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CryoWire: Wire-Driven Microarchitecture Designs for Cryogenic computing 27

CryoWire: Wire-Driven Microarchitecture Designs for Cryogeni...

引用

27th acm International Conference on Architectural Support for Programming Languages and Operating systems (ASPLOS)

作者： Min, Dongmoon Chung, Yujin Byun, Ilkwon Kim, Junpyo Kim, Jangwoo Seoul Natl Univ Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781450392051

Cryogenic computing, which runs a computer device at an extremely low temperature, is promising thanks to its significant reduction of wire resistance as well as leakage current. Recent studies on cryogenic computing have focused on various architectural units including the main memory, cache, and CPU core running at 77K. However, little research has been conducted to fully exploit the fast cryogenic wires, even though the slow wires are becoming more serious performance bottleneck in modern processors. In this paper, we propose a CPU microarchitecture which extensively exploits the fast wires at 77K. For this goal, we first introduce our validated cryogenic-performance models for the CPU pipeline and network on chip (NoC), whose performance can be significantly limited by the slow wires. Next, based on the analysis with the models, we architect CryoSP and CryoBus as our pipeline and NoC designs to fully exploit the fast wires. Our evaluation shows that our cryogenic computer equipped with both microarchitectures achieves 3.82 times higher system-level performance compared to the conventional computer system thanks to the 96% higher clock frequency of CryoSP and five times lower NoC latency of CryoBus.

关键词： Cryogenic computing Pipelining Network on Chip Multicore Architectures Superscalar Architectures Chip Multi Processor

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PiaMuscle: Improving Piano Skill Acquisition by Cost-effectively Estimating and Visualizing Activities of Miniature Hand Muscles 25

PiaMuscle: Improving Piano Skill Acquisition by Cost-effecti...

引用

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

作者： Liu, Ruofan Peng, Yichen Oku, Takanori Liao, Chen-Chieh Wu, Erwin Furuya, Shinichi Koike, Hideki School of Computing Institute of Science Tokyo Tokyo Japan Sony Computer Science Laboratories Inc. Tokyo Japan Shibaura Institute of Technology Tokyo Japan NeuroPiano Institute Tokyo Japan Digital Human Lab Huawei Tokyo Research Center Tokyo Japan

ISBN: (纸本)9798400713941

Understanding neuromusculoskeletal mechanisms significantly impacts skill specialization and proficiency. While existing methods can infer large muscle activities during gross motor movements, the estimation of dexterous motor control involving miniature muscles remains underexplored. Targeting the coordinated hand muscles in advanced piano performance, we learn spatiotemporal discrete representations of electromyography (EMG) data and hand postures utilizing a multimodal dataset. Subsequently, we train a precise and cost-effective neural network model. Based on this model, PiaMuscle is introduced to investigate if visualizing muscle activities during piano training enhances piano performance. Quantitative and qualitative results of a user study with highly skilled professional pianists demonstrate that PiaMuscle provides reliable muscle activation data to support and optimize force control. Our research underscores the potential of a naturalistic workflow to estimate small muscles' activities from readily accessible human-centric information and more accurately when combined with tool-centric data, thereby enhancing skill acquisition. © 2025 Copyright held by the owner/author(s). Publication rights licensed to acm.

关键词： Electromyography Estimation Multimodal Learning Muscle analysis

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Creating Concise and Efficient Dynamic Analyses with ALDA 27

Creating Concise and Efficient Dynamic Analyses with ALDA

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27th acm International Conference on Architectural Support for Programming Languages and Operating systems (ASPLOS)

作者： Cheng, Xiang Devecsery, David Georgia Inst Technol Atlanta GA 30332 USA Meta Platforms Inc Seattle WA USA

ISBN: (纸本)9781450392051

Dynamic program analyses are essential to creating safe, reliable, and productive computing environments. However, these analyses are challenging and time-consuming to construct due to the low-level optimization required to achieve acceptable performance. Consequently, many analyses are often never realized, or have inefficient implementations. In this work we argue that many analyses can and should be constructed with a high-level description language, leaving the burden of low-level optimizations to the analysis instrumentation system itself. We propose a novel language for dynamic analysis called ALDA. ALDA leverages common structuring of dynamic analyses to provide a simple and high-level description for dynamic analyses. Through restricting the supported behaviors to only essential operations in dynamic analyses, an optimizing compiler for ALDA can create analysis implementations with performance on-par to hand-tuned analyses. To demonstrate ALDA's universality and efficiency, we create an optimizing compiler for ALDA targeting the LLVM instrumentation framework named ALDAcc. We use ALDAcc to construct 8 different dynamic analysis algorithms, including the popular MemorySanitizer analysis, and show their construction is succinct and simple. By comparing two of them (Eraser and MemorySanitizer) with their hand-tuned implementations, we show that ALDAcc's optimized analyses are comparable to hand-tuned implementations.

关键词： Domain specific language dynamic analysis compiler optimization

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Research proposal: AI-assisted Affective computing and Spatial Audio for Interactive Multimodal Virtual Environments 13

Research proposal: AI-assisted Affective Computing and Spati...

