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31st ieee International symposium on High Performance Computer Architecture, HPCA 2025

作者： Jia, Siyao Jiao, Bo Zhu, Haozhe Chen, Chixiao Liu, Qi Liu, Ming Frontier Institute of Chip and System Fudan University State Key Laboratory of Integrated Chips and Systems China

ISBN: (纸本)9798331506476

Chiplet-based 3DICs have emerged as modular solutions for large-scale, high-performance computing systems. However, unlike monolithic Network-on-Chips (NoCs), 3DICs using active interposers encounter difficulties in managing heterogeneous and high-load network traffic patterns. The emerging field of Networks-on-Active-Interposer (NOAI), independently designed from top dies' interconnects, is desired to address these challenges by supporting flexible topologies, low-latency memory access, and reduced traffic congestion. To satisfy the requirements, we propose a heterogeneous dual-layer interconnect architecture, EIGEN, for chiplet-interposer systems, along with a reinforcement learning (RL)-based routing framework, which can provide efficient and flexible communication for chiplet-based 3DICs. EIGEN features an application-aware switch-programable interconnection layer (AspLayer) and a dynamic packet-routing interconnection layer (DynLayer) on the interposer, respectively. To optimize inter-chiplet data communication, we also develop an RL-based path routing framework tailored to this dual-layer architecture. The RL framework's state space includes topology, network, and memory metrics, while the RL reward is set as the product of average packet latency and link utilization. The effectiveness of EIGEN is demonstrated through different applications where CPU, GPU, and AI accelerator chiplets are integrated via NOAI. Simulation results show that the proposed EIGEN achieves up to {6 7. 1 6%} latency reduction, {5 3. 8 9%} hops reduction and {1 1. 2 1%} runtime reduction compared to the state-of-the-art (SOTA) chiplets interconnect architectures. Furthermore, sensitivity analysis of network scaling shows that the EIGEN architecture and framework exhibit strong scalability, with latency reduction of {2 0. 9 6%} to {5 2. 7 7%} and hop reduction of {1 9. 7 2%} to {4 3. 4 1%} as the system scales from 4 × 4 to 32 × 32 chiplets. © 2025 ieee.

关键词： Sensitivity analysis

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EmBARDiment: an Embodied AI Agent for Productivity in XR

EmBARDiment: an Embodied AI Agent for Productivity in XR

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ieee Annual International symposium Virtual Reality

作者： Riccardo Bovo Steven Abreu Karan Ahuja Eric J Gonzalez Li-Te Cheng Mar Gonzalez-Franco Google USA Imperial College London UK University of Groningen Netherlands Northwestern University USA

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

XR devices running chat-bots powered by large Language Models (LLMs) have the to become always-on agents that enable much better productivity scenarios. Current screen based chat-bots do not take advantage of the the full-suite of natural inputs available in XR, including inward facing sensor data, instead they over-rely on explicit voice or text prompts, sometimes paired with multi-modal data dropped as part of the query. We propose a solution that leverages an attention framework that derives context implicitly from user actions, eye-gaze, and contextual memory within the XR environment. Our work minimizes the need for engineered explicit prompts, fostering grounded and intuitive interactions that glean user insights for the chat-bot.

关键词： Productivity visualization Three-dimensional displays Extended reality large language models Memory management Gaze tracking User interfaces Chatbots Artificial intelligence

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Visual Clustering Factors in Scatterplots

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2021年第5期41卷 79-89页

作者： Xia, Jiazhi Lin, Weixing Jiang, Guang Wang, Yunhai Chen, Wei Schreck, Tobias Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Cent South Univ Comp Sci & Technol Changsha 410083 Hunan Peoples R China Cent South Univ Visual Analyt Lab Changsha 410083 Hunan Peoples R China Cent South Univ Changsha 410083 Hunan Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 250100 Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou 310027 Peoples R China Graz Univ Technol Fac Comp Sci & Biomed Engn Inst Comp Graph & Knowledge Visualizat A-8070 Graz Styria Austria

Cluster analysis is an important technique in data analysis. However, there is no encompassing theory on scatterplots to evaluate clustering. Human visual perception is regarded as a gold standard to evaluate clustering. The cluster analysis based on human visual perception requires the participation of many probands, to obtain diverse data, and hence is a challenge to do. We contribute an empirical and data-driven study on human perception for visual clustering of large scatterplot data. First, we systematically construct and label a large, publicly available scatterplot dataset. Second, we carry out a qualitative analysis based on the dataset and summarize the influence of visual factors on clustering perception. Third, we use the labeled datasets to train a deep neural network for modeling human visual clustering perception. Our experiments show that the data-driven model successfully models the human visual perception, and outperforms conventional clustering algorithms in synthetic and real datasets.

