Reviewerly/exHarmony huggingface dataset:
Implementation Of Cooperative Learning Method Type Jigsaw For Improving Skill Of Speaking English Of Cadet At Barombong Maritime Polytechnic Makassar

The purpose of this study is to know the effectiveness of implementation of cooperative learning method type jigsaw to improve skill of speaking English of cadet at Barombong Maritime Polytechnic (Politeknik Pelayaran Barombong) through a class action research (penelitian Tindakan kelas) during 3 cycles.Based on the result of research, researcher is able to conclude that implementing cooperative learning method type Jigsaw effectively improving skill of speaking English. This can be seen from scoring result in every cycle getting higher. The score in cycle II is higher than score in cycle I, while the score in cycle III is higher than score in cycle II. Finally, researcher is able to state “If cooperative learning method type Jigsaw is implemented in English class, the skill of speaking English of student will be improved”.&#x0D;
The Causes of Workplace Conflicts among Teachers’ Performance: A Case Study of Government - Aided Secondary Schools in Bukonzo County, Kasese District

The study sought to establish the effects of workplace conflicts on teachers’ performance in the government –aided secondary schools in Bukonzo County, Kasese District. It focused on establishing the categories, causes and the effects of workplace conflicts on teachers’ performance in the government –aided secondary schools in Bukonzo County Kasese District. A non-experimental descriptive survey design was adopted which used interviews and structured self-administered questionnaires to collect data from a sample of 178 participants. The head teachers, deputy head teachers and directors of studies were sampled using purposive sampling and the teachers were sampled using stratified random sampling. This sample comprised of 11 head teachers, 11 deputy head teachers, 11 directors of studies and 145 teachers. The schools were sampled using census sampling. The causes of workplace conflicts are said to be scarcity of resources, misappropriation of funds and role ambiguities. In light of the above findings, the following recommendations were made; school administration should find out the root causes of workplace conflicts in the government -aided secondary schools and involve all the stakeholders in the school management in order to enhance discipline for effective conflict management strategies to be put in place. Keywords: Causes, workplace, hazards, conflicts, secondary schools, Uganda
Research in China and Abroad

Software is a technical discipline that takes computer software as the object and studies its paradigm, development method s, operation support, and quality metrics evaluation. The foundation of trustworthy softwareTrustworthy software research is software trustworthinessSoftware trustworthiness. It performs basic research on software and related interdisciplinary studies. Software engineering is its basic content.
Impact of Factory Reset on Android Devices

The usage of smartphones is increasing day by day compared to other gadgets, and as a result, the devices have become common exhibits in most criminal investigations. Forensic analysis of smart phones is incredibly challenging due to the security features enforced on them. The factory reset facility available in smart phones clears all the user-generated data, including third-party applications, from the internal memory and completely resets the device back to its original state as a new phone. Therefore, evidential artefacts such as contacts, messages, call logs, photos, videos, and documents get deleted from the device memory, and recovery becomes difficult.Forensic acquisition of data remnants from mobile phones after factory reset is of utmost importance to forensic investigators. This paper discusses the impact of factory resets on Android devices and describes in detail the evidential artefacts left behind after the factory reset of Android phones.
Lightweight Ring Learning With Errors Cryptographic Scheme for Secure Cloud-IoT Systems

The Internet of Things is giving rise to a hyper-connected world in which people as well as things can communicate with one another. Integration of the Internet of Things technology with Cloud Computing has been recommended as a new paradigm, to overcome the present restrictions of IoT such as low power, volume and performance. However, there are security issues which need to be fixed in the new converged paradigm so as to reduce the risks associated with information protection and management. This work proposes a lightweight Ring Learning With Errors (RLWE) homomorphic encryption communication protocol for user and message management to secure the Cloud-IoT systems. The experiment shows the feasibility of the proposed lightweight RLWE for usage on resource constrained IoT or Edge Computing type of environments for the future smart application ecosystems.
A Critical Study of Challenges and Risk in Healthcare sector based on Cloud Computing

