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SMART: Serverless Module analysis and Recognition Technique for Managed Applications

SMART: Serverless Module Analysis and Recognition Technique ...

引用

IEEE/acm International Symposium on Cluster computing and the Grid (CCGRID)

作者： Adi Ashkenazi Edita Grolman Aviad Elyashar Dudu Mimran Oleg Brodt Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Serverless Function-as-a-Service (FaaS) environments enable developers to build and run cloud applications without the need to manage the underlying servers and computing infrastructure, allowing them to focus on implementing the application logic. Such environments contain numerous functions and dynamic resources, e.g., APIs and databases, making it challenging to gain insight and context of internal events i.e., recognize modules. Module in a serverless application is a set of functions and resources, that represents a functional unit that shares logical context. This paper presents SMART, a method for automatic analysis and recognition of modules for managed serverless applications. The proposed method creates an event-based graph by analyzing the standard serverless logs that document events involving the application’s functions and resources and utilizes well-known community detection algorithms (such as Louvain), with graph centrality metrics (such as degree centrality) to recognize the modules. SMART enables high-level visibility of the application’s structure and logical context which can facilitate security analysis and contribute to improved decision-making of incident response handlers, who typically do not have direct access to the application’s design and code, which can lead to challenges in fully understanding the system’s intricacies. We focused on the popular Amazon Web Services (AWS) Lambda serverless computing platform and evaluated the proposed method on three different demo applications (Airline Booking, VOD, and E-commerce). We compared SMART’s performance to four overlapping community detection algorithms and showed that it outperformed them in the task of module recognition, with a maximum improvement of 61% on the omega index metric compared to the Speaker-Listener Label Propagation algorithm. In addition, we demonstrate that the use of large language models (LLMs) with the knowledge gained by SMART can enrich security analysis insights.

关键词： measurement Web services Serverless computing Computer architecture Security Indexes Electronic commerce Detection algorithms Standards Airline industry

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ICDAR 25: Intelligent Cross-Data analysis and Retrieval 25

ICDAR 25: Intelligent Cross-Data Analysis and Retrieval

引用

proceedings of the 2025 International Conference on Multimedia Retrieval

作者： Takahiro Komamizu Marc A. Kastner Minh-Son Dao Michael Alexander Riegler Duc-Tien Dang-Nguyen Son Tran Nagoya University Nagoya Japan Hiroshima City University Hiroshima Japan National Institute of Information and Communications Technology (NICT) Tokyo Japan Simula Metropolitan Center for Digital Engineering Oslo Norway University of Bergen Bergen Norway Deakin University Melbourne Australia

ISBN: (纸本)9798400718779

The sixth edition of the Intelligent Cross-Data analysis and Retrieval (ICDAR) workshop continues to serve as a forum for researchers and practitioners addressing the integration, analysis, and retrieval of heterogeneous data sources. While individual modalities such as wearable sensors, lifelogging cameras, and social media have been well studied, analyzing cross-data that incorporates multiple perspectives remains a crucial yet challenging task for advancing human-centered applications. In 2025, the workshop received 19 submissions, of which 7 were accepted following a careful peer-review process, resulting in an acceptance rate of 37%. The accepted papers covered a wide range of topics, including zero-shot composed image retrieval, vision-language scene understanding, adaptive modality fusion, lightweight fine-tuning with truncated SVD, and real-world federated split learning on mobile devices. By fostering interdisciplinary collaboration across domains such as well-being, disaster mitigation, mobility, food computing, and smart cities, the workshop continues to highlight emerging challenges and solutions for building intelligent, sustainable, and human-centric systems driven by cross-modal and multimodal data analytics.

关键词： 3d scene understanding

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KAIROS: Talking Heads and Moving Bodies for Successful Meetings 21

KAIROS: Talking Heads and Moving Bodies for Successful Meeti...

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22nd International Workshop on Mobile computing systems and Applications, HotMobile 2021

作者： Choi, Jun-Ho Constantinides, Marios Joglekar, Sagar Quercia, Daniele Yonsei University Incheon Korea Republic of Nokia Bell Labs Cambridge United Kingdom

ISBN: (纸本)9781450383233

Successful meetings create a safe environment for contribution;one that attendees feel engaged in and part of. Previous research has shown that meetings success depends not only on execution, but also on whether attendees feel psychologically safe. While this aspect is, to a great extent, partly observable through certain body cues during in-person meetings, they are often overlooked in virtual ones. To partly fix that, we developed "Kairos"-a system for multi-modal monitoring of virtual meetings that captures subtle body cues. We deployed it in 55 real-world corporate meetings and, upon six metrics for body cues, we built a model to predict a meeting's self-reported success, achieving an AUC as high as 79%. We found that certain body cues were more predictive of a meeting's success (defined as a linear combination of execution and psychological safety) than others (head movements, for example, were twice as predictive as hand movements), not least because they captured three typical meeting phases (its initiation, collective discussions, and turning points) whose presence (or absence) greatly mattered for success. © 2021 acm.

