) B-DaKnow demonstrates commendable robustness, as evidenced by only slight fluctuations in prediction results across different problems.

Meisya Syahtira, Nurdin Nurdin, Fajriana Fajriana

Energi listrik merupakan kebutuhan vital bagi masyarakat dan menjadi penunjang utama dalam berbagai sektor, termasuk rumah tangga, bisnis, hingga industri. Seiring meningkatnya permintaan listrik setiap tahunnya, PT. PLN (Persero) ULP Lhokseumawe dituntut mampu melakukan perencanaan distribusi dan kapasitas daya yang akurat. Prediksi yang kurang tepat dapat menimbulkan ketidakseimbangan antara pasokan dan kebutuhan energi. Penelitian ini membandingkan dua metode peramalan, yaitu Single Exponential Smoothing (SES) dan Double Exponential Smoothing (DES), untuk memprediksi konsumsi energi listrik di wilayah Lhokseumawe. Data yang digunakan berupa konsumsi listrik bulanan per kecamatan periode 2022–2024, dengan proyeksi prediksi hingga tahun 2027. Tahapan penelitian meliputi pengumpulan data, pra-processing, penerapan metode SES dan DES, evaluasi akurasi menggunakan MAPE, serta perancangan sistem berbasis web menggunakan Python dan Flask. Hasil penelitian menunjukkan bahwa metode SES memiliki tingkat akurasi lebih tinggi dengan nilai MAPE sebesar 5,85%, sedangkan metode DES memperoleh nilai MAPE sebesar 7,87%. Hal ini menegaskan bahwa SES lebih sesuai digunakan untuk data dengan pola fluktuatif acak. Sebaliknya, DES lebih cocok diterapkan pada data dengan pola tren. Melalui perbandingan nilai MAPE, diperoleh gambaran metode mana yang lebih optimal digunakan dalam konteks prediksi konsumsi listrik di Lhokseumawe. Penelitian ini diharapkan dapat memberikan kontribusi praktis bagi PT. PLN (Persero) ULP Lhokseumawe dalam menyusun strategi distribusi energi listrik yang lebih efektif dan efisien.

Elena Kakoulli

Edge artificial intelligence (AI) workloads generate bursty, heterogeneous traffic on Networks-on-Chip (NoCs) under tight energy and latency constraints. Hybrid NoCs that overlay electronic meshes with silicon photonic express links can reduce long-path latency via wavelength-division multiplexing, but thermal drift and intermittent optical availability complicate routing. This study introduces a decentralized, photonic-aware controller based on Deep Reinforcement Learning (DRL) with Proximal Policy Optimization (PPO). The policy uses router-local observables—per-port buffer occupancy with short histories, hop distance, a local injection estimate, and a per-cycle optical validity signal—and applies action masking so chosen outputs are always feasible; the controller is co-designed with the router pipeline to retain single-cycle decisions and a modest memory footprint. Cycle-accurate simulations with synthetic traffic and benchmark-derived traces evaluate mean packet latency, throughput, and energy per delivered bit against deterministic, adaptive, and recent DRL baselines; ablation studies isolate the roles of optical validity cues and locality. The results show consistent improvements in congestion-forming regimes and on long electronic paths bridged by photonic links, with robustness across mesh sizes and wavelength concurrency. Overall, the evidence indicates that photonic-aware PPO provides a practical, thermally robust control plane for hybrid NoCs and a scalable routing solution for AI-centric manycore and edge systems.

Payel Dhar, B. Jose Ravindra Raj, Amayappanallur Kannan Dasarathy et al.

