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ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Bhamidipati, Padmaja Vemuri, Ranga Univ Cincinnati EECS Cincinnati OH 45221 USA

ISBN: (纸本)9798350354119

Network-on-Chip (NoC) architectures have gained prominence in modern computing systems for their scalability and efficiency. However, the globalization of NoC design and fabrication exposes them to security threats. Quantitative analysis plays an important role in exposing vulnerabilities by quantifying packet information and traffic flow across components and pathways. It uses numerical data and mathematical models to understand complex systems, revealing patterns, and vulnerabilities invisible through qualitative methods. Our proposed QANoCs (Quantitative analysis for Trojan Detection in Networkon-Chips) methodology leverages the power of quantitative analysis to protect NoCs against security threats. Our methodology dynamically evaluates network traffic to identify potential vulnerabilities. This novel lightweight approach introduces a quantifiable metric to identify traffic patterns, anomalies, attack scenarios, and potential information leakage points in NoCs. We evaluate our methodology on 4x4 and 8x8 mesh NoCs using real benchmarks. With Trojan infection rates as low as 1.5-2%, our approach shows an improvement of up to 3% in latency and 4% in throughput savings. Furthermore, our results show a substantial reduction in latency by over 50%, with a 2% to 10% Trojan infection scenario.

关键词： Network-on-Chip security Trojan detection Packet duplication Quantitative analysis

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Immersive 3D Medical visualization in Virtual Reality using Stereoscopic Volumetric Path Tracing 31

Immersive 3D Medical Visualization in Virtual Reality using ...

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ieee Conference on Virtual Reality and 3D User Interfaces (VR)

作者： Taibo, Javier Iglesias-Guitian, Jose A. Univ A Coruna CITIC Ctr ICT Res La Coruna Spain

ISBN: (纸本)9798350374025;9798350374032

Scientific visualizations using physically-based lighting models play a crucial role in enhancing both image quality and realism. In the domain of medical visualization, this trend has gained significant traction under the term cinematic rendering (CR). It enables the creation of 3D photorealistic reconstructions from medical data, offering great potential for aiding healthcare professionals in the analysis and study of volumetric datasets. However, the adoption of such advanced rendering for immersive virtual reality (VR) faces two main limitations related to their high computational demands. First, these techniques are frequently used to produce pre-recorded videos and offline content, thereby restricting interactivity to pre-defined volume appearance and lighting settings. Second, when deployed in head-tracked VR environments they can induce cyber-sickness symptoms due to the disturbing flicker caused by noisy Monte Carlo renderings. Consequently, the scope for meaningful interactive operations is constrained in this modality, in contrast with the versatile capabilities of classical direct volume rendering (DVR). In this work, we introduce an immersive 3D medical visualization system capable of producing photorealistic and fully interactive stereoscopic visualizations on head-mounted display (HMD) devices. Our approach extends previous linear regression denoising to enable real-time stereoscopic cinematic rendering within AR/VR settings. We demonstrate the capabilities of the resulting VR system, like its interactive rendering, appearance and transfer function editing.

关键词： Computing methodologies-Virtual reality Computing methodologies-Ray tracing- Human-centered computing-Graphical user interfaces

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CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical data

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 756-766页

作者： Zhang, Wei Wong, Jason K. Wang, Xumeng Gong, Youcheng Zhu, Rongchen Liu, Kai Yan, Zihan Tan, Siwei Qu, Huamin Chen, Siming Chen, Wei State Key Lab of CAD&CG Zhejiang University China Laboratory of Art and Archaeology Image Zhejiang University Ministry of Education China Hong Kong University of Science and Technology Hong Kong TMCC CS Nankai University China Fudan University China

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation. © 2022 ieee.

关键词： visualization

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Visual analysis of Big data of Geographical Indications Products in Guangdong Province Based on GIS 7

Visual Analysis of Big Data of Geographical Indications Prod...

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7th International Conference on data Science and Information Technology, DSIT 2024

作者： Xie, Ru Xiao, Guangjiang Zhang, Lingyun Guangdong Academy of Agricultural Sciences Institute of Agricultural Economics and Information Guangzhou China Ministry of Agriculture and Rural Affairs Key Laboratory of Urban Agriculture in South China Guangzhou China

ISBN: (纸本)9798350384093

In order to solve the problems of large volume, multiple dimensions and difficult analysis in the big data of geographical indication products (litchi, banana, etc.) of Guangdong province, a big data processing and visualization method based on GIS for geographical indication products of Guangdong province was proposed. By constructing GIS technology to pre-process the data of geographical indication products (litchi, banana, etc.) in Guangdong province, the number of data samples and dimensions are reduced;and then through spatio-temporal grid computing and various visualization techniques, the market development situation of geographical indication products (litchi, banana, etc.) is visually displayed. This proposal can achieve fast, interactive and visual big data analysis and mining of litchi, banana and other agricultural products brands, providing technical support for the analysis of geographical indication agricultural products brands in Guangdong province. © 2024 ieee.

