Reviewerly/exHarmony huggingface dataset:
Widely-Linear MMSE Estimation of Complex-Valued Graph Signals

In this paper, we consider the problem of recovering random graph signals with complex values. For general Bayesian estimation of complex-valued vectors, it is known that the widely-linear minimum mean-squared-error (WLMMSE) estimator can achieve a lower mean-squared-error (MSE) than that of the linear minimum MSE (LMMSE) estimator. Inspired by the WLMMSE estimator, in this paper we develop the graph signal processing (GSP)-WLMMSE estimator, which minimizes the MSE among estimators that are represented as a two-channel output of a graph filter, i.e. widely-linear GSP estimators. We discuss the properties of the proposed GSP-WLMMSE estimator. In particular, we show that the MSE of the GSP-WLMMSE estimator is always equal to or lower than the MSE of the GSP-LMMSE estimator. The GSP-WLMMSE estimator is based on diagonal covariance matrices in the graph frequency domain, and thus has reduced complexity compared with the WLMMSE estimator. This property is especially important when using the sample-mean versions of these estimators that are based on a training dataset. We then state conditions under which the low-complexity GSP-WLMMSE estimator coincides with the WLMMSE estimator. In the simulations, we investigate two synthetic estimation problems (with linear and nonlinear models) and the problem of state estimation in power systems. For these problems, it is shown that the GSP-WLMMSE estimator outperforms the GSP-LMMSE estimator and achieves similar performance to that of the WLMMSE estimator.
HST-Detector: A Multimodal Deep Learning System for Twitter Spam Detection

Twitter is one of the most famous microblogging and Online Social Networks (OSNs). Nowadays, the unimodal spam filtering systems in Twitter achieved a good detection rate. To bypass the existing systems and reduce their recognition rate, attackers inject spam information into various portions of tweets and fuse them. Therefore, a powerful Heterogeneous Spam Tweets (HST) filtering system is necessary to fully protect users. To address the above challenge, we propose a Deep Learning (DL) system combining a Convolutional Neural Network model (CNN) and a Long Short-Term Memory model (LSTM) to classify heterogeneous malicious tweets. To ensure the efficient filtering of spam hidden in text or images, we apply a multimodal combination approach in which LSTM and CNN models deal respectively the text and image portion of heterogeneous tweets to obtain two predictions. Afterward, a fusion model is used to decide if the tweet is malicious or legitime. This system is more efficient than the existing spam filtering systems trained on Image Spam Hunter (ISH), Dredze, and Honeypot datasets. The proposed system can achieve accuracies of 98% on heterogeneous tweet datasets.
Source-free Unsupervised Domain Adaptation for Privacy-Preserving Intelligent Fault Diagnosis

Rolling bearing is of vital significance in industrial applications and Intelligent fault diagnosis (IFD) have been widely exploited in this field. However, cross-machine variations hinder the model performance across different machines in varying working conditions. To solve this issue, many unsupervised domain adaptation (UDA) approaches have been proposed recently, requiring direct access to the fully labeled source domain. However, privacy concerns are raised in traditional UDA scenario, since raw signals contain various private information. To address this issue, the source-free unsupervised domain adaptation (SFUDA) scenario is considered, where only the pre-trained source model is required, instead of the fully labeled source domain. In this paper, a SFUDA approach for intelligent fault diagnosis (IFD) is proposed. It consists of two steps: 1) source model generalization, where virtual adversarial training, R-Drop and label smoothing techniques are adopted to improve the generalization ability of the source model; and 2) target model adaptation, where information maximization is used for cluster assumption and mean teacher training paradigm is utilized to alleviate the catastrophic forgetting phenomenon. The proposed approach is verified on the Case Western Reserve University (CWRU) dataset. Experimental results on the show that the proposed SFUDA approach outperforms several typical UDA approaches, and its effectiveness is verified.
Integrating Trusted Computing Mechanisms with Trust Models to Achieve Zero Trust Principles

One of the main pillars for creating a sustainable model to address changes in the threat landscape is the Zero Trust principle of "Never Trust/Always Verify". Zero Trust focuses on addressing insiders and creeping insiders problem including those existing in clouds, IoT and social networks environment. We noticed Zero Trust did not yet get enough attention from academia and most of the work done on Zero Trust is coming from industry. Majority of the work on this domain are vendor specific and not well covered from an enterprise security architecture point of view. This results in narrow focus and inconsistencies. The use of trust models to implement the "Never Trust" principle, and the "Always Verify" concept utilizing trusted computing techniques is our major contribution. The integration of Trusted Computing mechanisms with Trust Models would strongly help in achieving the Zero Trust principles.
Learning and Generalization

