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Toward Using Representation Learning for Cloud Resource Usage Forecasting

Toward Using Representation Learning for Cloud Resource Usag...

引用

Workshop on AI For systems (AI4Sys)

作者： Moundir Ghorab, Razine Dimitra Doudali, Thaleia IMDEA Software Inst Madrid Spain

ISBN: (纸本)9798400706523

Cloud providers face issues with cost efficiency due to the inefficient utilization of resources within their clusters. To improve efficiency, cloud resource management systems use techniques such as autoscaling and overcommiting resources across users, where it is necessary to predict future resource usage. However, it is very challenging to generate accurate predictions, due to the wide variety of observed and unseen patterns. Recent works show that representation learning is very effective in generic time series forecasting. Inspired from this, we propose a system prototype for predicting cloud resource usage using representation learning. Our approach transforms time series into embeddings and explores their spatial proximity to generate predictions about future resource usage. Our experimental analysis acts as a proof-of-concept and shows that the proposed approach delivers highly accurate forecasts, significantly outperforming current machine learning baselines. Future directions of this work will focus on refining certain aspects of our proposed system and address remaining challenges and limitations.

关键词： Cloud computing Representation Learning Machine Learning Forecasting Prediction Resource Usage

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Leveraging Human-AI Partnership Approach to Unravel Human Psychological Constructs

Leveraging Human-AI Partnership Approach to Unravel Human Ps...

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Meng, Han School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713958

Human psychological constructs form the cornerstone of our understanding of cognition, emotion, and behavior, which are crucial to health informatics and HCI studies. However, current psychological construct analyses may lack theoretical grounding, cultural sensitivity, context, and socio-digital tailored interventions. My thesis explores how these human-centered computing methods, in particular human-AI partnerships, can unravel psychological constructs, using mental health stigma as a case study. Specifically, the study takes a multi-faceted approach: designing conversational agents for multilingual, cross-national data collection from over 1,000 participants;conducting human-AI collaborative qualitative analysis to interpret the embodied psychological constructs;modeling causal relationships from human-chatbot conversations to decompose psychological factors and their interplay;dissecting cross-sociocultural variation;and exploring digital techniques for restructuring psychological constructs. The overall goal is to enhance human-centered AI approaches for psychological-construct analysis, foster cultural inclusivity, inform the design of culturally appropriate healthcare technologies, and promote social justice by operationalizing psychological constructs. © 2025 Copyright held by the owner/author(s).

关键词： Culture Human-centered AI Mental health stigma Psychological construct

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A Novel Method for Trust-Sensitive Design: Applying Conjoint analysis to Machine-Assisted Genome Annotation

A Novel Method for Trust-Sensitive Design: Applying Conjoint...

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： McGrath, Melanie Cooper, Patrick Duenser, Andreas Irons, Jessica Li, Xiaomei Suchecki, Rad Data61 CSIRO Melbourne Australia The University of Melbourne Parkville VIC Australia Data61 CSIRO Clayton VIC Australia Data61 CSIRO Hobart TAS Australia Data61 CSIRO Eveleigh NSW Australia CSIRO Marsfield NSW Australia CSIRO Adelaide SA Australia

ISBN: (纸本)9798400713958

Human trust is recognized as being central to integration of AI systems into organizational settings and workflows. However, trust is a highly context-dependent, state-based attitude that can be influenced by hundreds of user, technology, and environmental factors. In this paper we describe the novel application of conjoint analysis, an established behavioral science method, to quantify the relative contribution of four design features to trust in an AI application to support scientists engaged in genome annotation. We found that Transparency had the greatest relative importance to trust, followed by Confidence, Control, and Accuracy. Our results demonstrate the utility of a conjoint analysis approach for identifying and prioritizing trust-sensitive design features, offering a cost- and time-effective approach to guide development efforts. © 2025 Copyright held by the owner/author(s).

关键词： artificial intelligence conjoint analysis genome annotation trust

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Decoding Attack Behaviors by Analyzing Patterns in Instruction-Based Attacks using gem5 35

Decoding Attack Behaviors by Analyzing Patterns in Instructi...

