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GPU Reliability Assessment: Insights Across the Abstraction Layers

GPU Reliability Assessment: Insights Across the Abstraction ...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Yang, Lishan Papadimitriou, George Sartzetakis, Dimitris Jog, Adwait Smirni, Evgenia Gizopoulos, Dimitris George Mason University Department of Computer Science United States University of Athens Dept. of Informatics & Telecomm. Greece University of Virginia Department of Computer Science United States William & Mary Department of Computer Science United States

ISBN: (纸本)9798350358711

Graphics Processing Units (GPUs) are widely de-ployed and utilized across various computing domains including cloud and high-performance computing. Considering its extensive usage and increasing popularity, ensuring GPU reliability is cru-cial. Software-based reliability evaluation methodologies, though fast, often neglect the complex hardware details of modern GPU designs. This oversight could lead to misleading measurements and misguided decisions regarding protection strategies. This paper breaks new ground by conducting an in-dept. examination of well-established vulnerability assessment methods for modern GPU architectures, from the microarchitecture all the way to the software layers. It highlights divergences between popular software-based vulnerability evaluation methods and the ground truth cross-layer evaluation, which persist even under strong protections like triple modular redundancy. Accurate evaluation requires considering fault distribution from hardware to software. Our comprehensive measurements offer valuable insights into the accurate assessment of GPU reliability. © 2024 IEEE.

关键词： Program processors

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LSTM-Based Classification of Psychiatric Disorders Using Ultra-Short Raw ECG Signals

LSTM-Based Classification of Psychiatric Disorders Using Ult...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Tsakmaki, Paraskevi V. Tasoulis, Sotiris Georgakopoulos, Spiros V. Plagianakos, Vassilis P. Dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Dept. of Mathematics University of Thessaly Lamia Greece

ISBN: (纸本)9798331508333

Psychiatric disorders, including bipolar disorder, depression, and schizophrenia, pose substantial public health challenges due to the intricate nature of their diagnosis and management. Electrocardiogram (ECG) signals offer a noninvasive method to assess autonomic nervous system function, which is frequently dysregulated in these conditions. However, conventional ECG analysis techniques often require prolonged recordings and extensive preprocessing, limiting their applicability in real-time or large-scale settings. This study presents an approach leveraging Long Short-Term Memory (LSTM) networks for medical data classification, specifically for the identification of psychiatric disorders using ultra-short, raw ECG signals. Our methodology efficiently captures the temporal dynamics of ECG signals within brief windows, significantly reducing the need for extensive preprocessing and manual feature extraction. The proposed LSTM-based classifier achieved an impressive mean accuracy of 97.12% ± 0.006, underscoring its precision and efficacy in distinguishing between different psychiatric conditions. Our findings indicate that LSTM networks, with their capacity to effectively model complex temporal patterns in short ECG recordings, represent a promising advancement in the diagnostic landscape of psychiatry. © 2025 IEEE.

关键词： Electrocardiography

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning 13

Phoneme Classification in Imagined Speech Using Explainable ...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Kim, Sejin Kang, Hyunwook Jeong, Ji-Hoon Lee, Minji Dept. Artificial Intelligence The Catholic University of Korea Bucheon Korea Republic of Dept. Electrical and Computer Engineering Inha University Incheon Korea Republic of Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36%, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke’s area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke’s area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Precision Localization: An Experimental Study on BLE Fingerprinting and Trilateration with ESP32 1

Precision Localization: An Experimental Study on BLE Fingerp...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Elhusseiny, Ahmed H. Zamzam, Marwa Zaghloul, Yasmine The German International University Dept. of Electrical Engineering Cairo Egypt The German International University Dept. of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350348057

This paper presents a method for indoor localization using two complementary techniques: Fingerprinting (FP) and Trilateration. The system is based on Bluetooth Low Energy (BLE) technology and a network of wireless anchors to determine the location of a mobile device. The FP approach collects Received Signal Strength Indicator (RSSI) data from BLE anchors in various indoor locations and creates a map of the environment in an offline stage. During the online stage, the device's location is determined by comparing its current RSSI measurements to the map. The FP technique showed an accuracy of 79.31% without positions on the other side of a separating concrete wall and 71.88% including those positions. In contrast, trilateration calculates the position of the device using distance measurements from multiple anchors, achieving an accuracy of 44.44%. Both techniques were tested in various environments and layouts, and the results indicate that FP is more suitable for indoor localization, particularly in large buildings with many rooms. This paper highlights the potential of these two techniques for practical indoor localization applications. Moreover, one of the experimental setups for the fingerprinting technique was tested in multiple rooms separated by a concrete wall to provide information about the potential scalability of the technique. © 2023 IEEE.