Cloud computing has been critical to the world's modern technological growth. Cloud computing is one of the most advantageous solutions to digital challenges. Security is a major concern in cloud computing technology, and this study tries to investigate cloud computing security concerns and potential solutions. A systematic review was conducted utilizing the databases Scopus, PubMed, Science Direct, and Web of Science. Firstly, this paper discussed the basics of cloud computing environment and mapped that how this technology can be helpful for health care sector. Technical, managerial, security and legal aspects while implementing cloud-based health care has also been observed and discussed. We have also discussed various cloud-based healthcare solutions offered by various companies such as Apple Health Kit, Google Health, AWS e-Health. Paper also presents the workflow of using cloud-based solution in health care sector.
Real-Time Network Monitoring: A Big Data Approach

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs.
Automatic Marks Identification from Test Notes using Deep Learning Algorithm

The process of detecting and categorising handwritten digits into the relevant numerical values is known as handwritten digit recognition. It has numerous practical applications, including optical character recognition (OCR) for digitizing handwritten documents, check processing, and automated mail sorting. The Convolutional Neural Networks (CNNs) is one of the deep learning algorithm that have given impressive results in handwritten digit recognition tasks, achieving accuracy levels close to human performance. These algorithms learn to recognize the patterns and shapes of digits by analyzing large amounts of labeled data and adjusting their weights and biases accordingly. So, to reduce the time-consuming work of entering marks individually, we proposed a system that recognizes the digit in the answer sheet and retrieves it in excel format
Joint Distributions in Probabilistic Semantics

Various categories have been proposed as targets for the denotational semantics of higher-order probabilistic programming languages. One such proposal involves joint probability distributions (couplings) used in Bayesian statistical models with conditioning. In previous treatments, composition of joint measures was performed by disintegrating to obtain Markov kernels, composing the kernels, then reintegrating to obtain a joint measure. Disintegrations exist only under certain restrictions on the underlying spaces. In this paper we propose a category whose morphisms are joint finite measures in which composition is defined without reference to disintegration, allowing its application to a broader class of spaces. The category is symmetric monoidal with a pleasing symmetry in which the dagger structure is a simple transpose.
Integration of Captive Movement Systems in Virtual Environments for Rehabilitation in People with Disabilities

Generally, virtual reality (VR) systems used to improve physical and cognitive skills in people with disabilities generate a limited play area, allowing only a few meters of movement. This work proposes the integration of the Virtuix Omni Pro device, a captive movement system that enables unlimited displacement. This represents an opportunity in VR research for gait training in people with disabilities and adds value in terms of realism and immersion for the practice of activities in which movement is essential, such as shopping in a supermarket or moving freely through an urban environment
Unraveling False Positives in Unsupervised Defect Detection Models: A Study on Anomaly-Free Training Datasets

Unsupervised defect detection methods have garnered substantial attention in industrial defect detection owing to their capacity to circumvent complex fault sample collection. However, these models grapple with establishing a robust boundary between normal and abnormal conditions in intricate scenarios, leading to a heightened frequency of false-positive predictions. Spurious alerts exacerbate the work of reconfirmation and impede the widespread adoption of unsupervised anomaly detection models in industrial applications. To this end, we delve into the sole available data source in unsupervised defect detection models, the unsupervised training dataset, to introduce a solution called the False Alarm Identification (FAI) method aimed at learning the distribution of potential false alarms using anomaly-free images. It exploits a multi-layer perceptron to capture the semantic information of potential false alarms from a detector trained on anomaly-free training images at the object level. During the testing phase, the FAI model operates as a post-processing module applied after the baseline detection algorithm. The FAI algorithm determines whether each positive patch predicted by the normalizing flow algorithm is a false alarm by its semantic features. When a positive prediction is identified as a false alarm, the corresponding pixel-wise predictions are set to negative. The effectiveness of the FAI method is demonstrated by two state-of-the-art normalizing flow algorithms on extensive industrial applications.
An Empirical Analysis of Relationship between Economic Growth and Freedom: A Cross Country Study using Machine Learning Approach

While there is sufficient exploration on the role of labour, capital and technology as determinants of economic growth, less is analysed on the role of economic and political freedom as factors influencing the economic growth of a country. The economic growth of some countries is high and in some low. The reason for this variation has been a matter of discussion in economic literature. In this paper, the relationship between economic growth and economic freedom along with labour and capital is analysed. The multi-linear regression model is used to estimate the relationship. The results obtained indicate that there exists a significant relationship between economic growth, economic freedom, labour and capital. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score was 0.929 which states that the dependent variable is explained well by the independent variables.
Model-Based Reinforcement Learning with Isolated Imaginations