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35th International Workshop on Rapid System Prototyping, RSP 2024

作者： Awais, Muhammad Mushtaq, Maria Naviner, Lirida Bruguier, Florent Yahya, Jawad Haj Benoit, Pascal Institut Polytechnique de Paris Comelec Telecom-Paris 19 place Marguerite Perey Palaiseau France University of Montpellier Lirmm Cnrs 161 rue Ada Montpellier34095 France Rivos Ins 3315 Scott Boulevard Santa Clara United States

ISBN: (纸本)9798331519537

The diversity of Instruction Set Architectures (ISAs), each with its unique constraints and optimization strategies, presents significant opportunities and challenges in processor design. Modern processor vendors exploit these ISAs to enhance security, reliability, and performance. Recent security vulnerabilities, notably Spectre and Meltdown, have highlighted the critical need for robust hardware security measures. In this paper, we employ gem5, a state-of-the-art cycle-accurate simulation tool, to simulate the Spectre attack. We developed and modified scripts for both x86 and ARM architectures to ensure compatibility with gem5 version 23.1. Our simulation setup involved running attack scenarios under various configurations to gather comprehensive data on cache misses, cache hits, mispredicted branches, and level 2 cache hits and misses. In the simulation, we analyzed the trace files generated by gem5, utilizing a range of debug flags such as Exec for disassembly (dasm) insights. By detailed analysis of cache and branch prediction using detailed debug data revealed by gem5 traces, we identify some specific attack patterns that are useful for automating the detection of the attacks. Our future work aims to expand this analysis to include additional attack vectors and find more attack patterns, thereby strengthening our attack pattern recognition capabilities. © 2024 IEEE.

关键词： Attacks Patterns Attacks Simulation Embedded systems Hardware Attacks Hardware Security

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Power Side-Channel analysis and Mitigation for Neural Network Accelerators based on Memristive Crossbars 24

Power Side-Channel Analysis and Mitigation for Neural Networ...

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29th Asia and South Pacific Design Automation Conference (ASP-DAC)

作者： Sapui, Brojogopal Tahoori, Mehdi B. Karlsruhe Inst Technol KIT Inst Comp Engn Karlsruhe Germany

ISBN: (纸本)9798350393545

The modern trend of exploring Artificial Intelligence (AI) in various industries, such as big data, edge computing, automobile, and medical applications, has increased tremendously. As functionalities grow, energy-efficient hardware for AI devices becomes crucial. To address that, Computation-in-Memory (CiM) using Non-Volatile Memories (NVMs) offers a promising solution. However, security is also an important concern in this computation paradigm. In this work, we analyze the vulnerability for power side-channel attacks on Multiply-Accumulate (MAC) operations implemented in CiM architecture based on emerging NVMs. Our results show that peripheral devices such as Analog-to-Digital Converters (ADCs) leak much more sensitive information than the crossbar itself because of its significant power consumption. Therefore, we propose a circuit-level countermeasure based on hiding for the ADCs of memristive CiM architecture to mitigate the power attacks. The efficiency of our proposed countermeasure is shown by both attacks and leakage assessment methodologies using a maximum of one million measurement traces.

关键词： Energy efficiency

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CODiT: Conformal Out-of-Distribution Detection in Time-Series Data for Cyber-Physical systems 23

CODiT: Conformal Out-of-Distribution Detection in Time-Serie...

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14th acm/IEEE International Conference on Cyber-Physical systems (ICCPS) part of CPS-IoT Week

作者： Kaur, Ramneet Sridhar, Kaustubh Park, Sangon Yang, Yahan Jha, Susmit Roy, Anirban Sokolsky, Oleg Lee, Insup Univ Penn Philadelphia PA 19104 USA Georgia Inst Technol Atlanta GA 30332 USA SRI Int Menlo Pk CA 94025 USA

ISBN: (纸本)9798400700361

Uncertainty in the predictions of learning enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning enabled component (LEC) is one source of uncertainty in the LEC's predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher's method leads to the proposed detector CODiT with bounded false alarms. We illustrate the efficacy of CODiT by achieving state-of-the-art results in autonomous driving systems with perception (or vision) LEC. We also perform experiments on medical CPS for GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject's body. Code, data, and trained models are available at https://***/kaustubhsridhar/time-series-OOD

关键词： Learning Enabled Components Uncertainty Cyber-Physical systems Time-Series Out-of-Distribution Conformal Detection

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Performance measurement on Heterogeneous Processors with PAPI 24

Performance Measurement on Heterogeneous Processors with PAP...

