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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fu, Xingcheng Wang, Jian Gao, Yisen Sun, Qingyun Yuan, Haonan Li, Jianxin Li, Xianxian Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)157735897X

Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to study graph connectivity and information propagation efficiency with a geometric perspective, and has been raised in recent years to explore the efficient message-passing structure of GNNs. However, most empirical studies are based on directly observed graph structures or heuristic topological assumptions and lack in-depth exploration of underlying optimal information transport structures for downstream tasks. We suggest that graph curvature optimization is more in-depth and essential than directly rewiring or learning for graph structure with richer message-passing characterization and better information transport interpretability. From both graph geometry and information theory perspectives, we propose the novel Discrete Curvature Graph information Bottleneck (CurvGIB) framework to optimize the information transport structure and learn better node representations simultaneously. CurvGIB advances the Variational information Bottleneck (VIB) principle for Ricci curvature optimization to learn the optimal information transport pattern for specific downstream tasks. The learned Ricci curvature is used to refine the optimal transport structure of the graph, and the node representation is fully and efficiently learned. Moreover, to reduce the computational complexity of Ricci curvature, we combine Ricci flow and VIB to deduce a curvature optimization approximation to form a tractable IB objective function. Extensive experiments on various datasets demonstrate the superior effectiveness and interpretability of CurvGIB. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Femtosecond Laser Enabled Supermetalphobic Double-Side Micropyramids Based Capacitive Tactile Sensor for Highly Sensitive, Antifouling and Robust Biomonitoring

SSRN

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SSRN 2022年

作者： Zhang, Chengjun Li, Zhikang Li, Haoyu Yang, Qing Wang, Hao Shan, Chao Zhang, Jingzhou Hou, Xun Chen, Feng School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing System Engineering Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Pursuing flexible tactile sensor with superior comprehensive performances is highly desired in their practical applications, such as personalized biomonitoring. However, current flexible tactile sensors suffer high-cost and low-efficiency in fabrication, as well as poor robustness and susceptible to contaminant in sensing performances, hindering their applications. Here, a highly sensitive, antifouling and robust all-flexible tactile sensor (AFTS) is presented with capacitive sensing that combines double-side micropyramids dielectric layer and liquid metal (LM) electrode. The double-side micropyramids dielectric layer leads to high sensitivity up to 2.78 kPa -1 , ultra-low limit of detection of ~ 3 Pa, fast response time of 80 ms, and ultra-high cycle durability of over 10000 times. The supermetalphobic micropyramids enable an ultra-high durability of the LM-based electrode due to the abundant nanoparticles induced by femtosecond laser. The used femtosecond laser enables programmable, high-efficiency, low-cost, and large-scale fabrication of supermetalphobic double-side micropyramids, which is difficult to implement using conventional techniques. Furthermore, the outer substrates are superhydrophobically treated by femtosecond laser, endow the AFTS with excellent antifouling ability, and stable sensing signals in the highly humid environment. Successful monitoring of various human physiological and motion signals demonstrates the potential of our developed AFTS for wearable biomonitoring applications. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Space-Oblivious Compression and Wear Leveling for Non-Volatile Main Memories 36

Space-Oblivious Compression and Wear Leveling for Non-Volati...

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36th International Conference on Massive Storage systems and technology, MSST 2020

作者： Liu, Haikun Ye, Yuanyuan Liao, Xiaofei Jin, Hai Zhang, Yu Jiang, Wenbin He, Bingsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore

Emerging Non-Volatile Main Memory (NVMM) technologies generally feature high density, low energy consumption and cost per bit compared to DRAM. However, the limited write endurance of NVMM poses a significant challenge of using NVMM as an substitute for DRAM. This paper proposes a space-oblivious compression and wear-leveling based memory architecture to improve the write endurance of NVMM. As memory blocks of many applications usually contain a large amount of zero bytes and frequent values, we propose a nonuniform compression encoding scheme that integrates Zero Deduplication with Frequent Value Compression (called ZD-FVC) to reduce bit-writes on NVMM. Moreover, we leverage the memory space available through compression to achieve an intra-block wear leveling policy. It rotates the writing positions of a compressed data block within the data’s initial memory space, and thus enhances write endurance by balancing the bit-writes per NVMM cell. ZD-FVC can be integrated into the NVMM module and implemented entirely by hardware, without any intervention of Operating systems. We implement ZD-FVC in Gem5 and NVMain simulators, and evaluate it with several programs from SPEC CPU2006. Experimental results shows that ZD-FVC is much better than several state-of-the-art approaches. Particularly, ZD-FVC can improve data compression ratio by 55% compared to Frequent Value Compression. Compared with the Data Comparison Write, ZD-FVC is able to significantly improve the lifetime of NVMM by 3.3X on average, and also reduces NVMM write latency by 31% and energy consumption by 19% on average. © 2020 36th International Conference on Massive Storage systems and technology, MSST 2020. All Rights Reserved.

