Reviewerly/exHarmony huggingface dataset:
What matters for short videos’ user engagement: A multiblock model with variable screening

The increasing number of short videos have permeated people’s daily life and played a vital role in information dissemination. However, the burgeoning video’s form brings challenges to discern the key features and improve the dissemination’s effect of short videos in affecting user engagement. Additionally, modeling the composition of likes, shares and comments as a response variable is essential in analyzing user engagement. Aiming to aggregate information from short videos and offer the quantitative method to advance the previous qualitative analysis on user engagement structure, we establish a framework for modeling multimodal data embedded in a short video. Building on deep learning and text mining method, we extract features from videos’ acoustics, textual, and visual data as high-dimensional variables. Then, we propose a new variable screening method for compositional response to reduce the vector dimensionality. These variables and other numerical variables are integrated into multiblock partial least squares to analyze the importance of each variable block. The numerical results show the effectiveness of the proposed method. Furthermore, the proposed method is empirically applied to the TikTok platform administered on various kinds of short videos to show the key factors that determine user engagement structure. Theoretical contributions and pragmatic implications to be gleaned from our research model and its subsequent empirical validation are discussed.
Tensor-DAAD: When tensor meets online early fault detection with transfer learning

This paper tries to address the concerns about online early fault detection in non-stop scenarios: (1) no information of running status and operating condition is available for online data; (2) False alarms are raised by irregular noise. This paper proposes a new self-supervised deep tensor domain-adversarial anomaly detection model, named Tensor-DAAD. A contrastive learning network is first built to generate pre-trained features and pseudo-labels for online data. Second, a new tensor hypersphere-adversarial network is designed. By calculating core tensors of two domains' features, this network builds hypersphere-based one-class detection rules from noisy data, and then realizes rule adaptation between the domains through adversarial training. Finally, a training algorithm with alternating minimization scheme is proposed to seek the optimal tensor decomposition and domain-invariant feature representation. Results on the two widely-used bearing datasets demonstrate that Tensor-DAAD can find early fault occurrence in an earlier location with a much lower false alarm rate.
Detecting outliers from pairwise proximities: Proximity isolation forests

Because outliers are very different from the rest of the data, it is natural to represent outliers by their distances to other objects. Furthermore, there are many scenarios in which only pairwise distances are known, and feature-based outlier detection methods cannot directly be applied. Considering these observations, and given the success of Isolation Forests for (feature-based) outlier detection, we propose Proximity Isolation Forest, a proximity-based extension. The methodology only requires a set of pairwise distances to work, making it suitable for different types of data. Analogously to Isolation Forest, outliers are detected via their early isolation in the trees; to encode the isolation we design nine training strategies, both random and optimized. We thoroughly evaluate the proposed approach on fifteen datasets, successfully assessing its robustness and suitability for the task; additionally we compare favourably to alternative proximity-based methods.
JoMIC: A joint MI-based filter feature selection method

Feature selection (FS) is a common preprocessing step of machine learning that selects informative subset of features which fuels a model to perform better during prediction or classification. It helps in the design of an intelligent and expert system used in computer vision, image processing, gene expression data analysis, intrusion detection and natural language processing. In this paper, we introduce an effective filter method called Joint Mutual Information with Class relevance (JoMIC) using multivariate Joint Mutual Information (JMI) and Mutual Information (MI). Our method considers both JMI and MI of a non selected feature with selected ones w.r.t a given class to select a feature that is highly relevant to the class but non redundant to other selected features. We compare our method with seven other filter-based methods using the machine learning classifiers viz., Logistic Regression, Support Vector Machine, K-nearest Neighbor (KNN), Decision Tree, Random Forest, Naïve Bayes, and Stochastic Gradient Descent on various datasets. Experimental results reveal that our method yields better performance in terms of accuracy, Matthew's Correlation Coefficient (MCC) and F1-score over 16 benchmark datasets, as compared to other competent methods. The superiority of our proposed method is that it uses an effective objective function that combines both JMI and MI to choose the relevant and non redundant features.
The Metaverse

