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Journal of software 2011年第1期6卷 91-99页

作者： Shu, Wanneng Ding, Lixin State key lab of software engineering Wuhan University Wuhan 430072 China College of Computer Science South-Central University for Nationalities Wuhan 430074 China Computer School Wuhan University Wuhan 430072 China

Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of fuzzy association rules obtained with the proposed method is larger than those obtained by applying other methods. © 2011 ACADEMY PUBLISHER.

关键词： Data mining

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Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning

arXiv

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arXiv 2024年

作者： Liu, Chengliang Wen, Jie Liu, Yabo Huang, Chao Wu, Zhihao Luo, Xiaoling Xu, Yong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus China College of Computer Science and Software Engineering Shenzhen University China

Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we focus on the complex yet highly realistic task of incomplete multi-view weak multi-label learning and propose a masked two-channel decoupling framework based on deep neural networks to solve this problem. The core innovation of our method lies in decoupling the single-channel view-level representation, which is common in deep multi-view learning methods, into a shared representation and a view-proprietary representation. We also design a cross-channel contrastive loss to enhance the semantic property of the two channels. Additionally, we exploit supervised information to design a label-guided graph regularization loss, helping the extracted embedding features preserve the geometric structure among samples. Inspired by the success of masking mechanisms in image and text analysis, we develop a random fragment masking strategy for vector features to improve the learning ability of encoders. Finally, it is important to emphasize that our model is fully adaptable to arbitrary view and label absences while also performing well on the ideal full data. We have conducted sufficient and convincing experiments to confirm the effectiveness and advancement of our model. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Negative Binomial Distribution Based Reputation Direct Update for Wireless Sensor Networks

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Journal of Electronic science and Technology 2011年第2期9卷 161-166页

作者： Zhe Wei Meng-Shu Hou Fang Wang School of Software University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science ＆ Engineering University of Electronic Science and Technology of ChinaChengdu 611731China College of Cornputer Science Civil Aviation Flight University of ChinaGuanghan 618307China

In the reputation modeling of wireless sensor networks（WSNs） many literatures have proposed creative reputation indirect update methods,such as reputation integration,discounting,aging to eliminate,and filtering malicious reputation information. However,few have discussed the reputation direct update. In this paper,based on sound statistical theories,a negative binominal distribution method in the reputation direct update for WSNs is proposed. Results show that the proposed method is more suitable and time-saving for the reputation update of the resource constraint WSNs and can improve the computation power efficiency as well.

关键词： Beta distribution negative binomial distribution reputation wireless sensor networks

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Predicting user influence based on improved linear threshold model in social networks

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Journal of Computational Information Systems 2014年第14期10卷 6151-6160页

作者： Tian, Zhongsheng Wang, Ying Zhao, Qiuyue Zuo, Wanli College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Lab. of Symbolic Computation and Knowledge Engineering Attached to the Ministry of Education Changchun China

With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. There are lots of works on this area, mainly covering the following aspects: Maximize Influence, Influence Diffusion, Predicting Influential Users and Predicting social influence. The common issue of above aspects is that they can't calculate the precise user influence. In this work, we propose a novel method to give each user influence a precise value in social networks. The proposed method is based on Improved Linear Threshold Model (ILTM). To build ILTM, we take a series of measures to overcome the deficiencies brought by Linear Threshold Model, such as assigning random threshold to node, cold start. So we adopt user similarity instead of random threshold and utilize cluster algorithm and link analysis algorithm to surmount cold start. © 2014 by Binary Information Press

关键词： Forecasting

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

Transformer-based Text Classification on Unified Bangla Mult...

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Information Technology (ACIT)

作者： Md Sakib Ullah Sourav Mohammad Sultan Mahmud Hua Zheng Mohammad Aljaidi Md. Simul Hasan Talukder Rejwan Bin Sulaiman Abdullah Hafez Nur Ahmad Al-Qerem School of Management Science and Engineering Shandong University of Finance and Economics Jinan China College of Computer Science and Software Engineering Shenzhen University Senzhen China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China Department of Computer Science Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Dhaka School of Computer Science & Technology Northumbria University Newcastle Tayne United Kingdom Northumbria UK Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world’s most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.

