Reviewerly/exHarmony huggingface dataset:
Exploring External Stakeholder Engagement Strategies and their Impact on Project Success

In the past few years, it can be observed that business norms and practices have changed and the market has now a wide circle of stakeholders as compared to past eras. The main purpose of the current study is to explore the impact of ESE strategies on project success in the IT industry (Information Technology) of Pakistan. The study is based on mixed methods. In the first phase, an in-depth Systematic Literature Review (SLR) was performed. In the next phase, face-to-face &amp; virtual interviews were conducted with senior IT leads to develop various themes. Moreover, a word cloud and frequency chart was extracted using NVivo 12 to strengthen the argument. The findings show that there is a strong relationship between stakeholder engagement and project success in the IT industry. Likewise, the findings of the study suggest that a successful Strategy for External Stakeholder Engagement (SESE) is possible by using a collaborative and participatory approach. The study provides greater insights into the engagement of external stakeholders. Moreover, the study recommends exploring new means of communication that remain helpful in engaging stakeholders and should be aligned with the local and organizational culture.
Variational Amplitude Amplification for Solving QUBO Problems

We investigate the use of amplitude amplification on the gate-based model of quantum computing&#x0D; as a means for solving combinatorial optimization problems. This study focuses primarily on&#x0D; QUBO (quadratic unconstrained binary optimization) problems, which are well-suited for qubit superposition states. Specifically, we demonstrate circuit designs which encode QUBOs as ‘cost oracle’&#x0D; operations UC, which when combined with the standard Grover diffusion operator Us lead to high&#x0D; probabilities of measurement for states corresponding to the optimal and near optimal solutions. In&#x0D; order to achieve these probabilities, a single scalar parameter ps is required, which we show can be&#x0D; found through a variational quantum-classical hybrid approach.
Edge Computing Systems for Streaming Video Analytics : Trail Behind and the Paths Ahead

The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.
STNWeb: A new visualization tool for analyzing optimization algorithms

STNWeb is a new web tool for the visualization of the behavior of optimization algorithms such as metaheuristics. It allows for the graphical analysis of multiple runs of multiple algorithms on the same problem instance and, in this way, it facilitates the understanding of algorithm behavior. It may help, for example, in identifying the reasons for a rather low algorithm performance. This, in turn, can help the algorithm designer to change the algorithm in order to improve its performance. STNWeb is designed to be user-friendly. Moreover, it is offered for free to the research community.
IoT-Based Smart Wheelchair for Disabled People and Patient Monitoring

Smart wheelchairs equipped with patient monitoring technology can provide enhanced safety and healthcare to disabled individuals. These wheelchairs use sensors and monitoring devices to collect data on vital signs, such as heart rate and blood pressure, and alert caregivers or medical professionals in case of abnormalities. In addition to patient monitoring. The integration of patient monitoring technology into smart wheelchairs has the potential to revolutionize healthcare for disabled individuals, enabling remote monitoring and timely interventions in case of medical emergencies. Overall, the combination of smart wheelchair technology and patient monitoring has the potential to improve the quality of life and healthcare outcomes for disabled individuals.
Automated Face Mask Detection using Artificial Intelligence and Video Surveillance Management

Surveillance camera has become an essential, ubiquitous technology in people's daily lives, whether applicable for home surveillance or extended to public workplace detection. The importance of the camera is irreplaceable in terms of the agent for an enclosed system to function correctly. The goal of ubiquitous computing is to keep different devices or technology communicating seamlessly, allowing them to expand to other areas instead of limiting it to one device. However, many research papers have been released on how the camera can aid in the current situation where COVID-19 is still raging worldwide, especially in crowded places. This paper aims to suggest a method by which surveillance cameras on the university campus can automatically detect student face mask status and notify them. Alongside that, this concept of applying a video management system within the university campus will assist in the automation of invigilating the student's daily mask status from the number of embedded surveillance cameras around the campus.
Advanced Optimization Techniques &amp; Its Application in AI-Powered Breast Cancer Classification