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based reinforcement learning approach that has two main contributions. First, we optimize the inverse dynamics to encourage the world model to isolate controllable state transitions from the mixed spatiotemporal variations of the environment. Second, we perform policy optimization based on the decoupled latent imaginations, where we roll out noncontrollable states into the future and adaptively associate them with the current controllable state. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild, such as self-driving cars that can anticipate the movement of other vehicles, thereby avoiding potential risks. On top of our previous work [1], we further consider the sparse dependencies between controllable and noncontrollable states, address the training collapse problem of state decoupling, and validate our approach in transfer learning setups. Our empirical study demonstrates that Iso-Dream++ outperforms existing reinforcement learning models significantly on CARLA and DeepMind Control.
Using Generative AI to Assist with Technology Adoption Assessment

Adoption of innovative solutions in healthcare remains a challenge and is a contributing factor to the barriers of their large scale uptake in both private and public healthcare settings. Traditionally, the study of technology adoption has been limited to considering the patient’s perspective, however, there is now an increasing appreciation that this should be expanded to consider adoption implications from a carer’s perspective in addition to healthcare professionals and indeed on a larger scale, from a healthcare service provider’s perspective. In this work we attempt to establish a proof of concept framework whereby technology adoption of innovative healthcare solutions can be built using generative AI. By considering established and validated clinical questionnaires for the purposes of assessing technology adoption for patients we have created a new suite of questionnaires that can be used for care givers. The approach was evaluated with a set of 28 patient focussed questions. All of the questions produced by the generative AI were deemed to be correct with an average Rouge-1 F1 score of 0.71.
Bitcoin and Its Energy, Environmental, and Social Impacts: An Assessment of Key Research Needs in the Mining Sector

In this study, we used a combination of AI-assisted analysis of social media discourse and collaboration with industry experts to delve into the key research needs associated with the Bitcoin mining industry. We identified primary threats, opportunities, and research questions related to the Bitcoin mining industry and its wider impacts, focusing on its energy use and environmental footprint. Our findings spotlight the industry’s move towards increasingly greater energy efficiency and an emerging commitment to renewable energy, highlighting its potential to contribute to the coming energy transition. We underscore the transformative potential of emerging applications in the Bitcoin mining sector, especially regarding demand response, grid flexibility, and methane mitigation. We suggest that targeted research on Bitcoin can serve policymakers, private sector decision-makers, research funding agencies, environmental scientists, and the Bitcoin industry itself. We propose that filling key information gaps could help clarify the risks and benefits of Bitcoin mining by encouraging collaboration among researchers, policymakers, and industry stakeholders and conducting research that provides baseline peer-reviewed evidence surrounding Bitcoin’s production and impacts. A collaborative approach could help mitigate the risks and realize the benefits of Bitcoin mining, including potentially positive and substantive contributions in alignment with the Sustainable Development Goals.
Magnetic and Geochemical Properties of Zagreb City Area Soils

This study was performed to derive the first insight into the distribution of low-field volume magnetic susceptibility (MS) throughout Zagreb city, based on in situ field measurements. The most interesting locations were selected for soil sampling and their geochemical contents were determined using ICP-OES. A geostatistical approach was applied to the MS and geochemical results. A median of 0.245 × 10−3 SI units was proposed as the average MS value in Zagreb. The mean concentrations of heavy metals in Zagreb’s soils (in µg/g) are Pb (36.82), Zn (87.77), Cu (30.84), Cd (0.66), Cr (29.04), Co (11.89), and Ni (28.40), and these measurements are relatively low in comparison to the Croatian legislation and the European and world average. Boxplot analyses demonstrate that 45% of the studied elements do not feature any anomalies, while most of the remaining elements indicate only one weak anomaly located at the same site as the MS anomalies. Our statistical analysis found significant correlations between MS and the following elements: Cd, Co, Fe, Mn, Na, Pb, Sb, and Zn. In situ MS measurements proved to be an efficient tool for the initial screening of large areas with elevated concentrations of heavy elements, enabling the cheap and fast assessment of the state of the environment.
تطوير الوسيلة التعليمية فليب بوك برو لتعليم المفردات العربية بالتطبيق على التلاميذ في المدرسة

