Reviewerly/exHarmony huggingface dataset:
A Review of Milgram and Kishino’s Reality-Virtuality Continuum and a Mathematical Formalization for Combining Multiple Reality-Virtuality Continua

We explore in this paper theoretical contributions that are related to Milgram and Kishino's Reality Virtuality Continuum by conducting a systematic literature review.From this study, we draw inspiration for our proposed mathematical formalization of combining multiple Reality-Virtuality Continua in a single, mixed reality experience.Also, we provide a definition for XR transition protocol.To complete our contribution, we discuss two potential examples that will exemplify our formalization and identify future work to be addressed.
User experience evaluation of a 3D virtual reality educational program for illegal drug use prevention among high school students: Applying the decomposed theory of planned behavior

Objective To evaluate user acceptability of an immersive three-dimensional virtual reality program for preventing illegal drug use and identify factors associated with continuous usage intention of three-dimensional virtual reality learning among high school students based on the decomposed theory of planned behavior. Methods In this cross-sectional observational study, we developed five educational modules and serious games based on three-dimensional virtual reality technology. Ninety student-participants’ experiences were assessed by a structured questionnaire based on the decomposed theory of planned behavior variables. We applied partial least squares structural equation modeling to examine the correlates of continuous usage intention. Results The proposed model demonstrated an acceptable fit to the observed data. Eight of the 11 hypotheses based on the decomposed theory of planned behavior were supported. Continuous usage intention was significantly associated with attitudes, subjective norms, and perceived behavioral control; these variables explained 55.4% of the variance in continuous usage intention. Perceived usefulness and compatibility were significant antecedents of attitude. The significant antecedent of subjective norms was support from school staff. Self-efficacy and resource-facilitating conditions were significant antecedents of perceived behavioral control. Conclusions Our findings support the applicability of the decomposed theory of planned behavior as a framework for evaluating a three-dimensional virtual reality program for illegal drug use. We recommend that the program be included as teaching material for illegal drug prevention education in senior high schools.
Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications

We conducted a Systematic Literature Review on scientific papers that examined the interaction between operators and drone swarms based on the use of a command and control center.We present the results of a meta-analysis of nine scientific papers published in the ACM DL and IEEE Xplore databases.Our findings show that research on human-drone swarm interaction shows a disproportionate interest in hand gestures compared to other input modalities for drone swarm control.Furthermore, all articles reviewed exclusively explored gestures and the size of the swarm used in the studies was limited, with a median of 3.0 and an average of 3.8 drones per study.We compiled an inventory of interaction modalities, recognition techniques, and application types from the scientific literature, which is presented in this paper.On the basis of our findings, we propose four areas for future research that can guide scientific investigations and practical developments in this field.
Optimized Image Authentication Algorithm using Redundant Wavelet Transform Based Sift Descriptors and Complex Zernike Moments

Due to the advanced multimedia editing tools and supported by sophisticated hardware, creating image/video manipulations for malicious purposes is increasing which is almost impossible to detect manually.Moreover, to conceal the traces, different post-processing operations are performed.Therefore, authenticity is a growing concern and important for identifying original and forged images.One of the popular image manipulations is copy-move forgery in which one or more regions in the image are duplicated to create a malicious effect within an image.The work in this article presents redundant wavelet transform based complex Zernike moment and Scale Invariant Feature Transform (SIFT) keypoint matching technique for copy-move image forgery operation detection.SIFT is robust against scale and rotation that works on identifying similarity using exhaustive search of SIFT features.After extracting SIFT keypoint features agglomerative hierarchical clustering is employed for grouping and key point matching operation is performed.Finally, block matching operation is evaluated and forged regions in the manipulated images are marked and displayed.This work also presents optimized SIFT key-point feature computations resulting in lower computation time, often one of the requirements in real time deployment.The proposed algorithm performance is evaluated using Precision, Recall, F-Measure and average detection accuracy on popular and publicly available MICC-220 database.The proposed technique demonstrates improved speedup and detection rate compared to existing approaches.
IMPROVING MATHEMATICS LEARNING OUTCOMES THROUGH THE USE OF PEN TABLET WITH ZOOM APPLICATION

