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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Justyna Zawalska Minh Chung Katarzyna Rycerz Laura Schulz Martin Schulz Dieter Kranzlmüller Institute of Computer Science AGH University of Krakow Krakow Poland Academic Computer Center CYFRONET AGH Krakow Poland Leibniz Supercomputing Centre (LRZ) Garching bei München Germany MNM-Team Ludwig-Maximilians-Universität München (LMU) Germany Chair for Computer Architecture and Parallel Systems Technical University of Munich (TUM) Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP; the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar–Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load.

关键词： Measurement Upper bound NP-hard problem High performance computing Heuristic algorithms Qubit Load management Synchronization Optimization Load modeling

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks 8

Machine Learning based Cybersecurity Technique for Detection...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Lakshmi, Kanchi Lohitha Shobana, S. Kumar, Bathula Prasanna Glory, K.B. Kumar, Bonda Kiran Rani, Sandya Vasireddy Venkatadri Institute of Technology Department of Computer Science and Engineering Guntur India R.M.K Engineering College Department of It India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering AP Guntur India Koneru Lakshmaiah Education Foundation Department of Engineering English Andhra Pradesh State Vaddeswaram522302 India Kl Deemed to Be University Kl School of Architecture Department of Architecture Guntur District Vaddeswaram522302 India St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350396638

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks. © 2023 IEEE.

关键词： Network security

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Higher-Order DeepTrails: Unified Approach to *Trails

arXiv

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arXiv 2023年

作者： Koopmann, Tobias Pfister, Jan Markus, André Carolus, Astrid Wienrich, Carolin Hotho, Andreas University of Würzburg Department of Computer Science CAIDAS Chair for Data Science Germany University of Würzburg Institute Human-Computer-Media Psychology of Intelligent Interactive Systems Germany University of Würzburg Institute Human-Computer-Media Media Psychology Germany

Analyzing, understanding, and describing human behavior is advantageous in different settings, such as web browsing or traffic navigation. Understanding human behavior naturally helps to improve and optimize the underlying infrastructure or user interfaces. Typically, human navigation is represented by sequences of transitions between states. Previous work suggests to use hypotheses, representing different intuitions about the navigation to analyze these transitions. To mathematically grasp this setting, first-order Markov chains are used to capture the behavior, consequently allowing to apply different kinds of graph comparisons, but comes with the inherent drawback of losing information about higher-order dependencies within the sequences. To this end, we propose to analyze entire sequences using autoregressive language models, as they are traditionally used to model higher-order dependencies in sequences. We show that our approach can be easily adapted to model different settings introduced in previous work, namely HypTrails, MixedTrails and even SubTrails, while at the same time bringing unique advantages: 1. Modeling higher-order dependencies between state transitions, while 2. being able to identify short comings in proposed hypotheses, and 3. naturally introducing a unified approach to model all settings. To show the expressiveness of our approach, we evaluate our approach on different synthetic datasets and conclude with an exemplary analysis of a real-world dataset, examining the behavior of users who interact with voice assistants. © 2023, CC BY.

关键词： User interfaces

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HCMonitor: An accurate measurement system for high concurrent network services

HCMonitor: An accurate measurement system for high concurren...

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作者： Song, Hui Zhang, Wenli Liu, Ke Shen, Yifan Chen, Mingyu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Frontier Research Center Peng Cheng Laboratory Shenzhen China

This article aims to enhance the monitoring accuracy of high concurrent network services. As modern network services grow rapidly in data centers, tail latency has become one of the most crucial deciding factors on user experience. Latency measurement and anomaly detection are essential in evaluating service performance. Existing monitoring tools can be divided into two categories according to estimation methods. First, approaches based on sample traffic sample network packets to unburden the measurement. Second, approaches based on full traffic like wrk, analyze all of the packets from the kernel network stack and load the client-side overhead into response delay. Therefore, we propose a high-performance monitor system named HCMonitor, which computes the server-side response latency and the round-trip time of per-request. It can afford full traffic monitoring on the basis of userspace, "zero copy" and pipeline. By switch mirroring, the measured latency eliminates the kernel network stack overhead and the queuing delay of the client-side. Such measurement results in improved accuracy, online analysis, anomaly detection, real-time display and transparent to network services. Our evaluations show HCMonitor obtains a higher throughput compared with tcpdump by over 200 times. Compared with wrk, the tail latency accuracy shows an increase by up to 72%–76% in high concurrent networks. © 2021 John Wiley & Sons Ltd.

