Reviewerly/exHarmony huggingface dataset:
Developing The Creative Potential Of Students Through Group Activities

The belief that a new, original product can be generalized exclusively by a unique, brilliant mind is no longer a certainty.History has proved to us that most of the time the novelty appears in work teams, because of the group effort.In addition to declarative knowledge and cognitive skills, the current context requires several other soft skills that include the dimension of developing creativity and teamwork skills.In this paper we aim to identify modalities to develop creative student's skills by integrating them into activities that can be performed in groups.These approaches must be compatible with the particularities of the groups in the class and adapted to the curricular requirements, if they are implemented within the formal activities.But this idea can also be integrated into extracurricular activities.The big challenge for teachers is to emphasize the role of group activities and the benefits they have on the development of students' personality, and to put them in the context of activating creative potential.
The Use Of Educational Technologies In The Students’ Pedagogical Practice Activities

The process of digitalization has a great influence on every context of people's life: social, work, political, communicative, economic and last but not least educational.The rapid rate of development of educational technologies offers new opportunities for the forming and development of competencies of the future and current teachers, the attitude towards these new technologies being a necessary condition for their adoption in the educational act.This paper addresses the way in which educational technologies have been used in the activities within the discipline Pedagogical Practice both by the mentor teachers from pre-university educational institutions and by the students from the faculties of the Technical University of Cluj-Napoca -North University Center of Baia Mare in the practical testing of didactic competencies.Understanding attitudes towards new technologies has an active role in determining the perceptions of future teachers about their use in the teaching-learning-evaluation process.The present paper aims to investigate, using the survey as research method and a questionnaire applied to students as research instrument, the attitudes of personal and didactic use of technologies, the perception of digital skills of students as future teachers, and the way in which technology is used in lower secondary and upper secondary pre-university education, given that the initial teaching training offers through practical pedagogically activities an opportunity for the students to get acquainted with the teaching profession and digital native pupils.
Academic And Career Paths For Phd Graduates

The Europe 2020 Strategy highlights the importance of investing in research and innovation to ensure the economic and social development of the European society.In order to achieve this objective, a great responsibility is assigned to universities.They must ensure excellence in research and teaching, regional innovation, lifelong learning, community-level interaction, and the development of collaborative activities between specialists and public and private stakeholders.Research and innovation activities mainly take place within the doctoral schools.On the one hand, they ensure the knowledge transfer to the socioeconomic environment, and on the other hand, by hiring PhD graduates and involving them in innovation activities, these schools prepare experts who will later contribute to the consolidation of research in different sectors.Our paper focuses on the academic and career paths for PhD graduates.We aim to highlight their path and motivation to graduate from a doctoral program, respectively what are the implications of the doctoral degree on their professional activity.Data was collected within the European funded project SmartDoct (code no.123008) implemented by the University of Oradea.Results are obtained based on the responses from 83 PhD graduates who completed their doctoral training between 2014-2019 at University of Oradea in different scientific domains.Data shows that PhD graduates mostly develop their professional activity within academia.
Embodied and Immersed Is the New Professional - Reconstruction, Replication and Re-enactment in the Humanities and Social Sciences. Edited by Sven Dupré, Anna Harris, Julia Kursell, Patricia Lulof, and Maartje Stols-Witlox. Amsterdam: Amsterdam University Press, 2020; 298 pp. €124.00 cloth, e-book available. - Experimenting with Ethnography: A Companion to Analysis. Edited by Andrea Ballestero and Brit Ross Winthereik. Durham, NC: Duke University Press, 2021; 301 pp. $104.95 cloth, $27.95 paper…

Three recent anthologies in science and technology studies show how standards for scholarly professionalization are changing. Such changes may facilitate new alliances for theatre faculty plagued by old feuds and the increasingly neoliberal university.
Higher education peer mentoring programme to promote student community using Mobile Device Applications

