Reviewerly/exHarmony huggingface dataset:
PENERAPAN TEKNIK LOAD BALANCING DENGAN EMPAT ISP MENGGUNAKAN METODE PER CONNECTION CLASSIFIER (PCC) DAN PENDISTRIBUSIAN BANDWIDTH

In the Bengkalis Regency Education Office, a computer network was built to support administrative activities at the office. The Bengkalis Regency Education Office has four ISP computer network connections, namely Telkomsel, 3Three, XL and Axis. This connection is used as a network connection to the data server and is also used to serve users of the employee's computer network in the office. In this Final Report, a load balancing technique and connection is designed using four ISP lines in the Bengkalis Regency Education Office. The method used is the Per Connection Classifier (PCC) method. This method classifies network connections based on source address, destination address, source port, and destination port which are implemented using the Mikrotik Routerboard device. With this method all network requests from the user will go to the routerboard that has been configured with Load Balancing using the PCC method after which the connection will be distributed to the four existing ISPs. Then to find out whether this configuration is running or cannot be known based on the traffic generated by four LAN ISPs that are connected to the Routerboard interface as an output line when we do Steaming, Download, Browsing and Upload activities, besides that, the waiting time is tested. After that, to find out the quality of the resulting network, a Quality of Service (QoS) test is carried out to find out how good the network quality of the Load Balancing configuration with this PCC method is. Bengkalis District Education Office.
A Model Proposal for Predicting Students Academic Performances Based on Data Mining

The purpose of this research is to propose a model to predict the academic performances of students and prevent their possible academic failures in the future.This research was conducted as an educational data mining application.The academic data of 1570 students who graduated from four departments of Akdeniz University, Faculty of Education between the year 2012 and 2017 were used in the research.All the exam scores, final grades, weighted averages of semester grades, and graduation grades were used in the study.Two main models have been developed for predicting students' academic success using data mining techniques and algorithms.The First Model is the Student Graduation Grade Estimation Model.This model is aimed to predict the future graduation grades of the students.Sub-models were developed using Artificial Neural Networks and Multiple Linear Regression Analysis.It was observed that the developed models predict the graduation grade of the students with an accuracy of 94% to 97% from the 1st semester's data.The second model developed in this research is the Early Warning Model for Students' Possible Academic Failures in the Future.The model predicts whether the general weighted average grades will fall below 2 in the future, according to the students' 1st year's 1st-semester grades.Under this model, the accuracy of the sub-models which were developed using Logistic Regression and Decision Trees was found to be 72% to 87%.As a result of the research, a model was proposed to prevent the academic failures in the future by predicting the student's academic performances.It can be asserted that educational institutions can benefit effectively and efficiently to increase students success by using the proposed model.
The Exploration of a New Blended Teaching Model Based on 5G Network and Small Private Online Courses for Talent Education

Objectives : To explore a new blended teaching model based on 5G network and small private online courses (SPOCs) to promote talent education under the background of China’s policies to foster excellent doctors and improve teaching quality. Design: Students were randomized into intervention group and control group of 20 each. Students assigned to the intervention group were given a new blended teaching model called 5G+SPOC pedagogy in the virtual classroom and asked to complete a questionnaire after class. While, those assigned to the control group were taught by the pedagogy including teacher guidance and teacher and peer interaction in the brick-and-mortar classroom. Both groups completed a quiz after the class session that measured students’ comprehension of the knowledge with a total score of 100. Setting: A level-A tertiary hospital in Fujian Province in China, which can offer 5G network environment.Participants: A total of 40 medical students in the same grade aged 20-25 years who have been currently receiving the pre-five years of “5+3” model courses were enrolled in the study. Primary and Secondary Outcomes:(1)The mean scores of the quiz that reflects students’ understanding of the course and mastery level. (2) Positive responses to the new teaching model.Results: Forty complete sets of quizzes were returned the quiz performance in the intervention group was improved. The intervention group had higher mean scores than the control group, and the difference in the mean scores of the quiz between the two groups was statistically significant (intervention: 83.25±19.41750; 95% confidence interval, 74.16 to 92.34 vs. control: 59.50±28.41886; 95% confidence interval, 46.2 to 72.8; t=3.086, P=0.004, d=0.97584). While, the difference in the mean ages of the students between the two groups was not statistically significant (intervention: 22.25±1.140; 95% confidence interval, 21.59 to 22.91 vs. control: 22.60±1.353; 95% confidence interval, 21.97 to 23.23; t=0.801, P=0.428). The result of the questionnaire showed that 5G+SPOC pedagogy had positive response rates.Conclusions: 5G+SPOC is a new teaching model which provides students and teachers with better experience in distance learning featuring real-time interaction, live streaming, high-definition and flexible schedule as well as instant feedback. SPOC based on 5G network focuses on fostering a small group of students to be excellent doctors, which not only improved the quiz performance of the students, but also offered medical students an effective and flexible approach to learn. Teaching, learning and problem solving at any time and any place can be achievable by adopting this learning technique.
LibGuides: IN151 - CWRR II - O'Donoghue: Interlibrary Loan