关键词： Anomaly detection

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Countering DoS Threats in WSN: Inventive Machine Learning Strategies with a Minimalistic Twist

Countering DoS Threats in WSN: Inventive Machine Learning St...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Senthilnathan Chidambaranathan Vanitha C.S. Sasireka Naduvathezhath Nessariose Jose K. Madhusudhana Rao T. Lakshmi Devi Architecture and Design Virtusa Department of Computer Science and Engineering Sri Sairam Institute of Technolgoy Department of Computer Science and Engineering SRM Institute of Science and Technology Regent Strategic Solutions Rochester USA KKR&KSR Institute Of Technology & Science Guntur A.P Department of Data Science Siddhartha Institute of Engineering and Technology (SIET) (A UGC Autonomous Institute) Hyderabad Telangana India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

In the era of expanding wireless sensor network (WSN) applications, safeguarding these networks against a spectrum of threats, including Denial-of-Service (DoS) attacks, is paramount. The innate resource limitations of sensor nodes often render conventional security measures ineffective. This study introduces a pioneering approach integrating minimalist machine learning techniques to combat DoS attacks within WSNs. Our methodology adeptly leverages machine learning capabilities while strategically curbing computational and resource demands. By distilling essential features from network traffic patterns, our model adeptly identifies aberrations signaling DoS attacks, all while preserving the efficiency of resource-limited sensor nodes. Its minimalist framework seamlessly integrates with existing WSN architectures, negating the need for substantial hardware upgrades and maintaining network performance. Results underscore the superior detection accuracy and efficiency of our minimalist machine learning strategy compared to traditional methods. Moreover, its scalability and adaptability render it ideal for deployment across varied WSN setups, fortifying them against DoS threats while safeguarding core network *** mitigating DoS attack impacts without burdening computational resources, our approach marks a significant stride towards bolstering the security and dependability of wireless sensor networks amid evolving threat landscapes.

关键词： Wireless sensor networks Renewable energy sources Scalability Machine learning Telecommunication traffic Network security Denial-of-service attack

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Security Assessment Algorithms for Transmission and Services in SDN-Based Mobile Networks

Security Assessment Algorithms for Transmission and Services...

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International Conference on computer and Communications (ICCC)

作者： Wei Zhu Chunhui Bao Tianxiang Chen Yi Zhuang Embedded Computer System Architecture Jiangsu Automation Research Institute Lianyungang China Faculty of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

In response to the challenge of comprehensively security assessment and analysis in existing mobile networks from both upper-layer services and network transmission levels, A SDN-based security assessment model for transmission and service security in mobile networks (SAMTS) is proposed. This model introduces security assessment algorithms, in which static configuration information and dynamic abnormal information are considered comprehensively to judge the trend and the risk degree of attack when evaluating the security of network nodes. Meanwhile, the node importance of service orientation and transmission is integrated for security evaluation. The effectiveness of the proposed method is verified by simulation and comparison experiments, demonstrating its higher evaluation accuracy compared with similar algorithms.

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Revisiting the Efficacy of Signal Decomposition in AI-based Time Series Prediction

Revisiting the Efficacy of Signal Decomposition in AI-based ...

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2024 International Conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Dan Jiang, Kexin Wu, Chuhan Peng, Yan Han, Zhaolong Chen, Yaoran Institute of Artificial Intelligence Shanghai University Shanghai200444 China Department of Computer Engineering and Science Shanghai University Shanghai200444 China Department of Electronic Engineering Tsinghua University Beijing100084 China School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Time series prediction is a fundamental problem in scientific exploration and artificial intelligence (AI) technologies have substantially bolstered its efficiency and accuracy. A well-established paradigm in AI-driven time series prediction is injecting physical knowledge into neural networks through signal decomposition methods, and sustaining progress in numerous scenarios has been reported. However, we uncover non-negligible evidence that challenges the effectiveness of signal decomposition in AI-based time series prediction. We confirm that improper dataset processing with subtle future label leakage is unfortunately widely adopted, possibly yielding abnormally superior but misleading results. By processing data in a strictly causal way without any future information, the effectiveness of additional decomposed signals diminishes. Our work probably identifies an ingrained and universal error in time series modeling, and the de facto progress in relevant areas is expected to be revisited and calibrated to prevent future scientific detours and minimize practical losses. © 2025 SPIE.

关键词： Information leakage

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Enhanced Neural SLAM with Semantic Segmentation in Dynamic Environments

Enhanced Neural SLAM with Semantic Segmentation in Dynamic E...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Zhengcheng Shen Minzhe Mao Linh Kästner Jens Lambrecht Faculty of Electrical Engineering and Computer Science Chair Industry Grade Networks and Clouds Berlin Institute of Technology Berlin Germany

Mobile robots face a significant challenge when navigating in dynamic environments such as human crowds. Existing research works typically employ separate data or simulators for dynamic object detection and navigation tasks. This paper combines the photo-realistic simulator Habitat with embedded dynamic objects to create a playground for optimizing vision-based navigation algorithms. To validate our system, we implement and train three approaches - zero masks, image memory inpainting, and semantic map filter - using semantically segmented information. We then implement and evaluate an adapted reinforcement-learning-based SLAM algorithm using the validation Gibson dataset. The results indicate that moderate localization bias occurs when the environment is small and the navigation process is short. However, the error will accumulate over time. Additionally, a dynamic environment leads to less accurate localization. All three approaches can reduce the localization error, while the semantic map filter approach shows the best overall performance.

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Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

arXiv

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arXiv 2023年

作者： Bhuiyan, Teham Kästner, Linh Hu, Yifan Kutschank, Benno Lambrecht, Jens Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Most commonly used sampling-based path planning approaches such as RRT require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing the approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, Deep Reinforcement Learning has shown remarkable results in dealing with unknown environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We evaluated our model against state-of-the-art sampling-based planners in several experiments. The results show the superiority of our planner in terms of path length and execution time. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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