关键词： Measurement Human computer interaction Sentiment analysis Analytical models Codes Accuracy Web 2.0 Text categorization Transformers Information technology

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Exception beyond Exception: Crashing Android System by Trapping in "Uncaught Exception"

Exception beyond Exception: Crashing Android System by Trapp...

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IEEE/ACM International Conference on software engineering: software engineering in Practice Track (ICSE-SEIP)

作者： Jingzheng Wu Shen Liu Shouling Ji Mutian Yang Tianyue Luo Yanjun Wu Yongji Wang State Key Laboratory of Computer Sciences Institute of Software General Department Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9781538627181

Android is characterized as a complicated open source software stack created for a wide array of devices with different form of factors, whose latest release has over one hundred million lines of code. Such code is mainly developed with the Java language, which builds complicated logic and brings implicit information flows among components and the inner framework. By studying the source code of system service interfaces, we discovered an unknown type of code flaw, which is named uncaughtException flaw, caused by un-well implemented exceptions that could crash the system and be further vulnerable to system level Denial-of-Service (DoS) attacks. We found that exceptions are used to handle the errors and other exceptional events but sometimes they would kill some critical system services exceptionally. We designed and implemented ExHunter, a new tool for automatic detection of this uncaughtException flaw by dynamically reflecting service interfaces, continuously fuzzing parameters and verifying the running logs. On 11 new popular Android devices, ExHunter extracted 1045 system services, reflected 758 suspicious functions, discovered 132 uncaughtException flaws which are 0-day vulnerabilities that have never been known before and generated 275 system DoS attack exploitations. The results showed that: (1) almost every type of Android phone suffers from this flaw, (2) the flaws are different from phone by phone, and (3) all the vulnerabilities can be exploited by direct/indirect trapping. To mitigate uncaughtException flaws, we further developed ExCatcher to re-catch the exceptions. Finally, we informed four internationally renowned manufacturers and provided secure improvements in their commercial phones.

关键词： Smart phones computer crashes computer crime Message systems software Java

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Optimal local data exchange in fiber-wireless access network: A joint network coding and device association design

Optimal local data exchange in fiber-wireless access network...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jin Wang Kejie Lu Jianping Wang Chunming Qiao School of Computer Science and Technology Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology University of Puerto Rico at Mayagüez Department of Computer Science City University of Hong Kong Department of Computer Science and Engineering State University of New York at Buffalo

ISBN: (纸本)9781467399548

For many emerging mobile broadband services and applications, the source and destination are located in the same local region. Consequently, it is very important to design access networks to facilitate efficient local data exchange. In the past few years, most existing studies focus on either the wired or wireless domains. In this paper, we aim to exploit both the wired and wireless domains. Specifically, we consider a Fiber-Wireless access network in which a passive optical network (PON) connects densely deployed base stations. In such a scenario, we propose a novel access scheme, namely, NCDA, where the main idea is to utilize both network coding and device association. To understand the potentials of NCDA, we first formulate a mixed integer nonlinear programming (MINLP) to minimize the weighted number of packet transmissions (WNT), which is related to both the system capacity and energy consumption. We then theoretically analyze the tight upper bounds of the minimal WNT in the PON, which helps us to approximate the original problem by a mixed integer linear programming (MILP). Next, we develop efficient algorithms based on linear programming relaxation to solve the optimal NCDA problem. To validate our design, we conduct extensive simulation experiments, which demonstrate the impact of important network parameters and the promising potentials of the proposed scheme.

