Reviewerly/exHarmony huggingface dataset:
SAR-BSO meta-heuristic hybridization for feature selection and classification using DBNover stream data

Abstract Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm.
Computational Intelligence and Soft Computing Paradigm for Cheating Detection in Online Examinations

Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online examinations. We have also presented state-of-the-art methods followed, which are capable of mitigating online cheating, namely, face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and detection of IP spoofing. A discussion on improvement of existing online cheating detection systems has also been presented.
An improved lightweight high-resolution network based on multi-dimensional weighting for human pose estimation

Human pose estimation is one of the key technologies in action recognition, motion analysis, human-computer interaction, animation generation etc. How to improve its performance has become a current research hotspot. Lite-HRNet establishes long range connections between keypoints and exhibits good performance in human pose estimation tasks. However, the scale of this method to extract features is relatively single and lacks sufficient information interaction channels. To solve this problem, we propose an improved lightweight high-resolution network based on multi-dimensional weighting, named MDW-HRNet, which is implemented by the following aspects: first, we propose global context modeling, which can learn multi-channel and multi-scale resolution information weights. Second, a cross-channel dynamic convolution module is designed, it performs inter-channel attention aggregation between dynamic and parallel kernels, replacing the basic convolution module. These make the network capable of channel weighting, spatial weighting and convolution weighting. At the same time, we simplify the network structure to perform information exchange and information compensation between high-resolution modules while ensuring speed and accuracy. Experimental results show that our method achieves good performance on both COCO and MPII human pose estimation datasets, and its accuracy surpasses mainstream lightweight pose estimation networks without increasing computational complexity.
Examining the antecedents of employee retention among Jordanian private Universities: The moderating role of knowledge sharing

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A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

Due to the development of digital transformation, intelligent algorithms are getting more and more attention. The whale optimization algorithm (WOA) is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems. However, with the increased dimensions, higher requirements are put forward for algorithm performance. The double population whale optimization algorithm with distributed collaboration and reverse learning ability (DCRWOA) is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems. In the DCRWOA algorithm, the novel double population search strategy is constructed. Meanwhile, the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area. Numerical experiments are carried out using standard test functions with different dimensions (10, 50, 100, 200). The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algorithm. The results show that the DCRWOA algorithm has higher optimization accuracy and stability, and the convergence speed is significantly improved. Therefore, the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.
Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

With the development of digitalization in healthcare, more and more information is delivered and stored in digital form, facilitating people’s lives significantly. In the meanwhile, privacy leakage and security issues come along with it. Zero watermarking can solve this problem well. To protect the security of medical information and improve the algorithm’s robustness, this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform (NSST) and Schur decomposition. Firstly, the low-frequency subband image of the original medical image is obtained by NSST and chunked. Secondly, the Schur decomposition of low-frequency blocks to get stable values, extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decomposition of each low-frequency block and constructing the transition matrix from it. Then, the mean of the matrix is compared to each element’s value, creating a feature matrix by combining perceptual hashing, and selecting 32 bits as the feature sequence. Finally, the feature vector is exclusive OR (XOR) operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center. Experimental data show that the Normalized Correlation (NC) values of watermarks extracted in random carrier medical images are above 0.5, with higher robustness than traditional algorithms, especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.
Secure Blockchain-Enabled Internet of Vehicles Scheme with Privacy Protection

The car-hailing platform based on Internet of Vehicles (IoV) technology greatly facilitates passengers’ daily car-hailing, enabling drivers to obtain orders more efficiently and obtain more significant benefits. However, to match the driver closest to the passenger, it is often necessary to process the location information of the passenger and driver, which poses a considerable threat to privacy disclosure to the passenger and driver. Targeting these issues, in this paper, by combining blockchain and Paillier homomorphic encryption algorithm, we design a secure blockchain-enabled IoV scheme with privacy protection for online car-hailing. In this scheme, firstly, we propose an encryption scheme based on the lattice. Thus, the location information of passengers and drivers is encrypted in this system. Secondly, by introducing Paillier homomorphic encryption algorithm, the location matching of passengers and drivers is carried out in the ciphertext state to protect their location privacy. At last, blockchain technology is used to record the transactions in online car-hailing, which can provide a security guarantee for passengers and drivers. And we further analyze the security and performance of this scheme. Compared with other schemes, the experimental results show that the proposed scheme can protect the user’s location privacy and have a better performance.
Security and Scalability of E-Commerce Website by OWASP threats.

