Reviewerly/exHarmony huggingface dataset:
Model-agnostic Adversarial Example Detection via High-Frequency Amplification

Authors:
raw_author_name: Jing Chen
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan 430072, China; Rizhao Institute of Information Technology, Wuhan University, Rizhao 276800, China
raw_author_name: Kun He
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan 430072, China
raw_author_name: Ruiying Du
raw_affiliation_string: Collaborative Innovation Center of Geospatial Technology, Wuhan 430072, China; Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan 430072, China
raw_author_name: Jisi She
raw_affiliation_string: Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan 430072, China

Abstract: Image classification based on Deep Neural Networks (DNNs) is vulnerable to adversarial examples, which make the classifier output incorrect predictions. One approach to defending against this attack is to detect whether the input is an adversarial example. Unfortunately, existing adversarial example detection methods heavily rely on the underlying classifier and may fail when the classifier is upgraded. In this paper, we propose a model-agnostic detection method that leverages high-frequency signals from adversarial noises in adversarial examples and does not need interactions with the underlying classifier. We amplify redundant high-frequency signals brought by adversarial noises and represent object boundaries with these signals in an image. Our key insight is that the boundaries extracted by redundant high-frequency signals have a strong correlation with the boundaries of images in adversarial examples, while this correlation does not exist in clean images. Furthermore, adversarial examples of large images have more high-frequency signals and make adversarial detection easier on large image datasets. Experimental results show that our method has good transferability and can accurately detect various adversarial examples on different datasets.
Complete multi-view subspace clustering via auto-weighted combination of visible and latent views

Authors:
raw_author_name: Bing Cai
raw_affiliation_string: School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China; School of Computer and Software Engineering, Anhui Institute of Information Technology, Wuhu, 241000, China
raw_author_name: Gui-Fu Lu
raw_affiliation_string: School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China
raw_author_name: Guangyan Ji
raw_affiliation_string: School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China
raw_author_name: Weihong Song
raw_affiliation_string: School of Computer and Software Engineering, Anhui Institute of Information Technology, Wuhu, 241000, China

Abstract: Multi-view subspace clustering aims to leverage the consensus and complementary information embedded within the data and effectively fuse the multiple views. However, existing methods have limitations in fully exploring the complementary information. Tensor-based methods tend to overlook the underlying structures, while latent representation-based methods struggle to capture high-order correlations, both of which are important components of complementary information. In this paper, we introduce a novel method called complete multi-view subspace clustering (CMVSC) using visible and latent views. The latent view with the underlying structures is obtained by the latent representation, along with visible views (i.e., original features) constitute the complete multi-view data. Then, the low-rank tensor constraint is imposed on the complete views to comprehensively explore the consensus and complementary information. Besides, to effectively combine the advantages of visible and latent views, we devise an auto-weighted strategy that can automatically assign the ideal weights to each view. Finally, an efficient alternating iterative algorithm using the augmented Lagrange multiplier method is designed to solve the CMVSC model. Experimental results show that our model outperforms the state-of-the-art counterparts while achieving almost perfect clustering performance on several benchmark datasets.
Multi-object tracking algorithm based on interactive attention network and adaptive trajectory reconnection

Authors:
raw_author_name: Sugang Ma
raw_affiliation_string: School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China; School of Information Engineering, Chang’an University, Xi'an 710064, China
raw_author_name: Zhiqiang Hou
raw_affiliation_string: School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
raw_author_name: Wangsheng Yu
raw_affiliation_string: School of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
raw_author_name: Lei Pu
raw_affiliation_string: School of Operational Support, Rocket Force Engineering University, Xi'an 710025, China
raw_author_name: Xiangmo Zhao
raw_affiliation_string: School of Information Engineering, Chang’an University, Xi'an 710064, China

