Reviewerly/exHarmony huggingface dataset:
Machine Intelligence by Central Pivot Ranges (MICPR): An Optimal Resource Scheduling Strategy for Cloud Services

Abstract Alongside the increasing demand for cloud computing solutions, the other critical factor in focusing on cloud computing solutions is effective cloud resource sensing and scheduling. In the real-time scenario, there are numerous resource scheduling models available to support effective managing resource scheduling. However, resource sensing and scheduling challenges remains in the increasing complexities of background processing in the cloud servers. Thus, in this manuscript, a machine intelligence model that provides significant insights into how the load balance can be sensed and handled using the sever performance's critical metrics is proposed. The model is an automated machine intelligence (MI) approach that uses pivot range (CPR), support, and resistance models for estimating if the load of the system as per the metrics chosen is running heavy load or normal. Accordingly, the decision can be triggered automatically by MICPR to choose the fundamental resource sensing and scheduling algorithms. The experimental study of the model refers to potential ways in which the system can be feasible, and if implemented on a large scale, it can help reduce the backend load on the cloud servers.
Faster Grasping of High-Calorie Food Objects in Virtual Reality

Abstract Purpose : Theoretical models and behavioral studies indicate faster approach behavior for high-calorie food (approach bias) among healthy participants. A previous study with Virtual Reality (VR) and online motion-capture quantified this approach bias towards food and non-food cues in a controlled VR environment with hand movements. The aim of this study was to test the specificity of a manual approach bias for high-calorie food in grasp movements compared to low-calorie food and neutral objects of different complexity, namely, simple balls and geometrically more complex office tools. Methods : In a VR setting, healthy participants repeatedly grasped or pushed high-calorie food, low-calorie food, balls and office tools in randomized order with 30 item repetitions. All objects were rated for valence and arousal. Results : High-calorie food was less attractive and more arousing in subjective ratings than low-calorie food and neutral objects. Movement onset was faster for high-calorie food in push-trials, but overall push responses were comparable. In contrast, responses to high-calorie food relative to low-calorie food and to control objects were faster in grasp trials for later stages of interaction (grasp and collect). Non-parametric tests confirmed an approach bias for high-calorie food. Conclusion : A behavioral bias for food was specific to high-calorie food objects. More systematic variations of object fidelity and in clinical populations are outstanding. The utility of VR in assessing approach behavior is confirmed in this study by exploring manual interactions in a controlled environment. Level of evidence: Basic science
Identification testing via sample splitting -- an application to Structural VAR models

In this article, a novel identification test is proposed, which can be applied to parameteric models such as Mixture of Normal (MN) distributions, Markow Switching(MS), or Structural Autoregressive (SVAR) models. In the approach, it is assumed that model parameters are identified under the null whereas under the alternative they are not identified. Thanks to the setting, the Maximum Likelihood (ML) estimator preserves its properties under the null hypothesis. The proposed test is based on a comparison of two consistent estimators based on independent subsamples of the data set. A Wald type statistic is proposed which has a typical $\chi^2$ distribution. Finally, the method is adjusted to test if the heteroscedasticity assumption is sufficient to identify parameters of SVAR model. Its properties are evaluated with a Monte Carlo experiment, which allows non Gaussian distribution of errors and mis-specified VAR order. They indicate that the test has an asymptotically correct size. Moreover, outcomes show that the power of the test makes it suitable for empirical applications.
Task-Based Language Teaching: Perceptions and Implementation in Teaching Speaking

This study explores teachers' and students’ perceptions of task-based language teaching in teaching speaking and the teacher's techniques in implementing task-based language teaching in teaching speaking. This study employs an explanatory case study design with an English teacher and 35 students of vocational schools in Indonesia involved in this study. Semi-structured interviews and classroom observations were used to collect the data. The data were analyzed qualitatively using interactive models of Miles, Huberman, and Saldana (2014). The finding indicates that both teacher and students had a positive perception toward task-based language teaching, especially its implementation in speaking class. Implementing task-based language teaching in speaking classes makes the classroom environment more effective. The implication indicates that how the teacher sets the knowledge into classroom implementation is related to what the teacher has understood about task-based language teaching.
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables

We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality is particularly well-suited for ternary random variables, which naturally show up in a variety of problems, including analysis of excess losses in classification, analysis of weighted majority votes, and learning with abstention. We demonstrate that for ternary random variables the inequality is simultaneously competitive with the kl inequality, the Empirical Bernstein inequality, and the Unexpected Bernstein inequality, and in certain regimes outperforms all of them. It resolves an open question by Tolstikhin and Seldin [2013] and Mhammedi et al. [2019] on how to match simultaneously the combinatorial power of the kl inequality when the distribution happens to be close to binary and the power of Bernstein inequalities to exploit low variance when the probability mass is concentrated on the middle value. We also derive a PAC-Bayes-split-kl inequality and compare it with the PAC-Bayes-kl, PAC-Bayes-Empirical-Bennett, and PAC-Bayes-Unexpected-Bernstein inequalities in an analysis of excess losses and in an analysis of a weighted majority vote for several UCI datasets. Last but not least, our study provides the first direct comparison of the Empirical Bernstein and Unexpected Bernstein inequalities and their PAC-Bayes extensions.
DIDACTIC POSSIBILITIES OF INFORMATION TECHNOLOGIES IN THE TRAINING OF FUTURE TEACHERS OF UKRAINIAN

The article reveals the didactic possibilities of information technology in the training of future teachers of the Ukrainian language. Screen and sound means as a kind of information technologies and requirements for their use are singled out. Considerable attention is paid to the use of video film both in academic classes and in extracurricular activities; its functions are revealed. Didactic requirements to technical means of training are defined. The significance and specifics of mass media in the formation of worldview and critical thinking of future teachers of the Ukrainian language are revealed. The specifics of the use of printed mass media and the peculiarities of their use in Ukrainian language classes are determined. Attention is paid to the personal computer as one of the means of information technology. It has been compared with other media (film, television, press, video). The functions of the computer as a tool of cognitive activity and a means of learning are highlighted. Its advantages and disadvantages are revealed. The need for a flexible rational combination of computer and traditional teaching methods is noted. The advantages of multimedia, features of electronic educational programs, in particular electronic textbook, are characterized. Didactic criteria and functions of the electronic textbook are revealed. The difference between the electronic textbook and the traditional linear one has been established. Its significance for distance learning and features of distance learning via the Internet are proved. Active methods of distance learning as a means of computer-mediated communication are proposed: debates, modeling, role-playing games, discussion groups, brainstorming, forums, and project groups. The specifics of the use of information technology by Ukrainian language teachers in the educational process are determined.&#x0D; Keywords: didactic opportunities; information technologies; training; future teachers; Ukrainian language; electronic textbook; screen and sound means; media; Internet.
KEMAMPUAN BERBICARA MELALUI VIDEO AUDIO VISUAL PADA PESERTA DIDIK KELOMPOK B2 TK KEMALA BHAYANGKARI PALU

The formulation of the problem of this research, namely how the forms and constraints of the ability to learn speaking through audio-visual videos in group B2 TK Kemala Bhayangkari Palu students and the purpose of this study, are to describe the forms and constraints of learning ability to speak through audio-visual videos in group B2 TK Kemala students. Bhayangkari Palu. The method used is a qualitative research method. The object of this research is the students of group B2 Kindergarten Kemala Bhayangkari Palu. Data collection techniques by observation, interviews, and documentation. Based on the analysis that has been carried out, it can be concluded that the form of learning to speak through audio-visual videos for group B2 Tk Kemala Bhayangkari Palu students is carried out through the following stages: providing learning materials, preparing various alternative media: Whatsapp Group, Youtube, and Zoom, monitoring, and uploading materials. learning in the form of tutorials, videos, student worksheets to media that have been determined or mutually agreed upon. The obstacles found by the teacher in learning speaking based on audio-visual videos for group B2 students were: (1) lack of parental assistance, (2) internet signal/connection, (3) teachers could not interact directly. With regard to the results of the study, the authors suggest that teachers can improve their competence in making innovations that can support the improvement of students' speaking skills by developing good plans for the learning process.
APLIKASI ALGORITMA DECISION TREE C4.5 SEBAGAI MODEL PENDUKUNG KEPUTUSAN SELEKSI ANGGOTA RESIMEN MAHASISWA UNIVERSITAS UDAYANA