In this paper, an advanced optimization technique will be used to find the cut-off of base model(s) and meta model along with the weights of the weighted blending. In this work, XGBoost, Random Forest, Logistic Regression have been used as the base model and also K-Fold cross validation has been used to capture the average score of individual base model. Here F-score will be used to assess the goodness of the models. The techniques have been applied for classification of Breast Carcinoma which is the one of the most prevailing diseases that thrives amid the human beings over decades. According to a report, published in March '21, in the web site of WHO, in 2020, about 2.3 million women diagnosed with breast cancer and according to International Agency for Research on Cancer (IARC) in December 2020, breast cancer has overtaken the lung cancer and has reached at the top position as a commonly diagnosed cancer. In order to determine the breast carcinoma, breast tumors are classified into two categories which are tagged as malignant or benign. For this study the WBCD dataset has been used as the dataset that contains 569 records derived from Fine Needle Aspirates (FNA) of human breast masses has no missing value and is a balanced dataset which minimizes the data pre-processing and EDA steps. In the Optimized weighted Blending, the F-1 Score goes maximum 0.99 (approx.) compared to other approaches within our scope.
Online Active Learning for Evolving Error Feedback Fuzzy Models within a Multi-Innovation Context

In data stream modeling problems, online active learning plays an important role for reducing model update times and costs (or efforts) associated with measuring and collecting target values, which are typically required for supervised updates in evolving fuzzy models. We propose an online active learning (oAL) approach for evolving error feedback fuzzy models (EEF-FM), which integrate an auto-regressive (AR) based noise correction component on consequent hyper-planes to gain higher robustness of predictions, especially in the case of measurement noise. Thereby, we first propose a multi-innovation based recursive update scheme for the consequent parameters, substituting conventional recursive fuzzily weighted least squares (RFWLS) approach in order to reduce the sub-optimality of consequent parameters in the case of structural changes in a fully single-pass manner <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> EEF-FM-MI. Our oAL technique applies a sample selection strategy in a fully single-pass manner to elicit those samples which are expected to be most important for improving the robustness of model parameters and enriching the model structure when being used for model updates. It is based on three criteria: i) maximization of the D-optimality criterion in the consequent space for addressing the problem to best reduce the parameter uncertainty; it relies on the determinant of the Hessian matrix, which is updated and represented in a multi-innovation context, where a threshold for selection is automatically derived by kernel density estimation; ii) overlap degree in the antecedent space for the purpose to ‘sharpen’ the local trends in the transition regions between the hyper-planes; iii) novelty content in the antecedent space for indicating required knowledge expansion through rule evolution. The results on noisy real-world data sets showed i) that multi-innovation RFWLS could significantly outperform conventional RFWLS in all cases, with achieving an even more significant out-performance of classical EFS (not using any AR-based correction component), ii.) that our oAL strategy was able to reduce the measurement effort by up to 90% with a small increase in the error trend lines, and this with about quartering the computation times for model updates.
Comparing the Lexical Density of Spoken and Written News

This paper is concerned with lexical density in spoken and written news on BBC News. The purpose of this study is to investigate the lexical density found in spoken and written news regarding the Afghanistan and Taliban conflict broadcasted by BBC. The method applied in this study was a qualitative method. The data consist of five spoken news taken from the BBC YouTube channel and five written news obtained from the BBC news website. The results show that generally, the average lexical density levels of both spoken and written news tend to be the same, that is, higher than 50%. Specifically, spoken news has an average lexical density level of 52.15% while written news has an average lexical density of 55.05%. Meanwhile, the lexical items which influence the density of the texts are nouns, verbs, adjectives, and adverbs with the noun to be dominant.
SceneGATE: Scene-Graph Based Co-Attention Networks for Text Visual Question Answering

Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image–question context, such as the brand name of a product or the time on a clock from an image. Most TextVQA approaches focus on objects and scene text detection, which are then integrated with the words in a question by a simple transformer encoder. The focus of these approaches is to use shared weights during the training of a multi-modal dataset, but it fails to capture the semantic relations between an image and a question. In this paper, we proposed a Scene Graph-Based Co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, the Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We create a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To permit explicit teaching of the relations between the two modalities, we propose and integrate two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conduct extensive experiments on two widely used benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperforms existing ones because of the scene graph and its attention modules.
THE INTERPLAY OF TEACHERS’ BELIEFS, ATTITUDES, AND THE IMPLEMENTATION OF DIFFERENTIATED INSTRUCTION IN INDONESIAN EFL CONTEXTS