关键词： Dynamic random access storage

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Unleashing the Power of Visuals: A Captivating Exploration of Scientific Data Visualization Methods and Techniques 8th

Unleashing the Power of Visuals: A Captivating Exploration o...

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8th International Visual Informatics Conference, IVIC 2023

作者： Baharum, Aslina Ismail, Rozita Ismail, Ismassabah Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Halamy, Suhaida Computing and Information System School of Engineering and Technology Sunway University Jalan Universiti Selangor Bandar Sunway Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Kajang Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Selangor Dengkil Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9789819973385

Scientific visualizations are the building blocks for conveying results and ideas to a wide range of audiences. It is one of the steps in the data science process, and it should be done before the data presentation phase. Because some data is not easy to read for humans, visualization can help transform the data into visual content such as graphs or plots, as the requirement for scientific visualization techniques and methods is increasing greatly. Reviewing various visualization methods is necessary to increase knowledge about it. In this paper, an overview of scientific data visualization methods and techniques has been reviewed. To conduct the experiment, a total of five peer-reviewed articles relating to scientific visualization techniques were chosen. The problem with this study is that people still do not have a clear idea of how to use or what is the importance of scientific visualization. The findings of this paper show that the occurrence of visualization techniques improves the effectiveness of reading data and helps people to understand data in a more vivid way. A systematic literature review has been adopted in this study to assist the author in doing a study without bias. Finally, this paper can help readers increase their knowledge of the scientific visualization technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data visualization

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Performance Analysis of LoRa for Rice Fading Channels

Performance Analysis of LoRa for Rice Fading Channels

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Takoua Mahjoub Adel Ben Mnaouer Hatem Boujemaa Maymouna Ben Said UCAR COSIM Lab. Higher School of Communications of Tunis Tunisia Faculty of Engineering Applied Science and Technology Canadian University Dubai College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

Long Range (LoRa) systems have received a lot of interest recently from the scientific community as well as for commercial application due to their ability to transfer data across large distances at a relatively low energy cost. In this paper, we derive the delay, the throughput, the Bit Error (BEP) and the Packet Error Probabilities (PEP) of LoRa for Rice channels. We derive packets' waiting time and total delay for LoRa in the presence of a Rice channel. Delay analysis of LoRA in the presence of Rice channel is not available in the literature.

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Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

arXiv

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arXiv 2022年

作者： Han, Yuehui Hui, Le Jiang, Haobo Qian, Jianjun Xie, Jin Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local structures of the graph. Also, the vector inner product based similarity metric cannot fully exploit the local structures of the graph to characterize the graph difference well. To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs. It aims to generate contrastive samples by capturing the intrinsic structures of the graph and distinguish the samples based on the features and structures of subgraphs simultaneously. Specifically, for each center node, by adaptively learning relation weights to the nodes of the corresponding neighborhood, we first develop a network to generate the interpolated subgraph. We then construct the positive and negative pairs of subgraphs from the same and different nodes, respectively. Finally, we employ two types of optimal transport distances (i.e., Wasserstein distance and Gromov-Wasserstein distance) to construct the structured contrastive loss. Extensive node classification experiments on benchmark datasets verify the effectiveness of our graph contrastive learning method. Source code is available at https://***/yh-han/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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3D Siamese Transformer Network for Single Object Tracking on Point Clouds

arXiv

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arXiv 2022年

作者： Hui, Le Wang, Lingpeng Tang, Linghua Lan, Kaihao Xie, Jin Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology China

Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance variation between the template and search area during tracking, how to learn the robust cross correlation between them for identifying the potential target in the search area is still a challenging problem. In this paper, we explicitly use Transformer to form a 3D Siamese Transformer network for learning robust cross correlation between the template and the search area of point clouds. Specifically, we develop a Siamese point Transformer network to learn shape context information of the target. Its encoder uses self-attention to capture non-local information of point clouds to characterize the shape information of the object, and the decoder utilizes cross-attention to upsample discriminative point features. After that, we develop an iterative coarse-to-fine correlation network to learn the robust cross correlation between the template and the search area. It formulates the cross-feature augmentation to associate the template with the potential target in the search area via cross attention. To further enhance the potential target, it employs the ego-feature augmentation that applies self-attention to the local k-NN graph of the feature space to aggregate target features. Experiments on the KITTI, nuScenes, and Waymo datasets show that our method achieves state-of-the-art performance on the 3D single object tracking task. Source code is available at https://***/fpthink/STNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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A Study of Multi-viewpoint Risk Assessment of Internet of Things (IoT) 9

A Study of Multi-viewpoint Risk Assessment of Internet of Th...

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9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

作者： Wangyal, Sonam Dechen, Tenzin Tanimoto, Shigeaki Sato, Hiroyuki Kanai, Atsushi Chiba Institute of Technology Faculty of Social System Science Chiba Japan The University of Tokyo Information Technology Center Tokyo Japan Hosei University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9781728173979

As global businesses now extensively depend on data, Internet of Things (IoT) sensors have becomes the primary source of real-time data that enable the digital transformation. According to Bain's Insight, the IoT market will grow to more than 520 billion by 2021. The technology has already been adopted for a wide array of use-cases, but due to the ever-expanding threat landscape, many customers have indicated that security remains the primary barrier when it comes to their acceptance of IoT. The current security risk management methodologies focus mostly on the cyber view. In this work, we identify 28 risk factors extracted using the risk breakdown structure method and expand this traditional view to include others (physical, psychological) that are critical to business operations. We also propose and quantitatively evaluate countermeasures for all the extracted risks. Our findings, help clarify IoT security and its relation to non-cyber risks for proper implementation of IoT systems. © 2020 IEEE.

关键词： Internet of things

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Developing a Computer-Aided Diagnostic system for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Radwa Taha Shereen Afifi Mohamed A. Abd-ElGhany Mohammed A.-M Salem Master Candidate in the Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Assistant Professor in Computer Science Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Professor in Electrical Engineering Faculty of Information Engineering and Technology German University in Cairo New Cairo Egypt Integrated Electronics Systems Lab TU Darmstadt Germany Digital Media Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt

In this research we aim to develop a Computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease.

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Shortest Path Priority-based RPL (SPPB-RPL): The Case of a Smart Campus

Shortest Path Priority-based RPL (SPPB-RPL): The Case of a S...

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2020 IEEE Conference on Application, information and Network Security, AINS 2020

作者： Onwuegbuzie, Innocent Uzougbo Razak, Shukor Abd Isnin, Ismail Fauzi Anuar, Nor Badrul School of Computing Faculty of Engineering Johor Malaysia Faculty of Computer Science and Information Technology University of Malaya Department of Computer System and Technology Kuala Lumpur Malaysia

ISBN: (纸本)9781728192406

Wireless Sensor Networks (WSN) is the network of embedded and resource-constrained nodes, that forms the foundation of the Internet of Things (IoT). These networks are designed to sense, aggregate and transport sensed data to locations that may be local or remote to the sensory field, for immediate or stored for later use. In most cases, WSN generates heterogeneous data that conveys information of varying priorities which may be real-time (RT) or non-real-time (NRT). However, the de facto routing protocol;Routing Protocol for Low-power and Lossy Networks (RPL) is not optimized for heterogeneous data routing. Likewise, routing schemes proposed in the literature have not proffered a lasting solution. In this paper, we proposed the Shortest Path Priority-based RPL (SPPB-RPL). The SPPB-RPL scheme not only routes data with varying priorities but also does so with optimal network performances. Its application in a smart campus is used as a case study. The scheme uses the mechanism of class of service (CS) to classify and prioritized data into various priority classes. Subsequently, the standard RPL DIO base object was improved upon to accommodate the prioritized data classes, and finally, the Dijkstra Algorithm (DA) was adopted to generate the optimal shortest routing path leading to the destination node which is guided by the priority of the data. Benchmarked with related schemes, SPPB-RPL performed optimally with a 99.98% packet delivery ratio with the least latency. This enhanced the network performance for a prolonged operational lifetime. © 2020 IEEE.

关键词： Routing protocols

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