Reviewerly/exHarmony huggingface dataset:
Pengembangan Learning Management System (LMS) dengan Menerapkan Video Based Learning dan Gamification Dalam Meningkatkan Motivasi dan Keterlibatan Mahasiswa

The learning model used greatly affects the learning process in the Covid-19 pandemic era. The online learning that has been passed in this one year has caused boredom. The learning process is too monotonous, the teacher's intonation is less varied, and not easy to interact directly with friends and teachers. Therefore, to achieve an effective and maximum learning process, the researcher proposes using video-based learning and gamification methods to increase deeper understanding of the material, motivation in learning, and student involvement in the learning process through the Learning Management System (LMS). The material presented will be transformed into more interactive and interesting videos such as simple animated videos, tutorial videos, podcast videos, and others. This research aims to provide positive benefits for students to be more active in discussing and collaborating and enthusiastic in doing all learning activities. The test to measure the level of motivation and involvement can be carried out in three stages, namely with pre-test and post-test, T-test and analytical data from student access to the LMS according to the indicators involved in this study such as video completion, total video, total comments, total badges, and completion of the game level. This study result indicates a positive influence from the application of video-based learning and gamification methods on LMS to increase student motivation and engagement.
A cross-language study of speech recognition systems for English, German, and Hebrew

Despite the growing importance of Automatic Speech Recognition (ASR), its application is still challenging, limited, language-dependent, and requires considerable resources. The resources required for ASR are not only technical, they also need to reflect technological trends and cultural diversity. The purpose of this research is to explore ASR performance gaps by a comparative study of American English, German, and Hebrew. Apart from different languages, we also investigate different speaking styles – utterances from spontaneous dialogues and utterances from frontal lectures (TED-like genre). The analysis includes a comparison of the performance of four ASR engines (Google Cloud, Google Search, IBM Watson, and WIT.ai) using four commonly used metrics: Word Error Rate (WER); Character Error Rate (CER); Word Information Lost (WIL); and Match Error Rate (MER). As expected, findings suggest that English ASR systems provide the best results. Contrary to our hypothesis regarding ASR’s low performance for under-resourced languages, we found that the Hebrew and German ASR systems have similar performance. Overall, our findings suggest that ASR performance is language-dependent and system-dependent. Furthermore, ASR may be genre-sensitive, as our results showed for German. This research contributes a valuable insight for improving ubiquitous global consumption and management of knowledge and calls for corporate social responsibility of commercial companies, to develop ASR under Fair, Reasonable, and Non-Discriminatory (FRAND) terms
Empirical Study on the Influencing Factors of WeChat Business Integrity

With the increasing popularity of the smart phones and electronic payment, WeChat shopping has become a trendy lifestyle for many people. However, the issue of WeChat business integrity has gradually appeared due to the virtuality of the Internet. This paper analyzed the development of WeChat business and its business integrity. The influencing factors of WeChat business integrity and related hypotheses had been studied based on theoretical and practical analysis. The reliability and validity of the data collected through questionnaires were tested with SPSS24.0. Empirical analysis was done to the hypotheses by using the structural equation model. The results indicated that products and service quality, after-sale guarantee, payment security and Word-of-mouth had a prominent positive effect on the integrity of WeChat business.
Research on the Modes and Paths of Transformation of Scientific and Technological Achievements of Colleges and Universities in Guangdong-Hong Kong-Macao Greater Bay Area

Against the backdrop of the reality of the transformation of scientific and technological achievements in domestic colleges and universities, this paper summarized the common modes and major paths of the transformation of scientific and technological achievements in colleges and universities in Guangdong-Hong Kong-Macao Greater Bay Area (GBA).On the strength of the analysis of the issues and hindrances during the transformation, this paper explored the improvement of the modes and paths of the transformation of scientific and technological achievements.Besides, the specific countermeasures and suggestions were also proposed in this paper for the purpose of providing useful reference for promoting the effective transformation of scientific and technological achievements in the colleges and universities in the GBA.
Estimation of Terrestrial Solar Radiation and its Variation with other Meteorological Parameters over Lokoja, Nigeria

