Reviewerly/exHarmony huggingface dataset:
Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective

Authors:
raw_author_name: Yang Tao
raw_affiliation_string: School of Artificial Intelligence, Jilin University, Changchun, Jilin Province, 130012, China
raw_author_name: Yizhen Zheng
raw_affiliation_string: Department of Data Science & AI, Monash University, Melbourne, Victoria, 3168, Australia
raw_author_name: Shirui Pan
raw_affiliation_string: School of Information and Communication Technology, Griffith University, Brisbane, Queensland, 4222, Australia
raw_author_name: Xiaofeng Cao
raw_affiliation_string: School of Artificial Intelligence, Jilin University, Changchun, Jilin Province, 130012, China
raw_author_name: Yi Chang
raw_affiliation_string: School of Artificial Intelligence, Jilin University, Changchun, Jilin Province, 130012, China

Abstract: Dimensional collapse in graph contrastive learning (GCL) confines node embeddings to their lower-dimensional subspace, diminishing their distinguishability. However, the causes and solutions of this curse remain relatively underexplored. In statistics, whitening presents a powerful tool to eliminate correlations among multiple variables. This motivates us to relieve the dimensional collapse of GCL from a whitening perspective. In this paper, we propose an intuitive analysis suggesting that high similarity scores of node embeddings may cause dimensional collapse, providing more evidence for its presence. Considering the success of whitening in statistics, we introduce a new plug-and-play module called the hitening raph ontrastive earning (WGCL) to address the dimensional collapse issue in existing GCL methods. WGCL plugin standardises the covariance matrices of dimensions, eliminating correlations among node embeddings' dimensions. Additionally, we enhance the conventional GCL training objective by introducing a mutual information maximisation loss between input features and node embeddings to maintain information capacity. Our experiments demonstrate that WGCL effectively addresses dimensional collapse, leading to an average improvement of 0.93% (up to 2.0%) in classification accuracy across three GCL backbones on nine widely-used datasets. The code to reproduce the experiments is available at https://github.com/acboyty/WGCL.
Multi-scale split matching three-dimensional reconstruction method of surface microcracks on the silicon nitride bearing roller

Authors:
raw_author_name: Dahai Liao
raw_affiliation_string: Laboratory of Ceramic Material Processing Technology Engineering, Jiangxi Province, Jingdezhen, 333403, Jiangxi, China
raw_author_name: Jianbo Le
raw_affiliation_string: School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, Jiangxi, China
raw_author_name: Nanxing Wu
raw_affiliation_string: Laboratory of Ceramic Material Processing Technology Engineering, Jiangxi Province, Jingdezhen, 333403, Jiangxi, China

Abstract: Aiming at the characteristics of parametric discrete and non-significant depth of silicon nitride bearing roller surface microcracks. A multi-scale split matching method is proposed. To realize the full reconstruction of the characteristics of the three-dimensional morphology of the surface microcracks. Combined with the parametric discretization characteristics of the surface microcracks, a box filter is defined to do second-order Gaussian differentiation on the image. Construct a multiscale Hessian matrix in the scale space of the surface microcrack image. Determine the covariance-discrete feature points, and obtain the global covariance-discrete sparse point cloud of silicon nitride bearing roller surface microcracks by non-maximum value suppression. For the non-significant depth features of silicon nitride bearing roller surface microcracks. Dissect the surface microcrack distortion images. Divide each image into multiple regional pixel blocks and construct an aggregation function to determine the exact depth value of each pixel block at the cost of matching each pixel block with its surrounding pixel blocks. Dense point cloud reconstruction of non-significant depth features of silicon nitride bearing roller surface microcracks is realized. The average distribution of dense clusters of reprojection errors of the dense point cloud features is less than 0.4pix, the median residual of the field of view of the surface microcrack image is 0.57pix, and the total number of effectively extracted feature points is about 92.9 %. The detailed lossless recovery reconstruction of the parametric discrete, non-significant depth features of silicon nitride bearing roller surface microcracks is realized. A new idea is provided for the establishment of molecular dynamics geometric modeling of the surface microcrack extension process on the silicon nitride bearings roller.
Parametric Lie group structures on the probabilistic simplex and generalized Compositional Data

