Reviewerly/exHarmony huggingface dataset:
An Efficient Scene Content-Based Indexing and Retrieval on Video Lectures

Recently, the popularity for massive online open course (MOOC) learning among student community is increasing day by day. In most of the MOOC learning Web sites, indexing and retrieval techniques are based on the keywords like names, titles and Web addresses of the videos. As a result, the response for content-based queries is not good in most of the scenarios. In this paper, we propose an efficient scene content-based indexing and retrieval (ESCIR) framework for lecture videos. The proposed ESCIR framework consists of two phases: offline phase and online phase. In offline phase, we apply novel block-level key frame extraction (BLKFE) technique to segment video frames into shots and choose the right frame. The optical character recognition (OCR) tool is applied to the generated key frames in the videos for extracting the text. The jaccard similarity coefficient measure is used to eliminate the duplicate text frames, and then use of stop word removal and stemming algorithms are applied to get meaningful keywords from the scene. We used single-pass in-memory indexing (SPIMI) technique for building the indexing with the help of extracted keywords. In online phase, search and matching algorithms will map input queries given by user to the corresponding videos. We applied the okapi BestMatch25 (BM25) ranking function to rank those matched videos for best relevance results. The proposed ESCIR framework has been validated on standard video lectures and found to give better results than the existing state of art algorithms in terms of precision and accuracy. We compared our results with existing methods of lecture video techniques reflects improvising robust performance on lecture videos.
Forensic Acquisition Methods for Cloud Computing Environments

Cloud computing environments add an inherent layer of complication to a digital forensic investigation. The content of this article explores current forensic acquisition processes, how current processes need to be modified for cloud investigations, and what new acquisition methods can help when it is necessary to garner evidence from a cloud computing-based environment. A section will be included that provides a recommendation on how to acquire evidence from cloud-based environments while maintaining chain of custody. A final section will include recommendations for additional areas of research in the area of investigating cloud computing environments and acquiring cloud computing-based evidence.
Usability Evaluation of KAI Access Application

Abstract The Government of Indonesia develops some distribution of transportation especially land transportation, i.e. train. This transportation only available in Java Island, but now available in Sumatra Island. The existence of this train was supported by the development of the Industry 4.0 which is train tickets purchased by Applications that can minimize cash payment, increasing the efficiency and effectiveness of customers. User ratings for KAI Access Application on Google Playstore is not good enough, there are still a lot of users who give complaints and bad ratings on this Application. From the data, there is a usability issue in this application. The purpose of this research is to evaluate KAI Access application with a usability approach in Sumatera Island which can be used as a basis for Increase performance of KAI Access application after comparing with Java Island users. This evaluation using a usability approach with the System Usability Scale (SUS) questionnaire, mapping complaints and task completion time for user performance. The result of this research are alternatives to improve this application such as redesign interface for home page and improvement of the database. Positive correlation is found between type of user (novice and expert) and origin of user (Java and Sumatra) where results show significant difference.
Vision Based Upper Limbs Movement Recognition Using LSTM Neural Network

This paper presents the theoretical background and the implementation of a Long Short-Term Memory (LSTM) Neural Network architecture to recognize arm movements from video clips. The pose points (corresponding to the position of six body parts: shoulders, elbows and wrists) are extracted with a pre-trained Convolutional Pose Machine. Those points generate sequences over time with 66 (x, y) pairs, which are the input for a neural network, to classify them in 20 movement classes. Our architecture has 128 LSTM cells and presented $$92.5\%$$ of accuracy on testing data and an execution time of around 6.64 ms. Moreover, we present the methodology used to create our dataset, with 2400 samples of 20 different arms movements, recorded by 6 persons with different physical appearance in a controlled environment.
CSII-TSBCC: Comparative Study of Identifying Issues of Task Scheduling of Big data in Cloud Computing

The world moves day by day toward cloud computing and large data, where it includes huge information sets from many sources such as community medium, company, astronomic, healthcare, economics, depository and many additional field causes. All this information generates processing and use complexities. Due to improved hardware possessions and software algorithms, system throughput, information accuracy, locality, management, examination, storage space, data privacy and task scheduling, many variables can influence system efficiency. In big data and clouds, all these problems generate difficulties. This section describes the issues of scheduling caused by cloud computing.
IT Education in St. Petersburg State University

