Reviewerly/exHarmony huggingface dataset:
Application Of Snakes and Ladders Media Assisted By Number Cards to Improve Student Activity and Learning Outcomes In Class II Mathematics Subjects

This study focuses on the use of snakes and ladders media with the help of number cards in learning mathematics for addition and subtraction. This study aims to improve activity and learning outcomes in the addition and subtraction material for Class II Girijoyo State Elementary School. The research method uses classroom action research (CAR). The subjects of this research were 10 students of Class II Girijoyo State Elementary School. The results of the pre-cycle research showed that the percentage was 66.5%, the first cycle increased to 76.5%, and the second cycle increased to 84%. This can be interpreted, that through the observation of the activeness of the second grade students, the success indicator has reached 90% and is categorized as active. In addition to the results of observations, the tests that have been carried out have also increased, from the percentage in the pretest 30% of students who completed the KKM, in the posttest 1 in the first cycle, the percentage increased to 50% of the students who completed the KKM. In posttest 2 in cycle II the percentage of students' completeness rose to 90%. Thus the achievement of the resulting competence with a percentage of 90% of students categorized as complete, exceeding the indicator of success with the achievement of class competence reaching more than 75%.
The Relationship between Knowledge Acquisition and Knowledge Dissemination: Mediating Role of ISO 9001 Key Factors

This research aims to examine the impact of ISO 9001 factors on knowledge acquisition and knowledge dissemination to determine the key success elements for organizations.A questionnaire survey method is employed to identify and analyze the relationship between knowledge acquisition, knowledge dissemination, and ISO key factors within organizations.In addition, the research adopts factor analysis to determine key factors of ISO 9001 management system, knowledge acquisition and knowledge dissemination.Moreover, the correlations of these dimensions are examined with correlation analysis and hierarchical regression analysis.The research reveals that the three dimensions (i.e., key factors of ISO 9001, knowledge acquisition, and knowledge dissemination) show significant correlations.Feasible suggestions and corresponding action plans are put forward to provide new directions for knowledge management and serve as a reference for future research.These findings can contribute to promoting knowledge management, revealing behavioral intentions, and providing strategic advice for business operations.
Algorithm for assessing the credibility of online information

The present paper is devoted to an algorithm for evaluating the data credibility and analyzes a number of available tools for this. During the investigation, it was foundt hat the most tools are used by journalists in doing this. Currently, there are attempts to automate the process of evaluating the information credibility, aimed not only at journalism professionals, but also at the regular user. In this sense, two tools wer eanalyzed: NewsGuard and WOT. The analysis of these tools allows us to determine the mechanisms of collective online analysis (crowdsourcing) in order to evaluate thecredibility of online information with the possible usage of computational intelligence without the mandatory experts’ participation. The developed algorithm takes over the best functions of the studied tools and can be useful for evaluating the data credibility from various websites.
CliMetLab and Pangeo use case: Machine learning data pipeline for sub-seasonal To seasonal prediction (S2S)

&amp;lt;p&amp;gt;As machine learning algorithms are being used more and more prominently in the meteorology and climate domains, the need for reference datasets has been identified as a priority. Moreover, boilerplate code for data handling is ubiquitous in scientific experiments. In order to focus on science, climate/meteorology/data scientists need generic and reusable domain-specific tools. To achieve these goals, we used the plugin based CliMetLab python package along with many packages listed by Pangeo. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;Our use case consists in providing data for machine learning algorithms in the context of the sub-seasonal to seasonal (S2S) prediction challenge 2021. The data size is about 2 Terabytes of model predictions from three different models. We experimented with providing data in multiple formats: Grib, NetCDF, and Zarr. A Pangeo recipe (using the python package pangeo_forge_recipes) was used to generate Zarr data (relying heavily on xarray and dask for parallelisation). All three versions of the S2S data have been stored on an S3 bucket located on the ECMWF European Weather Cloud (ECMWF-EWC).&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;CliMetLab aims at providing a simple interface to access climate and meteorological datasets, seamlessly downloading and caching data, converting to xarray datasets or panda dataframes, plotting data, feed them into machine learning frameworks such as tensorflow or pytorch. CliMetLab is open-source and still a Beta version (https://climetlab.readthedocs.io). The main target platform of CliMetLab is Jupyter notebooks. Additionally, a CliMetLab plugin allows shipping dataset-specific code along with a well-defined published dataset. Taking advantage of the CliMetLab tools to minimize the boilerplate code, a plugin has been developed for S2S data as a companion python package of the dataset.&amp;lt;/p&amp;gt;
Modifiable Risk Factors as Predictors of Global Cognition in Adults: A Pilot Study of Two Rural Communities in Anambra State, Nigeria