The aims of this study were to examine the relationship between teachers’ beliefs and attitudes about differentiation and the implementation of this approach in English as Foreign Language (EFL) classrooms in Indonesia. The study also aimed to examine the impact of these practices on student engagement and motivation. A total of 100 teachers from several different schools participated in the study which were scattered from the major islands in Indonesia, and the data were collected using a self-report questionnaire that was administered online by provided with link. Correlations analysis were conducted to examine the relationships between the variables. The results showed that teachers’ beliefs and attitudes about differentiation had a strong positive correlation with the implementation of this approach in their classrooms (r = 0.75, p 0.001). in addition, professional development in differentiation was positively correlated with teachers’ beliefs and attitudes about this approach (r = 0.65, p 0.001). The results also indicated a strong positive relationship between teachers’ beliefs and attitudes about differentiation and student achievement in EFL classrooms (r = 0.80, p 0.001). These findings suggest that teachers’ beliefs and attitudes about differentiation play a significant role in their implementation of this approach in the classroom, and that differentiated instruction has the potential to improve student achievement and engagement in EFL classroom in Indonesia. However, further research with a larger and more diverse sample is needed to fully understand these relationships in the Indonesian context.
PREDICTION OF STUNTING PREVALENCE IN EAST JAVA PROVINCE WITH RANDOM FOREST ALGORITHM

Stunting or cases of failure to thrive in toddlers is one of the most serious health problems faced by the people of Indonesia. Based on data from the Ministry of Health and the Central Statistics Agency, East Java Province has a stunting prevalence value of 26.8% which is categorized as a high prevalence value according to the standards of the World Health Organization (WHO). Random forest is one of the machine learning algorithms in the field of artificial intelligence that can learn patterns from labeled data so that it can be used as a method for predicting or forecasting data. This approach is considered very suitable to be used in predicting the value of stunting prevalence because stunting prevalence data is usually accompanied by other data in the health sector according to survey results. Previous studies on the prediction of stunting prevalence used secondary data sourced from one survey only. Therefore, this study is one of the efforts to contribute in providing solutions for the stunting problem in East Java Province by combining several data from different surveys in the same year. The results of this study show that from 20 factor candidates for predicting stunting prevalence value, only 12 factors are suspected to be causative factors based on their correlation value. However, the prediction results obtained using the random forest algorithm in this study, with data consisting of 12 features and a dataset consisting of only 38 data, have results with error values of 1.02 in MAE and 1.64 in MSE that are not better than multi-linear regression which can produce smaller error values of 0.93 in MAE and 1.34 in MSE.
Research Trends of selected articles on Modern Instructional Materials and Academic Learning

This study examined the implications of modern instructional materials in academic learning. The criteria outlined in this study were applied to previous studies that could be analysed. The researchers looked at 100 studies that met the inclusion criteria and were conducted between 2012 and 2022. The researchers looked for studies that supported their argument from all over the world, but they focused primarily on Asia and Africa. Furthermore, the researchers sourced their articles from national and international databases such as "Science Direct," "Springer Link," "Educational Resource Information Center (ERIC)," "Google Scholar," and "JSTOR." They then used them to extract data so that they could compare and draw conclusions. The descriptive approach-based analysis produced the following results: 93 out of 100 articles stated that the use of modern instructional materials is beneficial, while 7 out of 100 studies stated that it is not beneficial in their learning, and that there are phenomenon and factors that affects the effectiveness of modern instructional materials. Many researchers believe that using modern instructional materials is beneficial in a variety of ways, and only a few do not. The researchers then investigated the underlying factors that caused it. These factors include their age, year of birth, environment, culture, and so on.
Perbandingan Evaluasi Metode Davies Bouldin, Elbow dan Silhouette pada Model Clustering dengan Menggunakan Algoritma K-Means