ABSTRACT The rapid growth of agri‐food industries has led to an alarming increase in waste generation, posing environmental, economic, and sustainability challenges. This review explores recent advancements in the valorization of agri‐food by‐products into value‐added products through green extraction and biorefinery technologies. It emphasizes the recovery of bioactive compounds such as polyphenols, flavonoids, carotenoids, and dietary fibers from fruit, vegetable, dairy, meat, and seafood wastes, highlighting their potential applications in the food, pharmaceutical, cosmetic, and bioenergy sectors. Emerging eco‐friendly extraction techniques—including supercritical and subcritical fluid extraction, enzyme‐assisted extraction, microwave‐ and ultrasound‐assisted methods, and pulsed electric field processing—offer improved yield, purity, and energy efficiency while reducing ecological impact. Despite technological progress, large‐scale adoption remains constrained by high costs, lack of standardization, and limited industrial integration. Key research gaps include the need for techno‐economic assessments, solvent recovery strategies, and life‐cycle evaluations to ensure process scalability and sustainability. Future research should focus on developing hybrid extraction systems, AI‐driven process optimization, and pilot‐scale biorefineries supported by robust policy frameworks and industry–academia collaboration. Overall, agri‐food waste valorization presents a viable pathway toward achieving environmental sustainability and circular bioeconomy goals, enabling a transition from waste‐intensive practices to resource‐efficient and climate‐resilient production systems.

Julián Garrido, Susana Sánchez, Edgar Ribeiro João et al.

The Square Kilometre Array Observatory (SKAO) faces unprecedented technological challenges due to the vast scale and complexity of its data. This paper provides an overview of research by the AMIGA group to address these computing and reproducibility challenges. We present advancements in semantic data models, analysis services integrated into federated infrastructures, and the application to astronomy studies of techniques that enhance research transparency. By showcasing these astronomy work, we demonstrate that achieving reproducible science in the Big Data era is feasible. However, we conclude that for the SKAO to succeed, the development of the SKA Regional Centre Network (SRCNet) must explicitly incorporate these reproducibility requirements into its fundamental architectural design. Embedding these standards is crucial to enable the global community to conduct verifiable and sustainable research within a federated environment.

K. Squire, Henry Jenkins

Malte Högemann, Malte Högemann, Laura Hein et al.

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Gianluca Aguzzi, Roberto Casadei, Mirko Viroli

Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library.

Shu Abe, Yuya Kodama, Hiroyoshi Yamada et al.

Chang Liu, Loc Hoang, Andrew Stolman et al.

Providing students with flexible and timely academic support is a challenge at most colleges and universities, leaving many students without help outside scheduled hours. Large language models (LLMs) are promising for bridging this gap, but interactions between students and LLMs are rarely overseen by educators. We developed and studied an LLM-powered course assistant deployed across multiple computer science courses to characterize real-world use and understand pedagogical implications. By Spring 2024, our system had been deployed to approximately 2,000 students across six courses at three institutions. Analysis of the interaction data shows that usage remains strong in the evenings and nights and is higher in introductory courses, indicating that our system helps address temporal support gaps and novice learner needs. We sampled 200 conversations per course for manual annotation: most sampled responses were judged correct and helpful, with a small share unhelpful or erroneous; few responses included dedicated examples. We also examined an inquiry-based learning strategy: only around 11% of sampled conversations contained LLM-generated follow-up questions, which were often ignored by students in advanced courses. A Bloom's taxonomy analysis reveals that current LLM capabilities are limited in generating higher-order cognitive questions. These patterns suggest opportunities for pedagogically oriented LLM-based educational systems and greater educator involvement in configuring prompts, content, and policies.

Kate Barnes, Mia Ellis-Einhorn, Carolina Chávez-Ruelas et al.

Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at the core and those in low-ranked departments are in the peripheries of the computer science coauthorship network. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated collaborations. Our intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers from highly ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted rank of their placement institution in the academic job market. This work was guided by an ameliorative approach: uncovering social inequities in order to address them. By targeting scholars for intervention based on institutional prestige, we are able to improve their centrality in the coauthorship network that plays a key role in job placement and longer-term academic success.

Cynthia L. Blitz, David J. Amiel, Teresa G. Duncan

High-quality computer science (CS) instruction is essential for preparing students to thrive in an increasingly technology-driven world. This research brief presents findings from a six-year longitudinal study of CS enrollments in seven public high schools from the 2018-2019 through the 2023-2024 academic years, drawing on the administrative data of over 15,000 students. Results show that overall enrollment in CS courses rose modestly from 10% to 15% between the 2018-2019 and 2022-2023 school years. Enrollment declined to 13% in 2023-2024, though the cause and persistence of this trend remains unknown. Additional analyses differentiate foundational and advanced CS courses as well as examine participation by sex and race, offering additional insight. As CS, artificial intelligence, and related fields become more important across our society, they also become a key component of a robust K-12 education. Analyzing and understanding these trends in CS enrollments is crucial to inform policy and instruction that encourage students to participate and succeed in CS; this research brief presents one such analysis.

