Reviewerly/exHarmony huggingface dataset:
Exploring Students’ Adoption of Virtual Reality in Journalism Practice Education: An UTAUT Perspective

Virtual reality (VR) has been widely adopted in education to equip students with the knowledge and skills they need to better adapt to the complex industry that operates today. The factors that affect the adoption of new learning technology in journalism education in Hong Kong, however, are not known because of the limited literature on the subject. This study explored students’ motivations by focusing on the factors influencing the acceptance of VR in journalism education at a Hong Kong-based university. The unified theory of acceptance and use of technology (UTAUT) model was used as the theoretical framework. Focus group discussions and surveys with the students who took VR classes were adopted as the research method. We contribute to the literature on students’ technology adaptation by examining their performance expectancy, effort expectancy, perceived social influence, and the university’s facilitating conditions. Five points of recommendations are made to researchers and educators of VR to enhance its effectiveness in journalism education.
Development of E-Modules Using Problem-Based Learning Flipbook Software on Class X Chemistry Material

The purpose of this study is to assess the viability of the Problem Based Learning learning model, which was created using flipbook software on chemistry content for class X even semester based on BSNP, and integrated with the E-Module. This report on research and development (R&D) employs the ADDIE
Quantum Computing: Applications and Future Trends

Quantum computing has emerged as a promising field with the potential to revolutionize various aspects of computation. This review aims to provide a comprehensive overview of the current state of quantum computing research. The primary question addressed in this review is: What is the current progress and potential applications of quantum computing? The review encompasses a wide range of major studies conducted in the field of quantum computing. It explores the fundamental principles of quantum mechanics that underlie quantum computing, including superposition and entanglement. Various quantum computing architectures, such as gate-based quantum computers and adiabatic quantum computers, are discussed, along with their advantages and limitations. The review also examines different quantum algorithms and their applications, including Short’s algorithm for factoring large numbers and Grover's algorithm for searching unsorted databases.
Semantic Document Indexing With Generative AI

The possibilities of processing abstract information using generative artificial intelligence systems, in particular ChatGPT, are presented to solve the problems of generating semantic maps, semantic indexing, analysis and visualization, which makes it possible to consider such systems as a useful analytical tools. The ChatGPT system was used to automatically extract basic concepts from documents from a thematic array on the topic of information technology in construction, i.e. perform semantic indexing, as well as build a semantic network for the selected subject area. A working model has been implemented that allows you to find relevant records by clicking on nodes and edges of the constructed semantic network, i.e. navigate through the source information array.
Mechatronic gripper system and delivery platform for the autonomous cargo transport with unmanned aerial vehicles

For intralogistics material transport with unmanned aerial vehicles (UAVs), special pickup and release devices are necessary to guarantee a reliable and efficient transport process in the field of application. Therefore, the paper presents a process-reliable transport system comprising a mechatronic gripper and a landing platform to conduct autonomous cargo pick-up and delivery. The presented configuration was used with a commercial UAV for the autonomous product handover between defined locations. To compare the performance against conventional methods of transporting small goods, different scenarios were carried out. The potential time saving is verified through comparisons with conventional conveyor systems. The cooperation of drone fleets and multi-UAV monitoring by a single operator are also discussed.
A study of blockchain adoption in the rail sector

This paper investigates the possible advantages deriving from blockchain adoption in railways. After reviewing the literature on blockchain applied to the sector, we examined its early applications in railways. We then identify areas where this technology can provide benefits for freight and passenger transport and application expected by academic literature and industrial applications. Cross-border operations, logistics, data distribution, MaaS, traffic management, signalling, and maintenance systems are amongst the topics with a higher overlapping between academia and industry. Lastly, the paper proposes areas such as LCC management, GHG production, MaaS functionalities and sovereign digital identity for further evaluating possible applications of the technology to the sector. Future research must provide a cross-sectoral overview to evaluate a possible EU-wide blockchain platform.
Developing personas to improve understanding of users’ needs in digital mobility: An experience of the DIGNITY project in the Barcelona Metropolitan Area

This paper contributes to the understanding of the end users' needs and capabilities in digital mobility by presenting a set of personas developed using data from a population-representative survey conducted among 601 Barcelona Metropolitan Area (BMA) residents. The questionnaires were carried out within the framework of the DIGNITY project. The results show that large parts of the population cannot access digital technologies or lack the skills required to use them effectively. As a result, they are not able to take advantage of many digital mobility solutions. The personas in this paper thus present important information on the diversity of potential users, in a way that designers and other stakeholders can find inspiring. This can help them to create inclusive mobility ecosystem, that fit the users' needs better, resulting in more people being able to use the solutions.
Trends in Motion Sickness Countermeasures for Autonomous Driving: review and future research