The metaverse can be described as the largescale societal shift from flat media viewed in the third person to immersive media experienced in the first person. While there is nothing inherently dangerous about immersive media technologies such as virtual and augmented reality, many policymakers have raised concerns about the extreme surveillance capabilities that powerful metaverse platforms could wield over users. What is often overlooked, however, is how surveillance-related risks become amplified when platforms are allowed to simultaneously target users with promotionally altered experiences. When considered in the context of control theory, the pairing of real-time surveillance and real-time influence raises new concerns, as large metaverse platforms could become extremely efficient tools for deception, manipulation, and persuasion. For these reasons, regulation should be considered that limit the ability of metaverse platforms to impart real-time influence on users based on real-time surveillance.
Sports in the Metaverse

The goal of this chapter is to present information on sports technology, specifically how the metaverse will affect the sports world and how well sports will adapt to life in the metaverse. The findings of the study conducted for the literature review have been described in the chapter, and the chapter has also investigated all the possible target outcomes. It has been proven that the sports industry is ready to enter the metaverse and has already taken action to do so as it guides the sporting world into the future.
Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion

Knowledge graph completion (KGC) tasks are aimed to reason out missing facts in a knowledge graph. However, knowledge often evolves over time, and static knowledge graph completion methods have difficulty in identifying its changes. Scholars have focus on temporal knowledge graph completion (TKGC). Most existing TKGC methods incorporate temporal information into triples and convert them into KGC tasks, ignoring the impact of temporal information on quaternions. Furthermore, existing embedding learning methods based on message-passing network aggregate features passed by neighbors with the same attention, ignoring the complex structure information that each node has different importance in passing the message. Therefore, to capture the impact of temporal information on quaternions and structural information on nodes, we proposed a TKGC method based on temporal attention learning (TAL-TKGC), which includes a temporal attention module and an importance-weighted GCN. The temporal attention module was designed to capture the deep connection between timestamps entities and relations at the semantic levels. The importance-weighted GCN considers the structural importance and attention of temporal information to entities for weighted aggregation. Finally, we conducted experiments on two public datasets, and the results proved the performance of our method. We also performed the speedup experiments in a distributed environment, and the proposed model has an excellent scalability on multiple GPUs.
Towards Backdoor Attacks and Defense in Robust Machine Learning Models

The introduction of robust optimisation has pushed the state-of-the-art in defending against adversarial attacks. Notably, the state-of-the-art projected gradient descent (PGD) -based training method has been shown to be universally and reliably effective in defending against adversarial inputs. This robustness approach uses PGD as a reliable and universal “first-order adversary”. However, the behaviour of such optimisation has not been studied in the light of a fundamentally different class of attacks called backdoors. In this paper, we study how to inject and defend against backdoor attacks for robust models trained using PGD-based robust optimisation. We demonstrate that these models are susceptible to backdoor attacks. Subsequently, we observe that backdoors are reflected in the feature representation of such models. Then, this observation is leveraged to detect such backdoor-infected models via a detection technique called AEGIS. Specifically, given a robust Deep Neural Network (DNN) that is trained using PGD-based first-order adversarial training approach, AEGIS uses feature clustering to effectively detect whether such DNNs are backdoor-infected or clean. In our evaluation of several visible and hidden backdoor triggers on major classification tasks using CIFAR-10, MNIST and FMNIST datasets, AEGIS effectively detects PGD-trained robust DNNs infected with backdoors. AEGIS detects such backdoor-infected models with 91.6% accuracy (11 out of 12 tested models), without any false positives. Furthermore, AEGIS detects the targeted class in the backdoor-infected model with a reasonably low (11.1%) false positive rate. Our investigation reveals that salient features of adversarially robust DNNs could be promising to break the stealthy nature of backdoor attacks.
Analyzing Programming Effort Model Accuracy of High-Level Parallel Programs for Stream Processing

Over the years, several Parallel Programming Models (PPMs) have supported the abstraction of programming complexity for parallel computer systems. However, few studies aim to evaluate the productivity reached by such abstractions since this is a complex task that involves human beings. There are several studies to develop predictive methods to estimate the effort required to develop software applications. In order to evaluate the reliability of such metrics, it is necessary to assess the accuracy in different programming paradigms. In this work, we used the data of an experiment conducted with beginners in parallel programming to determine the effort required for implementing stream parallelism using FastFlow, SPar, and TBB. Our results show that some traditional software effort estimation models, such as COCOMO II, fall short. In contrast, Planning Poker could contribute toward a parallel-aware effort model.
Sentiment Analysis For Arabic Low Resource Data Using BERT-CNN