This paper presents an Italian peer mentoring programme implemented by second year undergraduate students (peer mentors) to support first years (mentees). The aim of the peer mentoring was to promote student community building and endorse active learning. We recruited 19 mentors and 32 mentees into the programme. Data was extracted through the use of semi-structed questionnaires, which were exposed to qualitative analysis. Peer mentors decided to implement their peer mentoring activities using Mobile Device Applications [MDA]. A qualitative content analysis was employed to evaluate student’s perception. Our findings suggest that students were satisfied with the adoption of MDA to foster active learning for, and community building between, undergraduate students. However, participants have highlighted criticisms and improvements.&#x0D;
Modeling Analysis of COVID-19 in View Point of Rough Topology

Work done by us shows that rough topology is a good tool for dealing with things that happen in the real world. The concept of ”basis” has been used to show out the deciding elements of the recent outbreak of COVID-19 that was reported all over the world.
Analytic Thinking (Type 2 or “System 2”) for Large Language Models: using Psychology to address hallucination and reliability issues

State-of-the-art Large Language Models have recently exhibited extraordinary linguistic abilities which have surprisingly extended to reasoning. However, responses that are unreliable, false, or invented are still a frequent issue. It has been argued that scaling up strategies, as in increasing model size or hardware power, might not be enough to resolve the issue. Recent research has implemented Type 2 strategies (such as Chain-of-Thought and Tree-of-Thought), as strategies that mimic Type 2 reasoning, from Dual Process Theory, to interact with Large Language Models for improved results. The current paper reviews these strategies in light of the Predicting and Reflecting Framework for understanding Dual Process Theory and suggests what Psychology, drawing from research in executive functions, thinking disposition and creativity, can further contribute to possible implementations that address hallucination and reliability issues.
A Multi-Scale Object Detector Based on Coordinate and Global Information Aggregation for UAV Aerial Images

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.
Factors affecting the writing skills of the education students: A descriptive study

This study aimed to identify the factors affecting first-year BSED students' writing skills in a selected Local College in Misamis Oriental. To achieve this goal, a fishbowl method-based sampling technique was employed to gather 50 students, focusing on the student's motivation, classroom environment, reliance on applications, and feedback systems. The results revealed that students' motivation played a significant role in their writing abilities, as they were encouraged to write to express themselves and enhance their skills. Furthermore, the classroom environment was found to have an impact on writing skills, with students being more engaged in writing when there was appropriate ventilation. In terms of practical applicability, the use of grammar checkers was found to be useful in improving writing skills. Additionally, the study discovered that the feedback system influenced students' writing abilities, with automatic grading being preferred by students. Overall, the study suggests that the factors that affect motivation also impact writing skills, and creating a conducive learning environment can enhance students' writing abilities.
Models and Techniques for Domain Relation Extraction: A Survey

As the significant subtask of information extraction, relation extraction (RE) aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications including knowledge graphs, intelligent question answering, text mining, and sentiment analysis. Different from general knowledge, domain knowledge is pertinent to specific fields which include a wealth of proprietary entities and relations. Besides, most of the data are formed as documents rather than sentences. In this paper, the task of domain RE is defined, and the common domains are presented. Furthermore, we provide a systematic review of state-of-the-art techniques as well as the latest trends. We survey different neural network-based techniques for RE and describe the overall framework, training procedures, as well as the pros and cons of these techniques. Then, we introduce and compare the corpus and metrics used for domain RE tasks. Finally, we conclude and discuss future research issues of domain RE.
Identification of Online Educational Game Applications in Teaching English for Madrasah Students

This article identified many educational game applications we found online that were relevant for application among madrasa students. Many studies on learning English for madrasah students have been carried out. We have not found any studies with the theme of identifying some relevant game applications applied in madrasah environments whose learning is more Islamic in content and slightly different from other general schools in Indonesia. For this reason, we have conducted a series of data searches in the context of the scientific context of international experts and domestic publications. To get data that can answer the problem first, we conducted a comprehensive study that involved data coding, thorough evaluation, and interpretation of the data so that the data could be used as the highest answer to this elephant's question. After we studied it, we got the results and discussed them carefully. Finally, we can summarize several educational game applications we can access online that we apply to madrasas, including children's pictures dictionary, monkey puzzle game, little birds game, kids learning, Billy playing games, and several games. Another trendy game has been applied in several schools in international contacts. Hopefully, the results of this identification can be helpful in the following study.
INDONESIAN EDUCATION POLICY AND MANAGEMENT: BOS FUND IN PRIMARY SCHOOLS

