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arXiv 2024年

作者： Chen, Peilin Fang, Xiaohan Wang, Meng Wang, Shiqi Ma, Siwei Department of Computer Science City University of Hong Kong Hong Kong The School of Data Science Lingnan University Hong Kong National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China

The Human Visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupled with high generalization capability and energy efficiency. By contrast, the state-of-the-art Versatile Video Coding (VVC) standard achieves a compression ratio of around 1,000 times for raw visual data. This notable disparity motivates the research community to draw inspiration to effectively handle the immense volume of visual data in a green way. Therefore, this paper provides a survey of how visual data can be efficiently represented for green multimedia, in particular when the ultimate task is knowledge extraction instead of visual signal reconstruction. We introduce recent research efforts that promote green, sustainable, and efficient multimedia in this field. Moreover, we discuss how the deep understanding of the HVS can benefit the research community, and envision the development of future green multimedia technologies. © 2024, CC BY.

关键词： Green development

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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HENet: Hyperbolic-Based Encoder-Decoder Network for Word Spotting in Historical Mongolian Documents

HENet: Hyperbolic-Based Encoder-Decoder Network for Word Spo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jing Zhang Hongxi Wei Qing Zhang Xiandong Chen Jingtao Ma School of Computer Science Inner Mongolia University Hohhot China Provincial Key Laboratory of Mongolian Information Processing Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

In the domain of historical Mongolian document image retrieval (HMDIR), word spotting poses a inherent challenge due to the frequent appearance of out-of-vocabulary (OOV) words. Existing methods have mainly focused on query-by-example (QBE), neglecting the query-by-string (QBS) approach. Meanwhile, the hierarchical structure of word makes Euclidean space not the optimal choice for representing complex structured data. To address the aforementioned problems, we propose a novel method that leverages a shared hyperbolic space to effectively align text strings and word images. Specifically, we use the Pyramidal Histogram of Characters (PHOC) for text string embeding, and a robust encoder-decoder architecture for word image embedding, then map their embeddings in the shared hyperbolic space. Moreover, we propose a new dataset of historical Mongolian documents called Geser, which includes 143,508 word images and 10,951 vocabularies. Extensive experiments conducted on two datasets of historical Mongolian documents with an OOV partitioning scheme (Kanjur and Geser), demonstrate that our proposed method surpasses state-of-the-art methods and achieves outstanding results on Geser.

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Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature

arXiv

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arXiv 2024年

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of School of Software Engineering Huazhong University of Science and Technology China

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model’s incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method’s superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method’s effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-Da Department of Information Management Tainan University of Technology Tainan710 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung811 Taiwan Kaohsiung Medical University Department of Biomedical Engineering Kaohsiung807 Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung807 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung807 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

research on the study of houses, condominiums and buildings in Taiwan's metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors - the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index - significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenc

关键词： Houses

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex Networks 9

Risk Modeling and Traceability of Boundary Attacks on Intern...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Xin Qu, Zhaoyang Yu, Tong Xie, Ming Yu, Fu Ding, Wei Northeast Electric Power University School of Electrical Engineering Jilin132012 China People's Bank of China Qinghai Provincial Branch Xining810000 China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning530023 China China Guizhou Power Grid Co. Ltd. Bijie Power Supply Bureau Bijie551700 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350391367

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines. © 2024 IEEE.

关键词： Internet of things

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Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning

arXiv

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arXiv 2024年

作者： Du, Xiaohu Wen, Ming Zhu, Jiahao Xie, Zifan Ji, Bin Liu, Huijun Shi, Xuanhua Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China College of Computer National University of Defense Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab HUST Wuhan430074 China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security HUST Wuhan430074 China JinYinHu Laboratory Wuhan430077 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities, resulting in poor performance in real-world scenarios beyond the training instances. To tackle this challenge, we introduce VulLLM, a novel framework that integrates multi-task learning with Large Language Models (LLMs) to effectively mine deep-seated vulnerability features. Specifically, we construct two auxiliary tasks beyond the vulnerability detection task. First, we utilize the vulnerability patches to construct a vulnerability localization task. Second, based on the vulnerability features extracted from patches, we leverage GPT-4 to construct a vulnerability interpretation task. VulLLM innovatively augments vulnerability classification by leveraging generative LLMs to understand complex vulnerability patterns, thus compelling the model to capture the root causes of vulnerabilities rather than overfitting to spurious features of a single task. The experiments conducted on six large datasets demonstrate that VulLLM surpasses seven state-of-the-art models in terms of effectiveness, generalization, and robustness. © 2024, CC BY.