The use of learning media is essential in the learning process. The teacher needs help in choosing a proper learning media in the process of learning Arabic vocabulary. Students have difficulties memorizing vocabulary effectively and have been inattentive during the learning process. Thus, the researcher provides a solution using technology-based learning media, Flip Book Pro. It is a learning media that converts PDF files into various engaging digital book pages. The paper uses a research and development approach and uses the model of ADDIE (Analysis, Design, Development, and evaluation). The researcher conducted trials using qualitative and quantitative methods. The researcher obtained data on the effectiveness of students on the product using a qualitative approach. The researcher quantitatively analyzed the data using the Likert scale formula, data questionnaire analysis, and t-test. This paper was centered around how the process of developing flipbook pro learning media on Arabic vocabulary learning for elementary students of Baiturrohman Jember in the 2021/2022 Academic Year and finding out whether or not there is a significant effect in the effectiveness of Flipbook Pro learning media on learning Arabic vocabulary for elementary students of Baiturrohman Jember in 2021/2022 Academic Year.
Active-Knowledge-Sharing-Method through High School Students at Kalimantan Tengah

The purpose of this paper is to explain that learning is the main activity in the whole educational process in schools. Learning is a process to achieve the success of educational goals in schools. The success of student learning depends on how the learning process is experienced by students as students, and teachers as educators who are directly involved in the implementation of learning. To improve the learning process of students, teachers must be able to choose and apply appropriate learning methods to improve student achievement. The research method used in this research is descriptive research with qualitative analysis. active knowledge sharing is a strategy that can bring students of SMA Negeri-1 Manuhing to be ready and active in learning activities. This strategy can be used to see the level of ability of students in learning.
The Influence Of The Ability In Mastering Dialogue On Students Speaking Comprehension At SMP SWASTA CINTA RAKYAT 3 Pematangsiantar

The aim of this research was to find out the influence of the ability in mastering dialogue on students speaking comprehension at SMP SWASTA CINTA RAKYAT 3 PEMATANGSIANTAR in the academic year of 2021/2022. This research was classified as a true-experimental study. It involved 24 students from two groups, as the experimental group (12 students) and as the control group (12 students). The experimental group was students taught using picture sets dialogue where as the control group was students taught using lecture technique. The data were obtained by using pre-test and post-test. The data of the pre-test and post-test of both groups were analyzed by using descriptive and inferential statistic. (SPSS 22.0). The result of the research shows that the testing hypothesis of post-test score, it is found that tcount (9,326) &gt; ttable (8,233) at the significance level a = 0.05 and degrees of freedom (df) = 11. H0 is rejected and Ha is accepted. So, it means that the influence of the ability in mastering dialogue significantly on students speaking comprehension at SMP SWASTA CINTA RAKYAT 3 PEMATANGSIANTAR academic year of 2021/2022
Re-designing the Interaction of Day-to-Day Applications to Support Sustained Attention Level

Digital distractions in decreasing people’s attentional abilities have become a subject of increasing concern and scrutiny in recent years. Based on the existing literature regarding the negative impact of technology on attention, this paper examines various solutions, encompassing mostly reactive strategies. It also questions the prevailing design practices prioritising learnability and efficiency and proposes a shift towards designing interactions that minimise distractions and promote sustained attention while emphasising the negative consequences of distraction-prone interactions on users’ attentional resources. As a proactive solution, we present a novel interaction strategy called “Attention mode,” which selectively reduces distractions when using apps. We developed three prototypes that consistently incorporate this strategy, each used in a different application on a different device: a news website on a desktop, an e-book reading app on a tablet, and a video-watching app on a smartphone. We conducted usability testing with 13 participants to evaluate this design feature and address the implications of these in the interaction design knowledge and practice today. By advocating for considering users’ attentional abilities in design, this work contributes to the field of Human-Computer Interaction (HCI) and calls for a balance between cognitive well-being and traditional usability criteria.