关键词： Spatio-temporal data

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MAPPING STONY RISE LANDFORMS WITH REMOTE SENSING AND GEOPHYSICAL data USING A MACHINE LEARNING APPROACH

MAPPING STONY RISE LANDFORMS WITH REMOTE SENSING AND GEOPHYS...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Fraser, Shaye Soto-Berelov, Mariela Holden, Lucas Son, Robert Hew Webb, John Jones, Simon RMIT Univ STEM Coll Melbourne Vic Australia La Trobe Univ Discipline Ecol & Environm Melbourne Vic Australia

ISBN: (纸本)9798350360332;9798350360325

Elevated areas on basalt lava flows, locally known as stony rises, are volcanic landforms located in the Victorian Volcanic Plain (VVP) in southeastern Australia. Stony rises are recognized as having high geological, ecological, and cultural values. Currently, the mapping of stony rises is carried out on a study-by-study basis, via traditional field surveys. Mapping of such features could be performed more rapidly and at a landscape level by utilizing remotely sensed datasets such as LiDAR, satellite and aerial imagery, and geophysical data, and applying detection techniques such as machine learning. This study incorporates an ensemble of remotely sensed data and predictor variables to characterize stony rise morphology through a machine learning approach that uses an object based Random Forest. We were able to successfully detect stony rises across various lava flows in the VVP. Future geomorphological studies should aim to incorporate the use of spectral and geophysical predictor variables for large area landform detection and analysis.

关键词： Stony rises object-based image analysis random forests remote sensing machine learning

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EmoInt-Trans: A Multimodal Transformer for Identifying Emotions and Intents in Social Conversations

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ieee-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 290-300页

作者： Singh, Gopendra Vikram Firdaus, Mauajama Ekbal, Asif Bhattacharyya, Pushpak IIT Patna Dept Comp Sci Engn Patna 800013 Bihar India

In the natural language processing community, open-domain conversational agents, also known as chatbots, are gaining popularity. One of the difficulties is getting them to communicate in an emotionally intelligent manner. To generate dialogues, current neural response generation methods depend solely on end-to-end learning from large scale conversation data. Therefore, we introduce a large-scale multi Emotion and Intent guided Multimodal Dialogue (EmoInt-MD) dataset labelled with 32 emotions and 15 empathetic intents having 32 k dialogues taken from different movie genres. We propose a novel multi-task multimodal contextual Transformer framework for simultaneously identifying the emotions and intents in a given utterance utilizing audio and visual features in addition to the textual information. Experimental analysis proves that the proposed framework outperforms several unimodal and multimodal baselines on the EmoInt-MD dataset. This dataset along with our baseline and proposed framework implementations will be made publicly available for research purposes.

关键词： Motion pictures visualization Transformers Emotion recognition Feature extraction Task analysis Speech processing Emotion empathetic intent transformers fusion context

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Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第11期36卷 6962-6976页

作者： Song, Yaoxian Sun, Penglei Liu, Haoyu Li, Zhixu Song, Wei Xiao, Yanghua Zhou, Xiaofang Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai 200437 Peoples R China Zhejiang Lab Res Ctr Intelligent Robot Hangzhou 311121 Zhejiang Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Embodied AI is one of the most popular studies in artificial intelligence and robotics, which can effectively improve the intelligence of real-world agents (i.e. robots) serving human beings. Scene knowledge is important for an agent to understand the surroundings and make correct decisions in the varied open world. Currently, knowledge base for embodied tasks is missing and most existing work use general knowledge base or pre-trained models to enhance the intelligence of an agent. For conventional knowledge base, it is sparse, insufficient in capacity and cost in data collection. For pre-trained models, they face the uncertainty of knowledge and hard maintenance. To overcome the challenges of scene knowledge, we propose a scene-driven multimodal knowledge graph (Scene-MMKG) construction method combining conventional knowledge engineering and large language models. A unified scene knowledge injection framework is introduced for knowledge representation. To evaluate the advantages of our proposed method, we instantiate Scene-MMKG considering typical indoor robotic functionalities (Manipulation and Mobility), named ManipMob-MMKG. Comparisons in characteristics indicate our instantiated ManipMob-MMKG has broad superiority on data-collection efficiency and knowledge quality. Experimental results on typical embodied tasks show that knowledge-enhanced methods using our instantiated ManipMob-MMKG can improve the performance obviously without re-designing model structures complexly.