The mission of DS is to extract interesting information from data. In the previous chapters, the form of this information has been discussed: These are diverse types of mappings such as classifiers or quantitative models. In the present chapter, the requirements on data available for processing and discovering this information are reviewed.
A Metaheuristic Perspective on Learning Classifier Systems

Within this book chapter we summarize Learning Classifier Systems (LCSs), a family of rule-based learning systems with a more than forty-year-long research history, and differentiate LCSs from related approaches like Genetic Programming, Decision Trees, Mixture of Experts as well as Bagging and Boosting. LCS training constructs a finite collection of if-then rules. While the conclusion (the then-part) of each rule is based on a problem-dependent local submodel (e. g.linear regression), the individual conditions and, by extension, the global model structure, are optimized using a—typically evolutionary—metaheuristic. This makes the employed metaheuristic a central part of learning and indispensable for successful training. While most traditional LCSs solely rely on Genetic Algorithms, in this chapter, we also explore systems that employ different metaheuristics while still being similar to LCSs. Furthermore, we discuss the different problems that metaheuristics are solving when applied in this context, for example, discrete or real valued input domains, optimization of individual rule conditions or entire sets of rule conditions, and fitness functions that support nicheing or control bloating. We ascertain that, despite the optimizer being essential, it has been investigated less directly than the learning components so far. Thus, overall, we provide an analysis of LCSs with a focus on the used metaheuristics and present existing solutions as well as current challenges to other practioners in the fields of metaheuristic ML and rule-based learning.
Periodic Physical Activity Information Segmentation, Counting and Recognition From Video

The research on complex human body motion including sports and workout activity recognition is a major challenge and long-lasting problem for the computer vision community. Recent development in deep learning algorithms to track people’s workout activity characteristics based on video sensors can be used to infer the human body pose for further analysis. Specifically, tracking complex body movements while performing multi-pose physical exercise helps individuals provide fine granularity feedback including activity repetition counting and activity recognition. Therefore, this research proposes a system that provides a repetition counter and activity recognition of physical exercise from video frames (extracted 3D human skeleton using VIBE) based on the deep semantic features and repetitive segmentation algorithm. The proposed system locates both ends of the activity’s action and segments the activity into multiple unit actions which improves activity recognition, time intervals, # of sets, and other quantitative values of activity. The proposed system is evaluated on the physical activities dataset named “NOL-18 Exercise” through extensive experiments. The proposed system results show that the accuracy of the repetitive action segmentation is 96.27% with 0.23% time error, and action recognition reaches 99.06%. The system can be employed to fitness or rehabilitation centers and used for treating patients.
Investigating the Effects of Hyperparameters in Quantum-Enhanced Deep Reinforcement Learning

Quantum machine learning uses quantum mechanical concepts of superposition of states to make the decision. In this work, we used these quantum advantages to enhance deep reinforcement learning (DRL). Our primary and foremost goal is to investigate and elucidate a way of representing and solving the frozen lake problems by using PennyLane which contains Xanadu’s back-end quantum processing unit. This paper specifically discusses how to enhance classical deep reinforcement learning algorithms with quantum computing technology, making quantum agents get a maximum reward after a fixed number of epochs and realizing the effect of a number of variational quantum layers on the trainability of enhanced framework. We have analyzed that, as the number of layers increases, the ability of the quantum agent to converge to the optimal state also increases. For this work, we have trained the framework agent with 2, 3, and 5 variational quantum layers. An agent with 2 layers converges to a total reward of 0.95 after the training episode of 526. The other agent with layers converges to a total reward of 0.95 after the training episode of 397 and the agent which uses 5 quantum variational layers converges to a total reward of 0.95 after the training episode of 72. From this, we can understand that the agent with a more variational layer exploits more and converges to the optimal state before the other agent. We also analyzed our work in terms of different learning rate hyperparameters. We recorded every single learning epoch to demonstrate the outcomes of enhanced DRL algorithms with selected 0.1, 0.2, 0.3, and 0.4 learning rates or alpha values. From this result, we can conclude that the greater the learning rate values in quantum deep reinforcement learning, the fewer timesteps it takes to move from the start point to the goal state.
Stationary mobile behavioral biometrics: A survey

