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International Conference on Image Information Processing

作者： N. P. G. Bhavani R V Sudha K.Shanmuga Priya S R Arun Raj Vinay Kumar Yadav Allam Balaram Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Tamilnadu India Department of Computer Science and Business Systems Knowledge Institute of Technology Kakapalayam Selliampalayam Tamil Nadu India Department of Information Technology KSR Institute for Engineering and Technology Tiruchengode TamilNadu India Department of Electronics & Communication Engineering U.B.D.T College of Engineering Davanagere Karnataka India Glocal School of Science and Technology Glocal University Uttar Pradesh India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Pedestrian detection is a critical task in computer vision with the aim of accurately identifying pedestrians in images or video frames. In this work, we tackle the issue of enhancing pedestrian detection precision while reducing processing time. To achieve this, we propose a novel technique called Softsign Gaussian Deep Belief Neural Network (SGDBNN) for pedestrian detection. The proposed SGDBNN technique leverages the power of recurrent deep neural networks to handle the complexities of pedestrian detection. The input layer receives images as input, which are then processed in the hidden layer. Here, a Gaussian activation function is employed to extract relevant features, effectively detecting pedestrian objects in the images. The incorporation of deep learning and recurrent behavior in the neural networks results in reduced incorrect classifications, leading to a lower false positive rate and improved pedestrian detection accuracy. The softsign activation function at the hidden layer allows the proposed technique to efficiently compare extracted features with pre-stored templates, enabling effective pedestrian identification in the images. By effectively utilizing the recurrent behavior and advanced activation functions, the SGDBNN demonstrates superior performance compared to existing methods, such as Deep Convolutional Neural Networks and Extended Deep Model. Experimental evaluations illustrate the effectiveness of the proposed SGDBNN technique. It achieves a significant increase in Positive Detection Accuracy (PDA) of up to 20% and 16%, while simultaneously reducing the False Positive Rate (FPR) by up to 18% and 3% compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Additionally, the proposed SGDBNN reduces training time by up to 30% and 12% and decreases memory requirements by up to 47% and 30% when compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Likewise, the SGDBNN tec

关键词： Training Deep learning computer vision Pedestrians Computational modeling Memory management Information processing

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PMU-Spill: Performance Monitor Unit Counters Leak Secrets in Transient Executions

PMU-Spill: Performance Monitor Unit Counters Leak Secrets in...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Pengfei Qiu Qiang Gao Dongsheng Wang Yongqiang Lyu Chang Liu Xiaoyong Li Chunlu Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Tsinghua University Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing China Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland Maryland USA

ISBN: (纸本)9781665461153

The processor's Performance Monitor Unit (PMU) allows the recording of architectural and microarchitectural events for profiling purposes. In this study, we reveal a security issue caused by the fact that current PMU implementations are capable of recording some events that happened during transient executions. We propose the PMU -Spill attack, a new kind of attack that enables attackers to maliciously leak the secret data in transient executions. We demonstrate on real hardware that PMU -Spill attack can leak the secret data stored in Intel Software Guard Extensions (SGX). In addition, we perform a thorough study to reveal all the vulnerable PMU counters and find that 20 of them can be used to achieve PMU -Spill attack. Our experiments suggest that the throughput of PMU -Spill attack is up to 575.3 bytes per second (Bps) with an average error rate of 1.89% when leaking the SGX-protected secret data.

关键词： Side-channel attacks Throughput Phasor measurement units Hardware Software Recording Security

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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When Killing-Time Periods Don't Boost Notification Engagement: Insights from a Multimodal Large Language Model Analysis of Smartphone Screen Activity

SSRN

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SSRN 2025年

作者： Chang, Yung-Ju Cho, Mu-Jung Chi, Yikun Chen, Yu-Chun Lee, Yu-Jen Kao, Kuei-Chun Ram, Nilam Robinson, Thomas Reeves, Byron Information Systems Northeastern University San JoseCA United States Department of Computer Science University of California Los AngelesCA United States Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei Taiwan Department of Communication Stanford University StanfordCA United States Department of Psychology Stanford University StanfordCA United States Department of Pediatrics Stanford University StanfordCA United States Department of Medicine Stanford University StanfordCA United States

Prior research suggests that "killing-time" periods may be ideal for presenting users with notifications that require attention and engagement. This study examines whether this assumption holds across different types of phone activities. Using a two-week, screenshot-based study with 36 smartphone users, we collected automated screenshots and delivered four categories of notifications—news, advertisements, crowdsourcing tasks, and questionnaires. To obtain users’ screen activity information, we employed a multimodal large language model (MLLM), GPT-4o, to infer five categories of phone activities: communication, media consumption, functional tasks, gameplay, and other activities. The MLLM-generated classifications demonstrated strong reliability, achieving an inter-coder reliability score of 0.877 compared to human coders. Our findings reveal that while killing-time periods generally increase notification engagement, this effect varies by activity type. Specifically, engagement is amplified during communication and gameplay, but diminishes during media consumption and functional tasks. Furthermore, this positive relationship between killing time and notification engagement is more pronounced when screen activity is inferred using broader time frames (e.g., 1 or 5 minutes, rather than 5 seconds). We also observed distinct patterns in users’ screen activities prior to engaged vs. unengaged notifications, with these differences becoming more pronounced during killing-time periods. Finally, users were more likely to respond to task-based notifications than to content-based notifications, suggesting that both timing and notification type play critical roles in driving user engagement. © 2025, The Authors. All rights reserved.