关键词： visualization Shape Visual perception Clustering algorithms Deep learning Splines (mathematics) Computer science

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Towards In-situ Authoring of AR visualizations with Mobile Devices 20

Towards In-situ Authoring of AR Visualizations with Mobile D...

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20th ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Satkowski, Marc Luo, Weizhou Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany

ISBN: (纸本)9781665412988

Augmented Reality (AR) has been shown to enhance the data visualization and analysis process by supporting users in their immersive exploration of data in a real-world context. However, authoring such visualizations still heavily relies on traditional, stationary desktop setups, which inevitably separates users from the actual working space. To better support the authoring process in immersive environments, we propose the integration of spatially-aware mobile devices. Such devices also enable precise touch interaction for data configuration while lowering the entry barriers of novel immersive technologies. We therefore contribute an initial set of concepts within a scenario for authoring AR visualizations. We implemented an early prototype for configuring visualizations in-situ on the mobile device without programming and report our first impressions.

关键词： Human-centered computing-Mixed/Augmented Reality Human-centered computing-visualization

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A Novel Cloud-Native and Multi-Platform Parallelized SBAS-INSAR Algorithm

A Novel Cloud-Native and Multi-Platform Parallelized SBAS-IN...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peichen Yu Chao Wang Yixian Tang Weikang Zhang Haihang You Shaoyang Guan Lichuan Zou Hong Zhang Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China International Research Center of Big Data for Sustainable Development Goals Beijing China University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Facing massive Synthetic Aperture Radar (SAR) data, the challenge of achieving rapid and efficient data processing has garnered attention. Currently, most time-series InSAR processing solutions are designed to operate on a single computing platform, leading to drawbacks such as inflexible deployment and slow data transmission. Leveraging the open-source Kubernetes platform, we have employed cloud-native technology to deploy a cross-platform, multi-level parallelization algorithm across multiple nodes. The algorithm is designed based on modular concept, and its modules can successfully achieve cross-platform deployment by relying on both the Network File System (NFS) protocol and the Common Internet File System (CFIS) protocol. This overcomes the limitations of the previous data file storage system, which solely depended on the NFS protocol, leading to deployment on a single operating system. Deploying the algorithm on different operating systems, our results show that the speeds of Linux platform's algorithm parallel modules were improved 16% and 30%, respectively. The successful cross-platform operation of the algorithm enables the data processing workflow to assimilate the strengths of different operating platforms, enhancing data visualization capabilities and gaining support from diverse platform resources. This introduces a new approach for large-scale time-series InSAR processing.

关键词： Cloud computing Protocols File systems Operating systems Linux Geoscience and remote sensing data visualization

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NFTVis: Visual analysis of NFT Performance

NFTVis: Visual Analysis of NFT Performance

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Fan Yan Xumeng Wang Ketian Mao Wei Zhang Wei Chen State Key Lab of CAD&CG Zhejiang University College of Computer Science Nankai University Ministry of Education Laboratory of Art and Archaeology Image (Zhejiang University) China

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.

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Hardware Implementation of Double Pendulum Pseudo Random Number Generator

Hardware Implementation of Double Pendulum Pseudo Random Num...

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ieee International symposium on Embedded Multicore Socs (MCSoC)

作者： T. Hui Teo Maoyang Xiang Hen Rin Leao Keye Qian Jarrod Lim Tom Manuel Opalla Piccio Chua Min Jie Michelle Engineering Product Development Singapore University of Technology and Design Singapore Nano-Electronic Engineering and Design Singapore University of Technology and Design Singapore Chang Gung University Taiwan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

This paper presents a novel pseudo-random number generator (PRNG) implemented on an FPGA for encryption and data security applications. The system leverages environmental data captured by sensors to generate seeds, which are processed using a chaotic double pendulum algorithm. The generated random numbers exhibit strong randomness, demonstrated by a histogram analysis of over one million data points with no discernible pattern. However, the algorithm's complexity posed resource limitations on the FPGA, prompting partial offloading to an Arduino. Despite these challenges, the method provides a robust solution for secure random number generation, with potential improvements to be explored in future work.