This Best Practice writing aims to describe the improvement of mathematics learning outcomes through the use of a Pen Tablet with the Zoom application. The subjects of this best practice writing are students of class XII MIPA-3 SMAN 2 Tanggul in the odd semester 2021/2022 academic year, totaling 34 students on the Derivatives of Trigonometric Functions. Quantitative data were obtained from quiz scores and daily tests using multiple choice written test techniques using Google Form. From the implementation of mathematics learning using a pen tablet and the Zoom application, an increase in student learning outcomes was obtained, seen from the average value and classical student learning completeness. The average score for quiz 1 is 64.12 and quiz 2 is 79.12. This means that from quiz 1 to quiz 2 there is an increase of 15% From quiz 2 with an average of 79.12 to daily tests to 86, 76 increased by 7.64% . Judging from the mastery of learning also increased. Quiz 1 learning completeness classically is 32.35%, quiz 2 is 70.59%, and daily test is 85.29%. This means that there is an increase from quiz 1 to quiz 2 by 38.24% while from quiz 2 to daily tests it increases by 14.7%. This best practice shows an increase in mathematics learning outcomes through the use of a Pen Tablet with the Zoom application, especially for class XII MIPA-3 students of SMAN 2 Tanggul in the odd semester of the 2021/2022 academic year.
BREPubSub: A Secure Publish-Subscribe Model using Blockchain and Re-encryption for IoT Data Sharing Management

As a result of the incredible growth and diversity of IoT systems and applications over the past several years, an enormous amount of sensing data has been generated, which is critical for developing IoT-based intelligent systems.So far, it has taken a significant amount of time and money to collect sufficient sensing data for these smart systems leading to demands of sharing or exchanging available and valuable data to reduce the time and money spent on the data collection process.However, ensuring the data sharing process's integrity, security, and fairness is fraught with challenges.This paper proposes a Blockchain-based model that supports a secure publish-subscribe protocol for data sharing management by addressing three criteria such as confidentiality, integrity, and availability.In addition, the proposed model adopts a re-encryption technique to optimize shared data storage with multiple users and enhance the security of the data exchange process in a transparent and public environment like Blockchain.We have developed a DApp to demonstrate the feasibility of our design and evaluate its performance.
Analyzing WhisperGate and BlackCat Malware: Methodology and Threat Perspective

The increasing use of powerful evasive ransomware malware in cyber warfare and targeted attacks is a persistent and growing challenge for nations, corporations, and small and medium-sized enterprises.This threat is evidenced by the emergence of the WhisperGate malware in cyber warfare, which targets organizations in Ukraine to render targeted devices inoperable, and the BlackCat malware, which targets large organizations by encrypting files.This paper outlines a practical approach to malware analysis using WhisperGate and BlackCat malware as samples.It subjects them to heuristic-based analysis techniques, including a combination of static, dynamic, hybrid, and memory analysis.Specifically, 12 tools and techniques were selected and deployed to reveal the malware's innovative stealth and evasion capabilities.This methodology shows what techniques can be applied to analyze critical malware and differentiate samples that are variations of known threats.The paper presents currently available tools and their underlying approaches to performing automated dynamic analysis on potentially malicious software.The study thus demonstrates a practical approach to carrying out malware analysis to understand cybercriminals' behavior, techniques, and tactics.
The Impact of Design-level Class Decomposition on the Software Maintainability

The quality of the software's internal structure tends to decay due to the adaptation to environmental changes.Therefore, it is beneficial to maintain the internal structure of the software to benefit future phases of the software life cycle.A common correlation exists between decaying internal structures and problems like software smell and maintenance costs.Refactoring is a process to maintain the internal structure of software artifacts based on the smell.Decomposition of classes is one of the most common refactoring actions based on Blob smell performed at the source code level.Moving the class decomposition process to the design artifact seems to affect the quality and maintainability of the source code positively.Therefore, studying the impact of design-level class decomposition on source code quality and software maintainability is essential to ascertain the benefits of implementing design-level class decomposition.The metricsbased evaluation shows that the design-level class decomposition positively impacts the source code quality and maintainability with the rank biserial value is 0.69.
Oyster Mushroom (Pleurotus ostreatus) Growth and Production On Straw and Brands Substrate Supplied with Nasa Liquid Organic Fertilizer (POC)

