Reviewerly/exHarmony huggingface dataset:
비대면 전공 한국어 수업의 효율성 제고를 위한 플립러닝 활용 방안 연구

This research is aimed at presenting a case of flip-learning classes that can be applied even in non-face-to-face situations as a way to focus on major-related Content-Based Korean classes.Flipped learning is a good alternative in the reality that it is difficult to conduct Korean language classes related to majors due to the practical difficulties of collaborating with content-oriented experts. In addition, the flipped-learning in major Korean classes, where learners’ Korean proficiency and background knowledge do not match, can reduce the difference in proficiency among learners through video lectures before class. In addition, using real-time online classes, discussions based on active interactions between teachers and learners, and between learners and learners are possible. In short, non-face-to-face CBI Korean classes using flipped learning can be a way to lead learners to participate and use more multimedia materials to overcome space limitations and provide high-quality online classes to learners.
A Quick Review of Cloud Computing & its Service Providers

Cloud Computing is storing data remotely on internet rather than on local servers. It is also called as store data anywhere on internet & access anytime. The companies which provide these services are called as cloud providers. The cloud providers are Microsoft azure, AWS, google cloud etc. Cloud Computing is just like Electricity, pay as you use. cloud computing benefited us in various ways such as it has reduced the hardware cost, it provides easy access to the data, easy to scale up & scale down, provides better security than local servers due to centralized security location with encryption facility. The cloud computing can be classified as cloud deployment & cloud services. Cloud Computing Services takes care of IT infrastructure allows users to focus on development.
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN ($\text{H}^2$3D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed $\text{H}^2$3D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at \url{https://github.com/djiajunustc/H-23D_R-CNN}.
The MIT Supercloud Dataset

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to changes in deployment approaches of HPC clusters and the commercial cloud, as well as a new focus on approaches to optimized resource usage, allocations and deployment of new AI frame- works, and capabilities such as Jupyter notebooks to enable rapid prototyping and deployment. With these changes, there is a need to better understand cluster/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies in resource utilization, energy/power consumption, failure prediction, and identifying policy violations. In this paper we introduce the MIT Supercloud Dataset which aims to foster innovative AI/ML approaches to the analysis of large scale HPC and datacenter/cloud operations. We provide detailed monitoring logs from the MIT Supercloud system, which include CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This paper discusses the details of the dataset, collection methodology, data availability, and discusses potential challenge problems being developed using this data. Datasets and future challenge announcements will be available via https://dcc.mit.edu.
A Classifing Bibliography on Computer Operating Systems.(Dept.E)

This paper is essentially a get of references to further reading for the entire operating system topics. The selected references are representative of studied dealing with computer operating systems. The references are subgrouped into due to use categories.
An Efficient Approach to Restructure Natural Language Text

During the recent times, there is enormous growth found in the domain of software development across the world. Many organizations are automating all the activities in the organization and for the development of any customized application; the developing organization needs to gather the requirements from the client organization. The gathered requirements may be structured or unstructured because of the flexibility and ambiguity in the English language. The natural language sometimes has the problem of flexibility because of which the same term may be written in a different way. To overcome the problems of natural language statements, in this paper an automated tool is proposed to restructure the sentences, paragraphs, simple statements, compound statements and pages which are grammatically correct. This approach takes the input text and converts compound statements into simple statements, removing duplicate statements, places one statement per line and assigns sequence numbers to each physical statement. This enables us to extract the correct and complete meaning of the statements for further processing and output achieved is used for abstraction of design elements.
국제개발협력의 명분과 진화의 맥락

This study aims at reviewing the justification and evolutionary context of international development cooperation from historical view points. In this line, this paper reviews the paradigm formation of international development cooperation through the social evolution perspective, the contents of modernization theory, the impacts of the Marshall Plan since 1948 and the United Nations expert report (1951). This article also looks at the results of a series of high-level meetings organized by the Development Assistance Committee (DAC) on the changing approach of international development cooperation. The results of the review are as follows. First, international development cooperation is justified in terms of maintaining the global economic order. In this light, international development cooperation has been derived from unfair trade practices and past colonial rule. Second, international development cooperation has been based on the sustainability of human civilization, which should be jointly responded altogether to climate change and other environmental crises. Third, international development cooperation is justified from a humanitarian standpoint. This humanitarian stance is also linked to the security guarantee of the international community, which defines human basic needs as universal welfare levels for mankind.
MODEL KLASIFIKASI EMOSI BERDASARKAN SUARA DENGAN METODE MULTILAYER PERCEPTRON

