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CEEMS: A Resource Manager Agnostic Energy and Emissions Monitoring Stack

CEEMS: A Resource Manager Agnostic Energy and Emissions Moni...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Paipuri, Mahendra Orsay France

ISBN: (纸本)9798350355543

With the rapid acceleration of ML/AI research in the last couple of years, the energy consumption of the Information and Communication Technology (ICT) domain has rapidly increased. As a major part of this energy consumption is due to users' workloads, it is evident that users need to be aware of the energy footprint of their applications. Compute Energy & Emissions Monitoring Stack (CEEMS) has been designed to address this issue. CEEMS can report energy consumption and equivalent emissions of user workloads in real time for HPC and cloud platforms alike. Besides CPU energy usage, it supports reporting energy usage of workloads on NVIDIA and AMD GPU accelerators. CEEMS has been built around the prominent open-source tools in the observability eco-system like Prometheus and Grafana. CEEMS has been designed to be extensible and it allows the Data Center (DC) operators to easily define the energy estimation rules of user workloads based on the underlying hardware. This paper explains the architectural overview of CEEMS, data sources that are used to measure energy usage and estimate equivalent emissions and potential use cases of CEEMS from operator and user perspectives. Finally, the paper will conclude by describing how CEEMS deployment on the Jean-Zay supercomputing platform is capable of monitoring more than 1400 nodes that have a daily job churn rate of around 20k jobs. © 2024 IEEE.

关键词： cloud emissions Energy measurement GPU HPC monitoring

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Multimodal Humor Detection and Social Perception Prediction 5

Multimodal Humor Detection and Social Perception Prediction

引用

5th Multimodal Sentiment analysis Challenge and Workshop: Social Perception and Humor (MuSe)

作者： Bijoy, Mehedi Hasan Porjazovski, Dejan Nhan Phan Huang, Guangpu Grosz, Tamas Kurimo, Mikko Aalto Univ Espoo Finland

ISBN: (纸本)9798400711992

The parallel audio-visual-text data contains vast amount of information. Thus it is essential to develop machine learning algorithms that can utilise them efficiently. In this work, we investigated unimodal and multimodal solutions for MuSe Humor and Perception challenges. Our main goal was to explicitly show the contribution of each modality in the multimodal systems. In addition, for the Humor challenge, we examined the effect of extending the input context and smoothing the framewise predictions. For Perception challenge, we trained an attention-encoder-decoder model to predict all perceived labels with a single model. During the challenge, the best results were achieved by a fusion of unimodal and multimodal systems, AUC = 0.8645 for Humor, and mean Pearson's correlation rho = 0.3550 for Perception. By investigating the multimodal systems we found that using only part of the video for model training can be beneficial, suggesting that valuable information is condensed to certain parts of the video. The implementation of our models and experiments can be found at https://***/aalto-speech/MuSe-2024.

关键词： Multimodal Fusion Multimodal Sentiment analysis Affective computing Social Perception Humor Detection

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ICCAI 2024 - proceedings of the 2024 10th International Conference on computing and Artificial Intelligence

ICCAI 2024 - Proceedings of the 2024 10th International Conf...

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10th International Conference on computing and Artificial Intelligence, ICCAI 2024

ISBN: (纸本)9798400717055

The proceedings contain 72 papers. The topics discussed include: classification of degradation level of pineapple leaf spot disease using MobileNetV3;multi-processing depth map estimation for single-view 3D image generation;artistic outpainting through adaptive image-to-text and text-to-image generation;visual presentation learning with contrastive learning for radiology report generation;data-driven methodology for bike route identification to enhance urban cycling infrastructure in metropolitan manila;person re-identification from video surveillance systems using artificial intelligence methods;assessing the security posture of mobile automated fingerprint identification systems (MABIS): a comprehensive analysis for enhanced resilience;and facemasks detection with different orientation using deep learning algorithms.

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The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment analysis Tools and Their Identity-based Biases 24

The "Colonial Impulse" of Natural Language Processing: An Au...