This guide provides suggestions for research tools and techniques for IN151. Using ILLiad to make interlibrary loan requests
Implementation of Gabor Filter for Carassius Auratus’s Identification

Carassius Auratus, otherwise known as goldfish, is one of the most popular ornamental fish. Goldfish have many variations, such as differences in body shape, colors, size, and fins. Identifying goldfish manually is difficult to do. This is due to several species that have similar anatomy, so automatic fish identification is needed. This research aims to identify three species of goldfish, such as Fantail, Oranda, and Ranchu. Gabor filter was applied to extract the features of goldfish. Gabor filter consists of several steps, including parameter initialization, Gabor kernels, Gabor convolution, feature point. The parameters used were frequency, orientation, and kernel’s size. Gabor kernel was formed based on initialized parameters. The convolution process was produced by adding up the multiplication of 256x256 pixel goldfish’s images and Gabor kernels. The results of the convolution process were normalized to produce a feature vector matrix. A probability neural network was used to classify the goldfish. Probability Neural Network is a supervised network that finds its natural use in decision making and classification problems. This research used 216 of goldfish’s images. Seventy-two images were used for each species. The optimal parameters in this study were kernel size (5,5), frequency (3), orientation (5), and downsample (16,16), with accuracy up to 100%. Parameters of the frequency, orientation, kernel size, and downsample affect the level of accuracy. The greater the parameter value used, the more variations in feature vectors are obtained. Still, if too many variations of the feature vector cause redundancy data, it causes the classification process to be inefficient.
대학 온라인수업 학습자의 학습몰입과 학업성취도를 위한교수 실재감에 관한 연구

The purpose of the study was to analyze the effects of teaching presence on students’ learning flow and academic achievement in online education of universities, especially during in COVID 19 pandemic situation. In total, 277 validated questionnaires were analyzed by students who have studied by online class in universities. These results are based on research, and the outcomes are as follows: First, it was shown that among Teaching Presence Factors, Systematic Action Plan, Teaching Contents, Communication & Feedback, and Teacher Presence had significant effect on students’' learning flow. Particularly, readable and various contents for the class and sense of being there make students more focused during online classes. Secondly, Teaching Presence Factors, Teaching Contents, Communication & Feedback, and Teacher Presence had significant effect on students’' academic achievement from online classes. Third, students’ learning flow had significant effect on their academic acquirements. Even in online classes, teaching presence is really important factors to students’ more focused in their learning and achieve their academic goals. In particular, the results showed freshmen of university marked the lowest core in all areas. Due to COVID 19, freshmen are in very unusual situation to get through online classes without socializing with faculty and other students. To improve their online learning experiences, they have to be more cared and oriented by faculties and universities.
3D Object Detection For Intelligent Vehicles

This master thesis presents an experimental study on 3D person localization (i.e., pedestrians, cyclists)in traffic scenes, using monocular vision and Light Detection And Ranging (LiDAR) data. The performance of two top-ranking methods is analyzed on the 3D object detection KITTI dataset. In this evaluation, the effect of the Intersection over Union (IoU) threshold on the performance in terms of 3D bounding box location, size, and orientation is analysed. Since the KITTI 3D object detection dataset contains relatively few 3D person instances, the analysis will is to the EuroCity Persons 2.5D (ECP2.5D) datasets (both day and night), which is one order of magnitude larger. Using both datasets, additional experiments are performed to evaluate the influence of distance, the number of LiDAR points, occlusion, and intensity on the performance. Domain transfer experiments between the KITTI and ECP2.5D datasets are performed, to examine how these datasets generalize with respect to each other. Furthermore, Part-A2 net is used to evaluate the detection score which is given to the ground truth pedestrians. The relationship between the detection score and the distance, the number of LiDAR points, and occlusion is analyzed. Some objects are not detected although their ground truth detection score is high. This creates the potential to detect these pedestrians. Lastly, this thesis presents a method that uses the detections from the previous frame to increase the performance in the subsequent frame by adding the previous detections to the 3D proposals coming from the Region Proposal Network (RPN).
A Modern Approach To The Psychological And Pedagogical Diagnosis Of Developmental Disorders In Children

