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2022 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2022

作者： Hassan, Ahmad Fan, Wei Hu, Xiaoyu Wang, Wenhao Li, Hanxi School of Computer and Information Engineering Jiangxi Normal University 330022 China Research Center of Jiangxi Social Psychological Service System Construction 330022 China Jiangxi Key Laboratory of Psychology and Cognitive Science 330022 China School of Psychology Jiangxi Normal University 330022 China

ISBN: (纸本)9781510654846

Studying eye movement trajectory through eye tracker can provide insights into classification mechanism of memory and reasoning. For the given text or images, memory and reasoning based observations are useful in multiple key areas of psychology, cognition and related fields. For our project, the EyeLink 1000 Plus is used to inspect and capture fixation, saccades, blinks etc. in eye tracking protocols of each participant during trials. Based on fixation sequence of each participant our trained deep recurrent neural network architecture–LSTM classifies whether a participant is performing the given text-based task by memorizing it or by inference. To set trial sentences, syllogism– deductive reasoning type is followed. Sixty university students (mature readers) participated in both memory and reasoning-based trials. Participants were then divided into two equal groups and of which one group was instructed to perform memory task first while the second group was instructed to perform reasoning based task first and then their order of performing the given task was changed. From sixty-one different sentences, sixty randomly selected sentences were presented to each participant. Sequential signals from fixation of each participant were then processed to get the results. Our trained LSTM model’s high accuracy to classify the memory and the reasoning based reading of participants ensures the significance of our work which will provide a solid base for future works on eye movements to build intelligent techniques in the field of A.I backed psychology, healthcare and neuro-marketing. Our trained model has the potential for (a) achieving very high accuracy for memory and reasoning classification, (b) for data learning, it saves enormous time, (c) Economical and comfortable for trials to record data via EyeLink 1000 plus (d) and finally, possible future work opportunities are enlisted. © 2022 SPIE.

关键词： Eye movements

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Modeling leaf color dynamics of winter wheat in relation to growth stages and nitrogen rates

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Journal of Integrative Agriculture 2022年第1期21卷 60-69页

作者： ZHANG Yong-hui YANG Yu-bin CHEN Chun-lei ZHANG Kui-ting JIANG Hai-yan CAO Wei-xing ZHU Yan School of Computer Engineering Weifang UniversityWeifang 261061P.R.China Texas A&M University System AgriLife Research&Extension CenterBeaumontTX 77713United States College of Information Science and Technology Nanjing Agricultural UniversityNanjing 210095P.R.China National Engineering and Technology Center of Information Agriculture Nanjing Agricultural UniversityNanjing 210095P.R.China

The objective of this work was to develop a model for simulating the leaf color dynamics of winter wheat in relation to crop growth stages and leaf positions under different nitrogen(N) rates. RGB(red, green and blue) data of each main stem leaf were collected throughout two crop growing seasons for two winter wheat cultivars under different N rates. A color model for simulating the leaf color dynamics of winter wheat was developed using the collected RGB values. The results indicated that leaf color changes went through three distinct stages, including early development stage(ES), early maturity stage(MS) and early senescence stage(SS), with respective color characteristics of light green, dark green and yellow for the three stages. In the ES stage, the R and G colors gradually decreased from their initial values to steady values, but the B value generally remained unchanged. RGB values remained steady in the MS, but all three gradually increased to steady values in the SS. Different linear functions were used to simulate the dynamics of RGB values in time and space.A cultivar parameter of leaf color matrix(MRGB) and a nitrogen impact factor(FN) were added to the color model to quantify their respective effects. The model was validated with an independent experimental dataset. RMSEs(root mean square errors) between the observed and simulated RGB values ranged between 7.0 and 10.0, and relative RMSEs(RRMSEs)ranged between 7 and 9%. In addition, the model was used to render wheat leaves in three-dimensional space(3 D). The 3 D visualizations of leaves were in good agreement with the observed leaf color dynamics in winter wheat. The developed color model could provide a solid foundation for simulating dynamic crop growth and development in space and time.

