Hasil untuk "Electronic computers. Computer science"

Xing Zhang, Haozhe Wang, Junqing Lu et al.

Abstract In this paper, a searchable encryption scheme for cloud data is proposed to address the limitations of existing schemes, which suffer from inefficient index construction and search process, as well as a lack of support for dynamic updates or the complexity and inefficiency of update operations. Firstly, the efficiency and flexibility of index construction are improved by designing a reverse index based on B+ trees, coupled with Bloom filters. Vector matching ideas are employed to enhance the efficiency of searching encrypted cloud data. Secondly, leveraging the bitmap index, a combination design of forward and reverse indices is utilized to improve update efficiency, concealing update types to protect access patterns. Finally, the security analysis and comparative experimental results collectively demonstrate the feasibility, efficiency, and security of the proposed scheme, verifying its potential in practical applications.

Jens Palsberg, Jason Cong, Yufei Ding et al.

Quantum computing is entering a period in which progress will be shaped as much by advances in computer science as by improvements in hardware. The central thesis of this report is that early fault-tolerant quantum computing shifts many of the primary bottlenecks from device physics alone to computer-science-driven system design, integration, and evaluation. While large-scale, fully fault-tolerant quantum computers remain a long-term objective, near- and medium-term systems will support early fault-tolerant computation with small numbers of logical qubits and tight constraints on error rates, connectivity, latency, and classical control. How effectively such systems can be used will depend on advances across algorithms, error correction, software, and architecture. This report identifies key research challenges for computer scientists and organizes them around these four areas, each centered on a fundamental question.

Bin Wang, Chao Li, Chao Zhou et al.

In vehicle safety detection, the accurate identification of body markers on medium and large vehicles plays a critical role in ensuring safe road travel. To address the issues of the feature and gradient information loss in previous You Only Look Once (YOLO) series models, a novel Vehicle Body Markers YOLO (VBM-YOLO) model has been designed. Firstly, the model integrates the cross-spatial-channel attention (CSCA) mechanism proposed in this study. The CSCA uses cross-dimensional information to address interaction issues during the fusion of spatial and channel dimensions, significantly enhancing the model’s representational capacity. Secondly, we propose a multi-scale selective feature pyramid network (MSSFPN). By a progressive fusion approach and multi-scale feature selection learning, MSSFPN alleviates the issues of feature loss and target layer information confusion caused by traditional top-down and bottom-up feature pyramids. Finally, an auxiliary gradient branch (AGB) is proposed. During training, AGB incorporates feature information from different target layers to help the current layer retain complete gradient information. Additionally, the AGB branch does not participate in model inference, thereby reducing additional overhead. Experimental results demonstrate that VBM-YOLO improves mean average precision (mAP) by 2.3% and 4.3% at intersection over union (IoU) thresholds of 0.5 and 0.5:0.95, respectively, compared to YOLOv8s on the vehicle body markers dataset. VBM-YOLO also achieves a better balance between accuracy and computational resources than other mainstream models, exhibiting good generalization performance on public datasets like PASCAL VOC and D-Fire.

Kai-Ze Liau, Heru Agus Santoso

Recommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role in e-commerce applications as well, with industry players like Amazon, AliBaba, eBay using recommender systems within their ecosystems to give suitable and value-driven insights. However, recommender systems face some main concerns such as data sparsity, cold-start problems and so on. As a result, research is currently ongoing to solve these issues and provide high-quality recommendations to consumers. This review aims to identify prevailing gaps surrounding these issues by analysing existing research on generative Artificial Intelligence (AI) recommender systems within an e-commerce context. It explores the underlying framework of common generative AI techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, diffusion models and so on. VAEs and Transformers hold great potential within e-commerce as noted by most researchers due to their ease of training and qualitative generations. This review intends to enhance recommender systems better to improve the quality of life of digital users, providing better recommendations in e-commerce as well as maximizing the value of stakeholders. It also includes potential future work for researchers to advance existing knowledge in this sector.