关键词： Large datasets

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Improved Human Detection Algorithm by Indoor W-Band FMCW RADAR using K-means Technique 9

Improved Human Detection Algorithm by Indoor W-Band FMCW RAD...

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9th International Electrical engineering Congress, iEECON 2021

作者： Worasutr, Aman Worasawate, Denchai Pongthavornkamol, Tiwat Fukawa, Kazuhiko Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronic and Computer Technology Center Location and Automatic Identification System Research Team Pathum Thani Thailand Tokyo Institute of Technology Department of Communications and Computer Engineering Tokyo Japan

ISBN: (纸本)9781728195841

This paper proposes the improved indoor human detection algorithm from data provided by W-Band FMCW radar by using k-mean technique. The data provided by the radar contain both humans and nun-human objects. To identify non-human objects, the obtained data are filtered by the velocity and RCS of the detected objects. The K-means algorithm is applied to the filtered dataset to estimate the positions of humans. The estimated positions of humans are much closed to the actual ones. © 2021 IEEE.

关键词： K-means clustering

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Analysis of radiative heat transfer on electro-osmotic magneto nanofluid flow through ciliary propulsion

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ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik 2024年第4期104卷

作者： Imran, Ali Raja, Muhammad Asif Zahoor Awan, Saeed Ehsan Shoaib, Muhammad Department of Mathematics COMSATS University Islamabad Attock Campus Attock Pakistan Future Technology Research center National Yunlin University of Science and Technology Douliou Yunlin Taiwan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock Pakistan Yuan Ze University AI center Taoyuan Taiwan

A novel mathematical investigation is carried out to reveal the significance of thermal radiation on the dissipative magneto hydrodynamic electroosmotic ciliary propulsion of a Newtonian nanofluid (DMHECP-NNF) in an symmetric micro-channel by implementing the impact of an axial electrical as well as transverse magnetic fields. The ciliary transport model is explored by using conservation of mass and momentum, heat, nanoparticle concentration, and electric potential expressions along with associated boundary conditions. The pertinent system for the proposed flow problem DMHECP-NNF consisting of partial differential equations are converted into ordinary differential equations system by incorporating the strengths dimensionless mechanism. Then analytical expressions are found for electrical potential, axial velocity, stream function, pressure gradient, temperature and concentration profiles. Whereas numerical computations are carried out to study transverse velocity and rise of pressure as per wavelength. The impacts of significant physical quantities of DMHECP-NNF are investigated with the help of graphical manipulations. It is observed in this novel study that axial velocity rises initially and then decline with increase in the magnitude of electro-osmotic parameter and Helmholtz-Smoluchowski velocity. Also, it is seen that with the rising value of electric-osmotic parameter the complexion of the trapped bolus is inflated and surrounded by few streamlines. © 2024 Wiley-VCH GmbH.

关键词： Ordinary differential equations

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Successive Bayesian Reconstructor for FAS Channel Estimation

Successive Bayesian Reconstructor for FAS Channel Estimation

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IEEE Conference on Wireless Communications and Networking

作者： Zijian Zhang Jieao Zhu Linglong Dai Robert W. Heath Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Fluid antenna systems (FASs) can reconfigure their locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance loss. In this paper, we propose the successive Bayesian reconstructor (S- BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S- BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S- BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as the maximum a posterior (MAP) estimator of FAS channels. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

关键词： Accuracy Uncertainty Simulation Channel estimation Stochastic processes Estimation Bayes methods

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