Authors:
raw_author_name: Petre Birtea
raw_affiliation_string: Universitatea de Vest din Timișoara [România] = West University of Timișoara [Romania] = Université Ouest de Timișoara [Roumanie]

Abstract: In this paper we build a set of parametric quotient Lie group structures on the probabilistic simplex that can be extended to real vector space structures. In particular, we rediscover the main mathematical objects generally used when treating compositional data as elements associated to the quotient Lie group with respect to the equivalence relation induced by the scale invariance principle. This perspective facilitates the adaptation of the statistical methods used for classical compositional data to data that follows a more general equivalence relation.
Cost-sensitive thresholding over a two-dimensional decision region for fraud detection

Authors:
raw_author_name: Ricardo Cao
raw_affiliation_string: Department of Mathematics, Research Group MODES, CITIC, University of A Coruña, A Coruña, Spain

Abstract: Credit fraud poses a challenging task in terms of detection. It can result in significant losses depending on the amount, so a cost-sensitive perspective needs to be taken. Classical approaches focus on estimating the probability of fraud and selecting a decision threshold, but they often fail to consider the transaction amount or account for the cumulative losses incurred within the sample. Consequently, these approaches can result in sub-optimal strategies. A new thresholding approach is proposed, based on the construction of a two-dimensional decision space with an estimated probability and the credit amount. This expansion allows more freedom for the optimal classification rule search, which is performed with a new algorithm. The proposed method generalizes previous approaches, so an improvement is consistently achieved. In addition, it allows a restricted search. This is shown in a study of two real data sets, comparing the results obtained by a wide range of classifiers.
Outlier detection in a multiset-valued information system based on rough set theory and granular computing

Authors:
raw_author_name: Hai Lin
raw_affiliation_string: School of Mathematics and Information Science, Guangxi University, Nanning, Guangxi 530000, PR China
raw_author_name: Zhaowen Li
raw_affiliation_string: Fujian Key Laboratory of Financial Information Processing, Key Laboratory of Applied Mathematics in Fujian Province University, Putian University, Putian, Fujian 351100, PR China

Abstract: Outlier detection on data with missing information values is especially tricky because the uncertainty caused by missing information values may contribute to an object being an outlier. A multiset-valued information system (MSVIS) is an information system (IS) in which information values are multisets. This kind of IS is a useful way of handling datasets with missing information values. In this paper, we study outlier detection in an MSVIS based on rough set theory and granular computing. First, some concepts of multisets and probability distribution sets are reviewed, and the fact that a weak one-to-one correspondence exists between multisets and rational probability distribution sets is illustrated. In this way, multisets may be treated as rational probability distribution sets. Then, an MSVIS can be induced by an incomplete information system (IIS) and viewed as the result of information fusion of multiple categorical ISs. Next, a tolerance relation in an MSVIS is constructed with the induced rational probability distribution sets. Then, the outlier factor in an MSVIS is formulated, and the corresponding outlier detection algorithm is proposed. Finally, the performance evaluation by AUC (area under the curve) and F1-score shows the superiority of the proposed algorithm over some existing algorithms.
Fast two-party signature for upgrading ECDSA to two-party scenario easily

Authors:
raw_author_name: Binbin Tu
raw_affiliation_string: CETC Cyberspace Security Technology Co., Ltd., Beijing, 100070, China; Key Laboratory of Cryptologic Technology and Information Security of Ministry of Education, Shandong University, Qingdao, 266237, China; School of Cyber Science and Technology, Shandong University, Qingdao, 266237, China; State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, 100878, China
raw_author_name: Yu Chen
raw_affiliation_string: Key Laboratory of Cryptologic Technology and Information Security of Ministry of Education, Shandong University, Qingdao, 266237, China; School of Cyber Science and Technology, Shandong University, Qingdao, 266237, China; State Key Laboratory of Cryptology, P.O. Box 5159, Beijing, 100878, China
raw_author_name: Hongrui Cui
raw_affiliation_string: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Xianfang Wang
raw_affiliation_string: CETC Cyberspace Security Technology Co., Ltd., Beijing, 100070, China