The yearly and monthly estimation of terrestrial solar radiation over Lokoja using meteorological parameters of temperature and relative humidity during the period of twenty two years (July 1983 – June 2005) was investigated. The monthly variation of terrestrial solar radiation with meteorological parameters of global solar radiation, temperature, relative humidity and atmospheric pressure were investigated. The results indicated that high values of terrestrial solar radiation were observed during the rainy season than in the dry season. The highest yearly average values of terrestrial solar radiation observed during the period under investigation were found to be in the year 2005 and 1983 with 378.3521 Wm^(-2) and 368.3232 Wm^(-2) respectively. The highest and lowest monthly values of terrestrial solar radiation were estimated during the rainy and dry seasons in the months of May and January with 385.6133 Wm^(-2) and 358.5111 Wm^(-2) respectively. High values of terrestrial solar radiation with relative humidity and atmospheric pressure were observed during the rainy season and low values during the dry season. The reverse is the case for terrestrial solar radiation with global solar radiation and temperature.
Rancang Bangun Sistem Informasi Arsip Surat Menggunakan Metode Prototype

Letters archiving is the process of receiving, collecting, maintaining letters in a structured manner by an agency, however, the process of filing letters at the Unit Pelaksana Teknis Daerah (UPTD) Satuan Pendidikan Non Formal (SPNF) Sanggar Kegiatan Belajar (SKB) Salatiga still has problems such as recording letter archive data that still uses a letter agenda book which sometimes occurs errors in recording, archival document storage which is physically stored on a mailing cabinet which is prone to being lost, scattered, or damaged and the large number of existing documents causes employees to need extra time and effort to look for them. With the Letter Archive Information System designed using the prototype method, it allows employees to take part in the design of this system by providing opinions and suggestions on system design so that the results will be more optimal, maximum and can be a solution to the problems that exist in the Unit Pelaksana Teknis Daerah (UPTD) Satuan Pendidikan Non Formal (SPNF) Sanggar Kegiatan Belajar (SKB) Salatiga.
Research on the Fastest Detection Method for Weak Trends under Noise Interference

Trend anomaly detection is the practice of comparing and analyzing current and historical data trends to detect real-time abnormalities in online industrial data-streams. It has the advantages of tracking a concept drift automatically and predicting trend changes in the shortest time, making it important both for algorithmic research and industry. However, industrial data streams contain considerable noise that interferes with detecting weak anomalies. In this paper, the fastest detection algorithm “sliding nesting” is adopted. It is based on calculating the data weight in each window by applying variable weights, while maintaining the method of trend-effective integration accumulation. The new algorithm changes the traditional calculation method of the trend anomaly detection score, which calculates the score in a short window. This algorithm, SNWFD–DS, can detect weak trend abnormalities in the presence of noise interference. Compared with other methods, it has significant advantages. An on-site oil drilling data test shows that this method can significantly reduce delays compared with other methods and can improve the detection accuracy of weak trend anomalies under noise interference.
Causes of Deprivation of Elementary Education in Children Living in Slums

Education plays an important role in uplifting the Socio-economic Status of a society. Significant proportions of urban poor population in India live in slum areas and are deprived of education. Large number of children living in slums (age group of 6-14 years) are out of school due to varied reasons. The researcher focused the study on the causes of non-enrolment and dropout of such deprived children. Incidental sampling technique was employed to collect the data from various households of 8 different slum localities of Aligarh city, India. Data collected from the parents through Structured Interview Schedule indicates that 43.06% children between the age group of 6-14 years have never enrolled in schools while 33.57% of total children dropped out due to varied reasons. The causes underlying non-enrolment and dropout of children living in slums have been discussed in detail in this paper.
Monitoring HPC applications on the cloud with Zabbix

When a user overprovision cloud resources for his or her HPC application, it leads to underutilization and causes resources wastage. One solution is to monitor their usage. In this work, we propose a viable architecture to monitor HPC applications on cloud environment using the Zabbix tool. With this, we will be able to minimize cost and develop a methodology to monitor different kinds of HPC applications.
Fréchet single index models for object response regression

With the increasing availability of non-Euclidean data objects, statisticians are faced with the task of developing appropriate statistical methods for their analysis. For regression models in which the predictors lie in Rp and the response variables are situated in a metric space, conditional Fréchet means can be used to define the Fréchet regression function. Global and local Fréchet methods have recently been developed for modeling and estimating this regression function as extensions of multiple and local linear regression, respectively. This paper expands on these methodologies by proposing the Fréchet single index model, in which the Fréchet regression function is assumed to depend only on a scalar projection of the multivariate predictor. Estimation is performed by combining local Fréchet along with M-estimation to estimate both the coefficient vector and the underlying regression function, and these estimators are shown to be consistent. The method is illustrated by simulations for response objects on the surface of the unit sphere and through an analysis of human mortality data in which lifetable data are represented by distributions of age-of-death, viewed as elements of the Wasserstein space of distributions.
Machine Learning Algorithms for Spam Detection in Social Networks

Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.
A Corpus Analysis of Online Education Tweets During Covid-19

The current study investigates the writing or register style of 0.2 million tweets on the topic of online education during Covid-19 in July and August 2020.This pandemic delivered a massive jolt not only to the global economy, health, and business, but also to our educational system.According to the most recent UNESCO report, 1.3 billion students worldwide have been unable to attend schools or universities since March 2020.(McCarthy, 2020).As a result, online education has been regarded as grasping at straws.The study intends to highlight frequently used language structure and frequently used lexis from individual users' lexicons via tweets.Furthermore, a deliberately used stock of vocabulary items may be useful in scrutinizing and measuring learners' attitudes toward this pandemic around the world.Style was defined by Biber and Conrad (2009) as "frequent language forms used by speakers" and "an art of effective and forceful communication."As a result, the most frequent words/lemmas were extracted from the entire corpus first, and then a reduced vocabulary with statistical measures was rigorously studied.Furthermore, some other morphosyntactic linguistic/grammatical features were investigated using the MAT tagger, a statistical computational tool for determining the text type.
Social Awareness and Safety Assistance of COVID-19 based on DLN face mask detection and AR Distancing

The outbreak of coronavirus disease (COVID-19) has forced major countries to apply strict policy toward society. People must wear a facemask and always keep their distance from each other's to avoid virus contamination. Government employ officers to monitor citizen and warn them if not wearing a face mask. The warning message also spread through SMS and social media to ensure people about safety and awareness. This paper aims to provide face mask detection using the Deep Learning Network(DLN) and warning system through video stream input from CCTV or images then analyzed. If people not wearing a mask are detected, they will alert them through the speaker and remind them about a penalty. AR distancing very useful to give position toward violator location based on the detected person in a certain area. The system is designed to work intelligently and automatically without human intervention. With the accuracy of 99% recognition, it's expected that the system can help the government to increase people awareness toward the safety of themselves and people around them.
EC-Seg: Electronic Component Segmentation for PCB Assurance and Counterfeit Avoidance

Abstract PCB assurance currently relies on manual physical inspection, which is time consuming, expensive and prone to error. In this study, we propose a novel automated segmentation algorithm to detect and isolate PCB components called EC-Seg. Component segmentation and localization is a vital preprocessing step in the automation of component identification and authentication as well as the detection of logos and text markings. As test results indicate, EC-Seg is an efficient solution to automate quality assurance toolchains and also aid bill-of-material (BoM) extraction in PCBs. It also has the potential to be used as a region proposal algorithm for object detection networks and to facilitate sensor fusion involving artifact removal in PCB X-ray tomography.
Circuit Design for k-Coloring Problem and Its Implementation in Any Dimensional Quantum System

With the evolution of quantum computing, researchers nowadays tend to incline to find solutions to NP-complete problems using quantum algorithms to gain asymptotic advantage. In this paper, we solve k-coloring problem (NP-complete problem) using Grover’s algorithm in any dimensional quantum system or any d-ary quantum system for the first time to the best of our knowledge, where $$d \ge 2$$ . Till date, k-coloring problem has been implemented only in binary and ternary quantum systems, hence, we abide to $$d=2$$ or $$d=3$$ , that is for binary and ternary quantum systems for comparing our proposed work with the state-of-the-art techniques. Our comparator-based approach has reduced the qubit cost, compared to the state-of-the-art binary quantum systems (Saha et al. in IEEE Int Symp Smart Electron Syst 1:17–22, 2020). Further in this paper, with the help of newly proposed ternary comparator, a substantial reduction in quantum gate count for the ternary oracle circuit of the k-coloring problem than the previous approaches has been obtained. Later, this proposed comparator-based approach helps to generalize the implementation of the k-coloring problem in any dimensional quantum system. An end-to-end automated framework has been put forward for implementing the k-coloring problem for any undirected and unweighted graph on any available near-term quantum devices or Noisy Intermediate-Scale Quantum (NISQ) devices or multi-valued quantum simulator, which helps in generalizing our approach.
Hardware Schemes for Autonomous Navigation of Cooperative-type Multi-robot in Indoor Environment