引用

CHI Conference on Human Factors in computing Sytems (CHI)

作者： Das, Dipto Guha, Shion Brubaker, Jed Semaan, Bryan Univ Colorado Dept Informat Sci Boulder CO 80309 USA Univ Toronto Fac Informat Toronto ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9798400703300

While colonization has sociohistorically impacted people's identities across various dimensions, those colonial values and biases continue to be perpetuated by sociotechnical systems. One category of sociotechnical systems-sentiment analysis tools-can also perpetuate colonial values and bias, yet less attention has been paid to how such tools may be complicit in perpetuating coloniality, although they are often used to guide various practices (e.g., content moderation). In this paper, we explore potential bias in sentiment analysis tools in the context of Bengali communities who have experienced and continue to experience the impacts of colonialism. Drawing on identity categories most impacted by colonialism amongst local Bengali communities, we focused our analytic attention on gender, religion, and nationality. We conducted an algorithmic audit of all sentiment analysis tools for Bengali, available on the Python package index (PyPI) and GitHub. Despite similar semantic content and structure, our analyses showed that in addition to inconsistencies in output from different tools, Bengali sentiment analysis tools exhibit bias between different identity categories and respond differently to different ways of identity expression. Connecting our findings with colonially shaped sociocultural structures of Bengali communities, we discuss the implications of downstream bias of sentiment analysis tools.

关键词： Algorithmic audit Sentiment analysis tools Bias Identity Colonial

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Assessing Human Visual Attention in Retail Human Robot Interaction: A YOLOv8-Nano and Eye-Tracking Approach 32

Assessing Human Visual Attention in Retail Human Robot Inter...

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32nd acm Conference on User Modeling, Adaptation and Personalization (acm UMAP)

作者： Kumar, Kamlesh Chen, Yuhao Hu, Boyi Luo, Yue San Jose State Univ San Jose CA 95192 USA Univ Florida Gainesville FL USA

ISBN: (纸本)9798400704666

Objectives: This research delves into the dynamics of humanrobot interaction (HRI) in retail environments, with a focus on robot detection from videos captured via an eye-tracking system. Methods: The study employs YOLOv8-nano model for real-time robot detection during grocery shopping tasks. All videos were processed using the YOLOv8 model to test inference speed while performing eye-tracking data analysis as a case study. Results: The YOLOv8 model demonstrated high precision in robot detection, with a mean average precision (mAP) of approximately 97.3% for Intersection over Union (IoU), 100% precision, and 99.87% recall for box detection. The model's ability to process an average of 160.36 frames per second (FPS) confirmed its suitability for real-time applications. In the case study on the impact of a robot's presence on human eye movements, the presence of a robot contributes to greater consistency in gaze fixation behavior, potentially leading to more predictable patterns of visual attention. Conclusion: The study's findings contribute significantly to the design of safer and more efficient cobot systems. They provide a deeper understanding of human responses in real-world scenarios, which is crucial for the development of effective HRI systems.

关键词： Human Robot Interaction Object Detection Applied computing Physiological Patterns

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Fault-Tolerant Deep Learning Cache with Hash Ring for Load Balancing in HPC systems

Fault-Tolerant Deep Learning Cache with Hash Ring for Load B...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Lee, Seoyeong Khan, Awais Kim, Yoochan Park, Junghwan Hwang, Soon Lee, Jae-Kook Hong, Taeyoung Zimmer, Christopher Kim, Youngjae Sogang University Korea Republic of Oak Ridge National Laboratory United States KISTI Korea Republic of

ISBN: (纸本)9798350355543

Large-scale DL on HPC systems like Frontier and Summit uses distributed node-local caching to address scalability and performance challenges. However, as these systems grow more complex, the risk of node failures increases, and current caching approaches lack fault tolerance, jeopardizing large-scale training jobs. We analyzed six months of SLURM job logs from Frontier and found that over 30% of jobs failed after an average of 75 minutes. To address this, we propose fault-tolerance strategies that recache data lost from failed nodes using a hash ring technique for balanced data recaching in the distributed node-local caching, reducing reliance on the PFS. Our extensive evaluations on Frontier showed that the hash ring-based recaching approach reduced training time by approximately 25% compared to the approach that redirects I/O to the PFS after node failures and demonstrated effective load balancing of training data across nodes. © 2024 IEEE.