Abstract: ECDSA is a standardized signature scheme and is widely used in many fields. However, most two-party ECDSA require a complicated multi-party computation technique to compute the multiplication of many shared secrets and necessitate expensive zero-knowledge proofs to deal with malicious adversaries. In this paper, we propose a simple yet fast two-party signing protocol based on ECDSA security, whose signing operation of each party is similar to the original ECDSA signing algorithm. Our protocol can enjoy the same efficiency as ECDSA and upgrades the existing ECDSA application to two-party scenario friendly. For this purpose, we first introduce a variant of ECDSA called combinatorial ECDSA, which is as secure as the standard ECDSA and can be easily divided into two parts. Then, we devise a two-party signing protocol without complicated multi-party computation from the combinatorial ECDSA and give the security proof based on ECDSA. Compared with the state-of-the-art two-party ECDSA, the signing result of each party in our protocol maintains the structure of ECDSA signature, which can be verified easily and avoids expensive zero-knowledge proofs. Last, we conduct an experimental evaluation, demonstrating that the performance of our combinatorial ECDSA and two-party signature is similar to ECDSA and the experimental results show that our proposed schemes are practical.
A game-theory-based scheme to facilitate consensus latency minimization in sharding blockchain

Authors:
raw_author_name: Cheng Guo
raw_affiliation_string: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China; School of Software Technology, Dalian University of Technology, Dalian 116620, China
raw_author_name: Yingmo Jie
raw_affiliation_string: Key Laboratory of Liaoning Province for Data Analytics and Decision-Making Optimization, Dalian 116025, China; School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, China
raw_author_name: Yining Liu
raw_affiliation_string: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
raw_author_name: Yan Hu
raw_affiliation_string: School of Software Technology, Dalian University of Technology, Dalian 116620, China

Abstract: Unbalanced consensus latency in reaching a consensus on transactions occurs due to differences in the ability of different committees to process transactions. As the consensus latency accumulates, the transaction throughput of the sharding blockchain significantly decreases. This article proposes a consensus latency minimization scheme to improve the transaction throughput. According to a time-varying directed graph and a non-cooperative game model solved by a distributed algorithm, we show that the generalized Nash equilibrium for all committees are to balance the differences in consensus latency among various committees and to minimize the consensus latency throughout the whole period. The security of the proposed scheme is analyzed by considering the peaceful equilibrium, and the committee actions asymptotically converge to a generalized Nash equilibrium. Through the comparisons with other baseline algorithms via simulation experiments, we demonstrate that our proposed scheme could result in the optimal consensus latency and throughput. Therefore, the proposed consensus latency minimization scheme effectively avoids the existing problem of the transaction throughput in sharding blockchain and provides a viable and security instrument for the sharding blockchain operator to improve the transaction throughput performance throughout the whole period.
Intelligent Deep-Learning Tutoring System to Assist Instructors in Programming Courses

Authors:
raw_author_name: David Roldán-Álvarez
raw_affiliation_string: Escuela T&#x00E9;cnica Superior de Ingenier&#x00ED;a de Telecomunicaci&#x00F3;n, Universidad Rey Juan Carlos, Fuenlabrada, Spain

Abstract: Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized AI-based intelligent tutor that answers students programming questions and provides them with examples is presented. AI is becoming more and more popular as time passes, allowing to perform tasks automatically in a way that could not be done before. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, The tool has been tested by university students at the Universidad Rey Juan Carlos during the course Programming Fundamentals in the first course of their Biomedical Engineering degree to evaluate if it helps the students in the process of learning programming skills. One of the main goals was to provide guidance to the students without needing the instructor to be physically by their side. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses.
Differentially private trajectory event streams publishing under data dependence constraints

Authors:
raw_author_name: Yuan Shen
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan, Hubei, China; School of Software, Pingdingshan University, Pingdingshan, Henan, China
raw_author_name: Wei Song
raw_affiliation_string: Intellectual Computing Laboratory for Cultural Heritage, Wuhan University, Wuhan, Hubei, China; School of Computer Science, Wuhan University, Wuhan, Hubei, China
raw_author_name: Yang Cao
raw_affiliation_string: Graduate School of Information Science and Technology, Hokkaido University, Japan
raw_author_name: Zechen Liu
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan, Hubei, China
raw_author_name: Zhiyong Peng
raw_affiliation_string: Intellectual Computing Laboratory for Cultural Heritage, Wuhan University, Wuhan, Hubei, China; School of Computer Science, Wuhan University, Wuhan, Hubei, China