关键词： Modal analysis

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Large Scale analysis of Multitasking Behavior During Remote Meetings 21

Large Scale Analysis of Multitasking Behavior During Remote ...

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CHI Conference on Human Factors in computing systems

作者： Cao, Hancheng Lee, Chia-Jung Iqbal, Shamsi Czerwinski, Mary Wong, Priscilla Rintel, Sean Hecht, Brent Teevan, Jaime Yang, Longqi Stanford Univ Stanford CA 94305 USA Amazon Seattle WA USA Microsoft Redmond WA USA UCL London England Microsoft Cambridge England

ISBN: (纸本)9781450380966

Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people's productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715 -person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking).

关键词： Multitasking meeting collaboration remote work

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Application-Focused HPC Network Monitoring

Application-Focused HPC Network Monitoring

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ISC High Performance 2025 Research Paper proceedings (40th International Conference)

作者： Philipp A. Friese Olivier Marsden Martin Schulz Technical University of Munich Garching Germany ECMWF Reading United Kingdom

ISBN: (数字)9783982633619

The network hardware used in High-Performance computing (HPC) systems is one of the core differentiators to regular compute clusters and has seen substantial improvements over the years. However, to fully utilize its capabilities, careful application tuning is necessary, which in turn is only possible if insights into application behavior can be obtained. With the rise of increasingly complex HPC application software stacks, this requires new kinds of fine-grained network monitoring capabilities. While regular TCP/IP based traffic can be monitored using a large variety of existing tools, for Remote Direct Memory Access (RDMA) based communication, which is prevalent in HPC networks, only very few tools are available. Further, those few tools either largely depend on features of specific network hardware, are mostly limited to node-granular monitoring only, or are limited to specific programming models. In this paper, we introduce a novel network monitoring tool that directly layers on a portable network abstraction library, namely libfabric, while enabling application specific monitoring. With that, our tool provides network hardware and programming model agnostic, per-process monitoring of RDMA-based network utilization and is capable of always-on, system-wide monitoring of production environments. We conduct a detailed overhead analysis on several state-of-the-art HPC systems with a variety of network hardware and show that our monitoring tool induces low overhead in the monitored application. In addition, we apply our monitoring tool to a real-world HPC application running in a production environment.

关键词： Production systems Source coding High performance computing TCPIP Programming Hardware Libraries Software measurement Monitoring Tuning

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"This Whole Thing Smacks of Gender": Algorithmic Exclusion in Bioimpedance-based Body Composition analysis 21

"This Whole Thing Smacks of Gender": Algorithmic Exclusion i...

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acm Conference on Fairness, Accountability, and Transparency (FAccT)

作者： Albert, Kendra Delano, Maggie Harvard Law Sch Cambridge MA 02138 USA Swarthmore Coll Swarthmore PA USA

ISBN: (纸本)9781450383097

Smart weight scales offer bioimpedance-based body composition analysis as a supplement to pure body weight measurement. Companies such as Withings and Fitbit tout composition analysis as providing self-knowledge and the ability to make more informed decisions. However, these aspirational statements elide the reality that these numbers are a product of proprietary regression equations that require a binary sex/gender as their input. Our paper combines transgender studies-influenced personal narrative with an analysis of the scientific basis of bioimpedance technology used as part of the Withings smart scale. Attempting to include nonbinary people reveals that bioelectrical impedance analysis has always rested on physiologically shaky ground. White nonbinary people are merely the tip of the iceberg of those who may find that their smart scale is not so intelligent when it comes to their bodies. Using body composition analysis as an example, we explore how the problem of trans and nonbinary inclusion in personal health tech goes beyond the issues of adding a third "gender" box or slapping a rainbow flag on the packaging. We also provide recommendations as to how to approach creating more inclusive technologies even while still relying on exclusionary data.

关键词： data collection and curation sex/gender bioelectrical impedance analysis body composition critical data/algorithm studies science and technology studies critical HCI and the design of algorithmic systems

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Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion 1

Deepfake Detection System for the ADD Challenge Track 3.2 Ba...

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1st International Workshop on Deepfake Detection for Audio Multimedia, DDAM 2022

作者： Zhang, Yuxiang Lu, Jingze Wang, Xingming Li, Zhuo Xiao, Runqiu Wang, Wenchao Li, Ming Zhang, Pengyuan Institute of Acoustics Chinese Academy of Sciences Haidian District Beijing China University of Chinese Academy of Sciences Shijingshan District Beijing China Data Science Research Center Duke Kunshan University Jiangsu Province Kunshan China School of Computer Science Wuhan University Hubei Province Wuhan China

ISBN: (纸本)9781450394963

This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion. The proposed system is a score-level fusion of several light convolutional neural network (LCNN) based models. Various front-ends are used as input features, including low-frequency short-time Fourier transform and Constant Q transform. Due to the complex noise and rich synthesis algorithms, it is difficult to obtain the desired performance using the training set directly. Online data augmentation methods effectively improve the robustness of fake audio detection systems. In particular, the reasons for the poor improvement of score fusion are explored through visualization of the score distributions and comparison with score distribution on another dataset. The overfitting of the model to the training set leads to extreme values of the scores and low correlation of the score distributions, which makes score fusion difficult. Fusion with partially fake audio detection system improves system performance further. The submission on track 3.2 obtained the weighted equal error rate (WEER) of 11.04%, which is one of the best performing systems in the challenge. © 2022 Association for computing Machinery.

