Reviewerly/exHarmony huggingface dataset:
Adversarial example‐based test case generation for black‐box speech recognition systems

Abstract Test case generation techniques based on adversarial examples are commonly used to enhance the reliability and robustness of image‐based and text‐based machine learning applications. However, efficient techniques for speech recognition systems are still absent. This paper proposes a family of methods that generate targeted adversarial examples for speech recognition systems. All are based on the f irefly algorithm (F), and are enhanced with g auss m utations and / or g radient e stimation (F‐GM, F‐GE, F‐GMGE) to fit the specific problem of targeted adversarial test case generation. We conduct an experimental evaluation on three different types of speech datasets, including Google Command , Common Voice and LibriSpeech . In addition, we recruit volunteers to evaluate the performance of the adversarial examples. The experimental results show that, compared with existing approaches, these approaches can effectively improve the success rate of the targeted adversarial example generation. The code is publicly available at https://github.com/HanboCai/FGMGE .
Review of Ngo, Hood, Martin, Painter, Smith &amp; Zappavigna (2022): Modelling paralanguage using systemic functional semiotics: Theory and application

Abstract This article reviews Modelling paralanguage using systemic functional semiotics: Theory and application
Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

Abstract Breast cancer (BC) is the second leading cause of death in women worldwide, caused by the uncontrolled proliferation of malignant cells in the breast. It is very tough to differentiate cancer from non‐cancer cells because of their shape and structure. Therefore, early diagnosis and classification of breast tumors as benign is a hot research topic. However, existing methods for early diagnosis have shortcomings like high computational cost and low classification accuracy. To trounce those limitations, this paper presented a computer‐aided diagnosis (CAD) system employing metaheuristic algorithms and machine learning classifiers. The proposed system uses fused features of ultrasound (US) and elastography images to enhance classification accuracy. Firstly, both US and elastography images are separately preprocessed using an enhanced wavelet thresholding method to diminish speckle noise. Secondly, a new segmentation algorithm is proposed to extract the lesion region from the preprocessed US image. Then the contour of the segmented lesion is mapped on an elastography image to obtain the corresponding region of interest (ROI), and texture and morphology features are separately extracted and concatenated. Subsequently, the enhanced grasshopper optimization algorithm (EGOA) is adopted to select salient features and reduce the dimension. Finally, based on the selected features, two machine learning classifiers, linear discriminant analysis (LDA) and support vector machine (SVM), are employed to classify the lesions into benign and malignant. The effectiveness of the proposed method is validated on 520 tumor images, including both benign and malignant samples. The suggested performance has been assessed by computing the confusion matrix and receiver operating characteristic curve. The developed method, EGOA‐SVM, outperforms the other classifiers by reaching a higher classification rate of 99.4%, specificity of 100%, the sensitivity of 99.2%, and Matthew's coefficient of 0.979. The introduced method can improve the radiologist's performance in categorizing breast lesions with a good classification rate. It could be a promising tool for clinical use.
Incremental association rules update algorithm based on the sort compression matrix

Association rule algorithm has always been a research hotspot in the field of data mining, in the context of today’s big data era, in order to efficiently obtain association rules and effectively update them, based on the original fast update pruning (FUP) algorithm, an association rule incremental update algorithm (FBSCM) based on sorting compression matrix is proposed to solve the shortcomings of frequent scanning of transaction datasets. Firstly, The algorithm maps the transaction dataset as a Boolean matrix, and changes the storage mode of the matrix(that is, adding two columns and a row vector); Secondly, the matrix is compressed many times during the generation of frequent k-itemset; After that, the items in the matrix are sorted incrementally according to the support degree of the itemset; Finally, the original string comparison operation is replaced by the vector product of each column of the matrix. Experimental results and analysis show that the FBSCM algorithm has higher temporal performance than the traditional FUP algorithm in different incremental dataset sizes, different minimum support thresholds and different feature datasets, especially when the incremental transaction volume is large or the minimum support degree is small.
A kernel search algorithm for virtual machine consolidation problem in cloud computing