Automation raises fundamental questions, particularly regarding the driver-vehicle interaction. Increasingly diversified in-vehicle assistive systems will be at the disposal of the vehicle occupants; these will allow drivers to disengage from the driving task. Instead of keeping their eyes on the road, occupants will be free to engage in other tasks besides driving. However, this will most likely increase the levels of Motion Sickness (MS) experienced inside the car or, as it has been termed, autonomous carsickness or self-driving carsickness. The main objectives of this work were to: (1) present and review available countermeasures present in the literature intending to prevent or mitigate MS that, additionally, could be implemented inside a car; (2) identify current trends in countermeasures to MS and, (3) suggest future avenues of research for potential use-cases aiming to mitigate motion sickness in autonomous driving. Our research shows that multimodal approaches, exploring the application of different types of countermeasures simultaneously, are possible, and might be a solution for passengers travelling in future autonomous cars.
The Content of the Decision Support System for Assessing the Labor Intensity of the Development of Software Products Under Uncertainty

In [1], the structure of a decision support system was proposed to assess the complexity of software development in uncertainty. This article presents materials on its content. 2 variants of the content of the DSS are considered. The first option is based on the use of neural networks, the second is based on the use of the Pert method. The choice of the second option is justified. The possibility of using the assignment problem model to find the optimal distribution of work between performers is considered.
Piloting Virtual Field Trips During a Pandemic: Developing Curriculum, Lessons Learned, and Recommendations

Discover how to create a virtual field trip program at an informal science institution to support students during the pandemic.
Improving MaxSAT-Reordered Plans via Block Deordering

Partial-order plans (POPs) provide greater flexibility during execution compared to sequential plans due to their least commitment nature. Optimizing flexibility in a POP involves strategies such as plan deordering, which eliminates unnecessary action orderings, and plan reordering which modifies action orderings arbitrarily. Though traditional plan deordering techniques, such as EOG (explanation-based order generalization), can efficiently find partial orderings in polynomial time, they lack optimality guarantees. This limitation prompts MaxSAT reorderings to encode the optimization of a POP’s orderings as a partial weighted MaxSAT problem. To further elevate the flexibility of the MaxSAT solutions, this work introduces an algorithm that employs block deordering, a distinct form of plan deordering that consolidates coherent actions into blocks, on top of MaxSAT reorderings. Our experiments with benchmark problems from the International Planning Competitions demonstrate that our algorithm not only makes significant enhancements to the satisfiable MaxSAT reordered plans but also takes it a step further by improving the optimal reordered plans in mere seconds.
Energy and Latency-aware Computation Load Distribution of Hybrid Split and Federated Learning on IoT Devices

Split learning (SL) and Federated Learning (FL) are popular distributed learning frameworks used to increase data privacy and reduce computation loads of Internet of Things (IoT) devices. However, one of the major challenges of distributed learning on IoT devices is determining the portion of computation load to be assigned for the devices compared to the server side. The contributions of the existing works in the literature are either limited by consideration of homogeneous resources available at all IoT devices or by not distributing computation loads among the devices in an efficient way. In this paper, we propose an adaptive clustering-based computation load distribution method for IoT devices, with heterogeneous resource capacities, participating in the model training. The clustering makes the optimal determination of the split point of the learning model, which is scalable even for a large number of devices. The numerical evaluation of the proposed learning model implemented using Python 3.0 and the comparative performance results show that the proposed load distribution policy for the learning models reduces the time by 160 times on average compared to the usual brute force method.
Development of a Scale to Measure the Parental Competency of Science-Gifted Students in South Korea

This study aimed to develop and validate a scale of parental competency of science-gifted students in South Korea. The parental competency of science-gifted students is constructed in three dimensions (Learner, Fosterer, Scientist) from the literature review. We in-depth-interviewed science-gifted students and parents to discover the competency behavior indicators. To confirm the content validity, 15 experts in Korea conducted a Delphi method. We were determined lifelong learning, information utilization as the sub-elements of learner, partnership, parenting attitude, empathy of fosterer, and science capability, responsibility in science, and career preparation of scientist. As a result, we developed the initial instrument with 45 items. To validate the instrument, we carried out an online survey with 512 parents. Finally, 23 items of the measurement instrument for parental competency underwent development with three dimensions and eight sub-dimensions. This study provides a reliable instrument for parental competency and contributes to its enhancement for science-gifted students.
A Survey on Optimization of Multi-criteria RBFN

