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CANARI: A Monitoring Framework for Cluster analysis and Node Assessment for Resource Integrity 24

CANARI: A Monitoring Framework for Cluster Analysis and Node...

proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Ryan T. DeRue Jacob Verburgt Rosen Center for Advanced Computing Purdue University West Lafayette Indiana USA

ISBN: (纸本)9798350355543

In providing High Performance computing (HPC) systems to research faculty at universities, research computing facilitators must often strike a balance between providing the most up-to-date versions of commonly used software packages and libraries while also ensuring that the software ecosystem on the cluster is stable enough that version changes do not cause performance degradation to existing workflows. Additionally, the modern data centers where these ecosystems are running are very large, intricately complex systems that provide many points of failure. The sum of these two challenges present the need for tools that can help to ensure that these systems, and the software running on those systems, are continuing to perform at their expected levels. To this end, this paper will present a framework for Cluster analysis and Node Assessment for Resource Integrity that we call CANARI. CANARI was developed and used at the Rosen Center for Advanced computing (RCAC) to continuously monitor the availability of nodes in our clusters as well as their performance against synthetic benchmarks, ingest that performance data into a persistent database, mark nodes displaying performance regression offline, and provide summary reports and real-time updates to the Slack instance used at RCAC by using Slack's API.

关键词： Benchmarking

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TronicBoards: An Accessible Electronics Toolkit for People with Intellectual Disabilities 22

TronicBoards: An Accessible Electronics Toolkit for People w...

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CHI Conference on Human Factors in computing systems (CHI)

作者： Senaratne, Hashini Ananthanarayan, Swamy Ellis, Kirsten Monash Univ Dept Human Centred Comp Melbourne Vic Australia

ISBN: (纸本)9781450391573

Engagement with electronic toolkits enhances people's creative abilities, self-esteem, problem-solving skills and enables the creation of personally meaningful artifacts. A variety of simplifed electronics toolkits are increasingly available to help diferent user groups engage with technology. However, they are often inaccessible for people with intellectual disabilities (IDs), who experience a range of cognitive and physical impairments. We designed and developed TronicBoards, a curated set of accessible electronic modules, to address this gap. We evaluated it one-on-one with 10 participants using a guided exploration approach. Our analysis revealed that participants were able to create simple sensor-based interactive circuits with varying levels of assistance. We report the strengths and weaknesses of TronicBoards, considering participants' successes and challenges in manipulating and comprehending toolkit components, circuit building activities, and troubleshooting processes. We discuss implications for designing inclusive electronics toolkits for people with IDs, particularly in considering design elements that improve functionality, comprehensibility and agency.

关键词： Human-centered computing -> Accessibility design and evaluation methods User interface toolkits Social and professional topics -> People with disabilities

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User Perspectives and Ethical Experiences of Apps for Depression: A Qualitative analysis of User Reviews 22

User Perspectives and Ethical Experiences of Apps for Depres...

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2022 CHI Conference on Human Factors in computing systems, CHI 2022

作者： Bowie-Dabreo, Dionne Sas, Corina Iles-Smith, Heather Sünram-Lea, Sandra Department of Psychology Lancaster University United Kingdom School of Computing and Communications Lancaster University United Kingdom Research and Innovation Salford Royal Nhs Foundation Trust United Kingdom

ISBN: (纸本)9781450391573

Apps for depression can increase access to mental health care but concerns abound with disparities between academic development of apps and those available through app stores. Reviews highlighted ethical shortcomings of these self-management tools, with a need for greater insight into how ethical issues are experienced by users. We addressed these gaps by exploring user reviews of such apps to better understand user experiences and ethical issues. We conducted a thematic analysis of 2,217 user reviews sampled from 40 depression apps in Google Play and Apple App Store, totaling over 77,500 words. Users reported positive and negative experiences, with ethical implications evident in areas of benefits, adverse effects, access, usability and design, support, commercial models, autonomy, privacy, and transparency. We integrated our elements of ethically designed apps for depression and principles of nonmaleficence, beneficence, justice, autonomy, and virtue, and we conclude with implications for ethical design of apps for depression. © 2022 acm.

关键词： Ethical technology

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The complexity of soundness in workflow nets 22

The complexity of soundness in workflow nets

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37th Annual acm/IEEE Symposium on Logic in Computer Science, LICS 2022

作者： Blondin, Michael Mazowiecki, Filip Offtermatt, Philip Max Planck Institute for Software Systems Saarbrücken Germany Université de Sherbrooke Sherbrooke Canada

ISBN: (纸本)9781450393515

Workflow nets are a popular variant of Petri nets that allow for the algorithmic formal analysis of business processes. The central decision problems concerning workflow nets deal with soundness, where the initial and fnal confgurations are specifed. Intuitively, soundness states that from every reachable confguration one can reach the fnal confguration. We settle the widely open complexity of the three main variants of soundness: classical, structural and generalised soundness. The frst two are EXPSPACE-complete, and, surprisingly, the latter is PSPACE-complete, thus computationally simpler. © 2022 Copyright held by the owner/author(s). Publication rights licensed to acm.