Current security mechanisms in mobile devices such as PINs, passwords, patterned passwords, and biometrics are one-time entry-point authentication and vulnerable to attacks. Furthermore, advanced mechanisms like Multi-Factor Authentication (MFA) introduce friction in the user experience. In contrast, behavioral biometrics rely on user interaction with computing devices to authenticate a user and thus, can be continuous, non-intrusive, and cost-effective, representing a promising direction that complements existing authentication techniques. This survey focuses on stationary/non-walking (sitting, standing) mobile behavioral biometrics through motion events like acceleration, gyroscope, magnetometer, and orientation (rotation) with the optional support of other non-motion, sporadic modalities such as swipes and keystrokes. The focus on stationary behaviors can be justified because such behaviors represent the major way a user interacts with mobile devices. To help readers understand the broad landscape of user activities/behaviors, we categorize the state of the art into natural and designed behaviors and describe the underpinning of behavioral biometrics in cognitive psychology. Furthermore, we categorize the surveyed studies into three groups based on the fusion of motion modalities and characterize each study along dimensions such as task, datasets, modality, algorithms, and performance. Based on our survey, we identify several future directions of research.
A Performance Analysis of Machine Learning Algorithms for Malaria Parasite Detection using Microscopic Images

Plasmodium parasites are the cause of malaria, a blood-borne disease spread by mosquitoes. Preparing a blood sample, staining it, then using a microscope to look at the stained blood smear to determine which parasite genus is Plasmodium. is the standard method for detecting malaria, a promising area of research is computer-aided Plasmodium detection. For the purpose of detecting Plasmodium on blood smear images obtained through conventional microscopy, this study examines the efficacy of various machine learning strategies in this paper. To target effected blood smears for detection of malaria, this research work employs different algorithms of machine learning in contrast to the conventional approach and has some issues with sensitivity and specificity. Most malaria diagnoses are made using standard microscopy. Microscopy has also shown a range of clinical and transmission situations to have varied sensitivity and specificity. The suggested method does not require experts or blood stains; instead, it uses images of patient to detect malaria.
Implementation of Medical Insurance Price Prediction System using Regression Algorithms

Medical expenses are increasing day by day due to unexpected epidemic diseases. People have awareness about the medical insurance and claiming processes which is helping in critical situations. Predicting the medical insurance cost is very important work in health care sectors. Many researcheis applied different machine learning algoiithms to predict the insurance premium in Kaggle data set with seven attributes such as age, sex, bmi, children, smoker, region and charges. But these features only not suitable for prediction. In this paper, the dataset with 24 features including all relevant attributes needed for prediction of insurance cost was used. The implementation done using regression algorithms such as Linear Regression, Decision Tree Regression, Lasso Regression, Ridge Regression, Random Forest Regression, ElasticNet Regression, Support Vector Regression, K Nearest Neighbor Regression and Neural Network Regression in R Programming. There were seven metiices applied to measure the performance of the model namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), RSquared (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), Adjusted R-Squared (Adj. $\mathrm{R}^{2}$), and Explained Variance Score (EVS). Random Forest Regression outperformed with 0.9533 as RS quared value. This prediction system will reduce the manual work and give the prediction accurately in medical field.
Sentiment Analysis: Quantitative Evaluation of Machine Learning Algorithms

Sentiment analysis is a computer method for analyzing the opinions, feelings, and thoughts expressed in online comments and reviews. Sentiment analysis is a topic that has recently gained attention in the research areas of NLP and text analysis. Understanding public sentiment is greatly aided by doing sentiment analysis on this user-generated data. Two methods are used to extract emotions from text: the knowledge base approach and the machine learning methods. This research work has developed a machine learning model to find the best technique for performing sentiment analysis. For this purpose, various machine learning classifiers were compared by including reviews of movies. Accuracy and fl metrics were also usedfor the evaluation of algorithms.
Medical Image Denoising using Convolutional Autoencoder with Shortcut Connections

In recent times, medical image analysis has gained immense attention among researchers for diagnosing and treating deadly diseases, e.g., Cancer. Denoising of Images is considered as a crucial process in medical image analysis. Several Deep Learning techniques are effectively used for image denoising including Autoencoders. In this paper, a convolutional autoencoders based approach with shortcut connections is proposed for medical image denoising. The proposed approach is evaluated by using three medical images datasets. The results demonstrated that the proposed approach outperforms the current cutting-edge methods of medical image denoising on all the three datasets.
Machine Learning Models for News Article Classification