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13th acm Multimedia systems Conference (MMSys)

作者： Antonio De Rus, Juan Montagud, Mario Cobos, Maximo Univ Valencia Valencia Spain I2CAT Fdn Barcelona Spain

ISBN: (纸本)9781450392839

This paper contains the research proposal of Juan Antonio De Rus that was presented at the MMSys 2022 doctoral symposium. The use of virtual reality (VR) is growing every year. With the normalization of remote work it is to expect that the use of immersive virtual environments to support tasks as online meetings, education, etc, will grow even more. VR environments typically include multimodal content formats (synthetic content, video, audio, text) and even multi-sensory stimuli to provide an enriched user experience. In this context, Affective computing (AC) techniques assisted by Artificial Intelligence (AI) become a powerful means to determine the user's perceived Quality of Experience (QoE). In the field of AC, we investigate a variety of tools to obtain accurate emotional analysis by using AI techniques applied on physiological data. In this doctoral study we have formulated a set of open research questions and objectives on which we plan to generate valuable contributions and knowledge in the field of AC, spatial audio, and multimodal interactive virtual environments, one of which is the creation of tools to automatically evaluate the QoE, even in real-time, which can provide valuable benefits both to service providers and consumers. For data acquisition we use sensors of different quality to study the scalability, reliability and replicability of our solutions, as clinical-grade sensors are not always within the reach of the average user.

关键词： Affective computing Artificial Inteligence Spatial Audio Virtual Reality Multimodal Content Quality of Experience

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DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network 23

DrivCapsNet: A Driving Style Detection Algorithm Based on Ca...

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4th International Conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Wang, Yueyue Wang, Chenxing Luo, Haiyong Zhao, Fang Beijing University of Posts and Telecommunications Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

Driver behavior plays a fundamental role in the driver-vehicle-environment system, where the driving style can significantly impact vehicle emissions, fuel consumption, insurance expenses, road safety, and advanced driver assistance systems (ADAS). Nonetheless, detecting driver behavior is a complex and challenging task, traditional methods require a lot of data pre-processing and there is still no research on discriminative driving behavior with capsule networks which can capture the spatial relationships of data. However, it has not been fully studied and applied for driver behavior detection. To tackle these challenges, we propose an methodology for detecting driving style using a capsule network, named DrivCapsNet, which is capable of detecting various driving styles using either inertial measurement unit (IMU) data or camera data. A crucial advantage of this method is that its dynamic routing mechanism can extract the relationships between the parts and the entity, thereby improving detection accuracy. We performed comprehensive experiments on two realistic driving datasets to substantiate the efficacy of our proposed DrivCapsNet approach. The outcomes validate that our approach performs well and achieves accurate driving style detection, highlighting its potential to contribute significantly to the field of driver behavior analysis. © 2023 acm.

关键词： Advanced driver assistance systems

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RTune: Towards Automated and Coordinated Optimization of computing and Computational Objectives of Parallel Iterative Applications

RTune: Towards Automated and Coordinated Optimization of Com...

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2024 IEEE International Symposium on Performance analysis of systems and Software, ISPASS 2024

作者： Yan, Yonghong Yan, Kewei Wang, Anjia University of North Carolina at Charlotte Department of Computer Science CharlotteNC United States

ISBN: (纸本)9798350376388

Efforts to optimize parallel scientific applications have focused on enhancing metrics related to either the computing aspects, such as execution performance, or the computational aspects, such as computation precision. However, attempting to simultaneously optimize both aspects often proves unproductive due to the differing expertise, benchmarks, and applications utilized by computer scientists and computational scientists. This paper introduces a Runtime Tuning (RTune) method designed to facilitate automated, runtime-time analysis and adjustment of application and system parameters during the simulation process to achieve user-defined objectives. The RTune API allows users to program computing and computational objectives within parallel iterative programs, while the RTune system manages measurement, modeling, and analysis based on these objectives throughout the computation process. We have evaluated the RTune method through three use cases. In simulations of wave propagation and for the objective of finding region of interest of the simulation, an RTune-optimized LULESH can decrease the number of iterations needed by 48 % to 98 % compared to a full simulation. For solver acceleration, an RTune-tuned Jacobi kernel can automatically adjust iteration counts based on the desired solver result precision. Additionally, for parallel applications, RTune can automatically determine the optimal thread count to minimize execution time, demonstrating its capability to efficiently configure system settings. The RTune library is available as open-source software under the BSD license and can be accessed from https://***/passlab/rtune. © 2024 IEEE.

关键词： Open source software

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Secure and Expressive Authorized Keyword Search in Cloud-Assisted Cyber-Physical systems 25

Secure and Expressive Authorized Keyword Search in Cloud-Ass...