In the current age of fast demanding services related to the E-Commercial commodities, it becomes the need of the hour to protect each and every detail of our customer and the website because after all, security of the data is our prime priority. Moreover this field has good set of areas in which extensive studies and research could be done. Each year we see thousands of local website and their data which is leaked or get encountered by some of top programmers. In this paper, we will try to secure our website from some of the top listed threats on OWASP (Open Web Application Security Project), which is also known as OWASP 10. The paper is a detailed analysis of how to secure our website from phishing attack, broken authentication, sensitive data exposure, XSS Attack, parameter tampering and SQL injection. In terms of scalability we will go with the technique of database replication and. To make our study effective we will be using various cheat sheets by OWASP and some tools like Jenkins, burp suite, network scanner, wire-shark.
Performance Analysis of Machine Learning Algorithms Using Bagging Ensemble Technique for Software Fault Prediction

Software development comes with a lot of challenges. Developers face various issues with performance and bugs. These issues increase with the scale of the project and if fewer individuals work on the development. It has become necessary to fix various bugs during development to ensure better performance and reduce the chances of failure during the deployment of the software. As a result of this, faults in the software must be predicted during the earlier stages of development. This would help in reducing the cost of maintenance of the software post-deployment. Multiple software fault prediction (SFP) approaches have been proposed to tackle this problem. These approaches can be improved by implementing ensemble techniques. In this paper, we study the effect of the bagging technique and how it helps to improve the predictive capability across various datasets. These datasets are provided and made open source by NASA. Decision Tree Classifier (DTC), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Support Vector Classifier (SVC) were used as base classifiers for the bagging method. Random forest (RFC) is an ensemble learning algorithm that uses the bagging technique. Based on the outcome of the results, it was concluded that RFC was the best-performing algorithm.
Blockchain Interoperability for A Reputation-Based Drug Supply Chain Management

The pharmaceutical drug industry faces considerable challenges in supply chain management, particularly in regard to tracking and tracing medications. Counterfeit drugs pose a significant global threat, and existing supply chain management practices often prove insufficient to prevent adulteration. Blockchain technology offers a potential solution to these issues by establishing a decentralized and distributed ecosystem where stakeholders can foster a trustworthy relationship. To address these limitations, we propose a blockchain-based system for tracking drugs in the supply chain, incorporating a reputation mechanism to ensure transparency and accountability among entities. To mitigate the interoperability challenges between blockchain networks, we implement inter-blockchain communication using the burn-to-claim protocol. Hyperledger Fabric is used as the primary blockchain because it ensures confidentiality between multiple organizations, and Quorum is the other blockchain. Our solution prioritizes trust, traceability, transparency, and accountability to deliver safe, authentic medication to consumers.
Aspect Based Sentiment Analysis for Amazon Data Products using PAM

Recent years have seen a considerable evolution in online shopping, as well as the ability to get text-based client feedback, comments, and recommendations. The sentiment analysis method examines a large collection of text data to determine the customer’s opinion. One method for analyzing customer sentiment that is provided by natural language processing is AbSA. Understanding out precisely exactly consumers feel about a product is the aim of AbSA. In this research, we spoke about aspect-based sentiment analysis, which identifies the sentiment by using the aspect term. To illustrate our framework, we used user reviews for headphones and earphones that were gathered from the Amazon website. We used NLP to preprocess the data, and we then used the Pachinko Allocation model (PAM) to extract the aspect term and polarity from the review. Machine learning technique have been explored to evaluate the model.
Histogram Refined Local Ternary Pattern-based Bilateral LPP for Vision Sensor-based Robot Navigation Guidance under Challenging Environments

The present letter proposes a new variant of bilateral locality preserving projection (BLPP), a nonlinear manifold learning technique, for vision sensor-based robot navigation guidance in challenging environments, suffering from both variations in ambient illumination levels and vision sensor noises and uncertainties in image acquisition. In its basic form, the adjacency graph in BLPP is formulated both in feature subspace and spatial subspace simultaneously. The present work proposes to compute the feature weights using local ternary pattern (LTP), a lesser noise-sensitive feature descriptor that is also robust against illumination variations. In addition, histogram refinement has been performed on the feature descriptor to encode the local neighborhood structure of the data, proposing a further augmented version of BLPP named histogram refined local ternary pattern-based BLPP (HRLTP-BLPP). Extensive real-life experiments with challenging photometric conditions and sensor noises have been performed to show the usefulness of HRLTP-BLPP over other competing, state-of-the-art approaches utilized for robot navigation guidance.
THE USE OF THE METAVERSE STUDIO APPLICATION IN FOREIGN LANGUAGE TEACHING AS AN INNOVATIVE AR TECHNOLOGY