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

作者： Willow E. Cunningham Vincent M. Weaver Electrical and Computer Engineering University of Maine Orono Maine USA

ISBN: (纸本)9798350355543

Modern computer processors improve their computing power by having multiple cores. Traditionally these cores were homogeneous: many identical cores with the same capabilities. Instead it is possible to create processors that have heterogeneous (or hybrid) cores, where the various cores have differing capabilities. This can lead to energy savings and other efficiencies, but complicates performance analysis. Heterogeneous cores have been common for years in embedded ARM processors; recently support has appeared in x86 desktop processors as well. It is likely that before long server and high-performance systems will also gain hybrid *** look at current Linux support for heterogeneous processors and detail the various problems encountered when adding support for them to the PAPI performance measurement library.

关键词： Linux perf

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Designing and Evaluating an Advanced Dance Video Comprehension Tool with In-situ Move Identification Capabilities 24

Designing and Evaluating an Advanced Dance Video Comprehensi...

引用

CHI Conference on Human Factors in computing Sytems (CHI)

作者： Hassan, Saad Pataca, Calua de Lacerda Nourian, Laleh Tigwell, Garreth W. Davis, Briana Wagman, Will Zhenya Silver Tulane Univ New Orleans LA 70118 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9798400703300

Analyzing dance moves and routines is a foundational step in learning dance. Videos are often utilized at this step, and advancements in machine learning, particularly in human-movement recognition, could further assist dance learners. We developed and evaluated a Wizard-of-Oz prototype of a video comprehension tool that offers automatic in-situ dance move identification functionality. Our system design was informed by an interview study involving 12 dancers to understand the challenges they face when trying to comprehend complex dance videos and taking notes. Subsequently, we conducted a within-subject study with 8 Cuban salsa dancers to identify the benefits of our system compared to an existing traditional feature-based search system. We found that the quality of notes taken by participants improved when using our tool, and they reported a lower workload. Based on participants interactions with our system, we offer recommendations on how an AI-powered span-search feature can enhance dance video comprehension tools.

关键词： Dance Dance Learning Choreography Human Movement analysis Search systems

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Weakly Supervised Turn-level Engagingness Evaluator for Dialogues 8

Weakly Supervised Turn-level Engagingness Evaluator for Dial...

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8th Conference on Human Information Interaction and Retrieval

作者： Jiang, Shaojie Vakulenko, Svitlana de Rijke, Maarten Univ Amsterdam Amsterdam Netherlands Amazon Barcelona Spain

ISBN: (纸本)9798400700354

Engagingness is an important measurement for evaluating open-domain conversational systems. The standard approach to evaluating dialogue engagingness is by measuring conversation turns per session (CTPS), which implies that the dialogue length is the main predictor of the user engagement with a dialogue system. The main limitation of CTPS is that it can only be measured at the session level, i.e., once the dialogue is over. But a dialogue system has to continuously monitor user engagement throughout the dialogue session as well. Existing approaches to measuring turn-level engagingness require human annotations for training. We pioneer an alternative approach, Weakly Supervised Engagingness Evaluator (WeSEE), which uses the remaining depth for each turn as a heuristic weak label for engagingness. WeSEE does not require human annotations and also relates closely to CTPS, thus serving as a good learning proxy for this metric. We show that WeSEE achieves the new state-of-the-art results on the Fine-grained Evaluation of Dialog dataset (0.38 Spearman correlation coefficient) and the DailyDialog dataset (0.62 Spearman correlation coefficient).

关键词： Conversation analysis engagingness user experience

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Feature Relevance analysis to Explain Concept Drift - A Case Study in Human Activity Recognition

Feature Relevance Analysis to Explain Concept Drift - A Case...

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2022 acm International Joint Conference on Pervasive and Ubiquitous computing and the 2022 acm International Symposium on Wearable Computers, UbiComp/ISWC 2022

作者： Siirtola, Pekka Röning, Juha Biomimetics and Intelligent Systems Group Oulu Finland

ISBN: (纸本)9781450394239

This article studies how to detect and explain concept drift. Human activity recognition is used as a case study together with a online batch learning situation where the quality of the labels used in the model updating process starts to decrease. Drift detection is based on identifying a set of features having the largest relevance difference between the drifting model and a model that is known to be accurate and monitoring how the relevance of these features changes over time. As a main result of this article, it is shown that feature relevance analysis cannot only be used to detect the concept drift but also to explain the reason for the drift when a limited number of typical reasons for the concept drift are predefined. To explain the reason for the concept drift, it is studied how these predefined reasons effect to feature relevance. In fact, it is shown that each of these has an unique effect to features relevance and these can be used to explain the reason for concept drift. © 2022 Owner/Author.

关键词： Feature extraction

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