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arXiv 2023年

作者： Wang, Jingyao Qiang, Wenwen Su, Xingzhe Zheng, Changwen Sun, Fuchun Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models;(ii) over-constraining task diversity may incur the risk of under-fitting or overfitting during training;and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://***/WangJingyao07/AdaptiveSampler. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Analytical Models for QoT Estimations in Super-C and Super-C + L Bands Optical Transmission systems

Analytical Models for QoT Estimations in Super-C and Super-C...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Baoluo Yan Yijun Sun Ziwen Zhou Hong Liu Hongya Wang Chen Huan Zhiyong Zhao Lipeng Feng Anxu Zhang Zhenhua Feng Hu Shi WDM System Department of Wireline Product R&D Institute ZTE Corp. Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Optics Valley Laboratory School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Beijing China

We propose a novel physical model-based QoT estimation with achieved OSNR estimation error ≤ 1.5 dB in primary paths and OSNR prediction error ≤ 1.76 dB in unestablished lightpaths for both super-C bands and super-C... 详细信息

ISBN: (纸本)9781957171258

关键词： Hole burning Optical amplifiers Optical networks Optical signal to noise ratio Stimulated scattering Variable optical attenuators

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Algorithmic Fairness: A Tolerance Perspective

arXiv

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arXiv 2024年

作者： Luo, Renqiang Tang, Tao Xia, Feng Liu, Jiaying Xu, Chengpei Zhang, Leo Yu Xiang, Wei Zhang, Chengqi School of Software Dalian University of Technology Dalian116620 China Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC3353 Australia School of Computing Technologies RMIT University MelbourneVIC3000 Australia School of Economics and Management Dalian University of Technology Dalian116024 China MIoT IPIN Lab School of Minerals and Energy Resources Engineering University of New South Wales SydneyNSW2052 Australia School of Information and Communication Technology Griffith University Gold CoastQLD4222 Australia School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC3086 Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia

Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In response, this survey delves into the existing literature on algorithmic fairness, specifically highlighting its multifaceted social consequences. We introduce a novel taxonomy based on'tolerance', a term we define as the degree to which variations in fairness outcomes are acceptable, providing a structured approach to understanding the subtleties of fairness within algorithmic decisions. Our systematic review covers diverse industries, revealing critical insights into the balance between algorithmic decision making and social equity. By synthesizing these insights, we outline a series of emerging challenges and propose strategic directions for future research and policy making, with the goal of advancing the field towards more equitable algorithmic *** Codes 68T01, 68W40 © 2024, CC BY.

关键词： Decision making

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PiSLTRc: Position-informed sign language transformer with content-aware convolution

arXiv

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arXiv 2021年

作者： Xie, Pan Zhao, Mengyi Hu, Xiaohui The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing100191 China

Since the superiority of Transformer in learning long-term dependency, the sign language Transformer model achieves remarkable progress in Sign Language Recognition (SLR) and Translation (SLT). However, there are several issues with the Transformer that prevent it from better sign language understanding. The first issue is that the self-attention mechanism learns sign video representation in a frame-wise manner, neglecting the temporal semantic structure of sign gestures. Secondly, the attention mechanism with absolute position encoding is direction and distance unaware, thus limiting its ability. To address these issues, we propose a new model architecture, namely PiSLTRc, with two distinctive characteristics: (i) content-aware and position-aware convolution layers. Specifically, we explicitly select relevant features using a novel content-aware neighborhood gathering method. Then we aggregate these features with position-informed temporal convolution layers, thus generating robust neighborhood-enhanced sign representation. (ii) injecting the relative position information to the attention mechanism in the encoder, decoder, and even encoder-decoder cross attention. Compared with the vanilla Transformer model, our model performs consistently better on three large-scale sign language benchmarks: PHOENIX-2014, PHOENIX-2014-T and CSL. Furthermore, extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on translation quality with +1.6 BLEU improvements. © 2021, CC BY.

关键词： Convolution

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Robust Self-Expression Learning with Adaptive Noise Perception

SSRN

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SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Natural Image Matting Based on Surrogate Model

SSRN

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SSRN 2022年

作者： Liang, Yihui Gou, Hongshan Feng, Fujian Liu, Guisong Huang, Han School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu610000 China Laboratory of Intelligent Algorithms and Intelligent Software School of Software Engineering South China University of Technology Guangzhou510006 China

Image matting is important in digital image processing. The pixel pair optimization-based methods are some of the image matting methods that have distinct advantages in spatially disconnected foreground. However, they cannot provide high-quality alpha mattes with limited computing resources which limits their computing resource-sensitive applications. To address this problem, we present a natural image matting method based on a surrogate model. The surrogate model for pixel pair optimization is established in the presented method to approximate pixel pair fitness, and its analytical optimal solution is used as the approximate optimal solution of pixel pair optimization problem, which saves a lot of computing resources. Experimental results show that the image matting method based on a surrogate model provides high-quality matting mattes with low computing resources and its image matting performance is competitive against state-of-the-art pixel pair optimization-based methods which require a lot of computational resources. © 2022, The Authors. All rights reserved.

关键词： Pixels

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Nitin Naik Paul Jenkins Paul Grace Dishita Naik Jingping Song Shaligram Prajapat Durgesh Mishra Longzhi Yang Tossapon Boongoen Natthakan Iam-On School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Department of Computer and Information Sciences Northumbria University United Kingdom Department of Computer Science Aberystwyth University United Kingdom

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks.

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Graph Schema Storage in SQL Object-Relational Database and NoSQL Document-Oriented Database: A Comparative Study

Graph Schema Storage in SQL Object-Relational Database and N...

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作者： Ait El Mouden, Zakariyaa Jakimi, Abdeslam Hajar, Moha Boutahar, Mohamed Software Engineering and Information Systems Engineering Team Faculty of Sciences Technologies Errachidia Moulay Ismail University Meknes Morocco Operational Research and Computer Science Team Faculty of Sciences Technologies Errachidia Moulay Ismail University Meknes Morocco Faculty of Sciences Institute of Mathematics of Marseille Marseille France

Over the past years, the main objective of database management systems was to store data and give users the possibility to manipulate those data. SQL databases were sufficient in the first time, but with the evolution of computer science technologies, other factors start to appear;such as the volume of the stored data, the velocity of the transactions between the user and data, the possibility to store heterogeneous data (variety) and many other Big Data challenges. This paper presents the major differences between the SQL and NoSQL databases in term of variety, velocity and ease of programming. For SQL, we used the Oracle object-relational database and for NoSQL, we used MongoDB document-oriented database. The comparative study was applied to data modeled by graphs, where we consider a set of graphs and for each graph, we measure the time needed to insert the graph in the database, the size of the graph in the database and other factors. Measurements presents in this process are generally automatic and supported by a set of developed algorithms. © 2020, Springer Nature Switzerland AG.

关键词： Digital storage

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A Many-Objective Evolutionary Algorithm Assisted by Ideal Hyperplane

SSRN

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SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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