For many years, the world IT industry seems to have insatiable needs for qualified IT specialists. Russian higher education produces a large number of IT graduates each year, and the global market values them highly, recruiting both remotely and onsite. In this article we explore three success factors of the Russian IT education: thorough grounding in fundamental mathematics, adherence to international educational standards and close connections with IT industry. Since the early 1990s, Saint Petersburg State University (SPbU) accumulated an immense experience in collaboration with IT companies on education. Moreover, SPbU has developed a set of tools for IT education purposes, providing significantly better control of student user errors, both static and dynamic. This helps controlling the quality and progress of students in the real-life tasks of IT, alongside with their academic progress. In this article we compare the efficiency of preparing new IT specialists via joint educational programs of universities with IT companies, with the alternative of the traditional recruitment followed by additional training in the workplace.
Knowledge and User Specifics of Digital Competence of Teachers of Human and Social Sciences in Secondary Vocational Education

The article presents data from more extensive research (N = 2015) focused on digital competences of secondary school teachers. The research aimed to find out knowledge and user specifics of digital competence of teachers of human and social sciences in comparison with the whole dataset, specifically with teachers of ICT and technical sciences. As a data collection tool, we used a self-created questionnaire. Data analysis was performed with IBM SPSS Statistics version 24 using the Chi-Square test of independence, Chi-Square goodness of fit test, ANOVA, the independent samples T-test and paired sample T-test. Research showed that teachers use only simple digital tools in their teaching, which do not impose great demands on the didactic‑methodical application. On the other hand, more demanding didactic tools (e. g. E-learning or virtual laboratory) are used significantly less. It has also been shown that although teachers see the pedagogical potential of digital technologies positively, the forms and means of digital education are almost unknown to them. The sample, therefore, shows a discrepancy between the expectations of the pedagogical effects of the application of digital technologies and only the average developed knowledge and user aspects of digital competences. In terms of knowledge and user specifics, teachers of human and social science lag behind the teachers of ICT and technical subjects in terms of assessing their knowledge and user skills. They do not show statistically significant differences compared to the whole dataset, which consists of teachers of mathematics, natural, medical, agricultural and environmental sciences, teachers of practical training and education, although even here their results are usually slightly worse.
Combination of Information Technology and College English in the Age of Media

Since the beginning of the new century, with the development of modernization, education has undergone tremendous changes compared with the past. Excellent international education concepts and models have emerged, laying the foundation for the development of practical results in classroom teaching. For college English teaching, we must closely hold on the hard push at the beginning and strive to use information to promote the development and innovation of college English teaching. Aiming at common problems in front of the public social, we need try to use the model of flipped classrooms. We can also combine flipped classrooms with information technology, combine with new media technologies, and use information technology to carry out reform exploration and advanced education. It is combined with information technology which is useful for the improving of English teaching, and then improving efficiency in college English classrooms.
Dynamic deployment model for large-scale compute-intensive clusters

In big data computing application, there are jobs of multiple computing modes mainly based on MapReduce. Therefore, compute-intensive cluster needs to maintain multiple computing modes. The utilization of virtual computing resources is not high because of the change of computing load. In order to optimize the resource utilization of virtual cluster, a dynamic deployment model is designed with the support of lightweight Docker container technology. The deployment model can adjust the form of virtual cluster according to the change of the resource request of job, mainly changing the type and size of computing nodes in real time. Simulation experiments show that the dynamic deployment model can optimize the utilization of virtual resources, with CPU utilization increased by 5.2%, and the execution efficiency of computing jobs is optimized. The dynamic deployment model can be applied to cloud environment and large-scale computing clusters, not only to achieve peak-taggering computing of user jobs, but also to achieve the purpose of dynamic customization of job execution environment.
Exploring the Cultivation of Associative Thinking Under the Environment of Informatization

Along with the development of the technology of computer and information, people’s learning methods and lifestyles have undergone disruptive changes. This is an inevitable result of the technological revolution. Therefore, the proper and effective usage of information technology achievements is an effective way to promote the development of education. As an important design thinking method, associative thinking is widely used in teaching and design practice. This article is supported by the achievements of information technology and combined with the needs of teaching for cultivating associative thinking ability in the design field, researching and exploring new methods of cultivating associative thinking ability under the background of big data and information construction.
The Application of Computer Technology in Sports Training and Competition

