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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Kumar, Vijay Valizadeh, Nima Petri, Ioan Rana, Omer Nag, Ambarish Zisman, Sagi Tripp, Charles Paul, Shuva School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Engineering Cardiff University Cardiff United Kingdom Colorado United States Colorado United States

ISBN: (纸本)9783031812255

Edge computing devices have increased in number and capability over recent years. The ability to process data and execute machine learning in proximity to data generation and collection sources provides several advantages over using cloud- based data centers. We describe an orchestration mechanism that enables edge devices to make more effective use of energy resources in their proximity – a technique we refer to as "edge energy orchestration". A software "orchestrator" can take account of renewable generation to alter how task execution on edge devices is carried out. An application scenario is used to illustrate the use of the orchestrator in practice, followed by a discussion about how this approach can be generalized to a broader set of applications © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clean energy

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A framework for object tracking in videos with complex backgrounds and obstructions

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Neural Computing and Applications 2025年 1-20页

作者： Chang, Tsui-Ping Chen, Tzer-Long Hsiao, Tsung-Chih Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Gaoxiong807 Taiwan Department of Radio Television and Film Shih Hsin University Taipei11604 Taiwan

The research of object tracking in videos utilizes computer vision and machine learning techniques to identify and track objects in the consecutive image frames of videos. The popular algorithms used in the research are YOLO, DeepSORT, StrongSORT, and ByteTrack. YOLO has gained recognition for its hardware efficiency and impressive performance in object detection tasks. It efficiently detects objects in the consecutive image frames of videos. On the other hand, DeepSORT, StrongSORT, and ByteTrack leverage the prediction capabilities to estimate the positions of objects in the next image frames. These algorithms play a significant role to enhance the capabilities of object tracking systems. Recently, many researchers have proposed their methods to enhance the accuracy in object tracking tasks. However, the division of videos into the consecutive image frames for tracking objects can pose challenges, especially when the videos have complex backgrounds and many obstructions in the image frames. In fact, the complex backgrounds with many obstructions in image frames decrease the accuracy of tracking objects in applications. In this study, we present TraObs, a framework for object Tracking in complex backgrounds with Obstructions in the image frames of videos. In TraObs, two novel mechanisms CFM and M-controller are proposed for detectors and trackers to enhance the accuracy of object tracking performance. CFM addresses the tracked objects which are missing in some image frames of videos, while M-controller makes sure the tracked objects with the correct object ids in the system. Some experiments with four testing datasets and benchmark datasets are designed and used respectively to analyze the performance of TraObs. Furthermore, the HOTA and MOTA metrics are used to evaluate our proposed methods. The experimental results also prove the effectiveness of CFM and M-controller in our framework TraObs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd.,

关键词： Image enhancement

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of computer Science 2025年 1-31页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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HPPH: computer Vision-Based Service for High-Performance Pavement Health Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 15987-15996页

作者： Tsung, Chen-Kun Kristiani, Endah Chiu, Chen-Kang Liu, Jung-Chun Yang, Chao-Tung National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung City411030 Taiwan Tunghai University Department of Computer Science Taichung City407224 Taiwan Krida Wacana Christian University Department of Informatics Jakarta11470 Indonesia Tunghai University Research Center of Smart Sustainable Circular Economy Taichung City407224 Taiwan

Identifying pavement damage is a crucial component in road maintenance and infrastructure management. Prompt detection and corrective action of pavement defects can prevent severe deterioration, maintain safety, and prolong the useful life of road infrastructure. computer vision is widely applied to vehicle applications, such as driver assistance and self-driving, and that is a good platform to equip the pavement damage recognition service. Therefore, this work applies computer vision to develop the pavement damage detection service named high-performance pavement health recognition (HPPH) to detect common pavement damages, including longitudinal cracks, transverse cracks, alligator cracks, and potholes. The proposed HPPH can be deployed on a vehicle for regular road detection. To fit the requirements of vehicle applications, HPPH considers state-of-the-art techniques to optimize the performance of the recognition service on vehicles, e.g., DeepStream is applied to increase the inference performance. At the same time, YOLOv8s (You Only Look Once, YOLO) provides real-time inference. The experimental results reveal that HPPH provides an accuracy of 90.3% and an average precision 0.5 of 74.3%. Moreover, the optimized HPPH provides three times better than the pure YOLOv8s in terms of the inference speed. In summary, the proposed HPPH provides high recognition accuracy with high efficiency and is feasible to be applied to vehicle applications to realize distributed road maintenance. © 2025 IEEE.

关键词： Deep learning

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To Risk Analyse, or Not to Risk Analyse: That’s the Question 18th

To Risk Analyse, or Not to Risk Analyse: That’s the Ques...