This study aims to analyze the effect of adding NASA Liquid Organic Fertilizer (POC) supplements to the substrate on oyster mushroom (Pleurotus ostreatus) growth and production. The research design used in this study was a completely randomized design (CRD), with four levels of treatment with the addition of NASA POC. The treatment doses used were 0% NASA POC as control (A), 2.5% NASA POC (B), 3% NASA POC (C), and 3.5% NASA POC (D). Each treatment was repeated four times to obtain 16 treatment units. Each treatment unit consisted of 5 Baglogs, so the total sample observed was 80 Baglogs. Based on the parameters observed in this study, including the length of mycelium spread (LPM), the beginning of the emergence of pinheads (AMP), the number of pinheads (JP), the diameter of the oyster mushroom hood (DTJT), and the weight of the oyster mushroom (BBJT), the addition of NASA POC on the substrate. Growth has no significant effect on the growth and production of oyster mushrooms (Pleurotus ostreatus).
A 1-Gram Sentiment Analysis Algorithm for Detecting Cyberbullying in Online Social Networks

Online social networking (OSN) sites in addition to providing business and recreational opportunities are fast becoming a breeding ground for cyberbullying activities. Cyberbullying is an act of harassing or insulting a person by sending messages that are hurting or threatening in nature using electronic communication. Such messages include threats, harassment, and humiliating messages to victims. Other forms are sexual harassments, sexual predating, etc. Cyberbullying poses threat to the physical and mental health of the victims. In this study, sentiments analysis was used to computationally recognize and categorize the opinions, views, and ideas expressed in a piece of text in social media to determine and establish whether the writer's attitude towards a particular topic, person, or a product is positive or negative. The study adopted both quantitative and qualitative approaches. Posts from Facebook were collected and analyzed. The software developed during the research was able detect the presence of cyberbullying in user contents. Results showed promising ability of the software to detect and suspend cyberbullying contents.
A Novel Framework for Semi-supervised Multiple-label Image Classification using Multi-stage CNN and Visual Attention Mechanism

To train deep neural networks effectively, a lot of labeled data is typically needed.However, real-time applications make it difficult and expensive to acquire high-quality labels for the data because it takes skill and knowledge to accurately annotate multiple label images.In order to enhance classification performance, it is also crucial to extract image features from all potential objects of various sizes as well as the relationships between labels of numerous label images.The current approaches fall short in their ability to map the label dependencies and effectively classify the labels.They also perform poor to label the unlabeled images when small amount of labeled images available for classification.In order to solve these issues, we suggest a new framework for semi-supervised multiple object label classification using multi-stage Convolutional neural networks with visual attention (MSCNN)and GCN for label co-occurrence embedding(LCE) (MSCNN-LCE-MIC), which combines GCN and attention mechanism to concurrently capture local and global label dependencies throughout the entire image classification process. Four main modules make up MSCNN-LCE-MIC: (1) improved multi-label propagation method for labeling largely available unlabeled image;(2) a feature extraction module using multistage CNN with visual attention mechanism that focuses on the connections between labels and target regions to extract accurate features from each input image; (3) a label co-existence learning that applies GCN to discover the associations between different items to create embeddings of label co-occurrence; and (4) an integrated multi-modal fusion module.Numerous tests on MS-COCO and PASCAL VOC2007 show that MSCNN-LCE-MIC significantly improves classification efficiency on mAP 84.3% and 95.8% respectively when compared to the most recent existing methods.
Research on Recommendation Model of College English MOOC based on Hybrid Recommendation Algorithm

Establishing a reasonable and efficient compulsory education balance index system is very important to boost the all-around of compulsory education development, and then realize the course recommendation for students with different attributes.Based on this, the research aimed at the problems in college English education and evaluation, aimed to establish a college English MOOC education and evaluation system based on the improved neural network recommendation algorithm.The research first constructed the college English MOOC education and evaluation data elements, and then established a genetic algorithm improved neural network algorithm (BP Neural Network Optimization Algorithm Based on Genetic Algorithm, GA-BP), and finally analyzed the effect of the assembled model.These results show that the fitness of the GA-BP model reaches the set expectation when the evolutionary algebra reaches 10 times, and its fitness is 0.6.The corresponding threshold and weight are obtained, and the threshold and weight are substituted into the model.After repeated iterative training, the model finally reached an error of 10-3 when it was trained 12 times, and the expected accuracy was achieved.The R value of each set hovered around 0.97, and the fitting degree was high, which showed that the GA-BP model proposed in the study had a better fitting degree.The difference between the expected value and the output value is mainly distributed in the [-0.08083, 0.06481] interval.To sum up, the GA-BP model proposed in the study has an excellent effect on college English education and evaluation.This evaluation model has a faster learning rate and a higher prediction accuracy and more stable performance.
Employee Information Security Awareness in the Power Generation Sector of PT ABC