关键词： Online systems

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Tessera: Discretizing data analysisworkflows on a task level 21

Tessera: Discretizing data analysisworkflows on a task level

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2021 CHI Conference on Human Factors in computing systems: Making Waves, Combining Strengths, CHI 2021

作者： Yan, Jing Nathan Gu, Ziwei Rzeszotarski, Jefrey. M Cornell University IthacaNY United States

ISBN: (纸本)9781450380966

Researchers have investigated a number of strategies for capturing and analyzing data analyst event logs in order to design better tools, identify failure points, and guide users. However, this remains challenging because individual-and session-level behavioral diferences lead to an explosion of complexity and there are few guarantees that log observations map to user cognition. In this paper we introduce a technique for segmenting sequential analyst event logs which combines data, interaction, and user features in order to create discrete blocks of goal-directed activity. Using measures of inter-dependency and comparisons between analysis states, these blocks identify patterns in interaction logs coupled with the current view that users are examining. Through an analysis of publicly available data and data from a lab study across a variety of analysis tasks, we validate that our segmentation approach aligns with users' changing goals and tasks. Finally, we identify several downstream applications for our approach. © 2021 acm.

关键词： Data Analytics

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Binding Data Narrations - Corroborating the Plausibility of Scientific Narratives by Open Research Data 23

Binding Data Narrations - Corroborating the Plausibility of ...

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proceedings of the 2023 acm/IEEE Joint Conference on Digital Libraries

作者： Denis Nagel Till Affeldt Nils Voges Ulrich Güntzer Wolf-Tilo Balke Institute for Information Systems TU Braunschweig Braunschweig Germany Institute for Informatics University of Tübingen Tübingen Germany

ISBN: (纸本)9798350399318

Narratives determine our worldviews, but need evidence to be believable. For scientific narratives, hard evidence usually is provided by specially curated research data and experiments within each publication. The aim is to strengthen the narratives' overall plausibility: the less plausible a narrative is, the more it is bound to be challenged. As recent advances in NLP enabled the scientific community to tackle important problems in fact-checking and more profound semantic interpretations of structured data, there is now also hope to unlock the rich narrative knowledge that such data sets can offer. Yet, current strategies to extract such narrative knowledge still heavily rely on exhaustive bottom-up analysis to cast insights from data into a human-understandable form. In this paper, we take a novel integration-based approach to design a system that reduces the task of finding narrative evidence to applying a sequence of simpler top-down matching tasks. Our BiND system builds upon an expressive definition of structured narratives. It uses them as templates for schema and instance matching against web tables, thereby computing flexible bindings between narratives and data. By combining structured narratives with a carefully chosen selection of statistical metrics to assess the inherent relationships between different attributes of the matched data, our system allows us to reliably identify the most plausible witnesses for a given narrative. We demonstrate the applicability of our system in the real world on the vast open data repository of the World Health Organization (WHO).

关键词： narrative bindings

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Design and analysis of CXL performance models for tightly-coupled heterogeneous computing 1

Design and analysis of CXL performance models for tightly-co...

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1st International Workshop on Extreme Heterogeneity Solutions, ExHET 2022

作者： Cabrera, Anthony M. Young, Aaron R. Vetter, Jeffrey S. Oak Ridge National Laboratory United States

ISBN: (纸本)9781450393447

Truly heterogeneous systems enable partitioned workloads to be mapped to the hardware that nets the best performance. However, current practice requires that inter-device communication between different vendors' hardware use host memory as an intermediary step. To date, there are no widely adopted solutions that allow accelerators to directly transfer data. A new cache-coherent protocol, CXL, aims to facilitate easier, fine-grained sharing between accelerators. In this work we analyze existing methods for designing heterogeneous applications that target GPUs and FPGAs working collaboratively, followed by an exploration to show the benefits of a CXL-enabled system. Specifically, we develop a test application that utilizes both an NVIDIA P100 GPU and a Xilinx U250 FPGA to show current communication limitations. From this application, we capture overall execution time and throughput measurements on the FPGA and GPU. We use these measurements as inputs to novel CXL performance models to show that using CXL caching instead of host memory results in a 1.31X speedup, while a more tightly-coupled pipelined implementation using CXL-enabled hardware would result in a speedup of 1.45X. © 2022 acm.

关键词： Field programmable gate arrays (FPGA)

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