Human-computer interaction technology has developed, for example, speech recognition. One of the uses of speech recognition is to recognize human emotions. Computers can recognize and classify human emotions based on sound. There have been many studies related to various method of feature extraction and classification but the results are still not close to perfect. The feature extraction method uses the Mel Frequency Ceptral Coefficient (MFCC). The data used is secondary data sourced from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The system model will be able to recognize 8 types of emotions, namely neutral, calm, happy, sad, angry, scared, disgusted and surprised. The results of the model obtained accuracy for neutral emotions by 98%, calm emotions by 97%, happy emotions by 94%, sad emotions by 97%, angry emotions by 97%, fearful emotions by 94%, disgust emotions by 97% and shocking emotions. by 96%. So that the results of the average accuracy of the models that have been made are 96%.
Perancangan Sistem Informasi Instalasi Hemodialisa Pada Rumah Sakit Umum Daerah Karawang

The use of information, media and communication technology has changed both the behavior of society and human civilization globally. The development of hospitals in Karawang is increasingly mushrooming, as well as the increasing number of degenerative diseases such as hypertension and diabetes, the increasing prevalence of chronic kidney disease. So it requires the handling of renal replacement therapy (Renal Replacement Therapy) because it is increasingly needed for cases of acute and chronic renal failure. The purpose of this research is to build an integrated and controlled information system that can simplify the patient administration process and reduce the effectiveness of the patient's time for dialysis. The methodology that the authors use in this study includes methods of identification and system design. System identification includes identification of the organization, running systems, and problem identification. The problems faced at this time in the Hemodialysis Installation. Patient data is not the same between patients who are served by hemodialysis (dialysis) and those in the application report, the transaction value for using tools and materials is sometimes empty or there is no transaction value in the application, it will cause losses to the customer. hospital. It is hoped that this system can eliminate the problems that have been happening so far.
Face Mask and Social Distancing Detection Using ML Technique

Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.
Analisis Usability Aplikasi Pengolahan Data Berbasis Web Pada Perpustakaan Sekolah Berdasarkan Permodelan Nielsen

Web-based data processing applications have an important role to avoid human error, especially in school libraries. However, in developing a system, consideration of the level of user satisfaction is needed. So the purpose of this study is to analyze the usability of web-based data processing applications in school libraries based on Nielsen's modeling. The method of data collection was performed through filling out questionnaires by 51 students of Public Junior High School 2 Jejawi, OKI Regency. The questionnaire consists of 15 questions using a 4-point Likert scale. The results obtained indicate that the data are valid and reliable. In addition, the web-based data processing application shows that the five usability attributes of Nielsen, namely learning ability, efficiency, memories, few error, and satisfaction have good results, with an average usability score of 80.4. In conclusion, web-based processing applications are acceptable
Siamese network object tracking algorithm improved on backbone

The Siamese network base object tracking method usually removing the Padding part in the backbone network to ensure the invariance of convolution translation. That will case the trained network cannot extract the boundary information well. In order to solve this problem, in this paper a boundary expansion and redundancy cropping method is proposed to improve the boundary information extracting ability of object tracking Siamese network. Firstly, we used the proposed method to modify SiamFC (Fully-Convolutional Siamese Networks), which using AlexNet and VGGNet as backbone, respectively. The experimental results on expand Got-10k dataset show that the proposed boundary expansion and redundancy cropping method can improve tracking effect, obviously. Then we use different depth residual networks as the backbone to further test the effect of the proposed method on deep networks. The improved backbone using boundary extension and redundancy cropping can suppress performance degradation better than the original backbone without Padding. The results show that the improved backbone using boundary extension and redundancy cropping has higher accuracy at multiple depths than the original backbone without Padding. Results on OTB, Tcolor128 and other datasets also show that the proposed method can improves the success rate and accuracy rate on the premise of maintaining real-time performance.
Implementation of Machine Learning Algorithm for Task Scheduling In Cloud Computing Environment

