Reviewerly/exHarmony huggingface dataset:
Modelling Fade Transition in a Video Using Texture Methods

A video shot is an uninterrupted frame captured from a single video recorder. Shot transition is the change from one shot to another. Shot transitions are of two types: abrupt transition (cut) and gradual transition. The cut transition occurs in just one frame. Many methods have been proposed to detect cut transitions and have given good results. Gradual transitions, on the contrary, are not so easy to detect as the transition occurs over a number of frames. Gradual transition is of three types, fade, dissolve and wipe. In this paper, the authors have focused on Fade Transition, making use of well-known texture methods: Grey level Co-occurrence Matrix (GLCM), Statistical and Laws Texture methods to transform the video into texture domain and detect the transition using the response of characteristic properties of the histogram on texture frames. For the obtained responses, a polynomial equation is fit to give a mathematical justification to this work. This polynomial obtained is used in the Identification Phase to detect the kind of transition a raw input video belongs to. Quality of fit is used to determine the best polynomial fit among the polynomials obtained.
Lifelogging: el fenómeno de las “cajas negras” personales

Lifelogging enables people to digitally record aspects of their daily life events in varying amounts of detail, for a variety of purposes. This continuous record can support human memory or can act as a form of surrogate memory. As such, it somehow constitutes a comprehensive «black box» of certain daily activities and therefore can potentially mine knowledge about how we live our lives. Although «total capture» seems unfeasible for the time being, it is possible to generate records that, despite being inevitably partial or fragmented, are very useful in a good number of scenarios in which users can infer knowledge about themselves for their own benefit. However, keeping this personal data on remote hard disks can be risky and should be carefully considered.
PREDIKSI HASIL PEMILU LEGISLATIF MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR BERBASIS BACKWARD ELIMINATION

Elections in Indonesia from period to period have undergone some changes. Elections legislative candidates not determined voters, but instead became a political elite authority in accordance with the order of the list of legislative candidates and their number sequence. To perform a prediction one of them with data mining. Data mining can be applied in the political sphere for example to predict the results of the legislative election and others. K-nearest neighbor algorithm is one of the data mining algorithm that performs classification based on learning object against which are closest to the object. Election-related research has been done with the k-nearest neighbor algorithm, but accuracy is obtained that method is still too low, so it takes an additional algorithm to improve accuracy. In this study, the proposed method, namely the method of k-nearest neighbor method combined with backward elimination as a selection of features. The dataset that will be used in the study comes from the KPU Sidoarjo that has special attributes 1 and 13 regular attributes. From the results of the analysis and computation of some methods, it can be concluded that the method of k-nearest neighbor method combined with backward elimination produced some conclusions. First, of the 14 attributes in the dataset, retrieved 8 most influential attribute. Second, the best accuracy are of 96.03% when k = 2 and tested by 10 fold cross validation.
Video Shot Detection and Summarization Using Features Derived From Texture

Video Shot Detection and Summarization plays a vital role in analyzing the contents of the video. The algorithms and methodologies learnt from video shot detection and summarization have a wide range of applications starting from video browsing, content-based video retrieval and storage, surveillance and many more. In an earlier work [1], we have extracted six texture features using gray level co-occurrence matrix, one of the most popular texture feature extraction methods. Frames from input video sequence are converted in texture domain. These video sequences are used to determine the “CUT” transition. In the proposed work, we have used GLCM and texture spectrum to extract texture features from frames in video and used a simple video shot detection method to find “CUT” transition and analyzed the results using quality metric parameters to determine the best feature extractor among GLCM and texture spectrum. The clustering algorithm for video summarization is affinity propagation.
Students’ Proficiency and Challenges in Filipino-to-English Translation: The Case of Filipino Senior High School Students in a Private Institutio

Translation has been considered an important linguistic skill for ESL students and is used as a remedy to repair linguistic barriers. This study is interested in exploring the proficiency and challenges encountered by Filipino students in Filipino-to-English translation. A class of twenty-seven (27) senior high school students in the Science, Technology, Engineering and Mathematics (STEM) strand participated in this study. Employing sequential explanatory design, the researchers first administered a Filipino-to-English translation proficiency test, followed by a content analysis of their translations. Quantitative data showed that participants’ quality of translations was evaluated as poor, which is attributable to their lack mastery of both Filipino and English. This encapsulates their difficulty in achieving equivalency. Qualitative analyses revealed that the students encountered lexical-semantic, cultural, pragmatic, and grammatical challenges in translating. In particular, translation errors were mostly in grammar, specifically in pluralization, capitalization, inflections, tenses, subject-verb agreement, demonstrative pronoun use, and fragments. This study emphasizes the importance of effective language teaching practices especially in the Philippines where students are expected to possess proficiency in Filipino (the national language) and English, both official languages of the country
Sistem Informasi Pengelolaan Tugas Akhir Mahasiswa dan Jurnal Penelitian Internal Jurusan Teknik Informatika Politeknik Negeri Ketapang