Modifiable risk factors are behaviours and exposures that can raise or lower a person's risk of developing diseases. These risk factors by themselves are modifiable and have been shown to drive midlife chronic diseases. This study investigated the roles of modifiable risk factors (i.e., hypertension, diabetes, stroke, measures of adiposity, and physical activity among others) on global cognition in rural community dwellers from southeast Nigeria. One hundred and fifty participants (95 females and 55 males; age = 58.6; SD age = 4.8) were recruited for the study through a door-to-door approach. Montreal Cognitive Assessment was used to capture global cognition. Self-report measures and standardized instruments were used respectively, to gather information on modifiable risk factors and measures of adiposity. Because this was a pilot study, two rural communities were selected based on proximity and convenience for the research team. Linear analysis of variance and multiple regression statistics were used for data analysis. Result showed significant differences at p&lt; 0.05 level on global cognition between diabetic and non-diabetic groups as well as stroke and non-stroke groups. Physical activity was shown as significant predictor of global cognition while sleep and social relations were not. Equally, arm (Beta = 0.31) and waist (Beta= 0.12) circumferences significantly predicted global cognition and underweight participants performed significantly worse F(3,148) = 2.6) on global cognition.
Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations

With the continuous reform and innovation of Internet technology and the continuous development and progress of social economy, Big Data cloud computing technology is more and more widely used in people's work and life. Many parallel algorithms play a very important role in solving large linear equations in various applications. To this end, this article aims to propose and summarize a cloud computing task scheduling model that relies on the solution of large linear equations. The method of this paper is to study the technology of solving large-scale linear equations and propose an M-QoS-OCCSM scheduling model. The function of the experimental method is to solve the problem of efficiently executing N mutually dependent parallel tasks within limited resources, while fully satisfying users' expectations of task completion time, bandwidth rate, reliability, and cost. In this paper, the application experiment of large-scale linear equations in task scheduling is used to study task scheduling algorithms. The results show that when the task load is 10 and 20, the convergence speed of the MPQGA algorithm is 32 seconds and 95 seconds faster than that of the BGA algorithm, respectively.
Specificity Of Using Artificial Intelligence In Science And Education

The article analyzes the experience of using mass distance learning technologies (DLT) in the context of the coronavirus pandemic, as well as the specifics of using the artificial intelligence by educators and trainees in the remote pedagogical interaction situation. The psychological, cultural and futurological consequences of the carried out global pedagogical experiment are analyzed in connection with the epidemiological situation emergency. The theses on the irreversible nature of the implemented innovations, as well as the subsequent elimination of the teacher's figure from the structure of the pedagogical process, are substantiated. Assessing the situation that has developed in the field of modern professional education, the authors reasonably assert that the priority is rather a revivalist than reformist "scenario" of the development of events around the problems of artificial intelligence. In our opinion, the supporters of the idea of the development of artificial intelligence have thoroughly underestimated its capabilities, based on the position that the development of certain technologies can lead to the displacement of a person exclusively from the fields of activity related to physical labor, which will allow directing the released human resources into the field of intellectual labor. We have to state that already today the situation is such that artificial intelligence can potentially displace humanity from this sphere of activity. It can be assumed with a high degree of confidence that even texts like the one presented by us may be successfully modeled using appropriate computer programs, as well as other intellectual products, experiments and engineering projects.
Review on Camera based Surveillance Systems

Abstract: Surveillance systems that use cameras to monitor the environment are known as automated surveillance systems. Motion detection, crowd behavior, individual behavior, and interactions between persons, crowds, and their surrounding environment are all used to analyses the observed scenario. These automated systems perform a wide range of functions, including detection, interpretation, comprehension, recording, and the generation of alarms based on the analysis. By manipulating various elements of these systems, researchers have been able to improve monitoring performance while minimizing possible human failings. This study provides a thorough examination of such video surveillance systems and the components that go with them. The architectures that were employed are described, along with the most important analyses in these systems. Existing surveillance systems were compared in terms of to provide a wider picture and a more holistic understanding of the system.
Clustering and event similarity based fault characterization of post mining induced seismicity of Gardanne mine, France