Computer technology is widely used in the sports industry. It can help athletes to train, analyze the physical condition of athletes, improve the fairness of sports competitions, and promote the development of modern sports. Therefore, it is very necessary to study the application of computer information technology in modern sports. The article analyzes that computers provide good technical support for the development of modern sports, achieve scientific and reasonable arrangement of various sports activities, and strengthen the implementation of sports activities. This paper studies the application of computer technology in modern sports, and discusses the influence of computer technology on modern sports industry, the application of computer technology in sports training, and the application of computer technology in sports competition broadcasting.
A Study of Cooperative Thinking

Computational Thinking is a competence in computational problem solving. Cooperative Thinking (CooT) is an enhancement of Computational Thinking, supporting team-based computational problem solving. CooT is actually grounded on both Computational Thinking (CT) and Agile Values (AV) competencies, which focus respectively on the individual capability to think in a computational-oriented way (CT), and on the social dimension of software development (AV). However, CooT is not just the sum of CT and AV, rather it is a new overarching competence suitable to deal with complex software engineering problems. Previous papers focused on the conceptualization and the validation of Cooperative Thinking. We now analyze in depth the characteristics and consequences of this construct, with respect to the level of seniority and coding experience. Consequently, we run a Multi–Group Analysis of a representative stratified sample of High–School students, University students, and practitioners, through a Structural Equation Modeling technique. Our goal is to identify if there is a significant difference among groups with respect to the CooT model. Results show that seniority is a significant factor, suggesting as beneficial an early exposure of students to Cooperative Thinking practices.
Analysis of a new business model to fundraise non-governmental organizations using fuzzy cognitive maps

Fundraising is one of the most critical issues for non-governmental organizations (NGOs) to carry out their projects. In this paper, a search engine project which aims to find additional financial sources and increase donations for NGOs is proposed. The proposed search engine project is analyzed using fuzzy cognitive maps (FCMs) to define and manage factor influences on the success of the project. FCMs are useful tools to define long term effects of important factors for a system. First casual relations of the factors are determined and then using sigmoid function for learning algorithm, the equilibrium state for the system is obtained. It is found that the factors generating monetary values are the most important ones for the project to be successful in long term.
Software Component Technology

The data gives the key to make the decisions and the complexity of the information from the leads to the bad decisions. There it needed a method for improvement as a result of the amount of this data. This chapter discusses the developing of information systems, by their data, information, and the knowledge resulted by proposing a component identification approach and the management component for data management. The advantage of using component is to manage the information system by dividing it to several parts with less complexity.
Improving Semantic Graph Connectivity for Word Sense Identification

This paper aims to develop a fully unsupervised graph-based word sense identification method that make use of all possible semantic information in the used lexical resource to increases the semantic-graph connectivity in order to identify the correct sense of the words in the given context. The method operates as follows, given a context of words with several possible senses, the proposed method seeks to simultaneously disambiguate all the content words in the context, by aiming to finally assign the most relevant sense to each target word. This is accomplished all words at a time, by creating a context expansion graph around the words to be disambiguated. The method therefore tends to be an improved version of some of the well-known approaches of graph-based word sense identification. Empirical results demonstrate that the proposed improvement leads to better performance, as the method evaluated on several standard datasets through two different models of evaluation.
Obesity in Adolescents: A Study on Knowledge Attitude and Practice of Teachers

Objectives of this work is the study the prevalence of unhealthy behaviours in relation to the BMI status of students in a private school in Ranchi.A private coeducational school located in Ranchi was selected for the study.Data for predictors of obesity was collected from 325 students belonging to standard 6 th , 7 th & 8 th .A pretested questionnaire adapted from WHO"s Global School Health Survey Questionnaire was used.Using anthropometric measurements of the subjects, BMI was calculated and subjects were classified as per their nutritional status.Survey of the existing canteens services was done; and knowledge, attitude & practices of teachers regarding health, dietary and lifestyle behaviours were evaluated using a pretested questionnaire.Both undernutrition (28.0%) and overnutrition (20.9%) existed in the adolescents under study.Subjects showed high prevalence of unhealthy behaviours: high consumption of sweetened beverages (19.1%), frequent consumption of outside food (41.5%), low fruit and vegetable intake (49.5%), physical inactivity (46.8%), excessive TV viewing or playing computer games (41.5%).A total of 32.9% of subjects were "at risk" i.e. had >3 unhealthy behaviours out of which 29.2 % were underweight or normal and 47.1% were overweight or obese.Strong health and nutrition related policies and health promotion programs are very important to encourage an adolescent to adopt a healthy behaviour and to prevent under nutrition and obesity.There is an urgent need to sensitize school authorities and teachers as well as parents regarding healthy food practices and physical activity by students.
Multi task learning with general vector space for cross-lingual semantic relation detection