This article discusses the policy and management of Education: BOS Fund in Primary Schools. Based on Permendikbud No 161 of 2014 concerning Technical Guidelines for the Management of Regular School Operational Assistance Funds. BOS fund management has at least three indicators: Planning, implementation and reporting. These three indicators are the benchmark for managing BOS funds well. The research method uses a literature study with a meta-analysis of ten national journal review results based on BOS fund management indicators: Planning, implementation, and reporting. Results of this study: 1. Planning involves all school residents, including the principal, treasurer, teachers, employees, committees, and parents. This Planning is poured in the form of the preparation of RKAS. RKAS is made with various considerations and by the conditions of each elementary school. 2. The implementation of BOS fund management, starting with the withdrawal of funds; some are carried out in Dahab, and some are all at once; the collection adjusts to each condition and then allocates by the RKAS that has been made. The collection of BOS funds must be recorded in detail and clearly. As well as leaving a minimum balance as a precaution. 3. Reporting is carried out gradually and every day so that it is monitored in real and particular. This reporting is in the form of a unique BOS fund governance book.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0007/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Predicting Online Learning Success Based on Learners’ Perceptions: The Integration of the Information System Success Model and the Security Triangle Framework

Although online learning has become ubiquitous worldwide, earlier research has neglected the relationship between its actual use and security concerns. Learners’ lack of security awareness while using learning technologies remains rarely studied. This paper integrates Delone and McLean’s information system success (D&amp;M-ISS) model with the security triangle framework. Data from 2,451 higher education students at different universities and a wide variety of disciplines in Iraq were collected. In addition to the effectiveness of the D&amp;M-ISS factors, the research findings based on the structural equation model suggest that the three constructs of the security triangle framework—namely, confidentiality, integrity, and availability—were significant predictors of students’ use of online learning. This research can thus help academic organizations understand factors that can lead to the successful implementation of online learning and learners’ security awareness.
IMPLEMENTASI METODE IQRO’ DALAM PENGENALAN HURUF HIJAIYAH PADA SISWA RA PERWANIDA II MATARAM

This study aims to determine how the implementation of the Iqro’ method in introducinghijaiyah letters at the 4-5 years age students in RA Perwanida II Mataram. This studyused a descriptive qualitative research approach in order to obtain broad and in-depthinformation about purpose of the study. The data source in this study were the principalof RA Perwanida II Mataram, teachers of group A class students, parents of group Astudents. The data collection used observation, interview, and documentation. Theresults showed that the implementation of the Iqro’ method in introducing the hijaiyahletters in children consisted of planning, implementation, and evaluation. The use of theiqro’ method provides a basic understanding of the hijaiyah letters and the ease ofdistinguishing letters properly and correctly, both sequentially and randomly. The iqro’method also has advantages, such as it is easy to understand and use, while thedisadvantages are that it cannot be read with tartil rhythms and is not taught tajwidreadings.
ASM-based Formal Model for Analysing Cloud Auto-Scaling Mechanisms

The provision of resources to meet workloads demands has become a crucial responsibility for auto-scaling mechanisms (auto-scalers) on cloud infrastructures. However, implementing auto-scaling mechanisms on cloud frameworks has generated many technical challenges. A typical challenge is that, these auto-scalers are often designed on different cloud systems making their evaluation, comparisons and wider applicability problematic. We propose an Abstract State Machine (ASM) model to address this problem. Our ASM model was developed systematically according to the behaviours of several auto-scalers, covering the necessary system processes. Our model was checked and validated with the CoreASM Model Checker. The validation and evaluation proves that our model can be used to analyse auto-scaling mechanisms, even without conducting real-life experiments. Our model, therefore, provides the platform to evaluate the behaviours of algorithms executed on clouds.
Comparison of Three Time Series Forecasting Methods on Linear Regression, Exponential Smoothing and Weighted Moving Average