关键词： Bluetooth

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End-to-End n-ary Relation Extraction for Combination Drug Therapies 11

End-to-End n-ary Relation Extraction for Combination Drug Th...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Jiang, Yuhang Kavuluru, Ramakanth University of Kentucky Department of Computer Science LexingtonKY United States Univ. of Kentucky Division of Biomedical Informatics Dept. of Internal Medicine LexingtonKY United States

ISBN: (纸本)9798350302639

Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently there are over 350K articles in PubMed that use the combination drug therapy MeSH heading with at least 10K articles published per year over the past two decades. Extracting combination therapies from scientific literature inherently constitutes an n-ary relation extraction problem. Unlike in the general n-ary setting where n is fixed (e.g., drug-gene-mutation relations where n =3), extracting combination therapies is a special setting where n ≥2 is dynamic, depending on each instance. Recently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset, CombDrugExt, for extracting such therapies from literature. Here, we use a sequence-to-sequence style end-to-end extraction method to achieve an F1-Score of 66.7% on the CombDrugExt test set for positive (or effective) combinations. This is an absolute ≈5% F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. Our model seamlessly extracts all drug entities and relations in a single pass and is highly suitable for dynamic n-ary extraction scenarios. © 2023 IEEE.

关键词： Drug therapy

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On Min-Max Graph Balancing with Strict Negative Correlation Constraints 34

On Min-Max Graph Balancing with Strict Negative Correlation ...

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Kuo, Ting-Yu Chen, Yu-Han Frosini, Andrea Hsieh, Sun-Yuan Tsai, Shi-Chun Kao, Mong-Jen Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan Dept. of Mathematics and Informatics University of Florence Italy Dept. of Computer Science and Information Engineering National Chi-Nan University Puli Taiwan

ISBN: (纸本)9783959772891

We consider the min-max graph balancing problem with strict negative correlation (SNC) constraints. The graph balancing problem arises as an equivalent formulation of the classic unrelated machine scheduling problem, where we are given a hypergraph G = (V, E) with vertex-dependent edge weight function p: E × V 7→ Z≥0 that represents the processing time of the edges (jobs). The SNC constraints, which are given as edge subsets C1, C2, . . ., Ck, require that the edges in the same subset cannot be assigned to the same vertex at the same time. Under these constraints, the goal is to compute an edge orientation (assignment) that minimizes the maximum workload of the vertices. In this paper, we conduct a general study on the approximability of this problem. First, we show that, in the presence of SNC constraints, the case with maxe∈E |e| = maxi |Ci| = 2 is the only case for which approximation solutions can be obtained. Further generalization on either direction, e.g., maxe∈E |e| or maxi |Ci|, will directly make computing a feasible solution an NP-complete problem to solve. Then, we present a 2-approximation algorithm for the case with maxe∈E |e| = maxi |Ci| = 2, based on a set of structural simplifications and a tailored assignment LP for this problem. We note that our approach is general and can be applied to similar settings, e.g., scheduling with SNC constraints to minimize the weighted completion time, to obtain similar approximation guarantees. Further cases are discussed to describe the landscape of the approximability of this prbolem. For the case with |V | ≤ 2, which is already known to be NP-hard, we present a fully-polynomial time approximation scheme (FPTAS). On the other hand, we show that the problem is at least as hard as vertex cover to approximate when |V | ≥ 3. © Ting-Yu Kuo, Yu-Han Chen, Andrea Frosini, Sun-Yuan Hsieh, Shi-Chun Tsai, and Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： Computational complexity

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Enhancing Drug Detection Strategies for Post-Rehabilitated Juveniles: A Comprehensive Review and Empirical Study 1

Enhancing Drug Detection Strategies for Post-Rehabilitated J...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Gour, Prabha Bir, Lovish Ansari, Lubna Samnata, Rojalini Soundarya, R. Chandigarh University Dept. of Computer Science Engineering Chandigarh India Madhav University Dept. of Humanities and S.Science Pindwara India Chandigarh University Dept. of of Com. Science Engineering Chandigarh India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Usha Martin University Dept. of Pharamcy Jharkhand India Dhanalakshmi Srinivasan College Dept. of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350356816

Drug addiction is a complex problem that will affect individuals of all ages, including juveniles. It is a chronic and often relapsing disease that requires continued care and support to help individuals achieve and maintain long-term recovery. While rehabilitation programs are designed to help individuals overcome addiction, relapse rates remain high, making post-rehabilitation drug detection an important component of continued care. This study aims to investigate the efficacy of various drug detection techniques for juveniles who have completed their rehabilitation. The success of rehabilitation program for young drug addicts depends on the creation of efficient drug detection rules and procedures that can assist avoid relapse and increase program effectiveness overall. The research technique comprises both an empirical investigation of post-rehabilitated juvenile participants and a thorough evaluation of the literature of past studies on drug detection for young people. The results of this study will have a big impact on how well drug detection policies and practises for young people who have completed their rehabilitation are developed. © 2024 IEEE.