Virtual machine consolidation refers to the process of reallocating virtual machines across a set of target servers. It can be formulated as a mixed integer linear programming problem, which is known to be NP-hard. In this paper, we propose a kernel search (KS) heuristic algorithm based on hard variable fixing to quickly obtain high-quality solutions for large-scale virtual machine consolidation problems (VMCPs). Since existing variable fixing strategies in KS algorithms may result in some VMCP instances being infeasible, our proposed KS algorithm employs a more efficient strategy to choose a set of fixed variables based on their corresponding reduced costs. Our numerical results on VMCP instances demonstrate that our proposed KS algorithm significantly outperforms mixed integer linear programming solvers in terms of CPU time. Moreover, our proposed strategy of variable fixing improves the efficiency of the KS algorithm significantly, with negligible degradation in solution quality.
Feature Selection for Phishing Website by Using Naive Bayes Classifier

The Internet is gradually becoming a necessary and important tool of human’s everyday life. But internet users might have poor security for different kinds of web threats, which may have an effect on monetary damage and loss of clients’ trust in online trading and online banking. Phishing is described as a skill of impersonating a website and trustful project aiming to get private and secret information such as a user name and password and social security and credit card number. However, there is no single solution that can catch most phishing attacks. This paper discusses the Feature Selection for Phishing website by using the Naive Bayes classifier. The dataset used in this study has thirty-one attributes. The aim of this paper is to reduce the dataset and find the best performance system having the ability to make right classifications for phishing datasets. We used feature selection algorithms for reducing the dataset and system performance, also comparing among feature selection algorithms’ performance for each dataset, then making a classification for the dataset by the naïve classifier.
Survey of the Challenges and Solutions in Cybersecurity Awareness Among College Students

College students are of age and demographic that are mostly reliant on technology for their daily life, including education, socialization, and entertainment. Cybersecurity risks pose a significant threat to college students, as they are often unaware of the potential dangers associated with their online activities. Cyber threats like phishing, identity theft, and malware attacks can lead to financial loss, reputation damage, and personal harm. Therefore, improving and increasing cybersecurity awareness among college students is essential. This research paper will analyze the challenges in cybersecurity awareness among college students and propose various methods to improve cybersecurity education and awareness.
Shape Detection Using Air-Drawing

Computer vision is a branch of AI that enables computers and systems to extract and process meaningful information from visual inputs such as digital images and videos. Computer vision endows computers with the ability to see, observe, and understand the visual world, while AI endows them with the ability to think and reason. Computer vision has a wide range of applications such as image recognition, face recognition, fingerprint recognition, medical diagnosis, and self-driving. In this study, we have designed and implemented a novel hands-on air drawing software that allows users to draw in the air with their index finger as a virtual pen. The software can also recognize basic drawn shapes such as squares, circles, and triangles and display them on the screen. This software can be used in the video conferencing field to facilitate communication and collaboration through air drawing.
Distribution of High-Frequency Words: An Analytical Investigation

Function words in natural language texts usually differ from content words in terms of distributional properties. In this work, we formulate a logarithmic function that produces the inverse document frequency of high-frequency words with respect to the frequency ranks on a log-scale. It is also shown that the proposed function may be used for unsupervised identification of stop words by means of estimating a cutoff frequency based on the maximum curvature point. The procedure of fitting the function to empirical data is provided in detail with a robust parameter initialization technique. The proposed framework was tested on a wide variety of languages and found to accurately capture the distributional characteristics of high frequencies by statistical measures. By further analyses on residual errors as well as on the next frequency prediction task, it is demonstrated that the highest frequencies are by far the most difficult ones to predict, as if they potentially behave as individual outliers within a set of abnormally high frequency values.
Identifying Microbenchmark Signatures of Existing Microarchitectural Exploits Using the Unified Side Channel Attack - Model (USCA-M) Four-Phase Testing Process

This paper presents the Unified Side Channel Attack – Model (USCA-M) four-phase testing process that can been used to represent microbenchmark signatures of microarchitecture exploits. The microbenchmark signatures have been created from generated hardware performance counter (HPC) events from the critical code components at assembly level on an Intel Core i7 CPU. Identifying critical components from the thousands of lines of assembly code from the various exploit proof of concepts (POCs) was a key factor, otherwise it would render the POCs unsuccessful. The USCA-M four-phase testing process includes exploit placement into a USCA-M matrix, testing, verification, and validation. This four-phase testing process enabled identification of the critical components used within the exploit and at a low-level catch the HPC events that were represented as microbenchmark signatures. The use of microarchitectural exploits as the design benchmark served three purposes. Firstly, demonstrations of microarchitectural based attacks are published alongside open source POCs and exploit code. Secondly, using exploit code minimizes the need for complex test bed set up and expensive equipment required for other SCA type analysis. Lastly, there is a broad range of open-source resources available that could be used tools for exploit code analysis. The use of the USCA-M four-phased testing process provides a uniform categorization of exploits and the signatures generated could be used in intrusion detection systems to identify microarchitectural-based malicious code.
Universal Simulation Platform Online, A Multi-Participants I.E.D. Disposal Simulator

