Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of cross-coupled chaotic maps multi-image encryption scheme

Authors:
raw_author_name: Laiphrakpam Dolendro Singh
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India
raw_author_name: Rohit Thingbaijam
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India
raw_author_name: Ripon Patgiri
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India
raw_author_name: Khoirom Motilal Singh
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Information Technology, Imphal, 795002, Manipur, India

Abstract: Recently, Patro et al. proposed a multiple gray-scale image encryption scheme based on cross-coupled chaotic maps. The authors claimed the scheme could withstand known plain-text attacks and chosen-plaintext attacks. In this paper, we have thoroughly analysed the Patro et al. algorithm and reported the crucial loopholes. Patro et al. algorithm uses a row-wise and column-wise scrambling operation based on a secret sequence followed by a feed-forward XOR operation generating the cipher image. Exploiting the loopholes of feed-forward XOR operation and high correlation of pixels in standard images, a cryptanalysis algorithm is developed that deciphers the ciphered images generated by Patro et al. algorithm based on cipher-text only attack. Without using the secret keys, the proposed cryptanalysis algorithm successfully deciphers the cipher image and reveals the secret images.
Semantic information extraction and search of mineral exploration data using text mining and deep learning methods

Authors:
raw_author_name: Qinjun Qiu
raw_affiliation_string: Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China; School of Computer Science, China University of Geosciences, Wuhan 430074, China
raw_author_name: Miao Tian
raw_affiliation_string: Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China
raw_author_name: Liufeng Tao
raw_affiliation_string: School of Computer Science, China University of Geosciences, Wuhan 430074, China
raw_author_name: Zhong Xie
raw_affiliation_string: Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China; School of Computer Science, China University of Geosciences, Wuhan 430074, China
raw_author_name: Kai Ma
raw_affiliation_string: College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China; Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, Hubei, China

Abstract: Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers’ knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining.
Design of Motor Skill Recognition and Hierarchical Evaluation System for Table Tennis Players

Authors:
raw_author_name: Zhuoyong Shi
raw_affiliation_string: School of Electronic Information, Northwestern Polytechnical University, Xi&#x2019;an, China
raw_author_name: Guoqing Shi
raw_affiliation_string: School of Electronic Information, Northwestern Polytechnical University, Xi&#x2019;an, China
raw_author_name: Kexin Zhang
raw_affiliation_string: School of Foreign Studies, Northwestern Polytechnical University, Xi&#x2019;an, China
raw_author_name: Longmeng Ji
raw_affiliation_string: School of Electronic Information, Northwestern Polytechnical University, Xi&#x2019;an, China
raw_author_name: Dinghan Wang
raw_affiliation_string: School of Electronic Information, Northwestern Polytechnical University, Xi&#x2019;an, China

Abstract: With the rapid development of electronic science and technology, the research on wearable devices is constantly updated. Still, the current wearable devices are not yet comprehensive in recognizing and analyzing the movement patterns of special sports. Based on this, this paper improves wearable devices for table tennis, a single sport, and realizes the recognition and evaluation of table tennis players’ motor skills based on the pattern recognition method and hierarchical evaluation system. First, a table tennis player’s motion information acquisition device was designed and the collected motion data were processed. Secondly, a sliding window was designed to segment the collected motion data and establish a feature database of six table tennis benchmark movements. Secondly, the motion features were constructed based on feature engineering, and after dimensionality reduction, the different models were used for action skill recognition. Finally, a hierarchical evaluation system of movement skills was established and the loss functions of different evaluation indexes were given. The experimental results show that in table tennis players’ action skill recognition, the neural network model based on motor feature extraction proposed in this paper improves the recognition accuracy by 6.5% than the traditional convolutional neural network, and in table tennis players’ action skill evaluation, the ANOVA shows that the hierarchical evaluation system proposed in this paper has a significance level of p<0.05 for the athletes’ professionalism level. The conclusion of the study indicates that the proposed paper table tennis players’ motor skills recognition and hierarchical evaluation assessment system can realize the accurate recognition of athletes’ motor skills and the precise determination of their professional level.
Self-Supervised Monocular Depth Estimation Based on High-Order Spatial Interactions

Authors:
raw_author_name: Xiuling Wang
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Minglin Yu
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Haixia Wang
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Xiao Lu
raw_affiliation_string: College of Energy Storage Technology, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Zhiguo Zhang
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China

Abstract: Depth estimation plays a pivotal role in various applications, including autonomous driving and robot navigation. In contrast to depth estimation using multiple images, such as stereo depth perception, inferring depth relations from a monocular camera is notably more challenging yet highly valuable. Traditionally, convolutional neural networks with residual structures have been extensively employed for this task, but they inherently constrain the model’s feature extraction capabilities. Inspired by HorNet, in this paper, we propose a novel self-supervised monocular depth estimation framework based on high-order spatial interactions, referred to as the Hor-Depth. Furthermore, the Hor-Depth improves feature fusion efficiency in the depth network decoder by incorporating the attentional feature fusion module based on first-order spatial interaction, leading to more refined predicted disparity maps. To address issues of loss fluctuations and training instability, we introduce a progressive scale-weight adjustment strategy-based loss function. This strategy applies varying constraints to the model at different training stages, effectively reducing training fluctuations and mitigating outliers that significantly deviate from the predicted values. The proposed approach demonstrates exceptional performance in self-supervised monocular depth estimation, surpassing certain stereo supervised or monocular supervised methods, as evidenced by its impressive results on the KITTI Eigen split benchmark.
An Adaptive Track Initiation Method Adopting Multiscan Spatiotemporal Information