关键词： Smartphones

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An iterative algorithm for artifacts removal in Fourier-domain optical coherence tomography 25

An iterative algorithm for artifacts removal in Fourier-doma...

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Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV 2021

作者： Wang, Mengyuan Ling, Yuye Shao, Shuo Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China Department of Electronic Engineering State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781510640962

ISBN: (纸本)9781510640955

FD-OCT is a widely used technology which could provide high-resolution 3D reconstruction images. Conventional OCT uses IDFT reconstruction method, which could obtain an FFT-limited axial resolution. Recently, several optimization-based methods have reportedly achieved a resolution improvement, while undesired noise artifacts might appear in their result, which could degrade the image quality. In this work, we proposed an iterative error reduction method in order to remove the artifacts as well as improve the resolution. A numerical simulation is designed to validate our algorithm. Two reconstruction methods including IDFT reconstruction, and l1 minimization are selected for a comparative study. Specifically, we conduct the simulation at four different noise levels. The result shows that our proposed method could greatly suppress the artifact and obtain a great reconstruction even when the SNR is reduced. © 2021 SPIE.

关键词： Image reconstruction

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Upgrading eHealth 2.0 Applications to eHealth 3.0 Based on QVTo Mapping

Upgrading eHealth 2.0 Applications to eHealth 3.0 Based on Q...

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Digital Age & Technological Advances for Sustainable Development (ICDATA), International Conference on

作者： Mint Mohamed Meyma Naziha Laaz Samir Mbarki Information Modeling and Communication Systems Team EDPAGS Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Mathematics Computer Science and Geomatics ASYR RT LAGES Laboratory Hassania School of Public Works EHTP Casablanca Morocco

The article presents an MDA approach for upgrading e-health applications, based on QVTo mapping which is defined by OMG as the language used to model the transformation. Its purpose is to transform abstract models into concrete ones so that the reader can understand how the complete transformation takes place. The work focuses on the modernization of eHealth applications by applying MDE principles, techniques and tools. To do so, a comparative study was conducted to see the difference between eHealth 2.0 and eHealth 3.0 applications. EHealth 2.0 applications are characterized by the use of social web, wikis, blogs. As for the 3.0 applications, they rely on artificial intelligence and semantic web in addition to web 2.0 properties. So, a development process is proposed that takes as input two PIM models, derived from both UML and IFML standards, and as output the two PSM models that respect ODM and HTML5 respectively.

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning

Environment Transformer and Policy Optimization for Model-Ba...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Pengqin Wang Meixin Zhu Shaojie Shen Division of Emerging Interdisciplinary Areas Hong Kong University of Science and Technology Hong Kong SAR China Systems Hub Hong Kong University of Science and Technology (Guangzhou) Civil and Environmental Engineering Department Sensing and Computation for Ubiquitous Internet of Things Hong Kong University of Science and Technology and Guangdong Provincial Key Lab of Integrated Communication Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible solution. On the one hand, it eliminates the requirements of interaction with the actual environment. On the other hand, it learns the transition dynamics and reward function from the offline datasets and generates simulated rollouts to accelerate training. Previous model-based offline RL methods adopt probabilistic ensemble neural networks (NN) to model aleatoric uncertainty and epistemic uncertainty. However, this results in a great increase in training time and computing resource requirements. Furthermore, these methods are easily disturbed by the accumulative errors of the environment dynamics models when simulating long-term rollouts. To solve the above problems, we propose an uncertainty-aware sequence modeling architecture called Environment Transformer. It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter. Benefiting from the accurate modeling of the transition dynamics and reward function, Environment Transformer can be combined with arbitrary planning, dynamics programming, or policy optimization algorithms for offline RL. In this case, we perform Conservative Q-Learning (CQL) to learn a conservative Q-function. Through simulation experiments, we demonstrate that our method achieves or exceeds state-of-the-art performance in widely studied offline RL benchmarks. Moreover, we show that Environment Transformer's simulated rollout quality, sample efficiency, and long-term rollout simulation capability are superior to those of previous model-based offline RL methods.

关键词： Training Uncertainty Q-learning Computational modeling Transformers Probabilistic logic Probability distribution Dynamic programming Planning Optimization

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Co‑packaged optics(CPO):status,challenges,and solutions

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Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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An ns-3 model for multipath communication with terrestrial and satellite links 1

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20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing systems, MMB 2020

作者： Deutschmann, Jörg Hielscher, Kai-Steffen Jens German, Reinhard Department of Computer Science 7 Computer Networks and Communication Systems University of Erlangen-Nürnberg Martensstraße 3 Erlangen91058 Germany

ISBN: (数字)9783030430245

ISBN: (纸本)9783030430238

Some terrestrial Internet access links provide only low data rates. Internet access via geostationary satellites on the other hand provides data rates of up to 50 Mbit/s, but suffers from high latencies. The combination of low data rate, low latency Internet access (e.g., DSL light) and high data rate, high latency Internet access (e.g., geostationary satellites) can utilize the advantages of both. We discuss multipath communication architectures and scheduling strategies for such heterogeneous link combinations. The solution described in this paper relies on Split TCP and Performance Enhancement Proxies. We implement and evaluate the solution with the network simulator ns-3. A well-established traffic model for loading websites is enhanced with different HTTP variants. The results show that the combination of heterogeneous link types can result in page load times close to as if there was a high data rate, low latency Internet access. © Springer Nature Switzerland AG 2020.

关键词： Geostationary satellites

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