Abstract: Multi-object tracking (MOT) detects multiple targets in an image and assigns a unique identifier to each target. However, challenges such as rapid motion, occlusion, and camera motion in the tracking scene may lead to identity switches (IDs) and missing trajectory problems, which degrade the performance of the tracker. To address these issues, this paper presents an MOT algorithm based on an interactive attention network and adaptive trajectory reconnection. First, an interactive attention network was created to learn the features for two different tasks of detection and tracking to alleviate feature conflicts in order to extract sufficient feature information. A new cost matrix was then designed to fuse the motion and feature information, thereby reducing the number of IDs. Meanwhile, the extreme gradient boosting reconnection module was used to achieve adaptive trajectory reconnection and reduce missing trajectories. The proposed algorithm achieved 61.5 % and 55.4 % HOTA using the standard MOT17 and MOT20 datasets, respectively. In comparison to FairMOT, our algorithm showcased notable enhancements of 3% and 1.6% on these datasets. Furthermore, when compared to state-of-the-art algorithms, the proposed algorithm demonstrated superior tracking performance.
Does bitcoin still enhance an investment portfolio in a post Covid-19 world?

Authors:
raw_author_name: Michael Gorman
raw_affiliation_string: Jack Welch College of Business and Technology, United States of America

Abstract: We investigate the ability of bitcoin to enhance investment portfolios of traditional assets. We find evidence of a structural break in the correlation and volatility processes around the onset of the Covid-19 pandemic. The correlations between bitcoin and traditional assets have increased, thereby reducing bitcoin’s diversification benefits. As a result prior studies using data from pre-2020 may have inadvertently overestimated the value of bitcoin in improving Sharpe ratios and certainty equivalent returns in the post-pandemic era.
MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion

Authors:
raw_author_name: Qianyu Li
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou, China
raw_author_name: Bozheng Feng
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou, China
raw_author_name: Xiaoli Tang
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, Singapore
raw_author_name: Han Yu
raw_affiliation_string: School of Computer Science and Engineering, Nanyang Technological University, Singapore
raw_author_name: Hengjie Song
raw_affiliation_string: School of Software Engineering, South China University of Technology, Guangzhou, China

Abstract: Recent years have witnessed increasing interest in the few-shot knowledge graph completion due to its potential to augment the coverage of few-shot relations in knowledge graphs. Existing methods often use the one-hop neighbors of the entity to enhance its embedding and match the query instance and support set at the instance level. However, such methods cannot handle inter-neighbor interaction, local entity matching and the varying significance of feature dimensions. To bridge this gap, we propose the Multi-Level Attention-enhanced matching Network (MuLAN) for few-shot knowledge graph completion. In MuLAN, a multi-head self-attention neighbor encoder is designed to capture the inter-neighbor interaction and learn the entity embeddings. Then, entity-level attention and instance-level attention are responsible for matching the query instance and support set from the local and global perspectives, respectively, while feature-level attention is utilized to calculate the weights of the feature dimensions. Furthermore, we design a consistency constraint to ensure the support instance embeddings are close to each other. Extensive experiments based on two well-known datasets (i.e., NELL-One and Wiki-One) demonstrate significant advantages of MuLAN over 11 state-of-the-art competitors. Compared to the best-performing baseline, MuLAN achieves 14.5% higher MRR and 13.3% higher Hits@K on average.
Hardness of (M)LWE with Semi-Uniform Seeds

Authors:
raw_author_name: Wenjuan Jia
raw_affiliation_string: School of Telecommunications Engineering, Xidian University, Xi'an, Shaanxi 710071, China; State Key Laboratory of Cryptology, P.O. Box 5159, Beijing 100878, China
raw_author_name: Jiang Zhang
raw_affiliation_string: State Key Laboratory of Cryptology, P.O. Box 5159, Beijing 100878, China
raw_author_name: Binwu Xiang
raw_affiliation_string: State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
raw_author_name: Baocang Wang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, Shaanxi 710071, China