Authors:
raw_author_name: Jiaqi Zhang
raw_affiliation_string: National Key Laboratory of Radar Signal Processing and the School of Electronic Engineering, Xidian University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Cao Zeng
raw_affiliation_string: National Key Laboratory of Radar Signal Processing and the School of Electronic Engineering, Xidian University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Haihong Tao
raw_affiliation_string: National Key Laboratory of Radar Signal Processing and the School of Electronic Engineering, Xidian University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Yuhong Zhang
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Minti Liu
raw_affiliation_string: National Key Laboratory of Radar Signal Processing and the School of Electronic Engineering, Xidian University, Xi&#x2019;an, Shaanxi, China
raw_author_name: Shihua Zhao
raw_affiliation_string: National Key Laboratory of Radar Signal Processing and the School of Electronic Engineering, Xidian University, Xi&#x2019;an, Shaanxi, China

Abstract: Trajectory initiation of dense newborn targets is an important research topic in multi-target tracking, however, few reports concerning the related studies can be found. In response to the problem of accurately obtaining spatiotemporal information of densely populated newborn targets, we propose a novel adaptive track initiation method adopting multi-scan spatiotemporal information. Firstly, by combining the spatial information measured from multiple scans, an adaptive track initiation method is proposed to obtain the measurement sequence of newborn targets. Secondly, by establishing an adaptive newborn density model, the newborn state estimation of dense targets is obtained from the measurement sequence of newborn targets. Finally, the state estimation of the targets is input to the labelled random finite set filter to obtain the trajectory information. By quantifying the posterior density error of the labelled random finite set, the truncation error of the newborn state estimation is presented. The error analysis suggests that our proposed method can achieve track initiation with small boundary L1 error. The simulation results verify that the proposed method can effectively cope with the situation of dense newborn targets. Compared with the existing adaptive birth algorithms, our proposal can realize more accurate track initiation when newborn targets occur simultaneously.
Correntropy-Induced Weighted GAN for Bridge Anomaly Detection

Authors:
raw_author_name: Jiankai Tu
raw_affiliation_string: College of Information Science and Electronic Engineering and the Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou, China
raw_author_name: Huan Liu
raw_affiliation_string: College of Information Science and Electronic Engineering and the Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou, China
raw_author_name: Chunguang Li
raw_affiliation_string: College of Information Science and Electronic Engineering and the Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou, China
raw_author_name: Pengfei Liu
raw_affiliation_string: Envision Group Company, Shanghai, China

Abstract: Bridges play an essential role in transportation and economics. Since the loss is significant in the event of bridge damage, the monitoring of bridge structure health is important. One of the important tasks of bridge health monitoring is the bridge anomaly detection. The bridge anomaly detection is a challenging problem since the real-world bridge data collected by the sensors is usually contaminated by large non-Gaussian noises. To address this issue, we propose a correntropy-induced weighted generative adversarial network (CWGAN) for bridge anomaly detection. Specifically, we first propose a correntropy-based generator, which has an encoder-decoder structure with a memory structure, and optimize it based on the maximum correntropy criterion. Then, derived from the correntropy of the reconstruction errors, we specially design a weight module to obtain a weight variable that measures the contamination-free degree of the corresponding sample. Afterwards, we take advantage of the weight module and transform the Generator and Discriminator modules of CWGAN into weighted versions to achieve robustness against contamination and obtain smaller reconstruction errors for non-contaminated normal data. We integrate and iteratively perform the reconstruction and weighting process to achieve better anomaly detection performance. After the model optimization, we detect the anomalies with reconstruction errors. We conduct experiments on a model bridge to demonstrate the effectiveness of the proposed CWGAN.
3-D Object Detection With Balanced Prediction Based on Contrastive Point Loss

Authors:
raw_author_name: Jiaxun Tong
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Kaiqi Liu
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Xia Bai
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Wei Li
raw_affiliation_string: Beijing Institute of Technology, Beijing, China

Abstract: Object detection on point clouds is widely used in autonomous driving technology. Recent researches have demonstrated that good feature representation is the key to 3D object detection, especially for point-based methods. Meanwhile, contrastive learning has shown its effectiveness to learn the general visual representation of images as a pre-training paradigm. Motivated by this, the contrastive learning is extended to the task of object detection for better distinguishing different types of objects. In this paper, a simple and effective single-stage detector, named 3D Object Detection with Balanced Prediction based on Contrastive Point Loss (BP-CPL), is proposed with a Contrastive Point Loss and a Re-balanced branch. Through the Contrastive Point Loss, similar representations among points of the same category and discriminative information among points of different categories can be learned. In order to keep the contrastive points consistency, a point filter is proposed. In addition, compared to the original point-wise feature, the self-supervised learned embedding is more robust to few-sample categories. Therefore, a re-balanced branch combined with the origin classification branch in a cumulative learning manner is proposed to re-balance the prediction results during the training phase. Extensive experiments show the effectiveness of the proposed method, especially for few-sample objects. The code will be available at https://github.com/Tongjiaxun/BP-CPL.
Using easy coefficients conjecture for rotation symmetric Boolean functions