The article is devoted to the analysis of the educational potential of the Metaverse Studio application as an innovative AR technology in foreign language teaching. The research is based on a literature review, synthesis, generalization, and systematization of the existing research and personal experience of the use of the software. The presented study proves the efficiency of the introduction of AR technology in foreign language teaching due to its versatility, immersion and engagement, motivation, and better learning outcomes it provides. It has been proven and revealed that the Metaverse Studio application applies to various language aspects such as phonetics, grammar, and vocabulary. Due to the rich variety of scenes introduced by the aforementioned AR software, it could be also used as a storytelling tool, puzzle, and trivia games tool as well as the way of effective introduction or a visual element of a project work via the creation of a media wall. Moreover, the use of the Metaverse Studio application offers poll features that could serve in terms of a warm-up at any lesson or topic or as a way for getting quick feedback, or as a reflection tool. The methodical aspects and the importance of sequencing in terms of the use of the software are highlighted and presented in the video posted on the author’s YouTube channel. The specifics of the software’s dashboard mode, storyboard mode, and setup behind the experience of content creation are visualized and explained in the article. The links to the official website and Metaverse AR platform YouTube are also provided since the support of the software is not available anymore. The benefits of the use of AR software in foreign language teaching as an innovative AR technology and Metaverse Studio application, in particular, are emphasized and arguments in the study.
Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the proposed aspect2class annotation algorithm, we labeled bulk unlabeled tweets according to their contained aspect terms. However, we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets. To address this issue, we proposed the Volatile Stopwords Filtering (VSF) technique to reduce sparsity and enhance classifier performance. The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21% when using the random forest as a meta-classifier. Through testing on three benchmark datasets, we found that the SABAC ensemble framework performed exceptionally well. Our findings showed that international students during the pandemic faced various issues, including stress, uncertainty, health concerns, financial stress, and difficulties with online classes and returning to school. By analyzing and summarizing these annotated tweets, decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.
Leveraging Large Text Corpora For End-To-End Speech Summarization

End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech recognition (ASR) and text summarization models, the E2E approach is more promising because it mitigates ASR errors, incorporates nonverbal information, and simplifies the overall system. However, since collecting a large amount of paired data (i.e., speech and summary) is difficult, the training data is usually insufficient to train a robust E2E SSum system. In this paper, we present two novel methods that leverage a large amount of external text summarization data for E2E SSum training. The first technique is to utilize a text-to-speech (TTS) system to generate synthesized speech, which is used for E2E SSum training with the text summary. The second is a TTS-free method that directly inputs phoneme sequence instead of synthesized speech to the E2E SSum model. Experiments show that our proposed TTS- and phoneme-based methods improve several metrics on the How2 dataset. In particular, our best system outperforms a previous state-of-the-art one by a large margin (i.e., METEOR score improvements of more than 6 points). To the best of our knowledge, this is the first work to use external language resources for E2E SSum. Moreover, we report a detailed analysis of the How2 dataset to confirm the validity of our proposed E2E SSum system.
Machine autonomy for rehabilitation of elderly people: A trade-off between machine intelligence and consumer trust

This is an exploratory study with the objective to understand elderly people's expected behavior to accept and use machine autonomy controlled by ambient intelligence. It was conducted on a proposed theoretical framework, the expected trust model for machine autonomy (ETM4MA). A detailed survey was collected among elderly people living at home with their family members in a developing country, Bangladesh. Based on the results obtained from structural equation modeling (SEM) on the sample, it was observed that elderly people's acceptance of automation systems driven by ambient intelligence as a substitute for caregiving support directly from human beings was linked to their concerns about emotional belongingness and social interaction as well as their ability to use the technology. Thus, in developing trust in this machine autonomy, their expectations around personal ability and the feeling of caring are two important issues for elderly people. To form the behavioral intention to accept this machine autonomy, trust plays a crucial role; however, this might vary depending on the differences in personality and behavioral attitude.
Dynamic Fair Node Representation Learning

Many real-world networks such as social networks, traffic networks vary over time, which can be modeled as dynamic graphs. Despite the significant number of systems that can facilitate from the algorithmic tools over dynamic graphs, dynamic graph representation learning is an under-explored research area. Furthermore, while the fairness of algorithms is essential for their deployment in real-world systems, this issue has never been considered in the context of dynamic graphs to the best of our knowledge. Motivated by this, the present study proposes an efficient online node representation learning framework over dynamic graphs that can also mitigate bias. Specifically, the proposed technique combines different observations (graph structure and nodal attributes) of the same source (attributed graph) in a complementary way while also reducing the intrinsic bias in the learned representations. Experimental results on dynamic graphs show that the proposed online strategy can improve the group fairness measures for node classification together with comparable/better utility to the baselines.