Information system is a matter that must be owned by an agency to be able to support managerial activities and performance in any field. Each agency must have its own information system. The system built in this study is the Final Project Management and Internal Research Journal of the Informatics Engineering Department at the Ketapang State Polytechnic. This analysis is used by collecting data in research. In designing, the authors create a computerized work through the process of designing component models, databases, and user interface components. This system is designed to provide information for students about each Final Project from students of the Informatics Engineering Study Program at the State Polytechnic of Ketapang. Final Project Management can provide services to students. Therefore this study aims to develop IT technology in the Informatics Engineering Department of the Ketapang State Polytechnic. This system was built to facilitate users such as admins and students in managing student final assignments.
A STUDY ON CLASSIFICATIONS OF CLOUD

“The interesting thing about cloud computing is that we’ve redefined cloud computing toinclude everything that we already do.” - Larry EllisonCloud computing is an emerging field that is expected to change the entire informationtechnology processes and IT market in the future. It is changing how we develop, deploy, anduse the information technology and the infrastructure that it runs on. Cloud Computing canbe defined as Internet-based computing in which virtually shared servers that are data centersprovide software, platform, infrastructure, policies and many resources.Clouds can be classified in various types: Public, Private, Hybrid and Virtual Private. In thispaper we will understand the Different Classifications of Clouds – IaaS, PaaS, SaaS – Public,Private, Community, Hybrid and also match Cloud Providers to Our Needs – Amazon, Google,Microsoft.
The STL Containers

The Standard Template Library (STL) consists of a standard set of functionality that implementers are required to support. Creating a standard ensures that code can be used interchangeably on different platforms and operating systems as long as the supplied implementations conform to that standard. A large part of the standard defines a set of containers that can be used to store data structures. This chapter looks at different scenarios where each of the STL containers proves useful.
The Effects of Google Word Coach Game and Vocabulary Mastery on Students’ Speaking Skill

The research aims to examine the effects of google word coach game and vocabulary mastery on students' speaking skills. This quasi-experimental study uses a 2x2 nonequivalent control group design with a factorial design carried out in four parallel classes, namely class XI IPS 1, XI IPS 2, XI IPS 3 and XI IPS 4 MA Negeri 1 Serang. Learning outcome data are obtained through pretest and posttest activities. The samples are 80 students randomly taken by adjusting to the condition and aim of the research. The results show a significant difference between the posttest of the experimental group and that of the control group. FO value is 5.225 and Sig. is 0.025 &lt; 0.05. Thus, it shows that the null hypothesis is rejected, and the research hypothesis is accepted. It also means that there is a difference between the interaction effect of Google Word Coach game and that of vocabulary mastery on students’ speaking skills.
USING DYNAMIC ALLOCATION ON MAPPED MEMORY TO PROCESS LARGE POINT CLOUDS WITH THE PCL LIBRARY

The article proposes a solution to the problem of processing point clouds whose size exceeds available RAM by using Point Cloud Library (PCL).The problem of lack of RAM is solved using the dynamic allocation mechanism on the mapped memory.The methods of introducing such a mechanism into the PCL library are analyzed, and the performance of processing point clouds and the amount of memory consumed using RAM or mapped (external) memory is evaluated.
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning

Deep reinforcement learning (RL) algorithms frequently require prohibitive interaction experience to ensure the quality of learned policies. The limitation is partly because the agent cannot learn much from the many low-quality trials in early learning phase, which results in low learning rate. Focusing on addressing this limitation, this paper makes a twofold contribution. First, we develop an algorithm, called Experience Grafting (EG), to enable RL agents to reorganize segments of the few high-quality trajectories from the experience pool to generate many synthetic trajectories while retaining the quality. Second, building on EG, we further develop an AutoEG agent that automatically learns to adjust the grafting-based learning strategy. Results collected from a set of six robotic control environments show that, in comparison to a standard deep RL algorithm (DDPG), AutoEG increases the speed of learning process by at least 30%.
Clustering by Constructing Hyper-Planes

As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data points. It relies on the marginal space between the points. Then we combine these hyper-planes to determine centers and numbers of clusters. Because the algorithm is based on linear structures, it can approximate the distribution of datasets accurately and flexibly. To evaluate its performance, we compared it with some famous clustering algorithms by carrying experiments on different kinds of benchmark datasets. It outperforms other methods clearly.
Study on Recent Method of Dynamic Time-Based Encryption (DTBE) and Searchable Access Control Scheme for Personal Health Record in Cloud Computing