Ethan Dickey, Andres Bejarano, Rhianna Kuperus et al.

Generative AI (GenAI) is rapidly entering computer science education, yet its effects on student learning, skill development, and perceptions remain underexplored. Concerns about overreliance coexist with a gap in research on structured scaffolding to guide tool use in formal courses. This study examines the impact of a dedicated "AI-Lab" intervention -- emphasizing guided scaffolding and mindful engagement -- on undergraduate students in Data Structures and Algorithms, Competitive Programming, and first-year engineering courses at Purdue University. Over three semesters, we integrated AI-Lab modules into four mandatory and elective courses, yielding 831 matched pre- and post-intervention survey responses, alongside focus group discussions. Employing a mixed-methods approach, we analyzed quantitative shifts in usage patterns and attitudes as well as qualitative narratives of student experiences. While the overall frequency of GenAI usage for homework or programming projects remained largely stable, we observed large effect sizes in comfort and openness across conceptual, debugging, and homework problems. Notably, usage patterns for debugging also shifted statistically significantly, reflecting students' more mindful and deliberate approach. Focus group discussions corroborated these results, suggesting that the intervention "bridged the gap" between naive GenAI usage and more nuanced, reflective integration of AI tools into coursework, ultimately heightening students' awareness of their own skill development. These findings suggest that structured, scaffolded interventions can enable students to harness GenAI's benefits without undermining essential competencies. We offer evidence-based recommendations for educators seeking to integrate GenAI responsibly into computing curricula and identify avenues for future research on GenAI-supported pedagogy.

Behnam Faghih, Amin Shoari Nejad, Joseph Timoney

Abstract Performing musical notes correctly does not mean that all the performers will play the notes at the exact same pitch and duration. However, it does imply that they are performing the notes within acceptable psychoacoustic ranges. Therefore, this article aims to find the range of a note’ duration and pitch according to its position in a piece of music by analysing several parameters in trained-professional singers’ behaviours in singing notes. To achieve the goal, the variations of eight variables on 2688 solo singing recorded files by trained professional singers were investigated to find the relationships between a performed note’s F0 and duration with these variables. The variables considered in this study are the interval to the following and previous notes, the existence of rest before or after the note, the note’s MIDI pitch code and duration in a music score, and the particular singing technique applied. The Bayesian hierarchical model was used to find the effect of the variables on the pitch and duration of a note sung by professionals, mainly in opera style, singers. The investigation confirms that these parameters affect the pitch and duration of notes performed by professional singers. Finally, this paper proposes formulas to calculate the pitch frequency and duration of the notes according to the variables to simulate the behaviour of the trained-professional singers in performing notes’ pitches and duration.

TAN Ruoqi, DONG Minggang, ZHAO Weixiao, WU Tianhao

Holding non-motorized vehicles accountable for legal violations effectively enhances urban traffic safety. Non-motorized vehicle license plates are characterized by small size, dense distribution, and ease of being obscured, which leads to significant feature information loss during the detection process in traditional deep learning-based methods. A non-motorized vehicle license plate recognition and localization method based on semantic alignment and hierarchical optimization is proposed. In this method, a semantic alignment module is designed for the underlying information fusion. During the upsampling process, low-level target information is used to guide the fusion of high-level semantics downwards, addressing the loss of small target features caused by conflicts between high- and low-level semantics. Subsequently, a hierarchical optimization module is constructed within the CSP structure to replace the deep ELAN module. This module uses a stack of a few convolutional kernel modules to extract the target information, reducing the number of network layers and preventing the loss of feature information at deeper levels. In the final stage, the K-Means++ algorithm is employed to cluster and obtain the initial anchor boxes suitable for non-motorized license plates to reduce the matching error during the training process. This approach aims to improve the accuracy of small-object recognition and localization. The experimental results demonstrate that the proposed method achieves a recognition and localization accuracy of 90.95% on a non-motorized vehicle license plate dataset. Compared with representative methods such as YOLOv7 and YOLOv8, it improves the accuracy by at least 3.58%. The proposed approach is effective for non-motorized vehicle license plate recognition and localization.