For each application, efficient scheduling of heterogeneous workloads to heterogeneous processors is critical to achieving high performance. Cloud computing gives a diverse environment in which to conduct numerous tasks. In a cloud context, scheduling user requests (tasks) is an NP-hard optimization problem. To propose a sub-optimal solution to the problem, researchers present a variety of heuristic and metaheuristic strategies. In this research, we present an Ant Colony Optimization (ACO) based task scheduling (ACOTS) algorithm for lowering the average waiting time and optimising the system's makespan. In the CloudSim simulator, the designed method is implemented and simulated. Simulation results are compared to Round Robin and Random algorithms, both of which produce good results.
A New Galactic Swarm Optimization Algorithm Enhanced with Grey Wolf Optimizer for Training Artificial Neural Networks

This paper proposes a new Galactic Swarm Optimization (GSO) algorithm enhanced with Grey Wolf Optimizer (GWO).The proposed algorithm is used to train a feedforward Neural Network for function approximation. Galactic swarm optimization isa popular swarm algorithm that has been used to solve optimization problems. It is motivated by stars movement and thesuperclusters of a galaxy in the universe. The algorithm allows using multiple levels of exploitation and exploration of searchspace. At the explorative level, different sub-populations independently explore search space and at the exploitation level, the bestsolution of different sub-populations is considered as a super swarm and moves towards finding the best solution position found bythe super swarm. The algorithm uses Particle Swarm Optimization (PSO) algorithm’s update equation in both levels. PSO has beenproven to get stuck in the local minimum due to its ability to converge prematurely. In this work, Galactic swarm optimizationenhanced with grey wolf optimizer is proposed. The use of the entire pack of wolves for exploring the search space in the GWO hasproven to escape local minima. Thus, the GSO’s explorative phase is done with the GWO and for the exploitation phase quicklyconverging PSO is used. The proposed algorithm ability is tested by training a feedforward neural network for functionapproximation of benchmark optimization problems. The proposed GSOGWO outperformed the classical GSO in most of thefunctions.
Web Content Analysis of Five Selected Indian Institutes of Science Education and Research (IISERs) Libraries' Website: An Evaluation

The study assessed the content of library websites of five IISERs Bhopal, Kolkata, Mohali, Pune and Thiruvananthapuram. This study is done with 8 checkpoints of qualitative features for descriptive information of the library. 155 checkpoints of quantitative features are used for various aspect of the library of five selected IISERs such as; general features, multimedia features, library services, library resources, my library, web 0.2/library 0.2, currency/accuracy/relevance, organizational structure, links and maintenance, user interface, search features, informative feedback and support. Finding of this study reveals that IISER Thiruvananthapuram is scored 100 out of 155 (64.51%) ranked above medium followed by IISER Mohali 97 (62.58%) and IISER Bhopal 95 (61.29%) ranked above medium. IISER Pune scored 85 (54.83%), and IISER Kolkata 74 (47.74%) ranked medium.
Primer on Suitability for Consumer Data Use and Product Design

Internationally, suitability or appropriateness in finance is conceptualized as degree to which the product or service offered by the intermediary matches the retail client's financial situation, investment objectives, level of risk tolerance, financial need, knowledge and experience (Basel Committee on Banking Supervision, 2008). A suitability-based approach to financial services provision places obligations on providers to make assessments of whether a product is suitable prior to the sale to a consumer. The effort is to move away from the current approach that places the burden solely on consumers to make complex financial choices. In India, financial sector regulators have supported the need to emphasize on suitability to varying extents. The Reserve Bank of India, for instance, upholds the consumers’ right to suitability in its Charter of Customer Rights. However, practitioners have often asserted that suitability is challenging to implement in practice because of the costs of granular suitability assessments and the absence of the detailed consumer information needed for them. With the growing use of personal information and analytics in provision of financial services, that challenge appears solvable — unlocking the potential for providers to undertake suitability assessments when selling or designing financial products. These new techniques however raise a new set of issues for consideration. First, the inappropriate use of personal information may itself create new harms for consumers. This raises questions about the boundaries of appropriate data use by all stakeholders in this new digital environment. Second, suitability frameworks may need to be updated for a new world where users can interact with integrated platforms as their “front-end” to finance. Such integrated front-ends allow various product features to be brought together by multiple providers. This begs the question of whether suitability frameworks evolved for a more analogue financial landscape need to be updated for digital financial services. These and related concerns re-open many questions on the need for new suitability frameworks for providers and regulators operating in the data-driven environment of modern digital finance.
Library Guides: Innovation Design Workshop (Berks): Innovation Design Workshop