Users share opinions and discussions on the internet through social media platforms. Nowadays, a significant number of internet users speak the Arabic language. They tend to express their opinions using different dialects. Therefore, understanding people's opinions and emotions become an urgent matter. The Arabic sentiment analysis is challenging because of linguistic complexity, data availability, and data quality, and it has multiple dialects. Therefore, research for low resources sentiment analysis became necessary. This study proposes a Bidirectional Encoder Representations from Transformers (BERT) that uses Convolutional Neural Network (CNN) as a classification head for Arabic low data resources for sentiment analysis. The classification head includes the CNN layer, drop-out layer, and a Relu activation function. The proposed approach experimented on three datasets collected from Twitter containing different dialects. The last four BERT layers were fined-tuned and while other layers were frozen. The suggested model outperforms current state-of-the-art models' accuracy with 50% fewer batch size, fewer training layers, and ∼20% fewer epochs.
Towards Secure Agile Software Development Process: A Practice-Based Model

Agile methods are a well-established paradigm in the software development field. Agile adoption has contributed to improving software quality. However, software products are vulnerable to security challenges and susceptible to cyberattacks. This study aims to improve security of software products when using an agile software development process. A multi-methods qualitative research approach was adopted in this study. First, we conducted semi-structured interviews with 23 agile practitioners having varied years of cybersecurity experiences. An approach informed by grounded theory methodology was adopted for data analysis. Second, we developed a novel practice-based agile software development process model derived from the results of the data analysis conducted. Third, we validated the model through a focus group comprising five senior agile cybersecurity professionals to evaluate its relevancy and novelty. The study has identified 26 security practices, organized into the six - software development life-cycle phases: planning, requirements, design, implementation, testing, and deployment. We have mapped the practices onto four swim lanes each representing an agile role. The self-organizing team is exclusively involved in three security practices, the security specialist in nine, penetration tester in one and the DevOps team collaborates on one with the security specialist. There are also seven practices that are collaboratively performed by the self-organizing team and the security specialist. Each of the practices in the model was examined during the validation phase of the study. There are two contributions in this study. First, the paper proposes a novel practice-based model comprising of 26 security practices mapped to agile roles. Second, we propose a new practice, in response to an observed lack of collaborative ceremonies, to disseminate awareness of and hence compliance with security standards.
Complex Human Activities Recognition Based on High Performance 1D CNN Model

Human activity recognition (HAR) is an emerging scientific research field that has wide area of applications in different fields such as healthcare, social-sciences and human-computer interaction etc. In many cases, humans perform very complex physical activities that needs to be tracked in order to improve well-being, quality of life and health. In this study, a method for complex HAR based on One dimensional (1D) CNN model using tri-axis accelerometer sensor data was proposed. The sensor data was collected from a smartwatch for three complex human activities which are studying, playing games and mobile scrolling. 1D CNNs provides high accuracy as well as less computational complexity in performing HAR. The proposed 1D CNN model was trained and optimized on a self-prepared dataset in Python. The adapted model provides an accuracy of 98.28 %. A preliminary study shows that the proposed model could effectively recognize the intended activities as a baseline for extending future work in the HAR area.
Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network

Over the past decade, Smart cities have been advanced and minimizing traffic congestion become the main concern in the progression of smart cities. The rapid growth in the number of road vehicles has raised the number of road accidents and traffic congestion. To Solve this problem, Vehicular Network (VN) formulated several novel concepts which include traffic control, vehicular communications, and navigation. Machine Learning (ML) will be an effective technique for identifying hidden insights into ITS without being programmed clearly by learning from data. This article develops an Optimal Extreme Learning Machine based Traffic Congestion Control System (OELM-TCCS) in vehicular networks. The presented OELM-TCCS technique mainly focuses on the identification and recognition of traffic congestion in VANET. To perform this, the presented OELM-TCCS technique primarily designs a new ELM model to carry out the classification process. In addition, the presented OELM-TCCS technique executes the satin bowerbird optimization (SBO) algorithm for parameter tuning of the ELM method. To demonstrate the boosted performance of the OELM-TCCS model, a series of experiments were executed. The experimental outcomes indicate the betterment of the OELM-TCCS method over recent methods with maximum accuracy of 99.17%.
Optimized Sentiment Analysis of Hotel Reviews using Machine Learning Algorithms