The purpose of this study is to compare the 3 forecasting methods Linear Regression, Exponential Smoothing and Weighted Moving Average based on the smallest error value or close to zero. From the results of this study, the Linear Regression method was obtained as the correct method with a predicted value of 502 students, the smallest error value was MAD 27.83, MSE 1152.1 MAPE 8.1%. The Tracking Signal value moves between 1 and -1, the movement is within the control limits of the tracking signal standard deviation distribution 4 and -4, meaning that the method is correct. The Moving Range value moves between 68 and -46, this value is within the MR control limits of 117.83 and -117.83, this result shows that this means that this method has been tested for truth and can be accepted as well. Thus, indicating that the Linear Regression method as a forecasting method is appropriate and acceptable as a basis for future decision making. The level of accuracy of the error and the value in control shows that there is a time series data relationship between the x variable, namely time, and the variable y, namely actual data. In addition, it produces trending data movement patterns, meaning that data movements experience a significant increase over a long period of time or for 7 periods.
Actis: A Strictly Local Union-Find Decoder

Fault-tolerant quantum computing requires classical hardware to perform the decoding necessary for error correction. The Union-Find decoder is one of the best candidates for this. It has remarkably organic characteristics, involving the growth and merger of data structures through nearest-neighbour steps; this naturally suggests the possibility of its realisation using a lattice of simple processors with nearest-neighbour links. In this way the computational load can be distributed with near-ideal parallelism. Here we show for the first time that this strict (rather than partial) locality is practical, with a worst-case runtime $\mathcal O(d^3)$ and mean runtime subquadratic in the surface code distance $d$. A novel parity-calculation scheme is employed which can simplify previously proposed architectures, and our approach is optimised for circuit-level noise. We compare our local realisation with one augmented by long-range links; while the latter is of course faster, we note that local asynchronous logic could negate the difference.
Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate

This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. We present our method in detail and demonstrate it with examples that involve "time", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems.
Fault Location of Doukan-Erbil 132kv Double Transmission Lines Using Artificial Neural Network ANN

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's performance was evaluated, and tests were run. Line-to-ground faults were examined. The study demonstrates how effective, rapid, and precise this method is at locating faults. The neural network's performance was examined, and tests were run on it. The overall performance of the mean square error in the trained network execution was 0.11792 at 35 epochs. The correlation coefficient at the entire target was 0.99987 percent of an error on the Doukan-Erbil double transmission lines.
An Investigation of Consolidating Virtual Servers and Data Centers based on Energy Consumptions using various Algorithms

With sophisticated technologies in wireless communication and cloud environments, virtual machine data centers and their applications are exponentially increasing. Energy consumption and impact on environmental factors play a significant role in the design and implementation of cloud platforms. Virtualization is a mode of operation in a cloud to simplify and reduce the workload of data centers through optimized resource utilization and energy bandwidth utilization. Cloud technologies facilitate the virtualization model by migrating content from physical data centers to virtual data centers with a trivial suspension of services. Multiple virtual machines can be hosted on a physical machine space, reducing the actual energy consumption of physical devices. The process is termed to be server consolidation for conserving the energy demand. Server consolidation can be achieved through renowned techniques, accommodating various parameters and conditions. The purpose of consolidations is to eliminate the dependency on hardware underlying the overall architecture. The available migration techniques are categorized into manner, distance, and granularity aspects for a better understanding. Non-Live migration techniques are listed briefly for comparison against a detailed perspective of live migration techniques. User mobility is a significant parameter for fog computing in VM migrations during cloud architecture deployment. Algorithmic approaches are listed in detail, predominantly used in server consolidation. The open challenges and other considerable issues are expressed in this survey article.