Wenxi Guo, Haiquan Chen, Shuyun Peng

With the rapid development of artificial intelligence, the Internet of Things, and related technologies, human-smart object relationships have become increasingly diversified. As smart object become deeply embedded in human society, this has given rise to emerging ethical issues—particularly human-smart object attachment—whose characteristics and influencing pathways remain unclear. This study focuses on the context of wearable smart devices and adopts a two-stage mixed-methods approach: First, based on assemblage theory and existing literature, we construct a three-phase theoretical framework encompassing human-smart object assemblage formation, experience, and attachment. Subsequently, using grounded theory, we conduct in-depth interviews with users of wearable smart objects and employ a three-tier coding process to clarify the conceptualization, typology, and formation pathways of human-smart object attachment. The findings reveal that human-smart object attachment is essentially a psychological bond formed through “self-extension” and “self-expansion,” facilitated by human-smart object capability synergy. It encompasses cognitive, affective, and conative dimensions and is influenced by three key factors: the user, the smart object, and the interaction process. Furthermore, the study explores the impact of human-smart object attachment on user attitudes and behaviors. As a unique phenomenon, the complexity of human-smart object attachment calls for HCI scholars to adopt multidisciplinary perspectives to investigate its mechanisms and effects. Such insights can assist enterprises and communities in developing technology products that better align with user needs.

Robin Ohs, Gregory F. Stock, Andreas Schmidt et al.

Low-Earth Orbit (LEO) satellites are increasingly proposed for communication and in-orbit computing, achieving low-latency global services. However, their sustainability remains largely unexamined. This paper investigates the carbon footprint of computing in space, focusing on lifecycle emissions from launch over orbital operation to re-entry. We present ESpaS, a lightweight tool for estimating carbon intensities across CPU usage, memory, and networking in orbital vs. terrestrial settings. Three worked examples compare (i) launch technologies (state-of-the-art rocket vs. potential next generation), (ii) operational emissions of data center workloads in orbit and on the ground and, (iii) in-orbit aggregation with raw data transmission. Results show that, even under optimistic assumptions, in-orbit systems incur significantly higher carbon costs - primarily due to embodied emissions from launch and re-entry. Our findings advocate for carbon-aware design principles and regulatory oversight in developing sustainable digital infrastructure in orbit.

Brady D. Lund, Ting Wang

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Xinhuan Zhang, Dongping Li, Les Lauber et al.

Abstract This study addresses the challenge of quantitatively assessing ride comfort in tram travel in Growing Urban Environments, where multiple influencing factors complicate developing a unified evaluation index system. A comprehensive evaluation framework based on cloud theory is proposed to overcome this challenge. The approach involves defining five‐level comfort evaluation grades to capture passengers' experiences and perceptions accurately. The Criteria Importance through Inter‐Criteria Correlation (CRITIC) method is employed to ensure objectivity to establish objective weights for evaluation indices. Subsequently, a cloud model algorithm is utilized to generate evaluation benchmark and actual result clouds, providing intuitive representations of the evaluation outcomes. The efficacy and rationality of the methodology is illustrated through a case study focusing on Suzhou Tram Line 2. This research contributes valuable insights for enhancing public transportation experiences in new urban settings by offering a systematic and objective approach to assessing tram ride comfort.

Fachri Amsury, Nanang Ruhyana, Andri Agung Riyadi et al.

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.

Carolina Veiga, Ashish Sharma, Daniel de Oliveira et al.

The impacts of climate change are intensifying existing vulnerabilities and disparities within urban communities around the globe, as extreme weather events, including floods and heatwaves, are becoming more frequent and severe, disproportionately affecting low-income and underrepresented groups. Tackling these increasing challenges requires novel approaches that integrate expertise across multiple domains, including computer science, engineering, climate science, and public health. Urban computing can play a pivotal role in these efforts by integrating data from multiple sources to support decision-making and provide actionable insights into weather patterns, infrastructure weaknesses, and population vulnerabilities. However, the capacity to leverage technological advancements varies significantly between the Global South and Global North. In this paper, we present two multiyear, multidisciplinary projects situated in Chicago, USA and Niterói, Brazil, highlighting the opportunities and limitations of urban computing in these diverse contexts. Reflecting on our experiences, we then discuss the essential requirements, as well as existing gaps, for visual analytics tools that facilitate the understanding and mitigation of climate-related risks in urban environments.