关键词： Task analysis Knowledge graphs Artificial intelligence Knowledge based systems Robots Knowledge engineering visualization Multimodal knowledge graph scene driven embodied AI robotic intelligence

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Design of 5G NR-V2X DB analysis Software

Design of 5G NR-V2X DB Analysis Software

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Lee, Jimin Lim, Kitaeg Yoon, Sanghun Jang, Seonghyun An, Byoungman Mobility Platform Research Center Korea Electronics Technology Institute Korea Republic of

ISBN: (纸本)9798350371888

In this paper, we propose a large-capacity V2X communication data analysis and visualization tool for the development of high-safety/high-trust V2X communication systems, which are essential for Lv-4+ autonomous vehicles. Communication tests and result analysis in diverse situations should be carried out to guarantee the security of V2X communication systems and to give developers prompt and accurate feedback. In order to do this, we provided a description of a scalable message format for data collection and used a Jupyter-based software tool for data analysis and visualization. This visualization tool is expected to greatly contribute to accelerating the commercialization of autonomous vehicles by facilitating the analysis of standardized V2X data collection results in the future. © 2024 ieee.

关键词： data visualization

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Deep Learning-Enhanced Internet of Things for Activity Recognition in Post-Stroke Rehabilitation

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ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第7期28卷 3851-3859页

作者： Jin, Fangpeng Zou, Mi Peng, Xiaoyun Lei, Hua Ren, Yongjun Nanjing Univ Informat Sci & Technol Sch Comp Nanjing 210044 Peoples R China Sichuan Acad Med Sci Hosp Univ Elect Sci & Technol China Univ Elect Sci & Technol China Sichuan Prov Peoples HospDept RehabilSch Med Chengdu 610072 Peoples R China

Wearable sensors provide a more effective means of activity monitoring and management by recording patients' daily activity data for assessing their daily function and rehabilitation progress, as well as providing a convenient and practical solution for human activity recognition (HAR). However, during the motor rehabilitation of stroke patients, sensors provide vast amounts of high-dimensional data that are large and complex. To enhance the accuracy of activity monitoring and identification, as well as address the limitations of real-time processing, data visualization, and tracking in conventional monitoring approaches, it is essential to perform valid data processing and analysis. This paper combines deep learning models to explore the potential relationships and patterns between data to build an intelligent post-stroke rehabilitation system. This paper proposes a novel framework aimed at accurately recognizing activities performed by stroke patients. Our approach leverages a data fusion mechanism based on multiple sensors to construct a fusion tensor and employs a bidirectional long and short-term memory (BiLSTM) network enhanced with an attention mechanism. This network effectively captures temporal patterns and long-term dependencies within the data, resulting in improved performance for wearable sensor-based activity classification. Furthermore, we introduce an enhanced loss function to optimize the learning process. To assess the performance of the proposed model algorithm, two benchmark datasets were employed. These datasets served as the basis for evaluating and comparing the baseline method as well as other proposed methods. The experimental results clearly demonstrated that the proposed model outperformed the compared methods, indicating its superior performance in activity recognition.

关键词： Sensors Wearable sensors Stroke (medical condition) data models Monitoring Feature extraction Deep learning stroke activity recognition deep learning Internet of Things

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INTEGRATED data CUBES FOR large-SCALE SPATIOTEMPORAL analysis OF REMOTE SENSING data IN ARCGIS

INTEGRATED DATA CUBES FOR LARGE-SCALE SPATIOTEMPORAL ANALYSI...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Xu, Hong Zhan, Shengan Jain, Amit Gopaiah, Manne ESRI Redlands CA 92373 USA

ISBN: (纸本)9798350320107

Advancements in sensor technology have led to significant growth in the volume and variety of geospatial data. Optical, radar, lidar, and sonar sensor technologies have presented us with valuable opportunities to understand our changing world. However, a key challenge still lies in developing data models that can efficiently manage diverse types of spatial and temporal data collected from these sensors to enable information extraction. In this paper, we describe the integrated data cubes within ArcGIS that effectively handle spatial and temporal data. Our approach includes a mosaic dataset for managing satellite time series and gridded climate data, a cloud raster format (CRF) file for storing pixel-aligned analysis-ready data, and a trajectory dataset for managing points from altimetry sensors. We present several use cases to demonstrate the analysis capabilities based on these data models.

关键词： Time Series Cube Imagery Altimetry CRF Mosaic dataset Trajectory dataset

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