This study proposes about the autonomous navigation of a multi-robot for transportation in indoor environments. This transportation is integrated with three foldings of VLSI architectures; they are 1) shortest optimal path planning 2) behavioral control between multi-robot with leadership swapping methods as per dynamic conditions and 3) obstacle avoidance by multi-robot. The hardware schemes have been designed for navigation of multi-robot with shortest path planning, based on an extended Dijkstra algorithm along with the Delaunay triangulation method. The behavioral control mechanism between the multi-robot is another challenge at the time of navigation and obstacle avoidance at both static and dynamic conditions in real-time scenario. The leader and follower approaches are deployed for cooperation between multi-robot to accomplish the task. The VLSI architectures are proposed for multi-robot navigation in the warehouse-type indoor environment. It is developed using Verilog HDL, simulated and synthesized with Xilinx Vivado 17.1. The Zynq-7000 SoC ZC702 FPGA is used as the target device.
Implementation of Meltdown Attack Simulation for Cybersecurity Awareness Material

One of the rising risk in cybersecurity is an attack on cyber physical system. Today’s computer systems has evolve through the development of processor technology, namely by the use of optimization techniques such as out-of-order execution. Using this technique, processors can improve computing system performance without sacrificing manufacture processes. However, the use of these optimization techniques has vulnerabilities, especially on Intel processors. The vulnerability is in the form of data exfiltration in the cache memory that can be exploit by an attack. Meltdown is an exploit attack that takes advantage of such vulnerabilities in modern Intel processors. This vulnerability can be used to extract data that is processed on that specific computer device using said processors, such as passwords, messages, or other credentials. In this paper, we use qualitative research which aims to describe a simulation approach with experience meltdown attack in a safe environment with applied a known meltdown attack scheme and source code to simulate the attack on an Intel Core i7 platform running Linux OS. Then we modified the source code to prove the concept that the Meltdown attack can extract data on devices using Intel processors without consent from the authorized user.
Analysis and Classification of H&amp;E-Stained Oral Cavity Tumour Gradings Using Convolution Neural Network

Upper Aero Digestive Tract (UADT) cancer is one of the most common cancer types in any gender.Early detection and diagnosing such type of cancer will reduce the risk of death in human.In this paper we addressed the state-of-the-art solutions for two major pathological constraints like, dysplasia type tumour grading and tumour grade classification with artifact's present in biopsy images.A new Whole Slide and patch-based CNN model was proposed which involves different pre-processing techniques to detect and normalize the stain artifacts present in biopsy tissue samples.The proposed CNN model is developed to classify different oral cavity tumour sites and its gradings automatically.It was observed that the proposed CNN model achieved best accuracy of 98 % with zero classification error rate.These findings will throw a light for pathologists to handle digital biopsy images with artifacts and also will classify dysplasia type tumour grades automatically.
Application of BP Neural Network in Prediction of Ground Settlement in Shield Tunneling

Ground settlement is one of the control indicators that the shield tunnel construction pays attention to. Ground settlement is affected by multiple construction processes of shield excavation face cutting, shield crossing and shield tail protruding. This paper adopts a data-driven method and based on actual engineering data to establish the relationship between the disturbance stage of the soil in front of the shield cut, the disturbance stage of the soil above the shell, and the disturbance stage of the soil behind the shell after passing through the shell and the final settlement of the ground. The neural network model and sensitivity analysis are used to explore the influence of settlement at different stages on the final settlement. Preliminary summary of the influence degree and law of different geological conditions and the settlement value of each stage on the final consolidation settlement provides a reference for the setting of the settlement value control target value and the design of the control strategy in the actual construction process.
Developing Teachers’ Inclusive Competence by Means of ICT in Postgraduate Education

The traditional organization of increasing the teachers’ level of inclusive competence in the system of postgraduate education in the framework of stationary courses is not effective enough. One of the reasons for this is lack of access to a large amount of the latest information that is available in modern research. Rapid changes in information technologies development open up more opportunities for modern teacher to acquire the necessary professional knowledge for teaching children with special educational needs. The article proposes an improved organization for increasing the level of inclusive competence in the postgraduate education, in which, along with traditional training in the framework of stationary courses, teachers, using ICT, participate in online courses and webinars posted on various educational platforms in the Internet. The characteristics of educational platforms ed-era.com, futurelearn.com, elt.oup.com, coursera.org, vseosvita.ua were analyzed. Pedagogical experiment was carried out, in which 2 groups of English teachers participated. In the control group, professional development was carried out traditionally, and in the experimental group – within the framework of improved organization of training. The analysis of the results showed that in the experimental group the greater level of inclusive competence was achieved in comparison with the control group. It was proved that this result was achieved thanks to improved organization of training using ICT tools. The results of the pedagogical experiment indicate the advantage of proposed organization for teachers’ inclusive competence development in comparison with the existing one. In connection with the above, the work, in our opinion, is of international importance.