关键词： Petri nets

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EMVGAN: Emotion-Aware Music-Video Common Representation Learning via Generative Adversarial Networks 5

EMVGAN: Emotion-Aware Music-Video Common Representation Lear...

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5th International Joint Workshop on Multimedia Artworks analysis and Attractiveness computing in Multimedia, MMArt-acm 2022

作者： Tsai, Yu-Chih Pan, Tse-Yu Kao, Ting-Yang Yang, Yi-Hsuan Hu, Min-Chun Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science National Tsing Hua University Taiwan Institue of Information Systems and Applications National Tsing Hua University Taipei Taiwan Research Center for IT Innovation Academia Sinica Taiwan

ISBN: (纸本)9781450392402

Music can enhance our emotional reactions to videos and images, while videos and images can enrich our emotional response to music. Cross-modality retrieval technology can be used to recommend appropriate music for a given video and vice versa. However, the heterogeneity gap caused by the inconsistent distribution between different data modalities complicates learning the common representation space from different modalities. Accordingly, we propose an emotion-Aware music-video cross-modal generative adversarial network (EMVGAN) model to build an affective common embedding space to bridge the heterogeneity gap among different data modalities. The evaluation results revealed that the proposed EMVGAN model can learn affective common representations with convincing performance while outperforming other existing models. Furthermore, the satisfactory performance of the proposed network encouraged us to undertake the music-video bidirectional retrieval task. © 2022 acm.

关键词： Generative adversarial networks

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Design and Modeling of a New File Transfer Architecture to Reduce Undetected Errors Evaluated in the FABRIC Testbed

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proceedings of the acm on measurement and analysis of computing systems 2025年第2期9卷 1-42页

作者： Jain, Prateek Sarabi, Arash Matta, Abraham Syrotiuk, Violet R. Boston University Department of Computer Science BostonMA United States Arizona State University School of Computing and Augmented Intelligence TempeAZ United States

Ensuring the integrity of petabyte-scale file transfers is essential for the data gathered from scientific instruments. As packet sizes increase, so does the likelihood of errors, resulting in a higher probability of undetected errors in the packet. This paper presents a Multi-Level Error Detection (MLED) framework that leverages in-network resources to reduce undetected error probability (UEP) in file transmission. MLED is based on a configurable recursive architecture that organizes communication in layers at different levels, decoupling network functions such as error detection, routing, addressing, and security. Each layer Lij at level i implements a policy Pij that governs its operation, including the error detection mechanism used, specific to the scope of that layer. MLED can be configured to mimic the error detection mechanisms of existing large-scale file transfer protocols. The recursive structure of MLED is analyzed and it shows that adding additional levels of error detection reduces the overall UEP. An adversarial error model is designed to introduce errors into files that evade detection by multiple error detection policies. Through experimentation using the FABRIC testbed the traditional approach, with transport- and data link- layer error detection, results in a corrupt file transfer requiring retransmission of the entire file. Using its recursive structure, an implementation of MLED detects and corrects these adversarial errors at intermediate levels inside the network, avoiding file retransmission under non-zero error rates. MLED therefore achieves a 100% gain in goodput over the traditional approach, reaching a goodput of over 800 Mbps on a single connection with no appreciable increase in delay. © 2025 Copyright held by the owner/author(s)

关键词： Network function virtualization

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Energy Saving Evaluation Method for Energy Storage Technologies Based on the Intimate Data Method of Machine Learning

Energy Saving Evaluation Method for Energy Storage Technolog...

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2023 International Conference on Power, Communication, computing and Networking Technologies, PCCNT 2023

作者： Zhao, Yulin Sun, Chong Li, Lanqing Li, Yifeng Zhou, Hang State Grid Jiangsu Electric Power Co. Ltd. Jiangsu Nanjing210000 China NARI-TECH Nanjing Control Systems Ltd. Jiangsu Nanjing China

ISBN: (纸本)9781450399951

Power storage technology is an important technical measure to transfer peak power, develop low valley power, optimize resource allocation and protect ecological environment. The promotion and application of energy storage technology has just started, and although the scale of promotion and application is not large, the benefits are obvious and the potential is huge. The existing research methods are prone to model errors when dealing with small samples and cannot change the subjectivity of the weights obtained by hierarchical analysis. Therefore, this paper uses an intimate data approach based on machine learning to evaluate the ranking of electricity use for energy storage. The research method introduces the scope of the experimental area and the benchmark data, calculates the Euclidean distance of each sample from the best point and the Euclidean distance of the worst point by comparing them with other constructed measurement points, and integrates their affiliation with the best point and the worst point by using the minimum distance of the best point and the maximum distance of the worst point as references. The experimental results show that it is feasible to use the intimate data method for energy efficiency assessment of energy storage and electricity use technologies, that the method is easy to calculate, and that its results are reliable and valid, reducing environmental pollution and improving the utilization of available resources. © 2023 acm.