Materials for the Innovation Design Workshop delivered at Penn State Berks for Penn State Startup Week 2021.
Assessing Employees Performance and Its Impact In Enhancing Loyalty At Jordanian Private Universities In The Northern Territory: DOI: 10.46988/IJIHRM.01.02.2020.002

The purpose of the study was to identify employee performance appraisal and its effect on organizational loyalty: An applied study on the private universities in the northern province of Jordan from the viewpoint of universities managers, assistant managers, heads of departments, and employees. A questionnaire of (45) items was constructed, items were distributed on two dimensions (Employees Performance Appraisal and Organizational Loyalty) to identify the level of implementing the process of employee’s performance appraisal. The population of the study included all managers, assistant managers, heads of departments, and employees of Jordan private universities in the north province (N. 435). Questionnaire number (219) was circulated out of which (209) were retrieved, representing a response rate of (95.4%). Results of the study revealed statistically significant effects of employee performance appraisal on enhancing organizational loyalty in the private universities in the north region of Jordan. It also showed that the level of Jordan private universities in the northern province interest in the process of appropriate performance evaluation was average. According to the results the researcher recommends the need to work on activating the role of performance evaluation at the Jordanian private universities in the northern province through realizing (achieving) the highest degrees of justice and objectivity in the evaluation process, to relate the results of the evaluation with moral and material incentives and pay more attention to the process of performance evaluation due to its important role and effect on organizational loyalty among employees.
Prediksi Tingkat Penjualan Sepeda Motor dengan Metode Rough Set

The high level of motorcycle sales makes the showroom have difficulty in procuring variants of motorcycles to be sold. The many variants of motorcycles in one manufacturer, make different sales of each motorcycle variant, there are variants with high sales and some with low sales. So it is necessary to predict the level of motorcycle sales. This study uses data from one of the Honda motorcycle showrooms, namely the Hayati showroom, Pasaman branch. The data used is a recapitulation of motorcycle sales data in the second quarter of 2020. This study uses 24 data samples as a decision system. From the test results obtained 13 equivalence classes, then a reduction process is carried out to obtain 7 reducts and a rules generation process is carried out, then 41 rules are obtained with Motorcycle Prices as the dominant attribute in influencing the Sales Level attribute decisions with an incidence of 42% and min. support = 5.
Application of hyperspectral remote sensing in the longwave infrared region technology for assessing the influence of settled desert dust particles on soil surface

&amp;lt;p&amp;gt;Soil mineralogy holds important information on the soil origin and development. Most common minerals in soils&amp;amp;#8212;quartz, clay minerals and carbonates&amp;amp;#8212;present fundamental spectral features in the longwave infrared (LWIR) region (8.0&amp;amp;#8211;12 &amp;amp;#956;m range), whereas quartz is featureless in the optical region (0.4&amp;amp;#8211;2.5 &amp;amp;#956;m range). A procedure for determining the soil surface mineralogy from hyperspectral LWIR data was used to assess the interaction with desert dust particles that accumulate on the soil surface during dust storms. Ground- and field-based hyperspectral LWIR images of different types of Israeli soils, before and after dispersion of desert dust-like material on the surface, were acquired with the Telops Hyper-Cam sensor, to calculate the surface emissivity spectra of soils, representing the surface mineralogy. Identifying mineral-related emissivity features and calculating their relative intensities, using two created indices&amp;amp;#8213;SQCMI (Soil Quartz Clay Mineral Index) and SCI (Soil Carbonate Index)&amp;amp;#8213;enabled determining the content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner&amp;amp;#8212;from more to less abundant, and identifying changes in their abundance resulting from the dispersion of dust on the surface. The dust affected the mineral-related spectral features of the soil surface, depending on the mineral composition of the dust compared to soil surface mineralogy, and its amount. The ability to detect minor mineralogical changes on the soil surface using high spectral resolution LWIR data was demonstrated.&amp;lt;/p&amp;gt;