Literature review plays a very important role to the success of any research work. It involves perusing works done by other scholars in the research area. It forms the basis for understanding the research topic and helps to bring to light recent works done in the research area and areas still seeking for academic attention. Literature review surveys academic articles, books, and other sources relevant to a precise area of research. These sources provide adequate information to assist a researcher have a good overview of the research area and form an opinion as to which direction his/her research should take. These sources of literature have their different characteristics as well as their individual strengths and weaknesses. In view of this, it is recommended that, the various sources are used to complement each other in a research process. In this article, the authors examine different works done in the area of Radial Basis Functions Networks (RBFs) by reviewing different sources of literature. Radial basis function networks (RBFNs) $$\varphi \left( r \right)$$ are univariate continuous real valued functions whose output depends on only the distance between a point and a fixed point called centre. They are regarded as a class of feed forward networks with universal approximation capabilities. This makes RBFNs an exciting area in mathematics and thus beginning to receive lots of attention.
Green Cloud Computing: A Sustainable Energy-Efficiency Approach for Business Rapidity and the Environment

Cloud computing is significantly assisting in addressing IT issues. To lessen the environmental impact, a thorough investigation of cloud computing and energy-efficient algorithms is required. A green cloud computing strategy aims to build an environment for cloud computing that is more energy-efficient, maximizes resource consumption without overprovisioning, leaves fewer carbon footprints, reduces Greenhouse Gas emissions, and minimizes E-wastages. Some features and techniques that Green Cloud Computing focuses on bringing to the cloud environment include containerization, designing green data center facility infrastructure, server consolidation, virtual machine placement, designing green networks and protocols, green data management, sustainable software development, and safe disposal techniques with the three R’s (Reuse, Reduce and Recycle). This paper offers a thorough examination of green cloud computing and its traits. The main objective of this study is to investigate green cloud computing, including its various components and conversion processes, as well as to compare energy-efficient VM placement techniques for green cloud computing.
A Machine Learning-Based Approach for Sentiment Analysis of Movie Reviews on a Bangladeshi OTT Platform

Sentiment analysis is the examination of feelings and viewpoints in any kind of literature. Opinion mining is another phrase for sentiment analysis. The data’s sentiment analysis is quite helpful, to convey the collective, group, or individual viewpoint. This method is employed to ascertain a person’s attitude about a specific source. Huge amounts of data are present on social media and other online platforms in the form of tweets, blogs, status, postings, etc. The movie reviews were examined in this research using a variety of methods. On demand of movies on OTT platform several Facebook reviewer pages has been created in Bangladesh. For this work, almost 1000 Bangla reviews were gathered, containing some English word from Facebook. Customer tones were assessed in movie reviews. We use Unigram, Bigram, and Trigram features with a variety of models, including Decision Tree, Random Forest, Multinomial Naive Bayes, K-Neighbors, and Linear Support Vector Machine in n-grams. Random Forest is the most accurate, with 92.35 percent and 90.03 percent accuracy in Unigram and Trigram, respectively. The most accurate model in Bigram is Decision Tree, which has an accuracy of 89.50 percent. This proposed system will help to analysis reviews and give feedback about a movie.
Load Balancing in Cloud Environment Using Different Optimization Algorithms and Open-Source Platforms: A Deep Picture

Cloud computing is a very vast area and become very popular in IT industries and academics. It has many challenges that load balancing is a critical/Important issue. Load balancing is a very interesting topic for researchers. In this article, we are studying cloud computing, load balancing, Virtualization, Optimization algorithms of evolutionary and swarm-based algorithms, and open-source Cloud Platforms, with different-different parameters.
Energy Efficient Distributed Cloud Load Balancing Using Virtual Machines

Load balancing is a crucial element of any distributed system because it serves to spread workloads equally throughout numerous computing resources aiming to optimize speed and efficiency. However, traditional load-balancing techniques often consume a significant amount of energy, which can lead to increased operating costs and negative environmental impacts. We propose a dynamic round-robin approach for cloud load balancing in this paper to alleviate the strain on wireless networks. Our algorithm achieves 8% more efficiency compared to the traditional round-robin model. Additionally, our load-balancing virtual machine architecture exhibits an improvement in resource usage efficiency ranging from 15 to 42% compared to the current model. Overall, our model significantly increases the efficiency of the current load-balancing method.
A Hybrid Approach for Improving Task Scheduling Algorithm in the Cloud