Improvised explosive devices are unconventional weapons that are a significant threat to public safety. Explosive ordnance disposal personnel are tasked with determining methods and procedures for locating, neutralizing, and disposing of chemical, biological, radiological, nuclear and explosive threats in extreme environments. Training methods vary depending on the region and military unit, and may use a combination of tools to facilitate a learning process. In our previous work, we proposed a Virtual Reality simulation called Universal Simulation Platform to enable explosive ordnance disposal personnel to train in a safe virtual environment without being exposed to potentially harmful real-world situations whilst being cost effective. In this paper, we propose a multi-participant revision of the simulation software, which allows multiple participants to connect and join a session, work together and attempt to neutralize a bomb(s). A multi-participant improvised explosive device disposal simulation is a novel virtual reality training approach which introduces crucial neutralization training aspects such as teamwork and communication, often neglected functions of virtual reality training simulations.
Large-Scale Shill Bidder Detection in E-commerce

User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users’ feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.
Contextualized Recommendation Model Based Socio-Environmental Factors

Recommender Systems (RS) often face challenges with new user and data sparsity problems. To address these issues, this paper proposes a new REcommendation Model based sOcio-enVironmEntal factors called "RE-MOVE", which takes into account socio-environmental contextual information. By reviewing state-of-the-art recommendation algorithms, the study found that combining both social and environmental data can improve recommendation quality. The paper argues that modifying the vector of characteristics of items and users can significantly enhance the model. According to the authors, no previous research has investigated matrix factorization by modeling item vector characteristics and considering social and environmental data. The proposed approach was evaluated by comparing it to methods that incorporate contextual information. The results, based on Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), demonstrate that RE-MOVE can more effectively address the challenges and improve the quality of recommendations.
Bitcoin Price Prediction Considering Sentiment Analysis on Twitter and Google News

Cryptocurrencies are digital currencies that operate on the blockchain, which is the technology that offers security and decentralization. The principal characteristic of cryptocurrencies is that they are not generally issued by a central authority. Many factors can influence the volatility of prices. This paper enables to drive insights into the behavior of markets through the application of sentiment analysis of Tweets, Google news and machine learning techniques for the challenging task of cryptocurrency price prediction. Most of the studies have focused exclusively on the sentiment analysis of tweets. In this work, we propose the use of common machine learning tools and available Google News data for predicting the price of crypto. We present the results of the Long Short-Term Memory (LSTM) model using Tweets and Google News data.
Study on controllable set of contingency return trajectories for crewed lunar missions based on interval analysis

The controllable set of contingency return trajectories for crewed lunar missions is studied on the basis of interval analysis. Firstly, in the light of the statement of the contingency return trajectory departing from the low lunar orbit, the mathematical model of the controllable set problem is presented. Secondly, based on interval analysis and contingency return trajectory design, a controllable set algorithm is put forward by combining a modified branch and bound method. Thirdly, using numerical simulations, the accuracy of this algorithm is verified by comparing with the results acquired from the classical brute force method. In addition, the comparison indicates this algorithm has higher calculation efficiency. Finally, the controllable set parameter characteristic analysis of contingency return trajectories is performed. The investigation conclusions can supply a decision basis for the plan and selection of a contingency return scheme in the future crewed lunar missions.
Semantic-gap-oriented feature selection in hierarchical classification learning