关键词： Machine learning

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A Large-Scale Longitudinal analysis of Missing Label Accessibility Failures in Android Apps 22

A Large-Scale Longitudinal Analysis of Missing Label Accessi...

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CHI Conference on Human Factors in computing systems (CHI)

作者： Fok, Raymond Zhong, Mingyuan Ross, Anne Spencer Fogarty, James Wobbrock, Jacob O. Univ Washington Seattle WA 98195 USA Bucknell Univ Lewisburg PA 17837 USA

ISBN: (纸本)9781450391573

We present the frst large-scale longitudinal analysis of missing label accessibility failures in Android apps. We developed a crawler and collected monthly snapshots of 312 apps over 16 months. We use this unique dataset in empirical examinations of accessibility not possible in prior datasets. Key large-scale fndings include missing label failures in 55.6% of unique image-based elements, longitudinal improvement in ImageButton elements but not in more prevalent ImageView elements, that 8.8% of unique screens are unreachable without navigating at least one missing label failure, that app failure rate does not improve with number of downloads, and that effective labeling is neither limited to nor guaranteed by large software organizations. We then examine longitudinal data in individual apps, presenting illustrative examples of accessibility impacts of systematic improvements, incomplete improvements, interface redesigns, and accessibility regressions. We discuss these fndings and potential opportunities for tools and practices to improve label-based accessibility.

关键词： mobile app accessibility large-scale longitudinal analysis

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CHI 2022 - proceedings of the 2022 CHI Conference on Human Factors in computing systems

CHI 2022 - Proceedings of the 2022 CHI Conference on Human F...

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2022 CHI Conference on Human Factors in computing systems, CHI 2022

ISBN: (纸本)9781450391573

The proceedings contain 637 papers. The topics discussed include: ‘I don’t want people to look at me differently’: designing user-defined above-the-neck gestures for people with upper body motor impairments;understanding gesture input articulation with upper-body wearables for users with upper-body motor impairments;identifying factors that inhibit self-care behavior among individuals with severe spinal cord injury;understanding how people with limited mobility use multi-modal input;VocabEncounter: NMT-powered vocabulary learning by presenting computer-generated usages of foreign words into users’ daily lives;Affinder: expressing concepts of situations that afford activities using context-detectors;two heads are better than one: a dimension space for unifying human and artificial intelligence in shared control;supporting serendipitous discovery and balanced analysis of online product reviews with interaction-driven metrics and bias-mitigating suggestions;shared interest: measuring human-AI alignment to identify recurring patterns in model behavior;and scaling creative inspiration with fine-grained functional aspects of product ideas.

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PIPE: Privacy-preserving 6DoF Pose Estimation for Immersive Applications 25

PIPE: Privacy-preserving 6DoF Pose Estimation for Immersive ...

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proceedings of the 23rd acm Conference on Embedded Networked Sensor systems

作者： Nan Wu Ruizhi Cheng Songqing Chen Bo Han George Mason University Fairfax Virginia USA

ISBN: (纸本)9798400714795

Image-based mapping and localization offer six degrees of freedom (6DoF) pose estimation for immersive applications. This is achieved by matching, on a server, 2D visual features extracted from a mobile device's camera view and 3D features stored in a map. While effective, this process may lead to privacy breaches (e.g., exposure of sensitive information captured by camera views). To tackle this crucial issue, we present PIPE, a first-of-its-kind Privacy-preserving Image-based 6DoF Pose Estimation system. The design of PIPE is motivated by our key observation that uploading only a small amount of features extracted from camera views for pose estimation could reduce privacy leakage. However, trade-offs exist between privacy preservation, system utility (i.e., pose estimation accuracy), and system performance (e.g., end-to-end latency). To balance the trade-offs, PIPE deliberately explores the feature-detection space to reduce computation latency, designs an efficient feature ranking method by judiciously utilizing map data, and optimizes feature selection by jointly considering the features' ranking and spatial distribution to improve pose estimation accuracy. Moreover, we construct a learning-based metric to quantify the extent of privacy leakage in images. Our extensive performance evaluation reveals that PIPE can effectively preserve privacy and reduce end-to-end latency by up to 22.6%, while marginally affecting pose estimation accuracy (e.g., as low as 2.7%).

关键词： mobile spatial computing

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