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18th IFIP WG 11.12 International Symposium on Human Aspects of Information Security and Assurance, HAISA 2024

作者： Bergström, Erik Andersson, Simon Lundgren, Martin School of Engineering Jönköping University Jönköping Sweden Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden School of Informatics University of Skövde Skövde Sweden

ISBN: (纸本)9783031725586

Risk analysis is a key activity for organisations that are looking to protect their valuable information assets against threats, such as malicious actors. It is one of the essential parts of risk management and is used to justify and prioritise what assets require the attention of which potential security controls. Risk management, and more specifically, risk analysis, is an activity that should be performed continuously. However, recent studies indicate that this is not always the case. As such, this paper investigates risk analysis as it is performed in practice in different Swedish public sector organisations. The results are based on semi-structured interviews with 17 senior security experts, an analysis of standards, and a national method support aiming to fill the gap between standard and practice. The results are presented in three themes: how, when and why risk analysis is performed. Of note, we identify that there is an issue of overlooking specific assets or systems when establishing an organisational-wide risk profile and a general recognition of the necessity for risk analysis, albeit not always in alignment with a classic risk analysis. © IFIP International Federation for Information Processing 2025.

关键词： Cybersecurity Information security Risk analysis

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Progressive Semantic-Visual Alignment and Refinement for Vision-Language Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4271-4286页

作者： Liang, Yanjie Wu, Qiangqiang Cheng, Lin Xia, Changqun Li, Jia Peng Cheng Laboratory Shenzhen518000 China City University of Hong Kong Department of Computer Science Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China

In recent years, vision-language tracking has drawn emerging attention in the tracking field. The critical challenge for the task is to fuse semantic representations of language information and visual representations of vision information. For this purpose, several vision-language tracking methods perform early or late fusion to fuse visual and semantic features. However, these methods cannot take full advantage of the transformer architecture to excavate useful cross-modal context at various levels. To this end, we propose a new progressive joint vision-language transformer (PJVLT) to progressively align and refine visual embedding with semantic embedding for vision-language tracking. Specifically, to align visual signals with semantic signals, we propose to insert a semantic-aware instance encoder layer (SAIEL) into each intermediate layer of transformer encoder to perform progressive alignment of visual and semantic features. Furthermore, to highlight the multi-modal feature channels and patches corresponding to target objects, we propose a unified channel communication patch interaction layer (CCPIL), which is plugged into each intermediate layer of transformer encoder to progressively activate target-aware channels and patches of aligned multi-modal features for fine-grained tracking. In general, by progressively aligning and refining visual features with semantic features in the transformer encoder, our PJVLT can adaptively excavate well-aligned vision-language context at coarse-to-fine levels, therefore highlighting target objects at various levels for more discriminative tracking. Experiments on several tracking datasets show that the proposed PJVLT can achieve favorable performance in comparison with both conventional trackers and other vision-language trackers. © 1991-2012 IEEE.

关键词： Semantics

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Partition Strategies for Vertex-Centric Historical Graph Systems 25

Partition Strategies for Vertex-Centric Historical Graph Sys...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Alexandros Spitalas Georgios Tsolas Kostas Tsichlas Computer Engineering and Informatics Department University of Patras Rio Achaia Greece

ISBN: (纸本)9798400706295

This paper explores partitioning strategies for vertex-centric historical graph systems within distributed environments, focusing on efficient data management and query execution. Historical graphs, which capture the dynamic evolution of vertices and edges over time, present unique challenges for storage and computation due to their constantly changing nature. We investigate two partitioning approaches for offline and online environments. Both offline and online algorithms aim to minimize an appropriately defined notion of edge cuts within the historical graph setting; the former algorithm is based on BFS, while the latter is based on a greedy partitioning approach. Both algorithms are compared qualitatively and quantitatively to hash-based methods that are mainly used in this setting. They are evaluated on real-world datasets, using metrics such as weighted edge cut score ratio and load balance ratio. Our experiments reveal that our online method consistently reduces edge cuts with minimal overhead, making it suitable for real-time distributed processing systems for historical graphs. Similarly, the offline algorithm achieves much better results for weighted edge cuts, but it requires severely more computational resources to achieve this performance. Additionally, the study highlights how varying the number of workers and partitioning thresholds impacts system performance across different datasets. All our experiments have been carried out in a simulation environment. The results provide valuable insights into optimizing partition strategies for historical graphs, paving the way for more efficient graph analytics in distributed systems.

关键词： partitioning strategies

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AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 552-563页

作者： Gaidhani, Yashika Ramesh, Janjhyam Venkata Naga Singh, Sanjit Dagar, Reetika Rao, T. Subha Mastan Godla, Sanjiv Rao Baker El-Ebiary, Yousef A. Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of MBA-Sanjivani College of Engineering Savitribai Phule Pune University Pune India GD Goenka University Haryana Gurugram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Faculty of Informatics and Computing UniSZA University Malaysia