J. Sutton-Parker, R. Procter

Joseph T. Wong, Almaz Mesghina, Edward Chen et al.

The COVID-19 pandemic necessitated a systematic change in course modalities due to the nationwide suspension of in-person instruction, resulting in the transition to emergency remote distance learning via Zoom. This transition certainly facilitated affordances of flexibility and continuity, but with it brought issues of unfamiliarity, lack of confidence, anxiety, distractions, and validity from both the instructors and the student perspectives. This in situ study aimed to better understand the students' learning experiences with Zoom by assessing the social, cognitive, and behavioral factors influencing learner's mind-wandering and its effect on online engagement. Undergraduate students from 14 classes across two research institutions in California (N = 633) were recruited to participate in an online survey while distance learning through a pandemic. Structural equation modeling was used to conduct a path analysis to explain the factors impacting students' online engagement mediated by students' frequency to mind-wander. Study findings revealed that (1) self-efficacy and trait anxiety had significant direct effects on students' mind-wandering; (2) self-efficacy, trait anxiety, task-value beliefs, and mind-wandering had significant direct effects on students' online engagement; and finally (3) the frequency of students' mind-wandering partially mediated the relationship between self-efficacy and engagement and between trait anxiety and engagement. Identifying these structural relationships further confirmed our hypotheses on sources contributing to students' mind-wandering while learning remotely, provided insights into potential mechanisms underpinning students' online engagement, and suggests practical pedagogical learning experience design recommendations for instructors to immediately implement while teaching and learning with Zoom..

Joseph W. N. Paulus, Daniele Nantes-Sobrinho, Jorge A. Pérez

We study encodings of the lambda-calculus into the pi-calculus in the unexplored case of calculi with non-determinism and failures. On the sequential side, we consider lambdafail, a new non-deterministic calculus in which intersection types control resources (terms); on the concurrent side, we consider spi, a pi-calculus in which non-determinism and failure rest upon a Curry-Howard correspondence between linear logic and session types. We present a typed encoding of lambdafail into spi and establish its correctness. Our encoding precisely explains the interplay of non-deterministic and fail-prone evaluation in lambdafail via typed processes in spi. In particular, it shows how failures in sequential evaluation (absence/excess of resources) can be neatly codified as interaction protocols.

Simon Burton, Benjamin Herd

There is increasing interest in the application of machine learning (ML) technologies to safety-critical cyber-physical systems, with the promise of increased levels of autonomy due to their potential for solving complex perception and planning tasks. However, demonstrating the safety of ML is seen as one of the most challenging hurdles to their widespread deployment for such applications. In this paper we explore the factors which make the safety assurance of ML such a challenging task. In particular we address the impact of uncertainty on the confidence in ML safety assurance arguments. We show how this uncertainty is related to complexity in the ML models as well as the inherent complexity of the tasks that they are designed to implement. Based on definitions of uncertainty as well as an exemplary assurance argument structure, we examine typical weaknesses in the argument and how these can be addressed. The analysis combines an understanding of causes of insufficiencies in ML models with a systematic analysis of the types of asserted context, asserted evidence and asserted inference within the assurance argument. This leads to a systematic identification of requirements on the assurance argument structure as well as supporting evidence. We conclude that a combination of qualitative arguments combined with quantitative evidence are required to build a robust argument for safety-related properties of ML functions that is continuously refined to reduce residual and emerging uncertainties in the arguments after the function has been deployed into the target environment.

Jiachen Yang, Jiaming Huo, Jingyi He et al.

Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1.