The article highlights the issues of psychological and pedagogical diagnostics of children with developmental disorders. It has been substantiated that mental processes and personality traits are not the result of the maturation of individual zones or areas of the brain. They take shape in ontogenesis and depend on the social situation of the child's development.
Robust Design of Artificial Neural Network Methodology to Solve the Inverse Kinematics of a Manipulator of 6 DOF

In this work the artificial neural networks robust design methodology was used to find the optimal parameters in backpropagation artificial neural network architecture applied to the solution of the inverse kinematics of a six degrees of freedom robotic manipulator. A systematic and experimental strategy is proposed according to the requirements of the problem. Among the various parameters that affect the performance of a backpropagation neural network, four design variables were selected since they are the variables that can be controlled by the user: the number of neurons in the first hidden layer, the number of neurons in the second hidden layer, the momentum, and the learning rate. The results show that the robust design methodology can be used to find a better configuration with high performance and generalization capability, significantly reducing the time spent determining the optimal architecture of the neural network compared to the trial and error approach, which requires long periods of time.
Introduction and dissemination of innovations during the pandemic: the experience of educational organizations in Krasnoyarsk

The educational process, not only in our country, but also in the world, has undergone drastic changes since March 2020. The total transition from traditional classroom classes to distance education occurred due to the threat of the coronavirus pandemic (COVID-19). The changes affected not only the learning process, but also the innovative activities that were implemented in educational organizations. The purpose of this article is to describe and analyze the experience of spreading innovations in schools in the city of Krasnoyarsk during the pandemic to identify related problems and ways to solve them. The main idea of the work is to study the transformation of the phenomenon of educational innovations in school education in the context of universal distance learning. Within the framework of the study, the possibility of organizing distance learning and conducting innovative activities during the pandemic was monitored. The survey was conducted for all educational organizations in the city of Krasnoyarsk. Secondary analysis and interpretation of the survey results, systematization and classification of the theoretical and factual materials used, analysis of management practices and experience of educational organizations in the conditions of extreme transition to remote mode were carried out, which together made up the author’s research result. In the course of the work, the key problems faced by the education system of the city of Krasnoyarsk during the transition to remote operation were also identified.
The Use of Repeatable Components in Hybrid Models to Enhance Software Project Management Success

The management of software project development requires a dynamic and reactive environment to meet shorter time-to-market demands to address competition efficently in the software industry. This scenario requires the use of effective and robust methodologieswhere opportunities are not lost due to delays and failures in timely software project deliveries.The Agile Manifesto in 2001 which introduced 4 values and 12 principles was designed to develop and manage software projects in a more suitable and effective way to improvethe success rates of software projects. But, increase in overall success rates are still not significant with failure rates remaining plauteaued at about 30% over the last 10 years. Hybrids methodologies seem to have worked better as agile hybrid management methodshave shown more promise when compared to pure agile methods with an overall success rate increase of 16%. There is evidence too that by combining agile methodologies with traditional methodologies, there would be a further increase in success rates. Whilst many hybrid methodologies have been suggested and researched, the gaps in the literature review reveal there is a lack of hybrid models that have been empirically developed and studied as second order components. To build a robust hybrid model, it is important to gather the relevant information and careful consuideration must be given to the design of the questionnaireto fit second order components and models must incorporate and provide for the use repeatable ways to test models once the data is collected.This paper presents a review of the current gaps in hybrid methodologies and proposes a questionnaire design that supports the research methodology and empirical study to be undertaken with second order components (Constructs).Further it looks at the design approach in questionnaires which incorporates the use of repeatable constructs and the measures used and emphasizes this as an important ingredient for developing and testing hybrid models in research studies.
Student Perceptions To Critical Reading Subject In The Third Semester Department Of English Education Muhammadiyah University Of Surakarta

This research was conducted to determine students' perceptions to Critical Reading subject in the third semester of Department of English Education Muhammadiyah University of Surakarta. This study use a qualitative descriptive method, while the data collection techniques used are questionnaires and interviews, interviews were conducted to find out things that the researcher did not understand from the questionnaires answers filled out by the students . The subject in this study was 22 fourth semester students Department of English Education Muhammadiyah University of Surakarta that has been taken Critical Reading subject. The results show that students have different perceptions regarding Critical Reading. There are those who argue that critical reading is a subject that requires more understanding requires critical thinking, analyzes, interpret and evaluates text, some also argue that Critical Reading is a process of reading quickly.
Will it Unblend

Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data. Blends, such as “innoventor”, are one particularly challenging class of OOV, as they are formed by fusing together two or more bases that relate to the intended meaning in unpredictable manners and degrees. In this work, we run experiments on a novel dataset of English OOV blends to quantify the difficulty of interpreting the meanings of blends by large-scale contextual language models such as BERT. We first show that BERT’s processing of these blends does not fully access the component meanings, leaving their contextual representations semantically impoverished. We find this is mostly due to the loss of characters resulting from blend formation. Then, we assess how easily different models can recognize the structure and recover the origin of blends, and find that context-aware embedding systems outperform character-level and context-free embeddings, although their results are still far from satisfactory.
ANALYSIS OF RESIDENTIAL RESERVOIR CAPACITY AT THE MUTIARA PURI HARMONI 2 CIKARANG

&lt;p&gt;Cikarang is one of the areas in the administrative city of Bekasi Regency, which has several flood-prone areas. Every year, several areas of Cikarang often experience flooding, including the North Cikarang area. Precisely in Karanganyar Village, Karang Bahagia Subdistrict, where most of the land has been converted from rice fields to densely populated housing. Over time, Karang Bahagia Subdistrict became familiar with floods and puddles. Mutiara Puri Harmoni 2 Housing is one of the densely populated residential areas in the Karang Bahagia District. From the observation of the drainage system made by Mutiara Puri Harmoni 2 Housing, there is a lot of sedimentation and some of the sections have a lot of narrowing due to increased development in the housing area. For the drainage before it is drained into the nearest river, namely the Gemboro river, the Mutiara Puri Harmoni 2 housing drainage system creates a reservoir as a storage. By analyzing the reservoir section using the rainfall distribution method and the Log Pearson III method, the resulting 2-year rainfall intensity is 74.374 mm/hour with the resulting discharge of 3.339 m3/second. From the calculation results, the reservoir storage is 6057.216 m3/second with a cross section of 62.4 mx 55.62m. Taking into account the reservoir water balance using 17 years of rainfall data, the maximum discharge in February is 18323m3 and the lowest discharge is in August of 3038m3. Therefore, the results of the capacity analysis needed to avoid flooding are to deepen and expand the reservoir and/or add a pump so that the concentration time is not too long.&lt;/p&gt;
Chaos Splitting and Merging Security over Distributed Storage in Cloud Computing

Cloud Computing facilitates business by storing an enormous amount of data in the cloud transmitted over the Internet with seamless access to the data and no hardware compatibility limitations. However, data during transmission is vulnerable to man in middle, known plain text, chosen cipher text, related key and pollution attack. Therefore, uploading data on a single cloud may increase the risk of damage to the confidential data. Existing literature study uncovered multiple cryptography techniques such as SA-EDS, Reliable Framework for Data Administration (RFDA), Encryption and Splitting Technique (EST)to secure data storage over multi-cloud.However, existing methods are vulnerable to numerous attacks.In proposed system chaos merging technique is used to confuse attackers and protects data from various attackers.
Face illumination processing via dense feature maps and multiple receptive fields

Recently, illumination processing of facial image based on generative adversarial networks has made favourable progress. However, the image quality is not so satisfactory and the recognition accuracy is low when the face image under extreme illumination conditions. For these reasons, an elaborately-designed architecture based on convolutional neural network and generative adversarial networks for processing face illumination is presented. A novel dense feature maps loss that computes loss by using the varisized feature maps extracted from different convolutional layers of pre-trained feature network is put forward. Moreover, multiple-receptive-fields-based generator that uses multiple encoders during encoding phase is also proposed, and these encoders have the same structure with different kernel size. A variety of experimental results demonstrate that the method is superior to the state-of-the-art methods under various illumination challenges. Code will be available soon at https://github.com/ling20cn/IP-GAN
Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve Natural Language Generation

Human ratings are one of the most prevalent methods to evaluate the performance of NLP (natural language processing) algorithms. Similarly, it is common to measure the quality of sentences generated by a natural language generation model using human raters. In this paper we argue for exploring the use of subjective evaluations within the process of training language generation models in a multi-task learning setting. As a case study, we use a crowd-authored dialogue corpus to fine-tune six different language generation models. Two of these models incorporate multi-task learning and use subjective ratings of lines as part of an explicit learning goal. A human evaluation of the generated dialogue lines reveals that utterances generated by the multi-tasking models were subjectively rated as the most typical, most moving the conversation forward, and least offensive. Based on these promising first results, we discuss future research directions for incorporating subjective human evaluations into language model training and to hence keep the human user in the loop during the development process.