Semantic relation detection has an important role in natural language processing. In a supervised approach, the training process requires a sufficient amount of labeled data. However, in low-resource languages, labeled data are limited, whereas in rich-resource languages, labeled data are available in large quantities. In addition, various studies tend to model the single-task problem without considering the generalization with other tasks. Hence, a strategy that can utilize the availability of labeled data in rich-resource languages and generalize models to improve the identification of relations in a cross-lingual manner is needed. In this paper, we propose a framework to identify cross-lingual semantic relation using multi-task learning with a general vector space. The proposed method was designed to construct a general vector space and semantic relation identification. The experiments were conducted over three datasets: Indonesian–Arabic, English–Arabic, and English–Indonesia. The results show that the use of multi-task learning with a general vector space can overcome the problem of cross-lingual semantic relation identification. This is shown by the accuracy of the synonym and hypernym tasks that reached 84.9% and 84.8%, respectively.
A Cloud Computing Based Supporting Technology for the Lightweight Application Service

With the continuous advancement of dispatching and control cloud, rich applications are integrated on this platform. However, the overall resource utilization rate of the platform is low. How to use the platform resources reasonably and efficiently becomes a major difficulty in the process of dispatching and control cloud’s continuous promotion. The cloud platform needs to study the technology of lightweight application service life cycle management with the goal of service lightweight and resource optimal allocation, ensure the safe and stable operation of the cloud platform. In this paper, the container cluster management technology is used to realize the application service lightweight of the regulatory cloud platform and improve the ability of the large-scale resource optimal allocation of the cloud platform by automatic arrangement technology of container.
Adaptive Multiple-View Label Propagation for Semi-supervised Classification

We propose a novel Adaptive Multiple-view Label Propagation (MLP) framework for semi-supervised classification. MLP performs classification on multiple views rather than on the single view, and can exploit the complementarity of multiple views in the label prediction process. Moreover, MLP integrates the multi-view label propagation and the adaptive multiple graph weight learning into a unified model, where a linear transformation is used to enforce different weights form different view spaces. Thus, an optimal graph weight matrix can be constructed from each view. Due to the adaptive manner for defining the weight matrices from multiple views, MLP can avoid the complex and tricky process to select the neighborhood size or kernel parameter. Extensive results on real data show that MLP can deliver the enhanced performances, by comparing with other related techniques.
Practice on Human Posture Based on OpenCV

With the popularity of monitoring devices and the completion of monitoring systems, computer vision has been widely used. The analysis and research of human posture is an important part of computer vision and a hot research direction. This paper chooses human motion posture analysis in computer vision as the research direction. By inputting video information, the foreground image is extracted, and the outline of the human body in the foreground image is normalized to get the standard human posture image. Finally, the projection histogram of the human posture image is compared with that of the image in the template library to find the image with the highest similarity, and then the human posture can be determined. This paper realizes the determination of three postures: standing, sitting and lying. An experimental verification is carried out using OpenCV on VS2017 platform. The experimental results show that the proposed method is feasible.
Can emojis mean “Earthquake”?

With the increase in digital communications systems and social media, there is a need for simple graphics-based ways to signal the occurrence of major disasters. We describe the development and initial usability testing of a sign to indicate that an earthquake has occurred. We involved 264 Italian adults, who completed an online survey to evaluate the evocativeness, simplicity, and universality of 16 emojis depicting earthquakes. Through a Self-Organising Map analysis we identified four similar response profiles to the evocativeness scale, differing in their mean strength. A non-metric Multidimensional Scaling suggested that evocativeness was higher for the emojis featuring a damaged building. Linear Mixed Models indicated that emojis with fabricated vs. natural structures were judged as more evocative, simpler, and more universal when characterized by danger from falling objects. In some cases, adding behavioral elements increased evocativeness. Practical implications for informing the development of a new emoji for earthquakes are discussed.