The Reserve Officer Training Corps (ROTC) of the 901st Mayurajana Battalion. The 2000 Agreement caused the ROTC to be excluded from the Indonesian Military command structure, causing the ROTC in each campus to have a diverse standard of members due to the recruitment process which are done by students. C4.5 algorithm from chefboost was used to find the accuracy as well as assist the construction of an ideal C4.5 algorithm. The result shows the C4.5 algorithm by chefboost to have a 79.16% accuracy with the tendency to underestimate the data. During the modeling of the algorithm it is discovered that the source of the algorithm’s tendency to underestimate the data appears to be affected by the recruits administration and medical history.
Spectral Maps for Learning on Subgraphs

In graph learning, maps between graphs and their subgraphs frequently arise. For instance, when coarsening or rewiring operations are present along the pipeline, one needs to keep track of the corresponding nodes between the original and modified graphs. Classically, these maps are represented as binary node-to-node correspondence matrices and used as-is to transfer node-wise features between the graphs. In this paper, we argue that simply changing this map representation can bring notable benefits to graph learning tasks. Drawing inspiration from recent progress in geometry processing, we introduce a spectral representation for maps that is easy to integrate into existing graph learning models. This spectral representation is a compact and straightforward plug-in replacement and is robust to topological changes of the graphs. Remarkably, the representation exhibits structural properties that make it interpretable, drawing an analogy with recent results on smooth manifolds. We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism. Our approach bears practical benefits in knowledge distillation and hierarchical learning, where we show comparable or improved performance at a fraction of the computational cost.
Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction

In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information.These repositories include personal, historic, cultural and business event images.Image mining is a limited field in research where most techniques look at processing images instead of mining.Very limited tools are found for mining these images, specifically 3D images.Open source image datasets are not structured making it difficult for query based retrievals.Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D.This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors.Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects.Corresponding cultural 3D images are grouped for a 3D reconstruction engine's optimized performance.Spatial clustering techniques based on density like Particle Varied Density Based Spatial Clustering of Applications with Noise (PVDBSCAN) eliminate image outliers.Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers.Clustering based on Information theory is also used in this work to identify cultural object's image views which are then reconstructed using 3D motions.The proposed scheme is benchmarked with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to prove the proposed scheme's efficiency.Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques
Consideration of Usefulness of Leakage Prevention System for Critical Diagnostic Information Transmission

In our country, overlooking and failing to communicate the results of imaging examinations and pathological examinations to patients is a problem. This is because it affects the prognosis of the patients. With the introduction of this system, the situation improved within six months, so this system was useful. However, there are some things that remain unaddressed even after notification by the system, so human intervention as well as the system is considered necessary.
EXTRACT SEMANTIC AND REGIONAL LANGUAGE FROM IMAGES: A SURVEY

The aim of our project to automate the application to overcome from the language barrier among countries and also states within the country, the above mentioned application will perform the various features in the application. The application recognizes speech (human matter) in one language to another user defined language to communicate in expressive manner. It includes 4 modules voice recognition, translation and speech synthesis and image translation and gives audio of the translated language. Also the application accepts text written and converts it into the language needed. Application is able to recognize the text present in the image which stored in system or captured using camera and translate the text into the language needed and display the translation result back on to the screen of system.
On the Road to Designing Responsible AI Systems in Military Cyber Operations

Military cyber operations are increasingly integrating or relying to a specific degree on AI-based systems in one or more moments of their phases where stakeholders are involved. Although the planning and execution of such operations are complex and well-thought processes that take place in silence and with high velocity, their implications and consequences could be experienced not only by their targeted entities, but also by other collateral friendly, non-friendly, or neutral ones. This calls for a broader military-technical and socio-ethical approach when building, conducting, and assessing military Cyber Operations to make sure that the aspects and factors considered and the choices and decisions made in these phases are fair, transparent, and accountable for the stakeholders involved in these processes and the ones impacted by their actions and largely, the society. This resonates with facts currently tackled in the area of Responsible AI, an upcoming critical research area in the AI field that is scarcely present in the ongoing discourses, research, and applications in the military cyber domain. On this matter, this research aims to define and analyse Responsible AI in the context of cyber military operations with the intention of further bringing important aspects to both academic and practitioner communities involved in building and/or conducting such operations. It does that by considering a transdisciplinary approach and concrete examples captured in different phases of their life cycle. Accordingly, a definition is advanced, the components and entities involved in building responsible intelligent systems are analysed, and further challenges, solutions, and future research lines are discussed. Hence, this would allow the agents involved to understand what should be done, what they are allowed to do, and further propose and build corresponding strategies, programs, and solutions e.g., education, modelling and simulation for properly tackling, building, and applying responsible intelligent systems in the military cyber domain.
Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction

This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurred every month per case. The results showed that data pre-processing using min-max normalization was better than with Z-score normalization because the error prediction for pre-processing using min-max and z-score were 18% and 47%, respectively.
Exploration and Research on Teaching and Learning Model of “Case-based” in Higher Mathematics

In this paper, taking the Higher Mathematics B course offered by Beijing Institute of Petrochemical Technology for each major of economics and Management as an example, under the background of student-centered theory, combining with the analysis of mathematics situation of the major of economics and Management, the application and implementation of case-based teaching methodare discussed, and the organic integration of higher mathematics courses and students majors is promoted.
POTENSI BANJIR BANDANG MENGGUNAKAN ANALISIS MORFOMETRI DI DAERAH ALIRAN SUNGAI KLAGISON KOTA SORONG

Flash flood is water level increases rapidly in the river beyond flood level and occurs due to extreme weather. It's happened quickly without warning. The Klagison Watershed in Sorong City has grown rapidly and often had flooded. Settlements dominate almost on watershed areas and there are many hills that are deforested due to mining activities upstream. This study aims to examine the morphometric conditions and to analyze the spatial distribution of the potential flash flood in the Klagison Watershed of Sorong City based on morphometric analysis. GIS is used to obtain morphometric parameters such as linear, relief and aerial parameters. The correlation coefficient between each morphometric parameter was calculated using Pearson Correlation. Determination of potential flash flood areas using the priority zone formula. The result shows that the Klagison watershed has high erosion potential and large runoff based on morphometric analysis. This can be seen in the high value of the texture ratio (T) and low Constant channel maintenance (C) so that the runoff volume is also large. The Lof value in all sub-watersheds is small, which means that surface runoff will quickly to the channel. The flash flood potential in the Klagison watershed is very low (Sub-watershed 1 and Sub-watershed 5), moderate (Sub-watershed 3 and Sub-watershed 4), high (Sub-watershed 2) and very high (Sub-watershed 6 and Sub-watershed 7). It means that Sub-watershed 2, Sub-watershed 6 and Sub-watershed 7 need management priorities related to disaster mitigation.&#x0D; Keywords: flash flood; morphometry; watershed
Cluster Analysis of Inclusive Economic Development Using K-Means Algorithm

This study aims to cluster 38 Districts/Cities in East Java based on the 10 forming indicators of inclusive economic development and to determine the inclusive economic growth of Districts/Cities above or below the total average. 10 indicators used in this study are GRDP per capita, GRDP by business field, Labor force participation rate, Unemployment rate, Gini ratio, Expenditure per capita, the number of poverty, Life expectancy, expectation years of schooling, and mean years of schooling. There are 3 scenarios in this study, namely 2 clusters, 3 clusters, and 4 clusters. The method of clustering in this study is using the K-means algorithm. This study uses the silhouette coefficient to evaluate the best cluster of 3 scenarios. The best k-means algorithm in this study is using 2 clusters with a silhouette coefficient of 0.87. There are 29 Districts/Cities included in cluster 1 with inclusive economic development below the total average and 9 Districts/Cities included in cluster 2 with inclusive economic development above the total average. The members of cluster 1 are mostly district areas and located in coastal or border areas and the members of cluster 2 are mostly urban or industrial areas.
Impact of blockchain technology on agricultural product circulation system: A simulation analysis with agent-based modelling

Abstract In the context of the Internet, the agricultural product circulation system needs to undergo corresponding changes to adapt to the modern fast-paced social system. Based on the main characteristics of blockchain technology and the summary of domestic and foreign theoretical research, this paper simulates the impacts of blockchain technology on the agricultural product circulation system. The results reveal that blockchain technology can improve the qualification rate of agricultural products and thereby ensure their quality and safety. Moreover, blockchain technology can significantly enhance the efficiency of the agricultural product circulation system, thus greatly promoting the economic benefits. Finally, the introduction of blockchain technology can effectively promotes the governance level and reduces the supervision cost of the agricultural product circulation system. These findings enrich the research on the application of blockchain technology in the management and circulation of modern agricultural products.