Nathalie Bertrand, Patricia Bouyer, Anirban Majumdar

Broadcast networks allow one to model networks of identical nodes communicating through message broadcasts. Their parameterized verification aims at proving a property holds for any number of nodes, under any communication topology, and on all possible executions. We focus on the coverability problem which dually asks whether there exists an execution that visits a configuration exhibiting some given state of the broadcast protocol. Coverability is known to be undecidable for static networks, i.e. when the number of nodes and communication topology is fixed along executions. In contrast, it is decidable in PTIME when the communication topology may change arbitrarily along executions, that is for reconfigurable networks. Surprisingly, no lower nor upper bounds on the minimal number of nodes, or the minimal length of covering execution in reconfigurable networks, appear in the literature. In this paper we show tight bounds for cutoff and length, which happen to be linear and quadratic, respectively, in the number of states of the protocol. We also introduce an intermediary model with static communication topology and non-deterministic message losses upon sending. We show that the same tight bounds apply to lossy networks, although, reconfigurable executions may be linearly more succinct than lossy executions. Finally, we show NP-completeness for the natural optimisation problem associated with the cutoff.

Clemens Seibold, Anna Hilsmann, Peter Eisert

Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.

Stefka Fidanova, Maria Ganzha, Olympia Roeva

Hasindu Gamaarachchi

DNA sequencing is revolutionising the field of medicine. DNA sequencers, the machines which perform DNA sequencing, have evolved from the size of a fridge to that of a mobile phone over the last two decades. The cost of sequencing a human genome also has reduced from billions of dollars to hundreds of dollars. Despite these improvements, DNA sequencers output hundreds or thousands of gigabytes of data that must be analysed on computers to discover meaningful information with biological implications. Unfortunately, the analysis techniques have not kept the pace with rapidly improving sequencing technologies. Consequently, even today, the process of DNA analysis is performed on high-performance computers, just as it was a couple of decades ago. Such high-performance computers are not portable. Consequently, the full utility of an ultra-portable sequencer for sequencing in-the-field or at the point-of-care is limited by the lack of portable lightweight analytic techniques. This thesis proposes computer architecture-aware optimisation of DNA analysis software. DNA analysis software is inevitably convoluted due to the complexity associated with biological data. Modern computer architectures are also complex. Performing architecture-aware optimisations requires the synergistic use of knowledge from both domains, (i.e, DNA sequence analysis and computer architecture). This thesis aims to draw the two domains together. In this thesis, gold-standard DNA sequence analysis workflows are systematically examined for algorithmic components that cause performance bottlenecks. Identified bottlenecks are resolved through architecture-aware optimisations at different levels, i.e., memory, cache, register and processor. The optimised software tools are used in complete end-to-end analysis workflows and their efficacy is demonstrated by running on prototypical embedded systems.

Nadya Bliss, Lawrence A. Gordon, Daniel Lopresti et al.

Computing devices are vital to all areas of modern life and permeate every aspect of our society. The ubiquity of computing and our reliance on it has been accelerated and amplified by the COVID-19 pandemic. From education to work environments to healthcare to defense to entertainment - it is hard to imagine a segment of modern life that is not touched by computing. The security of computers, systems, and applications has been an active area of research in computer science for decades. However, with the confluence of both the scale of interconnected systems and increased adoption of artificial intelligence, there are many research challenges the community must face so that our society can continue to benefit and risks are minimized, not multiplied. Those challenges range from security and trust of the information ecosystem to adversarial artificial intelligence and machine learning. Along with basic research challenges, more often than not, securing a system happens after the design or even deployment, meaning the security community is routinely playing catch-up and attempting to patch vulnerabilities that could be exploited any minute. While security measures such as encryption and authentication have been widely adopted, questions of security tend to be secondary to application capability. There needs to be a sea-change in the way we approach this critically important aspect of the problem: new incentives and education are at the core of this change. Now is the time to refocus research community efforts on developing interconnected technologies with security "baked in by design" and creating an ecosystem that ensures adoption of promising research developments. To realize this vision, two additional elements of the ecosystem are necessary - proper incentive structures for adoption and an educated citizenry that is well versed in vulnerabilities and risks.