An enormous amount of data is produced these days on a daily basis. News articles are one of the major parts of the daily data, which people refer to and most of the data are unstructured or unorganized so a better strategy and tools that can enable a better management processing data iteration should be adopted. Categorization is one of the methods to organize this unstructured data into structured data. Article Classification is a need for structuring these online articles. Knowledge should be categorized into a useful set of groups because it is important and necessary. Automatic text classification is desperately needed to categorize these growing numbers of machine-readable data. Using text labelling, a supervised learning technique, unlabeled data is divided into predetermined groups of labelled data. In this paper, five different machine learning models are used to classify news articles into four classes. The best accuracy was achieved by the SVM (support vector machine) model, with a value of 89.35%.
What Is a Multi-Modal Knowledge Graph: A Survey

With the explosive growth of multi-modal information on the Internet, the multi-modal knowledge graph (MMKG) has become an important research topic in knowledge graphs to meet the needs of data management and application. Most research on MMKG has taken image-text data as the research object and used the multi-modal deep learning approach to process multi-modal data. In comparison, the structure of the MMKG is no uniform statement. This paper focuses on MMKG, introduces the related theories of multi-modal knowledge, and analyzes several common ideas about its construction. The survey also explains the structural evolution, proposes mirror node alignment to represent cross-modal knowledge for MMKG, lists some tasks' difficulties, and ultimately gives a sample MMKG for the news scene.
Challenges in Virtual Reality Studies: Ethics and Internal and External Validity

User studies on human augmentation nowadays frequently involve virtual reality (VR) technology. This is because VR studies allow augmentations of the human body or senses to be evaluated virtually without having to develop elaborate physical prototypes. However, there are many challenges in VR studies that stem from a multitude of factors. In this paper, we first discuss different types of VR studies and suggest high-level terminology to facilitate further discussions in this space. Then, we derive challenges from the literature that researchers might face when conducting research with VR technology. In particular, we discuss ethics, internal validity, external validity, the technological capabilities of VR hardware, and the costs of VR studies. We further discuss how the challenges might apply to different types of VR studies, and formulate recommendations.
Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s

Object detection of overhead transmission lines is a solution for promoting inspection efficiency for power companies. However, aerial images contain many complex backgrounds and small objects, and traditional algorithms are incompetent in the identification of details of power transmission lines accurately. To address this problem, this paper develops an object detection method based on optimized You Only Look Once v5-small (YOLOv5s). This method is designed to be engineering-friendly, with the objective of maximal detection accuracy and computation simplicity. Firstly, to improve the detecting accuracy of small objects, a larger scale detection layer and jump connections are added to the network. Secondly, a self-attention mechanism is adopted to merge the feature relationships between spatial and channel dimensions, which could suppress the interference of complex backgrounds and boost the salience of objects. In addition, a small object enhanced Complete Intersection over Union (CIoU) is put forward as the loss function of the bounding box regression. This loss function could increase the derived loss for small objects automatically, thereby improving the detection of small objects. Furthermore, based on the scaling factors of batch-normalization layers, a pruning method is adopted to reduce the parameters and achieve a lightweight method. Finally, case studies are fulfilled by comparing the proposed method with classic YOLOv5s, which demonstrate that the detection accuracy is increased by 4%, the model size is reduced by 58%, and the detection speed is raised by 3.3%.
Threshold-based Naïve Bayes classifier

Abstract The Threshold-based Naïve Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naïve Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests’ reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency.
<scp>BUS‐CAD</scp>: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

Abstract Breast cancer has become the most prominent type of cancer in the world. Early detection of breast cancer plays an important role in optimal treatment planning to decrease mortality. Breast ultrasound is widely used in diagnosing breast masses. Applications of machine learning in ultrasound imaging‐based classification have shown promising potential for early and accurate detection of breast cancer. In this study, a new computer‐aided diagnosis system based on machine learning techniques for breast cancer classification is proposed. Feature space is extended by using hybrid feature representations that combine both global and local texture statistics. A two‐step feature selection process is implemented using Boruta all‐relevant feature selection algorithm and iterative correlation analysis. A grid‐search strategy is followed along with 20 times repeated 10‐fold randomly stratified cross‐validation to optimize machine learning algorithms. Fourteen classification models based on random forest (RF) and support vector machine trained using all combinations of global features and the features driven from gray‐level co‐occurrence matrix and local binary patterns are tested. The experiments showed that the RF classifier on the hybrid feature vector that combines all global and local features achieved the best classification performance with average accuracy and area under the curve of 97.81% and 99.80%, respectively. The results suggest that the proposed system efficiently improves the classification performance of breast lesions on ultrasound images and can assist clinical decision‐making.