关键词： Addiction Drug Post Rehabilitated Rehabilitation

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Reconfiguration Cost for Reconfigurable Computing Architectures 23

Reconfiguration Cost for Reconfigurable Computing Architectu...

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23rd ACIS International Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Summer 2022

作者： Takano, Shigeyuki Amano, Hideharu Keio University Dept. of Informatics and Computer Science Kanagawa Japan

ISBN: (纸本)9798350396379

Reconfigurable computing systems show the advantage of high flexibility and high-performance or low-power relative to traditional computing systems. Reconfiguration can change user logic statically or dynamically. The reconfiguration is a key feature in the reconfigurable computing system, however, the reconfiguration techniques are not considered enough. One of the reconfiguration techniques must be chosen for usage target characteristics of reconfiguration. This article shows the cost of typical reconfiguration techniques which can be applied to not only field-programmable gate arrays (FPGAs) but also coarse-grained reconfigurable arrays (CGRAs). We call these microarchitectures, field-programmable logic (FPL). We focus on three device classes;traditional configurable FPL (ex. FPGA), a partially reconfigurable FPL and a multi-context FPL. We investigate how the size and frequency of reconfiguration can be taken care of and how much speed-up is theoretically expected from a perspective of reconfiguration cost. In addition, the investigation introduces temporal and spatial configuration cache techniques. © 2022 IEEE.

关键词： Reconfigurable architectures

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Netstaldi: A Modular Distributed Architecture for Incremental Network Discovery 8

Netstaldi: A Modular Distributed Architecture for Incrementa...

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8th Italian Conference on Cyber Security, ITASEC 2024

作者： Paier, Matteo Pizzolitto, Mattia Foresti, Gian Luca Miculan, Marino University of Udine Dept. of Mathematics Computer Science and Physics Italy IMT Alti Studi Lucca Italy Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Italy

Maintaining a comprehensive understanding of a network's connected devices is fundamental for robust security. This knowledge is crucial for tasks like vulnerability assessments, identifying potential attack surfaces, and network mapping. In this paper, we propose Netstaldi, a distributed architecture specifically designed for incremental and secure network discovery. Our architecture leverages non-intrusive network scanning techniques based on standard TCP/IP protocols, eliminating the need for monitoring agents on individual devices. This approach prioritizes scalability, modularity, and resilience, making it well-suited to handle large and dynamic network environments. We have implemented a prototype system based on this architecture, utilizing established open source tools. This system has been successfully tested on a large, real-world network. The results are promising: the tool efficiently scans the entire network within a few hours, and the intuitive GUI allows administrators to interactively explore the generated network map to identify and address potential vulnerabilities and misconfigurations. Copyright © 2024 for this paper by its authors.

关键词： Network security

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ECC's Achilles'Heel: Unveiling Weak Keys in Standardized Curves 8

ECC's Achilles'Heel: Unveiling Weak Keys in Standardized Cur...

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8th Italian Conference on Cyber Security, ITASEC 2024

作者： Talotti, Enrico Paier, Matteo Miculan, Marino University of Udine Dept. of Mathematics Computer Science and Physics Italy IMT Alti Studi Lucca Italy Ca' Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Italy

The strength of Elliptic curve cryptography (ECC) relies on curve choice. This work analyzes weak keys in standardized curves, i.e., private keys within small subgroups of the auxiliary group ∗p. We quantify weak key prevalence across standardized curves, revealing a potential vulnerability due to numerous small divisors in auxiliary group orders. To address this, we leverage the implicit baby-steps giant-steps algorithm, which transforms the complex elliptic curve discrete logarithm problem into a simpler problem within ∗p. This enables efficient detection of weak keys in small-order subgroups. Our findings highlight the importance of rigorous key testing in applications using standardized ECC. While random weak keys are unlikely, malicious actors could exploit this by manipulating key generation libraries. To this end, we show how users can assess their private key vulnerabilities and mitigate risks by eliminating weak keys. Hence, this work contributes to improved ECC security through proactive key management practices. Copyright © 2024 for this paper by its authors.

关键词： Geometry

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