引用

26th International Conference on Distributed computing and Networking, ICDCN 2025

作者： Routray, Kasturi Bera, Padmalochan Indian Institute of Technology Bhubaneswar Odisha Bhubaneswar India

ISBN: (纸本)9798400710629

Cloud-assisted cyber-physical systems (C-CPS) manage substantial volumes of sensitive data, necessitating robust security measures and secure search mechanisms to protect confidentiality and ensure efficient access to stored information. Attribute-based searchable encryption (ABSE) facilitates these goals by providing fine-grained access control and keyword search functionality for secure data sharing. However, most existing ABSE approaches compromise the privacy of sensitive access policies and lack expressive access control, limiting their effectiveness in dynamic C-CPS environments. To address these challenges, we propose a practical ciphertext-policy attribute-based searchable encryption (CP-ABSE) scheme that supports secure and expressive authorized keyword searches for efficient data retrieval. Our scheme allows data owners to encrypt keyword indexes under specific access policies, enabling searches only for users whose attributes satisfy the defined policies. Before uploading ciphertext to the cloud, each attribute of the access policy is scrambled using an anonymous key agreement protocol to secure the rules governing the keyword access. Our proposed CP-ABSE scheme supports flexible and expressive access structures, enabling the definition of policies using any monotone access formula over encrypted data. Additionally, it utilizes an efficient ciphertext blinding technique to ensure that unauthorized parties, including key generation centers and cloud providers, cannot access sensitive data. Furthermore, it securely offloads the computational overhead of search operations to the cloud server, eliminating the need for data owner interaction and reducing the load on resource-limited user devices. Our theoretical analysis validates the scheme's effectiveness and security, making it a robust and scalable solution for practical keyword-based data sharing in real-time C-CPS. © 2025 Copyright held by the owner/author(s).

关键词： Ciphertext

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Crescendo: Towards Wideband, Real-time, High-Fidelity Spectrum Sensing systems 23

Crescendo: Towards Wideband, Real-time, High-Fidelity Spectr...

引用

29th Annual International Conference on Mobile computing and Networking, MobiCom 2023

作者： Subbaraman, Raghav Mills, Kevin Schulman, Aaron Bharadia, Dinesh University of California San Diego La Jolla CA60030612 United States

ISBN: (纸本)9781450399906

Spectrum sensing systems provide real-time feedback essential for spectrum sharing. However, the growth of spectrum sharing is limited by the capabilities of these spectrum sensors. Sharing a new frequency band is only possible if sensors can detect activity in that band with sufficient time granularity and signal fidelity to meet spectrum sharing policy requirements. In this work, we introduce Crescendo, a system design that shows we can achieve wideband, real-time, high-fidelity spectrum sensing using sweeping spectrum sensors. We first provide an analysis that demonstrates there are operating points of sweeping sensors that can sense multiple popular protocols. Then we demonstrate these sensors can be built in practice with an adaptive gain super-heterodyne RF frontend with high-fidelity LO generation, and evaluate a prototype built with COTS components. In our benchmarks, Crescendo outperforms prior wideband spectrum sensors, achieving a 30 dB increase in dynamic range and 10 dB increase in SNR. © 2023 Owner/Author(s).

关键词： Real time systems

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What Do We Do? Lessons Learned from Conducting Systematic Reviews to Improve HCI Dissemination

What Do We Do? Lessons Learned from Conducting Systematic Re...

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2024 CHI Conference on Human Factors in computing Sytems, CHI EA 2024

作者： Ibrahim, Zaidat Karimi, Pegah Martin-Hammond, Aqueasha Harrington, Christina Siek, Katie A. Indiana University Bloomington BloomingtonIN United States Indiana University-Purdue University Indianapolis IndianapolisIN United States Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400703317

Systematic reviews are essential in helping researchers address pre-defined research questions through explicit, methodical, and reproducible techniques for identifying studies and comprehensively synthesizing their findings. We highlight our experiences conducting two systematic review studies in HCI: (1) women's reproductive health research in HCI and (2) the intersection of identity and older adults in health research. We identify patterns and lessons that can be applied to enhance the reporting and communication of our research. While these lessons may not be universally applicable, they provide HCI researchers with the opportunity for introspection regarding how we convey our findings to the broader research community. Additionally, these lessons contribute to upholding transparency and integrity in our work, rendering it more long-lasting and beneficial for secondary purposes, like literature reviews and study replication. We provide recommendations and, where feasible, good examples of how to effectively report participants' demographics and study methodology in our HCI work. © 2024 Owner/Author.

关键词： Best Practices HCI Research Lessons Learned Meta analysis Research Methods Systematic Literature Reviews

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proceedings of IPDRM 2022: 5th Annual Workshop on Emerging Parallel and Distributed Runtime systems and Middleware, Held in conjunction with SC 2022: The International Conference for High Performance computing, Networking, Storage and analysis

Proceedings of IPDRM 2022: 5th Annual Workshop on Emerging P...

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5th IEEE/acm Annual Workshop on Emerging Parallel and Distributed Runtime systems and Middleware, IPDRM 2022

The proceedings contain 3 papers. The topics discussed include: SABO: dynamic MPI+OpenMP resource balancer;LEC-PR: proactive recovery method in erasure-coded storage;and Petri nets for concurrent programming.

ISBN: (纸本)9781665475082

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