Abstract: Differential Privacy (DP), as a de facto privacy-preserving paradigm, can be widely applied in numerous event streams publishing schemes to protect private events (i.e., trajectory points of users). However, most existing schemes assume these events are independent, which is a vulnerable assumption that can lead to unforeseeable privacy degradation. The privacy-preserving for event streams publishing under data dependence is still challenging because existing dependence quantify models cannot dynamically measure the degree of dependent relationships between events. In this paper, we investigate the privacy degradation issue of trajectory event streams publishing and propose (Θ,ϵ)-dependent privacy mechanism to mitigate this issue. Specifically, we introduce spatial dependence to model the dependent relationship between events in trajectory data and extend it to quantify the influence of multiple dependent events on the target event. We define the notion of max dissimilarity to formalize the privacy level of DP mechanisms in trajectory event streams publishing. The proposed notion of global dependent coefficient helps the private mechanism to provide the expected privacy level by calibrating the added noise. We further achieve ω-event privacy protection over trajectory event streams under data dependence constraints with (Θ,ϵ)-dependent privacy mechanism. Theoretical analysis proves our scheme provides the expected privacy guarantee under data dependence constraints. Extensive experiments validate our scheme is efficient and effective.
Indoor semantic segmentation based on Swin-Transformer

Authors:
raw_author_name: Yunping Zheng
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
raw_author_name: Yuan Xu
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
raw_author_name: Shiqiang Shu
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
raw_author_name: Mudar Sarem
raw_affiliation_string: General Organization of Remote Sensing, Damascus, Syria

Abstract: In recent years, with the rapid development of Transformer in the field of natural language processing, many researchers have realized its potential and gradually applied it to the field of computer vision, with a proliferation of theoretical approaches represented by Vision Transformer (ViT) and Data-efficient image Transformer (DeiT). On the basis of ViT, the famous Swin-Transformer was proposed as one of the best computer vision neural network backbones, which can be widely used in tasks such as image classification, target detection and video recognition. However, in the field of image segmentation, the semantic segmentation of indoor scenes is still very challenging due to the wide variety of objects, large differences in object sizes, and a large number of overlapping objects with occlusion. Aiming at the problem that the existing semantic segmentation of RGB-D indoor scenes cannot effectively fuse multimodal features, in this paper, we propose a novel indoor semantic segmentation algorithm based on Swin-Transformer. It attempts to apply Swin-Transformer to the field of indoor RGBD semantic segmentation, and tests the performance of the model by conducting extensive experiments on the mainstream indoor semantic segmentation datasets NYU-Depth V2 and SUN RGB-D. The experimental results show that the Swin-L RGB+Depth setting achieves 52.44% MIoU on the NYU-Depth V2 data and 51.15% MIoU on the SUN RGB-D data set, which reflects an excellent performance in the field of indoor semantic segmentation. The improved performance of the Depth features on the indoor semantic segmentation model has also been demonstrated in the experiments by controlling the type of input features. Our source code is publicly available at https://github.com/YunpingZheng/ISSSW.
Residual attention fusion network for video action recognition

Authors:
raw_author_name: Ao Li
raw_affiliation_string: School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
raw_author_name: Yang Yi
raw_affiliation_string: Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510275, China; School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China; Xinhua College of Sun Yat-sen University, Guangzhou 510520, China
raw_author_name: Daan Liang
raw_affiliation_string: School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China

Abstract: Human action recognition in videos is a fundamental and important topic in computer vision, and modeling spatial–temporal dynamics in a video is crucial for action classification. In this paper, a novel attention module named Channel-wise Non-local Attention Module (CNAM) is proposed to highlight the important features both spatially and temporally. Besides, another new attention module named Channel-wise Attention Recalibration Module (CARM) is developed to focus on capturing discriminative features at channel level. Based on these two attention modules, a novel convolutional neural network named Residual Attention Fusion Network (RAFN) is proposed to model long-range temporal structure and capture more discriminative action features at the same time. More specifically, first, a sparse temporal sampling strategy is adopted to uniformly sample video data as input to RAFN along the temporal dimension. Secondly, the attention modules CNAM and CARM are plugged into residual network for highlighting important action regions around actors. Finally, the classification scores of four streams of RAFN are combined by late fusion. The experimental results on HMDB51 and UCF101 demonstrate the effectiveness and excellent recognition performance of our proposed method.