On social media, words and phrases reflect people's opinions about certain goods, services, organisations, laws, and events. In the discipline of NLP, the objective of sentiment analysis is to extract positive things or negative things from social media. Researchers are driven to complete their sentiment analysis research because of the government and corporate organisations' and individuals' exponentially growing needs. This study optimises sentimental analysis using four cutting-edge machine learning classifiers: Naivebayes, J48, BFTree, and OneR. This four classification methods' effectiveness are investigated and contrasted. While OneR appears more promise in producing the accuracy of correctly classified instances, Naivebayes was proven to be quick at learning. This study has observed that the four records are positively related, and the product's position in online market has also advanced over time. In light of the analysis of the favourable reviews and star ratings, this research study has suggested displaying a potentially successful or disastrous item based on the positive surveys. Finally, the relationship between the quantity of surveys and star ratings are also analyzed.
Software Effort Estimation using Machine Learning Algorithms

Accurate software work estimates is essential to the planning, management, and execution of a successful project on schedule and within budget. The necessity for accurate software work estimates is something that will never go away since both overestimation and underestimate provide substantial barriers to the development of additional software (SEE). Research and practise are aimed at finding the machine learning estimating technique that is most successful for a given set of criteria and data. This is the goal of the research and practise. Most academics working in a particular subject are not aware of the findings of previous studies that investigated different approaches to effort estimate in machine learning. The primary purpose of this investigation is to aid researchers working in the field of software development by assisting them in determining which method of machine learning produces the most promising effort estimate accuracy prediction.
DeepSORT: Real Time &amp; Multi-Object Detection and Tracking with YOLO and TensorFlow

One of the most significant and challenging areas of computer vision is object recognition and tracking, which is extensively utilised in many industries including health care monitoring, autonomous driving, anomaly detection, etc. The tracking of moving objects in videos is actively researched over the past two decades due to its practical applications in many fields such as event analysis, human-computer interaction, crowd analysis, video surveillance, behaviour analysis, etc. The effectiveness of object trackers and detectors has significantly increased with the rapid advancement of deep learning (DL) networks and GPU processing capability. New methods have been presented for object recognition and tracking in video as a result of extensive study in this field. This article addressed the several processes of object tracking in video sequences: object detection, object classification, and object tracking, in order to comprehensively comprehend the key advancements in the object detection and tracking pipeline. Additionally, we thoroughly examine the various approaches available for object recognition, categorization, and tracking.
Classification and Segmentation of Mitotic Cells using Ant Colony Algorithm and TNM Classifier

Breast cancer develops from breast tissue and leads to abnormally growing cells in the chest. Doctors usually look for tumors on a mammogram, and some mammograms contain abnormal macrocalcifications and microcalcifications when the image quality is very poor. The presence of these abnormal amounts of calcium deposits in the breast is a sign of early breast cancer and should never be ignored. The image quality should be of the highest quality for an accurate interpretation of this mammographic deposit. Proposed research work is ongoing, exploring other screening methods and the stages of breast cancer. Improved Adaptive Fuzzy C-Means (IAFCM), Ant Colony Algorithm (ACA), and TNM (The size of the breast tumour (T), adjacent lymph nodes and Metastasized methods are used which builds the proposed medical image processing systems into an efficient way. Modified Poisson Inverse Gradient, Metastasized classifier (MPIG) has been used for classification. More than 500 image modalities are involved in all of the approaches. Clinical practitioners who make decisions based on photographs are predicted to benefit from the findings of this study.
IABCP: an Integrated Approach for Breast Cancer Prediction

Breast cancer detection and prognosis may result in better medication being provided early, lowering the number of deaths caused by the illness each year and presenting a potentially significant danger to human health. Even though many academics have developed Additional research is also being undertaken in this field by other researchers employing Machine Learning (ML) approaches for the prognostication of chronic illnesses such as diabetes, heart disease, and cancer using both manually generated datasets and publicly available datasets. Based on the support vector machine (SVM) classifier, principal component analysis (PCA), and recursive feature elimination (RFE), this study suggested an Integrated Approach for Breast Cancer Prediction (IABCP) for predicting this chronic disease. Several experiments were carried out using the UCI-ML warehouse-based dataset WBCD, i.e., the Wisconsin breast cancer dataset (WBCD), and Python coding in a Jupyter notebook. Various experiments are taken place and results are obtained as accuracy, precision, recall, and f-measures of 97.87%, 98.55%, 98.77%, and 97.14% respectively. The results revealed that the recommended technique outperforms other typical approaches and is effective in the context of this research.