The advent of Artificial Intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery. This research introduces an extensive AI-based predictive analytics framework aimed at improving customer engagement, retention and satisfaction using advanced Machine Learning (ML) and Natural Language Processing (NLP) methodologies. By using XGBoost for churn prediction and BERT-based models for sentiment analysis, the system efficiently handles both structured and unstructured customer data. The methodology involves sophisticated feature engineering, customer segmentation via K-Means clustering, and Customer Lifetime Value (CLV) prediction to aid data-driven business strategies. An NLP-driven chatbot offers real-time, personalized support, response time and improving user experience. Evaluation metrics such as accuracy, precision, recall and F1-score demonstrate the better performance of the proposed system compared to conventional CRM approaches. This work also addresses important issues such as data privacy compliance, algorithmic bias and explainability of AI decision-making. Ethical deployment and transparency of AI are emphasized for building confidence in automated CRM systems. Future evolution will tackle the use of reinforcement learning to facilitate learning-based interaction schemes and federated learning for trusted, decentralized management of data. This architecture does not only provide better CRM functionality but also builds a platform towards intelligent, responsible and scalable solutions for customer relations across industries. © (2025), (Science and Information Organization). All rights reserved.

关键词： Reinforcement learning

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DR3DH: A DoS resistant extended triple Diffie–Hellman for mobile edge networks

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computer Networks 2025年 265卷

作者： Altaweel, Ala Bouridane, Ahmed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Mobile Edge Networks (MENs) are wireless networks that establish "close-to-end-users-clouds" to provide storage, computation, and communication services. MENs employ the Extended Triple Diffie–Hellman (X3DH) protocol to establish secret keys that provide end-to-end encryption among their nodes. X3DH relies on a cloud server that stores the public keys of the nodes to address the asynchronous scenarios when they operate offline. The cloud server in X3DH protocol represents a single point of failure (SPoF), as the exchange of secure keys is disrupted if the server fails or is compromised. Moreover, a malicious attacker, Eve, can launch various DoS attacks against the MEN nodes (e.g., packet dropping or packet modification) and wireless links (e.g., jamming) to disrupt the execution of X3DH during secure key exchange. This paper proposes the DoS-Resistant-X3DH (DR3DH) protocol to resolve the SPoF of the cloud server in X3DH and to address packet dropping, packet modification, and jamming DoS attacks. DR3DH employs Reed–Solomon Erasure Coding to encode the X3DH public keys into fragments that are distributed into a set of storage nodes. These storage nodes, which are determined by an Integer Linear Programming (ILP) solver, are optimum. That is, they have lowest probabilities to launch packet dropping/modification attacks and lowest probabilities that the wireless links toward them are jammed. The authors evaluated DR3DH through trace-driven simulations in MATLAB using two mobility traces and a proof-of-concept implementation in Java. The authors also compared DR3DH with two approaches that employ Random and Greedy policies when selecting the storage nodes for keys fragments. The results demonstrate the resistance of DR3DH to DoS attacks, achieving a high success rate that outperforms the Random and Greedy approaches by between 13.6% and 83.0%. Additionally, the results confirm the feasibility of DR3DH under different mobility models and node speeds, as demonstrated by

关键词： Packet loss

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How Lasers Exploit Photoacoustic and Photoelectric Phenomena to Inject Signals into MEMS Microphones

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Journal of Hardware and Systems Security 2025年 1-17页

作者： Cyr, Benjamin Sumaria, Vedant Long, Yan Tadigadapa, Srinivas Sugawara, Takeshi Fu, Kevin Computer Science and Engineering University of Michigan Ann Arbor USA Electrical & Computer Engineering Northeastern University Boston USA Department of Informatics The University of Electro-Communications Tokyo Japan

An amplitude-modulated laser can be used to generate false, yet coherent acoustic signals on the outputs of MEMS microphones. While this vulnerability has ramifications on the security of cyber-physical systems that trust these microphones, the physical explanation of this effect remained a mystery. Without an understanding of the physical phenomena contributing to this signal injection, it is difficult to design effective and reliable defenses. In this work, we show the degree to which the mechanisms of thermoelastic bending, thermal diffusion, and photocurrent generation are used to inject signals into MEMS microphones. We provide models for each of these mechanisms, develop a procedure to empirically determine their relative contributions, and highlight the effects on eight commercial MEMS microphones. We accomplish this with a precise setup to isolate each mechanism using several laser wavelengths and a vacuum chamber. The results indicate that the injected signal on the microphone is dependent on the wavelength of the incoming light. Shorter wavelengths (such as a 450 nm blue laser) exploit photoacoustic effects, and the periodic heating and expansion of air is the dominant factor in seven of eight sample microphones. Longer wavelengths (such as a 904 nm infrared laser) exploit photoelectric effects on the sensitive ASIC, generating signals that are between 2x and 100x stronger than photoacoustic signals in six of eight sample microphones. This understanding of the physical causality of laser signal injection leads to recommendations for future laser-resistant microphone designs. These include adding light-blocking structures at the system or device level, improving to glob top application, and adding simple light or temperature sensors for injection detection. Based on the fundamental causality, we also suggest potential vulnerabilities within other sensors with similar characteristics to MEMS microphones, such as conventional microphones, ultrasonic

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