Semantic gap is common issues in classification tasks. Different from traditional classification tasks, the semantic gap problem can introduce serious error propagation for hierarchical classification. In this paper, we propose a hierarchical feature selection method with label enhancement (HFSLE) to solve this challenge, which estimates the label distribution and selects subsets of features for each subtask. Firstly, HFSLE combines the label enhancement and hierarchical feature selection process so that the label enhancement benefits from the feedback of the loss function. Then, the label enhancement is constrained by the consistency of the parent-child distribution to obtain a plausible label distribution. Finally, feature selection is optimized based on intra-class consistency hierarchical regularization terms, which unifies feature subsets with hierarchical semantic descriptions to narrow the semantic gap. HFSLE gives a new perspective on the hierarchical feature selection based on label distribution rather than the original logical label. To demonstrate the superiority of the method, we compared it with six well-established feature selection methods on eight hierarchical data sets, and analyzed the convergence and robustness of the method. Extensive experiments demonstrate that the proposed method can perform favorably against advanced hierarchical feature selection approaches.
Combining layered label correction and mixup supervised contrastive learning to learn noisy labels

Deep neural networks trained using standard cross-entropy loss often induce overfitting of noisy labels that degrades performance. Label correction and contrastive learning have been theoretically demonstrated to be mutually beneficial in noisy label learning. However, the fusion of these two modules in practice has not been researched adequately, and the influence of each module on performance improvement has not been clarified. In this paper, we propose an effective noisy label learning framework that combines layered label correction with mixup supervised contrastive learning, based on hierarchical clustering using first neighbor relations and mixup contrastive loss. In particular, the framework consists of two modules—Layered Label Correction (LLC) and Mixup Supervised Contrastive Loss (MSCL). The LLC module aggregates the first neighbor's soft labels via label propagation among multilayer partitions based on the hierarchical clustering of the feature representation. Its purpose is to capture the underlying structure of learning representations at different levels of granularity. The MSCL module incorporates pseudo-labels into mixup contrastive learning directly to learn class-related representations of all instances of each class. Ablation studies are conducted to evaluate the contribution of individual modules to the overall performance enhancement of the framework. Moreover, experiments on synthetic datasets comprising noise of various patterns and levels demonstrate that the proposed framework yields a superior classification accuracy in comparison with existing alternatives.
Special section: Best papers of the 14th Mexican conference on pattern recognition (MCPR) 2022

Two recent works have shown the benefit of modeling both high-level factors and their related features to learn disentangled representations with variational autoencoders (VAE). We propose here a novel VAE-based approach that follows this principle. Inspired by conditional VAE, the features are no longer treated as random variables over which integration must be performed. Instead, they are deterministically computed from the input data using a neural network whose parameters can be estimated jointly with those of the decoder and of the encoder. Moreover, the quality of the generated images has been improved by using discrete latent variables and a two-step learning procedure, which makes it possible to increase the size of the latent space without altering the disentanglement properties of the model. Results obtained on two different datasets validate the proposed approach that achieves better performance than the two aforementioned works in terms of disentanglement, while providing higher quality images.
Vision Transformer (ViT)-based Applications in Image Classification

In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introduces the image classification imple- mentation process and the basic architecture of the ViT model, then analyzes and summarizes the image classification methods, including traditional image classification methods, CNN-based image classification methods, and ViT-based image classification methods, and provides a comparative analysis of CNN and ViT. Subsequently, this paper outlines the application prospects of ViT in image classification and its future development and also outlines some shortcomings of ViT and its solutions.
SG-RC: SG-CIM Grid Knowledge Graph Relationship Complementation Model Based on Entropy Uncertainty and Semantic Recognition

SG-CIM grid knowledge graph is a domain knowledge graph based on the actual business knowledge of State Grid Corporation of China. SG-CIM grid knowledge graph consists of the triad of "entity-relationship-entity " of the grid. The traditional construction of SG-CIM grid knowledge graph needs experts familiar with relevant fields to build it manually, which is a necessary work in the early stage of data accumulation. This work consumes a lot of labor and cost, and some relations are missing due to manual operation. However, completing the relationship on the incomplete SG-CIM grid knowledge graph will lead to the problem that some difficult samples cannot be mined. To solve this problem, this paper proposes an SG-CIM grid knowledge graph relationship complementation method SG-RC based on entropy uncertainty and semantic recognition.SG-RC consists of an active learning module based on entropy uncertainty calculation and a relationship prediction module based on semantic recognition. The SG-RC model first mines all possible entity pairs to calculate the entropy uncertainty, and those with higher entropy uncertainty The higher entropy uncertainty represents the poor relationship prediction effect of the model, and those entity pairs with higher entropy uncertainty are recommended to experts for relationship labeling, and multiple iterations improve the quality of prediction results of the semantic recognition relationship prediction module. Compared with randomly selected entity pairs, this method can improve the completion probability of relation completion.