Тураева Н.М.

В данной статье рассматривается математическая модель цифровой системы автоматического сопровождения цели по дальности, которая, в отличие от существующих, удовлетворяет по всем требованиям устойчивости и качеству системы измерения дальности и автоматического сопровождения цели. Также в статье показана структурная схема и построена математическая модель преобразователя «код-временная задержка», которая исследована на устойчивость и качество, определены допустимые области параметров цифрового управляющего устройства, обеспечивающие устойчивость работы построенной математической модели. Определены допустимые области параметров алгоритма работы цифрового управляющего устройства, при которых система автоматического сопровождения дальности соответствует своему предназначению.

Jie Ding, Lingyan Weng, Lili Fan et al.

Tourism demand projection is paramount for both corporate operations and destination management, facilitating tourists in crafting bespoke, multifaceted itineraries and enriching their vacation experiences. This study proposes a multi-layer self attention mechanism recommendation algorithm based on dynamic spatial perception, with the aim of refining the analysis of tourists’ emotional inclinations and providing precise estimates of tourism demand. Initially, the model is constructed upon a foundation of multi-layer attention modules, enabling the semantic discovery of proximate entities to the focal scenic locale and employing attention layers to consolidate akin positions, epitomizing them through contiguous vectors. Subsequently, leveraging tourist preferences, the model forecasts the likelihood of analogous attractions as a cornerstone for the recommendation system. Furthermore, an attention mechanism is employed to refine the spatial layout, utilizing the forecasted passenger flow grid to infer tourism demand across multiple scenic locales in forthcoming periods. Ultimately, through scrutiny of data pertaining to renowned tourist destinations in Beijing, the model exhibits an average MAPE of 8.11%, markedly surpassing benchmarks set by alternative deep learning models, thereby underscoring its precision and efficacy. The spatial layout optimization methodology predicated on a multi-layer attention mechanism propounded herein confers substantive benefits to tourism demand prognostication and recommendation systems, promising to elevate the operational standards and customer contentment within the tourism sector.

Frances Fengyi Yang, Michele Sasdelli, Tat-Jun Chin

Gate quantum computers generate significant interest due to their potential to solve certain difficult problems such as prime factorization in polynomial time. Computer vision researchers have long been attracted to the power of quantum computers. Robust fitting, which is fundamentally important to many computer vision pipelines, has recently been shown to be amenable to gate quantum computing. The previous proposed solution was to compute Boolean influence as a measure of outlyingness using the Bernstein-Vazirani quantum circuit. However, the method assumed a quantum implementation of an $\ell_\infty$ feasibility test, which has not been demonstrated. In this paper, we take a big stride towards quantum robust fitting: we propose a quantum circuit to solve the $\ell_\infty$ feasibility test in the 1D case, which allows to demonstrate for the first time quantum robust fitting on a real gate quantum computer, the IonQ Aria. We also show how 1D Boolean influences can be accumulated to compute Boolean influences for higher-dimensional non-linear models, which we experimentally validate on real benchmark datasets.

Akanksha Arora, Hitendra Garg, Shivendra Shivani

The increase in information sharing in terms of digital images imposes threats to privacy and personal identity. Digital images can be stolen while in transfer and any kind of alteration can be done very easily. Thus, privacy protection of digital images from attackers becomes very important. Encryption, steganography, watermarking, and visual cryptography techniques to protect digital images have been proposed from time to time. The present paper is focused on the enhancement of privacy protection of digital images utilizing watermarking and a QR code-based expansion-free and meaningful visual cryptography approach which generates visually appealing QR codes for transmitting meaningful shares. The original secret image is processed with a watermark image (copyright logo, signature, and so on), and then halftoning of the watermarked image has been done to limit pixel expansion. Then, the halftoned image has been partitioned using VC into two shares. These shares are embedded with a QR code to make the shares meaningful. Lossless compression has been performed on the QR code-based shares. The compression method employed in visual cryptography would save space and time. The proposed approach keeps the beauty of visual cryptography, i.e., computation-free decryption, and the size of the recovered image the same as the original secret image. The experimental results confirm the effectiveness of the proposed approach.