关键词： Optical network units Wireless communication Passive optical networks Network coding computer science Approximation algorithms

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Domain Contrastive Learning for Multi-Site Alzheimer's Disease Classification

Domain Contrastive Learning for Multi-Site Alzheimer's Disea...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Linda Delali Fiasam Yunbo Rao Collins Sey Esther Stacy E. B. Aggrey Seth Larweh Kodjiku Judith Ayekai Browne Juliana Mantebea Danso Chiagoziem C. Ukwuoma Emmanuel S. A. Gyarteng School of Information and Software Engineering University of Electronic Science and Technology of China School of Computer Science and Technology Zhejiang Gongshang University School of Computer Science and Technology University of Electronic Science and Technology of China University College of Nuclear Technology and Automation Engineering Chengdu University of Technology

In the field of medical imaging, accurately classifying Alzheimer's Disease (AD) poses a substantial challenge, primarily due to the inherent variability in imaging data across different clinical sites. This paper introduces a novel approach, Domain Contrastive Learning (DCL), to address this challenge by enhancing the robustness and transferability of AD classification models using multi-site Magnetic Resonance Imaging (MRI) data. DCL leverages a unique contrastive learning technique within an autoencoder's latent space, enabling the extraction of domain-invariant features essential for accurate diagnosis. We rigorously evaluate the proposed network on brain images from three distinct imaging sites, employing a Domain Generalization (DG) training protocol to ensure robustness and generalizability across varying data sources. Our model is evaluated on three binary classification tasks: AD vs. Normal Control (NC), AD vs. Mild Cognitive Impairment (MCI), and MCI vs. NC. Results demonstrate that DCL outperforms existing methods, especially in AD vs. NC classification, achieving higher accuracy, sensitivity, and specificity with 0.51%, 1.54% and 1.68% increments respectively. This underscores DCL's potential as a robust and generalizable method for enhanced AD diagnosis, marking a significant step forward in medical imaging challenges in a multi-site context.

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A Time Series Analysis-Based Forest Disturbance Time Backtracking Method in Northeastern China, the Changbai Mountain

A Time Series Analysis-Based Forest Disturbance Time Backtra...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Yuxiao Yan Changsheng Zhang Lei Fang Bin Zhang Xingjun Dong Ying Guo College of Software Northeastern University Shenyang PR China Environment Research Institute Shandong University Qingdao China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan PR China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Forest disturbance is a crucial factor in evaluating carbon sink changes in ecological studies. Current research on forest disturbances mainly focuses on forest disturbance detection, forest disturbance classification, and other analyses and statistics of forest changes methods. These studies are mostly based on time-series images or forest remote sensing features to achieve dynamic monitoring of forest changes. However, it is often impossible to construct disturbance information with a lack of images from early remote sensing. This paper addresses this limitation by proposes a method of forest disturbance time backtracking which can get historical information on forest disturbance over a wider temporal distribution. Using this method, we ultimately achieved forest disturbance detection on early remote sensing images from the 1960s. This method is based on machine learning and deep learning algorithms to construct two forest disturbance datasets to achieve this goal. The first dataset, for categorizing forest disturbances, is the first of its kind in Northeast China. This data can be widely used in similar studies. The second dataset is the forest disturbance recovery year dataset. We estimate the timing of early forest disturbance by classifying this dataset. The experimental results showed that the first dataset has an accuracy of over 90%, and the backtracking historical disturbance results are consistent with the actual historical information. This method is significant in its ability to fill in gaps in the forest disturbance classification present in Northeast China and enhance the confidence and time span of the forest disturbance detection method.

关键词： Surveys Backtracking Semantic segmentation Time series analysis Forestry Timing Sensors Pattern recognition Reliability Remote sensing

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Mirror-Like Gabor Features for Face Recognition under Varying Illumination Conditions

Mirror-Like Gabor Features for Face Recognition under Varyin...

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Yingnan Zhao Yan Ma Zhong Jin School of Computer and Software Nanjing University of Information Science and Technology Nanjing China College of Physics Science & Information Engineering Jishou University Jishou China School of Computer Science & Technology Nanjing University of Information Science and Technology Nanjing China

As is well known, face recognition under varying illumination conditions remains one of the most challenging tasks. How to achieve illumination invariant features is the key issue of face recognition. This paper provides a novel face recognition method using mirror-like Gabor features (MGF). The paper first presents the process of even/odd MGF extraction. It then demonstrates that the even MGF are superior to the odd ones in terms of robustness and efficient matching, providing relevant theoretical analysis from the point of view of the Fisher criterion and statistics. Finally, it describes comparison experiments on the YaleB face database and offers valuable conclusions.

关键词： Lighting Face recognition Face Robustness Correlation Mirrors

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