Abstract: Let (D,S,χ,m)-LWEn,q be the LWE problem in matrix form (A,y=As+emodq), where A,s,e are randomly chosen respectively from the seed distribution D over Zqm×n, secret distribution S over Zqn and noise distribution χm over Zm (or Rm), i.e., A←D,s←S,e←χm. For various secret-noise distributions (S,χ), the (D,S,χ,m)-LWEn,q problem is shown to be as hard as some standard worst-case lattice problems, but most of the known results require D to be the uniform distribution over Zqm×n. In this paper, we show that under the standard LWE assumption, the problem (D,S,χ,m)-LWEn,q can still be hard for some distribution D that is not (even computationally indistinguishable from) the uniform distribution over Zqm×n. Specifically, we show that if D is a semi-uniform distribution over Zqm×n (namely, D can be publicly derived from and has a “small difference” to the uniform distribution over Zqm×n), then for appropriate choices of (S,χ), the problem (D,S,χ,m)-LWEn,q is hard under the standard LWE assumption. Moreover, we also show that the semi-uniform MLWE problem is hard under the standard MLWE assumption. As a direct application, our results pave the way to prove the security of public-key encryptions with rounded public keys under the standard (M)LWE assumption.
A robust script independent handwriting system for gender identification

Authors:
raw_author_name: Shivakumara Palaiahnakote
raw_affiliation_string: School of Science, Engineering and Environment, University of Salford, Manchester, UK
raw_author_name: Maryam Asadzadeh Kaljahi
raw_affiliation_string: promiseQ, Berlin, Germany
raw_author_name: Swati Kanchan
raw_affiliation_string: Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India
raw_author_name: Umapada Pal
raw_affiliation_string: Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India
raw_author_name: Daniel Lopresti
raw_affiliation_string: Computer Science & Engineering, Lehigh University, Bethlehem, PA, USA
raw_author_name: Tong Lu
raw_affiliation_string: National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

Abstract: Gender identification at the word level in a multi-script environment is challenging due to variations posed by free-style handwriting of individuals and geographical differences in writing styles. This paper presents a new approach, Multi-Orientation-Scale Gabor Response Fusion (MOSGF), for gender identification at the word level using handwritten text. Our method has two steps: (i) word segmentation from unconstrained lines and (ii) gender identification at the word level. In the first step, the method explores the number of zero crossing points and gradient information for word segmentation from handwritten text lines. In the second step, employs Gabor responses at different orientations and scales to detect fine details in female and male handwriting. For each Gabor response, the proposed model estimates the correlation between average templates of all Gabor responses and the individual Gabor response to extract global consistency in writing. To strengthen correlation features, the proposed method uses the Mahalanobis distance measure, which extracts local similarity. Further, the proposed approach fuses correlation coefficient and distance-based features in a novel way. The fused features are then fed to a Neural Network (NN) for gender identification. Experiments on our dataset, which comprises Roman (English), Chinese, Farsi (Persian), Arabic, and Indian scripts, and a benchmark dataset, namely, IAM which includes English text, KHATT which includes Arabic, and QUWI which includes both English and Arabic, show that the proposed system outperforms the existing methods in terms of word segmentation and gender identification.
Secure and Decentralized Federated Learning Framework with Non-IID Data based on Blockchain

Authors:
raw_author_name: Feng Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
raw_author_name: Yongjing Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
raw_author_name: Shan Ji
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
raw_author_name: Zhaoyang Han
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.

Abstract: Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in practice, the data distributions among these organizations are often non-independent and identically distributed (non-IID), which poses significant challenges for traditional federated learning. To tackle this challenge, we present a hierarchical federated learning framework based on blockchain technology, which is designed to enhance the training of non-IID data., protect data privacy and security, and improve federated learning performance. The framework builds a global shared pool by constructing a blockchain system to reduce the non-IID degree of local data and improve model accuracy. In addition, we use smart contracts to distribute and collect models and design a main blockchain to store local models for federated aggregation, achieving decentralized federated learning. We train the MLP model on the MNIST dataset and the CNN model on the Fashion-MNIST and CIFAR-10 datasets to verify its feasibility and effectiveness. The experimental results show that the proposed strategy significantly improves the accuracy of decentralized federated learning on three tasks with non-IID data.
Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review