&amp;lt;p&amp;gt;In post-mining environments, seismic hazard is still not very well understood, as number of research studies remains limited. Seismicity is often considered in post-mining risk mitigation procedures as a precursory of failure initiation in rocks within the mining works leading to ground instabilities. However, flooding of the mines can also lead to perturbations of stress states and pore pressures within the rock mass leading to failure of pre-existing faults, which may have more important impact on public safety due to a potentially longer period of activity and possibly higher magnitudes of the induced seismic events depending on the fault size. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In a former coal mine in Gardanne, France, which was abandoned in 2003 and flooded afterwards, seismicity started appearing and raising concerns since 2010, when flooding reached the center of the mining basin. The seismic activity has been occurring approximately every two years in the form of crises. Events were also felt by the local population. A sparse temporary monitoring network has been installed in 2013 in this seismically active area. Based on research results so far, seismicity originates from the reactivation of faults underlying the mining excavations and is influenced by flooding, pumping of the water, and seasonal meteorological conditions.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We investigate the clustering behavior and multiplet occurrences within the seismic events recorded by the sparse temporary microseismic network between 2014 and 2017. Detailed cluster analyses, the spatio-temporal distribution, recurrence time patterns, and source parameters help to characterize seismically active structure(s) below the mining works. The triggering of the seismic activity in each cluster appears to be differently influenced by the hydro-meteorological conditions, with some clusters being more affected by rainfall, while other by dry period. The variations of the pumping rate strongly affect the rate of seismicity in this area as well. The analysis is complemented by incorporating a new dataset recorded by an enhanced monitoring network during 2019, which allows to follow the evolution of the cluster activity.&amp;amp;#160;&amp;lt;/p&amp;gt;
Adrienne Fairhall

Interview with Adrienne Fairhall, who studies the relationship between neuronal circuitry and the functional algorithms of computation at the University of Washington.
Analysis of Operational Assistance School Fund Management

This study aims to analyze the application of the principles of the School Expenditure Management Budget Plan (RAPBS) in improving the quality of education at SMP Negeri 6 Tolangohula. This study uses 5 types of principles of financial management of BOS funds according to Ministerial Regulation No. 6 of 2021. This type of research is descriptive qualitative research. The method used in this research is observation, interview, and documentation.The results of this study are SMP Negeri 6 Tolangohula has implemented 5 principles of financial management of BOS funds properly, in accordance with technical guidelines and legislation on BOS funds, both physically and mentally. Flexibility, Effectiveness, Efficient, Accountability, and Transparency.
The Role of School in Raising Awareness of Students About The Dangers of Drug Adicction

According to the research plan, the first chapert is dedicated for the dimensions of the study and the definition of concepts, the first is dedicated to the dimensions of the study, in which the research (problems, importance, objectives, and hypothesis) are presented. In the second case, the definition of concepts (role, school, addiction, and drug) is defined.&nbsp; The second chapter is dedicated to exposing the drug addiction of individuals. The third chapter is special to analyzing the phenomenon of drug addiction in the view of two social theories the second axis is the practical aspect of the research, which is presented in the fourth part (the research community, the research sample, the research method, the used statistical framework). The fifth chapter is dedicated to presenting and analyzing the information obtained, and then the results and suggestions are presented.
Online Learning for Reference-Based Super-Resolution

Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images, which is applicable to not only RefSR but also SISR networks. Experimental results show that our online learning method is seamlessly applicable to many existing RefSR and SISR models, and that improves performance. We further present the robustness of our method to non-bicubic degradation kernels with in-depth analyses.
Some Additional Notes on the Ancient Side Flute

Abstract Literary, archaeological, and iconographic testimonies attest to the side flute as an instrument that, from the 4th century BC to the 3rd century AD , played its part in the Greco-Roman musical landscape. Building on the conclusions of recent studies, this contribution aims to add to the discourse some further elements regarding both the panorama that the sources allow us to draw and the structural and acoustic features of the ancient side flute. A detailed analysis of the sources sheds light on more than a name, more than one typology, more than a performative context, for an instrument whose life extended far from the bucolic cliché. Plus, the side-blown reedless headjoint is a distinctive feature of the ancient side flute, documenting an experimental journey through acoustic effectiveness that sees eye to eye with the solutions adopted by modern craftsmanship to enhance sonority and focus.
CEKD:Cross ensemble knowledge distillation for augmented fine-grained data