Authors:
raw_author_name: Thomas W. Cusick
raw_affiliation_string: University at Buffalo, 244 Math. Bldg., Buffalo, NY 14260, United States of America

Abstract: A Boolean function in n variables is rotation symmetric (RS) if it is invariant under powers of ρ(x1,…,xn)=(x2,…,xn,x1). An RS function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ to a single monomial. Completing earlier research on special cases, the author showed in 2018 that for any RS function fn in n variables, the sequence of Hamming weights wt(fn) for all values of n satisfies a linear recurrence relation. It was also proved that the associated recursion polynomial could be explicitly calculated as the minimal polynomial of a rules matrix and an algorithm for computing the rules matrix was explained. Examples showed that the usual formula which gives the values of wt(fn)−2n−1 as a linear combination of powers of the irrational roots of the minimal polynomial has simple coefficients which are all 1/2 if the multiset made up of the roots of the characteristic polynomial of the rules matrix is used instead, no matter what the degree of the Boolean function is. The conjecture that this is always true is called the Easy Coefficients Conjecture (ECC). The special case of MRS quadratics was proved in an earlier paper. The present paper gives some ECC applications valid for any function for which the ECC is true, even though there is a proof only for the quadratic MRS case so far.
Stochastic first-order learning for large-scale flexibly tied Gaussian mixture models

Authors:
raw_author_name: Reshad Hosseini
raw_affiliation_string: School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
raw_author_name: Babak Nadjar Araabi
raw_affiliation_string: School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract: Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications. Flexibly-tied factorization of the covariance matrices in GMMs is a powerful approach for coping with the challenges of common GMMs when faced with high-dimensional data and complex densities which often demand a large number of Gaussian components. However, the expectation–maximization algorithm for fitting flexibly-tied GMMs still encounters difficulties with streaming and very large dimensional data. To overcome these challenges, this paper suggests the use of first-order stochastic optimization algorithms. Specifically, we propose a new stochastic optimization algorithm on the manifold of orthogonal matrices. Through numerous empirical results on both synthetic and real datasets, we observe that stochastic optimization methods can outperform the expectation–maximization algorithm in terms of attaining better likelihood, needing fewer epochs for convergence, and consuming less time per each epoch.
The area-efficient gate level information flow tracking schemes of digital circuit with multi-level security lattice

Authors:
raw_author_name: Xiaole Cui
raw_affiliation_string: Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, 518055, China
raw_author_name: Xiaoxin Cui
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, 518055, China; School of Integrated Circuits, Peking University, Beijing, 100871, China
raw_author_name: Xing Zhang
raw_affiliation_string: School of Integrated Circuits, Peking University, Beijing, 100871, China

Abstract: The Gate Level Information Flow Tracking (GLIFT) is an effective method to uphold the information security for high-assurance digital circuits. The GLIFT method associates a security label with each data bit and monitors these labels to expose the illegal information flow in the circuit under tracking. However, the label propagation usually consumes a significant area overhead, especially for the multi-level security lattices. This work aims to reduce the area cost of the GLIFT logic to enhance its applicability. A new implementation technique of GLIFT logic of basic gates is proposed by introducing the logic operation 'minus'. A decoupling-encoding technique of the security labels is also proposed to simplify the GLIFT logic. Furthermore, three improved GLIFT schemes are introduced for different scenarios based on these two techniques. These improved schemes are applied to some ISCAS'85 benchmarks. The evaluation results show that the improved schemes only consume 38.90%, 39.84% and 29.16% area of that of the original GLIFT scheme on average, respectively.
Accelerated Structured Matrix Factorization

Authors:
raw_author_name: Bernardo Nipoti
raw_affiliation_string: Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
raw_author_name: Antonio Canale
raw_affiliation_string: Department of Statistical Sciences, University of Padova, Padova, Italy

Abstract: Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insights, with interpretation favored by sparse structures. Sparsity, in addition, is beneficial in terms of regularization and, thus, to avoid over-fitting. By exploiting Bayesian shrinkage priors, we devise a computationally convenient approach for high-dimensional matrix factorization. The dependence between row and column entities is modeled by inducing flexible sparse patterns within factors. The availability of external information is accounted for in such a way that structures are allowed while not imposed. Inspired by boosting algorithms, we pair the proposed approach with a numerical strategy relying on a sequential inclusion and estimation of low-rank contributions, with a data-driven stopping rule. Practical advantages of the proposed approach are demonstrated by means of a simulation study and the analysis of soccer heatmaps obtained from new generation tracking data. Supplementary materials for this article are available online.