One of data mining model that is often used is the clustering model. The clustering model is used to create a grouping of a datasheet. Data clustering can be used to distinguish data in a datasheet. Many of the best groupings can be done with the clustering model evaluation process. In the clustering method, the evaluation process can use various evaluation methods. The research will cluster the results of a survey on reviews of tourist destinations consisting of 10 categories. The method used is a data mining method, namely Knowledge Discovery in Database (KDD). Stages in KDD include data selection, data cleaning, transformation, data mining process and evaluation of model results. The process of creating a clustering model uses a public datasheet, namely tripadvisor_review.csv. The data clustering process uses the K-means algorithm. The result of the clustering will be tested by comparing evaluation methods. This evaluation method is used to select the best amount of clustering. Testing to get the best clustering results is conducted by testing from clustering 2 to 15. The evaluation uses Davies Bouldin, Elbow and Silhouette methods. The result shows that the number of datasheet groupings with the three evaluation methods provides recommendations for grouping as many as 2 groups.
Understanding the Metaverse: A Review of Virtual Worlds and Augmented Reality Environments

This review paper attempts to provide a comprehensive understanding of the metaverse by analyzing virtual worlds and augmented reality environments. The metaverse is a virtual space in which users can interact with a computer-generated environment and other users in real time. With the rapid growth of technology, virtual reality (VR) and augmented reality (AR) have attracted major attention, providing immersive and engaging experiences. The paper looks at the key elements of virtual worlds and augmented reality environments, such as their technological foundations, user experiences, and societal implications. The paper also considers the potential societal impact of the metaverse, delving into areas such as education, entertainment, business, and communication. This study gives insights into the current state of the metaverse, challenges, and future possibilities by assessing current examples and trends. This paper can assist individuals, industries, and policymakers to better understand and navigate the evolving landscape of virtual worlds and augmented reality environments, allowing for more informed decisions and strategies in this rapidly evolving domain.
IMPORTANCE OF CYBERSECURITY AWARENESS TRAINING FOR EMPLOYEES IN BUSINESS

In today's digitally connected business landscape, the increasing reliance on technology exposes organizations to a growing number of cyber threats and security breaches. Cybercriminals exploit vulnerabilities in systems and networks, putting sensitive data, financial assets, and company reputation at risk. To fortify cybersecurity defenses effectively, organizations must recognize the pivotal role of employees. This article emphasizes the significance of cybersecurity awareness training, empowering employees as the first line of defense against cyber attacks and preserving customer trust. A systematic literature review reveals that training employees has a positive impact, reducing security incidents and fostering a culture of cybersecurity consciousness. Tailoring training for remote work enhances the organization's resilience. A holistic security strategy integrating technical measures and policies is crucial. By investing in comprehensive and ongoing cybersecurity awareness training, businesses can proactively protect assets and maintain a secure position in the digital age.
HIGH PERFORMANCE WORK SYSTEM AND ORGANIZATIONAL INNOVATION: THE MEDIATING ROLE OF INNOVATIVE WORK BEHAVIOUR

This research aims to examine the influence of High Performance Work System (HPWS) on organizational innovation Pakistan, with a focus on the mediating variable of Innovative Work Behavior (IWB) in the health sector of Pakistan. The study's sample comprises 530 doctors from the public and private hospitals within Sindh province of Pakistan both sectors. Data analysis was conducted using Smart PLS. The findings revealed that the intervening effect of IWB was partial, indicating its significant role in facilitating the correlation of HPWS with organizational innovation. Furthermore, the statistical hypotheses were accepted, confirming the presence of HPWS indicated positive and significant impact on both IWB and organizational innovation. The findings cast light on the significance of implementing HPWS in hospital settings to cultivate a culture of innovative work behavior, thereby enhancing organizational innovation. The current research adds to the existing reservoir of knowledge by providing empirical evidence on the mechanism by which HPWS influences innovation-related outcomes. These insights have practical implications for hospital management, emphasizing the need to prioritize and support HPWS practices to foster a more innovative work environment among healthcare professionals.