Authors:
raw_author_name: Jamin Rahman Jim
raw_affiliation_string: Advanced Machine Intelligence Research Lab, Banani, Dhaka, 1213, Dhaka, Bangladesh
raw_author_name: Md Apon Riaz Talukder
raw_affiliation_string: Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
raw_author_name: Md Mohsin Kabir
raw_affiliation_string: Faculty of Informatics, Eötvös Loránd University, H-1117 Budapest, Hungary
raw_author_name: Kamruddin Nur
raw_affiliation_string: Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
raw_author_name: M.F. Mridha
raw_affiliation_string: Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh

Abstract: Sentiment analysis is a method within natural language processing that evaluates and identifies the emotional tone or mood conveyed in textual data. Scrutinizing words and phrases categorizes them into positive, negative, or neutral sentiments. The significance of sentiment analysis lies in its capacity to derive valuable insights from extensive textual data, empowering businesses to grasp customer sentiments, make informed choices, and enhance their offerings. For the further advancement of sentiment analysis, gaining a deep understanding of its algorithms, applications, current performance, and challenges is imperative. Therefore, in this extensive survey, we began exploring the vast array of application domains for sentiment analysis, scrutinizing them within the context of existing research. We then delved into prevalent pre-processing techniques, datasets, and evaluation metrics to enhance comprehension. We also explored Machine Learning, Deep Learning, Large Language Models and Pre-trained models in sentiment analysis, providing insights into their advantages and drawbacks. Subsequently, we precisely reviewed the experimental results and limitations of recent state-of-the-art articles. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of sentiment analysis, covering its models, application domains, results analysis, challenges, and research directions.
User-defined weight based multi objective task scheduling in cloud using whale optimisation algorithm

Authors:
raw_author_name: Swati Gupta
raw_affiliation_string: Department of Computer Science and Engineering, IIT (BHU), Varanasi, Uttar Pradesh, India
raw_author_name: Ravi Shankar Singh
raw_affiliation_string: Department of Computer Science and Engineering, IIT (BHU), Varanasi, Uttar Pradesh, India

Abstract: Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.
L2-BiTCN-CNN: Spatio-temporal features fusion-based multi-classification model for various internet applications identification

Authors:
raw_author_name: Zhiyuan Li
raw_affiliation_string: Jiangsu Provincial Key Laboratory of Industrial Network Security Technology, Zhenjiang, Jiangsu, 212013, China; Jiangsu Ubiquitous Data Intelligent Perception and Analysis Application Engineering Research Center, Zhenjiang, Jiangsu, 212013, China; The College of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, China
raw_author_name: Xiaoping Xu
raw_affiliation_string: The College of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, China

Abstract: Identifying various types of Internet applications is a key issue for network management. Traffic classification is one of the important technologies for identifying Internet applications traffic. However, some secure technologies such as protocol encryption, information hiding, and traffic obfuscation pose new challenges to traffic identification and classification, especially in feature extraction. In addition, multi-classification has always been a difficult problem in traffic classification research. This paper proposes a spatio-temporal features fusion-based multi-classification model, named as L2-BiTCN-CNN. Our approach combines bidirectional temporal convolutional network (TCN) and convolutional neural network (CNN) to create an enhanced model. The L2-BiTCN model utilizes a bidirectional approach to learn both forward and backward sequence features of traffic, with a focus on the hierarchical structure of network traffic known as “byte-packet-session flow”. To fully extract timing characteristics between adjacent bytes and packets, this model incorporates attention mechanisms for both “byte hierarchy” and “packet hierarchy”. Filters of various sizes are utilizes in the CNN stage to capture packet features of different ranges. The spatial features of the packets are then obtained through two convolution and average pooling processes. The results of the experiments provide evidence of the effectiveness and superiority of our approach. Our model achieved an impressive overall accuracy of 99.839% and 97.186% in two public available datasets, namely USTC-TFC2016 and ISCX VPN-nonVPN2016, respectively. It outperforms the state-of-the-art models in terms of classification performance, which suggests that the model has the potential to be a valuable tool in various applications that require high accuracy.