Data augmentation has been proved effective in training deep models. Existing data augmentation methods tackle fine-grained problem by blending image pairs and fusing corresponding labels according to the statistics of mixed pixels, which produces additional noise harmful to the performance of networks. Motivated by this, we present a simple yet effective cross ensemble knowledge distillation (CEKD) model for fine-grained feature learning. We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem. The proposed model can be trained in an end-to-end manner, and only requires image-level label supervision. Extensive experiments on widely used fine-grained benchmarks demonstrate the effectiveness of our proposed model. Specifically, with the backbone of ResNet-101, CEKD obtains the accuracy of 89.59%, 95.96% and 94.56% in three datasets respectively, outperforming state-of-the-art API-Net by 0.99%, 1.06% and 1.16%.
Editorial for Special Issue “Perspectives and Challenges in Doctoral Research—Selected Papers from the 9th Edition of the Scientific Conference of the Doctoral Schools from the “Dunărea de Jos”

This Editorial is dedicated to the 9th edition of the Scientific Conference organized by the Doctoral Schools of “Dunărea de Jos” University of Galati (SCDS-UDJG), which was organized in June 2021 in Galati (Romania) [...]
Digitalization through the eyes of students: prospects, risks, obstacles

The analyze the peculiarities of the perception of digitalization of student youth, in whose life social networks play an important role. Different assessments of digitalization are presented, as well as internal and external factors that affect students ‘ perception of the digital future are high-lighted.
The Threat of Covert Channels in Network Time Synchronisation Protocols

Synchronized clocks are vital for most communication scenarios in networks of Information Technology (IT) and Operational Technology (OT). The process of time synchronisation requires transmission of high-precision timestamps often originating from external sources. In this paper, we analyze how time synchronization protocols impose a threat by being leveraged as carrier for network covert channels. This paper is an extended version version of our open-access paper [15] in which we performed an in-depth analysis of the Network Time Protocol (NTP) in regards to covert channels. In this extended version, we broaden the view and take a look and time synchronisation in a more general way as we provide two comprehensive threat scenarios regarding covert channels and discuss the applicability of such covert channels to another time synchronisation protocol, namely the Precision Time Protocol, PTP. While the Network Time Protocol (NTP) is the most prevalent protocol for synchronizing clocks in IT networks, the Precision Time Protocol (PTP) is mostly found in networks of Industrial Control Systems (ICS) due to higher demands regarding accuracy and resolution. To illustrate the threat of covert channels in such protocols we describe two threat scenarios, one for the Network Time Protocol and one for the Precision Time Protocol. For NTP we perform a systematic in-depth analysis of covert channels. Our analysis results in the identification of 49 covert channels, by applying a covert channel pattern-based taxonomy. The summary and comparison based on nine selected key attributes show that NTP proofs itself as a plausible carrier for covert channels. The analysis results are evaluated in regards to common behavior of NTP implementations in six major operating systems. Two channels are selected and implemented to be evaluated in network test-beds. By hiding encrypted high entropy data in a high entropy field of NTP we show in our first assessment that practically undetectable channels can be implemented in NTP, motivating the required further research. In our evaluation, we analyze 40,000 NTP server responses from public NTP server providers and discuss potential countermeasures. Finally, we discuss the relevance, applicability and resulting threat of these findings for the Precision Time Protocol.
What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development

Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs.In this paper, we present an approach to predict the time to fix, the risk and the complexity of debugging and resolution of a bug report using different supervised machine learning algorithms, namely Random Forest, Naive Bayes, SVM, MLP and XGBoost.Further, we investigate the effect of the application of active learning and we evaluate the impact of different text representation techniques, namely TF-IDF, Word2Vec, Universal Sentence Encoder and XLNet on the model's performance.The evaluation shows that a combination of text embeddings generated through the Universal Sentence Encoder and MLP as classifier outperforms all other methods, and is well suited to predict the risk and complexity of bug tickets.
PELATIHAN PENGARSIPAN SECARA ELEKTRONIK (E-FILLING) BAGI PERANGKAT DESA DI PEKON SUKANEGERI JAYA

This service activity in the form of electronic archiving training or e-filling is motivated by archiving activities carried out at the village office still using the traditional way, namely using paper. This method will cause many problems, so it is necessary to conduct electronic archiving training for village officials. The village office in Pekon Sukanegeri Jaya has been equipped with computer equipment and an internet network that can be used for electronic archive management. However, the skills and knowledge of village officials are still lacking, especially for archiving using Google Drive media. The purpose of implementing this service is that with the existence of electronic archiving training for village officials, it is hoped that archive management at the village office can be more organized and make it easier for village officials to re-search the archive files needed at any time.