Energy consumption is a big problem for cloud computing. Different task-scheduling algorithms in cloud computing take a different amount of energy, resources, and time for executing. In this paper, our goal is to minimize the consumption of energy within a short executing time and make a high resource utilizing a task scheduling approach which will help to make sustainable green cloud computing. The common scheduling is particle swarm optimization (PSO) and ant colony optimization (ACO). This algorithm is used in the cloud. In this paper, our goal is to combine these two algorithms and make a new approach for scheduling which performance is better than the algorithms. So, our new approach is the PSAC algorithm which is a combination of PSO and ACO. PSAC approach reduces energy consumption and takes less time for execution. It also uses more resources than two algorithms. The result of the new hybrid algorithm takes less energy so energy consumption is reduced and less time for task execution. Moreover, it can utilize more resources. The result shows that the hybrid PSAC algorithm can perform the same number of tasks using lower energy and execution time than the PSO and the ACO algorithm and also utilizes more resources than the two algorithms.
Application of Analytical Hierarchy Process (AHP) in Assessing the Risk of COVID-19 Contraction by the Urban Public Through Transport Services

Transport has emerged as one the factors increasing the risk of contracting COVID-19 virus hence the study deliberated on determining the most safe transport people can use during the pandemic period. This was determined amongst five transport operators in the city of Bulawayo, Zimbabwe which include, Zimbabwe United Passenger Company (ZUPCO) Buses, ZUPCO Minibuses, ZUPCO Omnibuses and Non-ZUPCO operators. Data was collected through a combination of independent observations. Observations were based on administration of COVID-19 prevention measures thus Temperature screening (TS), Sanitizer Administration (SA), Social Distancing (SD), Disinfection (D) and Proper Mask Wearing (PMW). These were in five categories ZUPCO Buses (N = 3452), ZUPCO Omnibus (N = 4450), ZUPCO Minibuses (N = 1339), Non-ZUPCO Omnibuses (N = 3950) and Pirating small cars (N = 2300). Binary recording system was used to capture administration of COVID-19 prevention measures (1 for administration and 0 otherwise). Analytical Hierarchy Process (AHP) was used to rank the transport operators in terms of COVID-19 prevention measures criterions. The computed composite weights are as follows ZUPCO buses (0.169), ZUPCO Omnibuses (0.174), ZUPCO Minibuses (0.290), Non-ZUPCO Omnibuses (0.052) and Pirating small cars (0.310). It was observed that Pirating small cars are the safest transport to use, however very few private car owners were willing to offer the service during the peak of the pandemic. This means the need for considering other alternatives in the following order of safety ZUPCO Minibuses, ZUPCO Omnibuses, ZUPCO Buses and lastly Non-ZUPCO Omnibuses. The contributions of this study are as follows (i) transport users are informed on which transport category has least risk of contracting COVID-19 and (ii) policy makers and law enforcement agents are hinted on grey areas in relation to transport operators that are lagging behind in terms of implementation of COVID-19 prevention measures.
Green Task Scheduling Algorithm in Green-Cloud

Cloud-dedicated servers can better meet green computing standards by being ecologically friendly. “Green cloud computing” refers to utilizing information technology and other technological achievements to help the environment. Task scheduling is one of the biggest challenges in cloud-based systems that must be addressed to improve system efficacy and user experience. The primary goal of this study is to develop an algorithm that focuses on minimizing green cloud computing execution times while remaining environmentally friendly. We compared task scheduling algorithms based on execution time, such as FCFS, SJF, and Round Robin, to the approach we recommended, the generalized priority (GP) algorithm. We experimented with evaluating our technique using the CloudSim 3.0.3 simulator. The algorithm we suggested has the shortest runtime out of all the algorithms which is 97.91.
No Simple Solutions to Complex Problems: Cognitive Science Principles Can Guide but Not Prescribe Educational Decisions

Cognitive science of learning points to solutions for making use of existing study and instruction time more effectively and efficiently. However, solutions are not and cannot be one-size-fits-all. This paper outlines the danger of overreliance on specific strategies as one-size-fits-all recommendations and highlights instead the cognitive learning processes that facilitate meaningful and long-lasting learning. Three of the most commonly recommended strategies from cognitive science provide a starting point; understanding the underlying processes allows us to tailor these recommendations to implement at the right time, in the right way, for the right content, and for the right students. Recommendations regard teacher training, the funding and incentivizing of educational interventions, guidelines for the development of educational technologies, and policies that focus on using existing instructional time more wisely.
Enhancing Sustainable Urban Landscapes through AI-Driven Low-Carbon Plant Selection: A Novel Approach

In the face of global environmental challenges, sustainable urban landscapes play a pivotal role in mitigating carbon emissions and fostering healthier cities. This paper presents a groundbreaking approach to low-carbon plant selection for landscape architecture, leveraging AI technology to address the limitations of existing language models in the field. Through the integration of professional databases, the introduction of judgment modules, low-carbon plant landscape recommendation modules, general dialogue modules, the use of prompt engineering technology, fine-tuning technology, our system offers precise, ecologically sound recommendations for low-carbon landscape designs.