Presidential Regulation No. 82 of 2022 demonstrates the Indonesian government's dedication to protecting Vital Information Infrastructure, which has become increasingly susceptible to cyber attacks.Intrusion detections at PT ABC reached 79,575 in 2021, and malware, botnets, targeted attacks, malicious websites/domains, and ransomware attacks may cause considerable financial losses.The implication of these incidents is that employees' awareness of information security is critical, in addition to security technologies like firewalls and monitoring tools.To enhance employees' knowledge of information security, this study aims to evaluate the information security awareness among PT ABC personnel using the HAIS-Q survey instrument alongside ISO/IEC 27001:2013 criteria.The study will provide valuable recommendations to improve the organization's security protocols.This research intends to investigate the correlation between employees' knowledge, attitude, and behavior towards information security.Data was collected through a questionnaire and analyzed using the Pearson Correlation, Cronbach's Alpha, descriptive statistics, linear regression, and Kruskal-Wallis test method.The study findings suggest that the overall information security awareness level among employees is "Good".However, certain areas like internet usage, information handling, asset management, incident reporting, and the use of mobile devices need improvement.To address these areas, the study recommends promoting information security awareness according to employee categories.
A Review of the Recent Progress on Crowd Anomaly Detection

Surveillance videos are crucial in imparting public security, reducing or avoiding the accidents that occur from anomalies.Crowd anomaly detection is a rapidly growing research field that aims to identify abnormal or suspicious behavior in crowds.This paper provides a comprehensive review of the state-of-the-art in crowd anomaly detection and, different taxonomies, publicly available datasets, challenges, and future research directions.The paper first provides an overview of the field and the importance of crowd anomaly detection in various applications such as public safety, transportation, and surveillance.Secondly, it presents the components of crowd anomaly detection and its different taxonomies based on the availability of labels, and the type of anomalies.Thirdly, it presents the review of the recent progress of crowd anomaly detection.The review also covers publicly available datasets commonly used for evaluating crowd anomaly detection methods.The challenges faced by the field, such as handling variability in crowd behavior, dealing with large and complex data sets, and addressing the imbalance of data, are discussed.Finally, the paper concludes with a discussion of future research directions in crowd anomaly detection, including integrating multiple modalities, addressing privacy concerns, and addressing crowd monitoring systems' ethical and legal implications.
Monetary Unification and Trade Outcome: Evidence from East African Community

"This paper examines monetary unification and trade outcome in the case of East African Community (EAC) using the Vector AutoRegressive (VAR) and Augmented Gravity Models for the period 1960-2016. We found that all countries in the EAC have challenges meeting the macroeconomic convergence targets, but Burundi is far from achieving most targets anytime soon. After separating various shocks using VAR, the cross-country correlation estimates show asymmetry of supply and monetary shocks despite external and demand shocks dominating; implying that the region is not an Optimum Currency Area. However, the gravity model shows that monetary unification itself will boost intra-EAC trade to around 60% (a factor of 4) from its current 14%. Thus, between 2006 and 2015, total trade with other EAC countries as percentage of total trade in Burundi averaged 22.5%. It was 30.5% in Rwanda and only 6.1%; 8.4% and 17.4% in Tanzania, Kenya and Uganda respectively. The three countries recorded more exports than imports in EAC leading to an overall intra-regional trade surplus balance while Rwanda and Burundi recorded overall intra trade deficit, as they import more from the EAC than they export. As a result, this study reveals that monetary unification may increase trade among member countries in the EAC since the trade creating effects will offset the business cycle shocks."
Evolutionary Design of a PSO-Tuned Multigene Symbolic Regression Genetic Programming Model for River Flow Forecasting

The earth's population is growing at a rapid rate, while the availability of water resources remains limited.Water is required for various purposes, including drinking, agriculture, industry, recreation, and development.Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity.Developing precise river flow forecasting models can greatly improve the management of water resources in many countries.In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States.In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model.In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters.Fine-tuning the MG-GP parameters improves the prediction accuracy of the model.The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively.
Intersymbol interval method for hiding secret information based on pseudo-random number sequences

In the article, an efficient method of hiding secret information in text-type containers based on changing inter-symbol intervals is suggested. Here, the hiding process is performed by selecting the symbols of the container in a pseudo-random sequence to increase reliability. To obtain a pseudo-random sequence the linear congruent method and Feigenbaum's quadratic function were used.