关键词： Distributed Deep Learning Fault Tolerance HPC NVMe Cache

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A Large-Scale measurement of Cybercrime Against Individuals 22

A Large-Scale Measurement of Cybercrime Against Individuals

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CHI Conference on Human Factors in computing systems (CHI)

作者： Breen, Casey Herley, Cormac Redmiles, Elissa M. Univ Calif Berkeley Berkeley CA 94720 USA Microsoft Res Redmond WA USA Max Planck Inst Software Syst Saarbrucken Germany

ISBN: (纸本)9781450391573

We know surprisingly little about the prevalence and severity of cybercrime in the U.S. Yet, in order to prioritize the development and distribution of advice and technology to protect end users, we require empirical evidence regarding cybercrime. Measuring crime, including cybercrime, is a challenging problem that relies on a combination of direct crime reports to the government - which have known issues of under-reporting - and assessment via carefullydesigned self-report surveys. We report on the first large-scale, nationally representative academic survey (n=11,953) of consumer cybercrime experiences in the U.S. Our analysis answers four research questions: (1) What is the prevalence and (2) the monetary impact of these cybercrimes we measure in the U.S.?, (3) Do inequities exist in victimization?, and (4) Can we improve cybercrime measurement by leveraging social-reporting techniques used to measure physical crime? Our analysis also offers insight toward improving future measurement of cybercrime and protecting users.

关键词： cybercrime network scale-up digitial inequity

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An Empirical analysis and Resource Footprint Study of Deploying Large Language Models on Edge Devices 24

An Empirical Analysis and Resource Footprint Study of Deploy...

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Annual acm Southeast Conference (acmSE)

作者： Dhar, Nobel Deng, Bobin Lo, Dan Wu, Xiaofeng Zhao, Liang Suo, Kun Kennesaw State Univ Marietta GA 30060 USA City Univ Macau Macau Peoples R China

ISBN: (纸本)9798400702372

The success of ChatGPT is reshaping the landscape of the entire IT industry. The large language model (LLM) powering ChatGPT is experiencing rapid development, marked by enhanced features, improved accuracy, and reduced latency. Due to the execution overhead of LLMs, prevailing commercial LLM products typically manage user queries on remote servers. However, the escalating volume of user queries and the growing complexity of LLMs have led to servers becoming bottlenecks, compromising the quality of service (QoS). To address this challenge, a potential solution is to shift LLM inference services to edge devices, a strategy currently being explored by industry leaders such as Apple, Google, Qualcomm, Samsung, and others. Beyond alleviating the computational strain on servers and enhancing system scalability, deploying LLMs at the edge offers additional advantages. These include real-time responses even in the absence of network connectivity and improved privacy protection for customized or personal LLMs. This article delves into the challenges and potential bottlenecks currently hindering the effective deployment of LLMs on edge devices. Through deploying the LLaMa-2 7B model with INT4 quantization on diverse edge devices and systematically analyzing experimental results, we identify insufficient memory and/or computing resources on traditional edge devices as the primary obstacles. Based on our observation and empirical analysis, we further provide insights and design guidance for the next generation of edge devices and systems from both hardware and software directions.

关键词： Large Language Models (LLMs) LLaMA-2 Edge Devices Edge computing

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10th IEEE/acm International Conference on Big Data computing, Applications and Technologies, BDCAT 2023

10th IEEE/ACM International Conference on Big Data Computing...

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10th IEEE/acm International Conference on Big Data computing, Applications and Technologies, BDCAT 2023

ISBN: (纸本)9798400704734

The proceedings contain 28 papers. The topics discussed include: real-time route planning to reduce pedestrian pollution exposure in urban settings;improving the validation of automotive self-learning systems through the synergy of scenario-based testing and metamorphic relations;EPSAPG: a pipeline combining MMseqs2 and PSI-BLAST to quickly generate extensive protein sequence alignment profiles;sleep well: pragmatic analysis of the idle states of intel processors;workload-aware cache management of bitmap indices;a framework for profiling spatial variability in the performance of classification models;privacy-preserving intrusion detection system for internet of vehicles using split learning;an approach for dynamic behavioral prediction and fault injection in cyber-physical systems;and cardiotocography signal abnormality detection based on deep semi-unsupervised learning.

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Towards an Optimized Heterogeneous Distributed Task Scheduler in OpenMP Cluster

Towards an Optimized Heterogeneous Distributed Task Schedule...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Neveu, Remy Ceccato, Rodrigo Leite, Gustavo Araujo, Guido Diaz, Jose M. Monsalve Yviquel, Herve Instituto de Computação Campinas Brazil

ISBN: (纸本)9798350355543

This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication *** work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts;second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments;and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods. © 2024 IEEE.

关键词： Distributed architectures Parallel systems Scheduling and task partitioning

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