关键词： winter wheat crop model virtual crop leaf color crop phenotype RGB Model

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Statistical static timing analysis for circuit aging prediction

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

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Multi-Layer Transformer for Video Classification 22

Multi-Layer Transformer for Video Classification

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22nd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2023

作者： Wu, Xing Tao, Chenjie Yao, Junfeng Qian, Quan School of Computer Engineering and Science Shanghai200444 China Key Laboratory of Silicate Cultural Relics Conservation Ministry of Education Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Zhejiang Laboratory Hangzhou Zhejiang311100 China CSSC Seago System Technology Co. Ltd Shanghai200010 China

ISBN: (纸本)9781643684307

Video classification is a challenging task because of the intricate spatiotemporal information present within videos. Current models often rely on 2D or 3D convolutional neural networks. However, convolutional neural networks are difficult to solve the long-range dependency problem. In addition, they are computationally expensive and memory-intensive. To address the challenges, a Multi-layer Transformer is proposed for video classification. The proposed method takes advantage of the high correlation between adjacent frames by grouping them and learning local and global information with a multi-layer structure based on Transformer. First, different frame sampling rates and grouping strategies are tested in the experiments, then comparing the method with state-of-The-Art models. The results demonstrate that the proposed method has advanced performance with TOP1 accuracy of 77.8% on the Kinetics-400 dataset and 64.9% on the Something-Something v2 dataset. © 2023 IOS Press. All rights reserved.

关键词： Classification (of information)

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

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

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing systems

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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International Conference on Data engineering

作者： Long Zheng Xiangyu Ye Haifeng Liu Qinggang Wang Yu Huang Chuangyi Gui Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering UNSW Sydney Australia

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating" version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value" that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency.

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Recommendation systems in Education : A Bibliometric Analysis

Recommendation Systems in Education : A Bibliometric Analysi...

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International Conference on Business and Industrial Research (ICBIR)

作者： Hendra Mayatopani Dina Fitria Murad Nor Adnan Yahaya School of Computing University Malaysia of Computer Science & Engineering Selangor Malaysia Information System Pradita University Tangerang Indonesia Information Systems Department Binus Online Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration.

关键词： Deep learning Filtering Collaborative filtering Education Bibliometrics Market research Information age International collaboration Recommender systems Optimization

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High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

arXiv

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arXiv 2025年

作者： Wei, Yuxuan Wang, Zehan Guo, Tian Liu, Hao Shen, Liquan Yuan, Hui School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China School of Computer and Control Engineering Yantai University Yantai264005 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200072 China

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity. © 2025, CC BY.

关键词： Wiener filtering

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Honeypot Comparison of Attack Detection and Mitigation of SSH Attack

Honeypot Comparison of Attack Detection and Mitigation of SS...

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Delhi Section Conference (DELCON), IEEE

作者： Wasif Ali Ahthasham Sajid Tanvi Ajit Ghodke Rida Malik Neeru Malik Keshav Kaushik Department of Cyber Security Riphah Institute of System Engineering Riphah International University Islamabad Pakistan Department of Computer Science and Engineering Pimpri Chinchwad University Maharashtra India School of Engineering & Technology Pimpri Chinchwad University Pune Maharashtra India Amity School of Engineering and Technology Amity University Mohali India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This research study addresses the importance of honeypots at multiple levels, which includes but not limited to placement in network or group of networks working alongside in an organization. This paper talks about the usage of multiple honeypots working alongside to log useful data for the mitigation of ongoing bot or actual attack this approach helps the organization better understand the threat they are dealing and the techniques which they can use to overcome the gap of the unseen security risks. The comparison will shed better light to the working and attack patterns of the ongoing attacks, gaining more confidence on using the honeypot for better protection. This allows strategic, optimized and critical insight to the evolving attack patterns.

关键词： Production systems computer hacking Prevention and mitigation Interactive systems Intrusion detection Organizations Real-time systems Security Reliability Protection

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M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Network for Segmenting Pathology Lesion of the Focal Lymphocytic Sialadenitis

M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Ne...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Hao Han Feng Lu Yanhan Deng Xiaofang Luo Hai Jin Wei Tu Xia Xie 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 Wuhan China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M 2 CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient.

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