关键词： visualization Statistical analysis Multicore processing Prototypes Generators Sensor systems Field programmable gate arrays Random number generation Testing Microphones

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Foot Traffic analysis Using Wi-Fi Sensor During the Tokyo 2020 Olympics and Paralympics 7

Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 20...

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7th International Conference on Information Technology, InCIT 2023

作者： Traganmaturapot, Peerada Sonehara, Noboru Hiruma, Nobuharu Cooharojananone, Nagul Jirapongwanich, Jirakit Yodsakuntip, Kanat Atchariyachanvanich, Kanokwan Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Institute for Policy Studies Tsuda University Tokyo Japan AREAPORTAL Inc Tokyo Japan King Mongkut's Institute of Technology School of Information Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798350358698

Various real-world factors, such as time, weather, distance, environment, the COVID-19 pandemic, or even protests, can all impact human decision-making. However, restrictions and unexpected occurrences may also influence people's decisions regarding their path at any given time. These factors can lead to challenges in managing foot traffic at largescale events. In response to these challenges, this paper proposes a data-driven web-based foot traffic management supporting dashboard for large-scale events based on limited pedestrian count data, consisting of sensor name, latitude, longitude, MAC address, Datetime, and RSSI, collected by Wi-Fi sensors around the Sendagaya area during the Tokyo 2020 Olympics and Paralympics. The results confirmed that our proposed web-based dashboard contributes to human behavior understanding and decision-supporting policymaking for foot traffic management, which improves the design of spectator movement between transportation and venues in large-scale events. Furthermore, the dashboard is valuable from various perspectives, including preventing crowd crushing, redesigning areas to increase engagement in the shopping district, and improving traffic management. © 2023 ieee.

关键词： data visualization

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ONTOLOGY-DRIVEN INTEGRATION AND VISUAL ANALYTICS FOR GEOSPATIAL data

ONTOLOGY-DRIVEN INTEGRATION AND VISUAL ANALYTICS FOR GEOSPAT...

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2024 ieee India Geoscience and Remote Sensing symposium, InGARSS 2024

作者： Bansu, Shilpali Chaudhari, Sangita Department of Computer Engineering Ramrao Adik Institute of Technology D. Y. Patil Deemed to be University Nerul Navi Mumbai India Department of Information Technology Ramrao Adik Institute of Technology D. Y. Patil Deemed to be University Nerul Navi Mumbai India

ISBN: (纸本)9798350390346

Recent advancements in spatial information technology have facilitated the acquisition of substantial spatial data from diverse sources characterized by varying levels of quality. Addressing the current challenge in geographical information sciences, researchers are focusing on how to effectively integrate these datasets to meet the evolving societal demands for heightened spatial data quality. Modern applications of geospatial data demand efficient, interactive, and real-time data integration. The assessment of semantic similarity plays a crucial role in the integration of ontology and spatial data. The increasing abundance of diverse and extensive geospatial data from various sources underscores the need for inventive solutions in the realms of integration and visualization. This paper introduces a conceptual framework that revolves around ontology-driven methodologies, aiming to seamlessly integrate and visually scrutinize geospatial data. Additionally, a case study implementation based on the conceptual framework is documented. © 2024 ieee.

关键词： Video analysis

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Human Blastocyst Image Generation Using Generative Adversarial Networks

Human Blastocyst Image Generation Using Generative Adversari...

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ieee International symposium on Biomedical Imaging

作者： Evangelos Tikas Lazaros Alexios Iliadis Sotirios Sotiroudis Achilles Boursianis Konstantinos-Iraklis D. Kokkinidis Achilleas Papatheodorou Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Embryolab Fertility Clinic Thessaloniki Greece Department of Electronics and Communication Engineering Bharath University Chennai India

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Several medical fields such as In Vitro Fertilization (IVF) suffer from limited databases d require augmentations, to be suitable for machine learning (ML) tasks such as classification. We test the use of various Generative Adversarial Network (GAN) architectures to tackle the problem of limited data by generating high-quality samples. We compare two GAN-based data augmentation techniques for a limited medical image database, utilizing synthetic images. A GAN can create synthetic images by learning to model a probability density function that can represent large data like images. Synthetic samples are then evaluated using metrics and visual evaluation with respect to the original "real" samples.

关键词： Measurement Training In vitro fertilization visualization Machine learning Probability density function Generative adversarial networks

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