An innovation which resides on virtualization that reduces the expense of IT infrastructure is cloud computing. It gives number of services for IT infrastructure at very low cost. Cloud computing enable users to keep in reserve and retrieve some data under the cloud network. User can demand any service based on their requirement from cloud at low price. But there are some security issues over cloud and some solution for these problems. In this paper we present a dynamic time-based encryption technique and searchable access control that empowers data to be securely redistributed to the cloud while keeping up the capacity to search the data efficiently.
Application of Multiple Intelligences Theory to English Vocabulary Teaching in College

Recent years have witnessed the problems of low efficiency in English vocabulary teaching and learning between college teachers and students. However, due to the fact that the teachers can not work out an effective way to help students learn the vocabularies, vocabulary teaching is a challenge for teachers in college. Hence, it is high time for the teachers to explore an efficient English teaching methods and strategies to arouse students' interest as well as improve their English vocabulary levels. In this paper, the author regards the current situations of college English vocabulary teaching as the object of research, and tries to draw a conclusion to indicate the feasibility of vocabulary teaching based on MIT which can provide enlightenment to vocabulary teaching. The author presents different teaching methods and analyzes their characteristics through observing classroom vocabulary teaching, which greatly reflects students’ performance in class. In brief, multiple intelligences theory has positive impact on the college English vocabulary teaching since it can improve our teaching quality and increase students’ learning interests, therefore it is well worth our studying.
한국어 교육에서의 어휘 교육 방안 -어종과 의미 관계를 중심으로-

The purpose of this study is to organize and categorize the composition of word contents for Korean vocabulary education in systemic group. The results of this study are summarized as follows: Firstly, word contents for Korean vocabulary is subdivided based on word type and semantic relation. Particles and affixes are excluded according to Korean Language Curriculum, Secondly, vocabulary education based on word type is analyzed into Pure-Korean words education for fluent conversation, Sino-Koreans word for professional vocabulary expansion and Loanwords education which is on the rise due to diversification of cultural contacts. Lastly, In order to make it easier to understand the meaning of words, the vocabulary is organized by dividing it into synonym and antonymous relation(oppositive relation), polysemy and homonymous relation, hypernym and hyponym depending on the semantic relation. Therefore, it establishes the word contents structure in Korean education by linking the correlation that the vocabulary has.
Quick response codes as a complement for the teaching of Electronics in laboratory activities

Augmented reality is gaining presence in the classroom because it allows to combine the physical environment and digital information in real time. An implementation of augmented reality that is attracting great attention are quick response codes because they can be read immediately in smartphones and tablets using free apps. A teaching field where it is beneficial to provide a prompt access to supplementary materials are laboratory sessions in scientific–technical disciplines. There, students realize complex measurement tasks using specific instrumentation, and so it is convenient to access technical documentation and explanations of the measurement process during the session. This work describes how quick response codes can be introduced in laboratory sessions to provide links to specifically designed supporting resources. Thanks to this strategy, an improvement in the acquisition of experimental skills has been observed as well as a better and quicker realization of the practical sessions.
Technology of Critical Thinking Development as a Factor of Achieving Personal Results in a Foreign Language Training

The article discusses the issue of achieving secondary school students’ personal learning outcomes using pedagogical technology of critical thinking development in foreign language classes. They are determined the advantages of this technology in comparison with traditional training. Analyze of the requirements of normative documents in relation to the personal results of the educational process, they are summarized in three groups: self-determination, semantic formation and moral and ethical orientation. The prerequisites and personal qualities of students necessary for the development of critical thinking are established. It is presented a three-stage model for the implementation of the critical thinking development technology in teaching foreign languages which includes the stages of challenge, comprehension and reflection.
Development of Adjective and Adverb Sentiment Lexicon with Book Review Corpus

초록·키워드 목차 오류제보하기 This paper presents a method of lexicon building for sentiment analysis using book review data. We show how to build sentiment lexicons automatically using book review data that includes readers" evaluation scores, basic positive and negative sentiment word lists using a semantic orientation measure, and sentiment word lists using Word2Vec. Specifically, we show how to automatically generate a useful lexicon of adjectives and adverbs for sentiment analysis. In summary, the process we describe is as follows: Basic word lists of positive and negative sentiments are created by relating word frequencies with reader"s book review scores. The expanded word lists are then built using the vector space representation of words and their cosine distance. This is made possible by assuming that words of similar sentiment and semantically co-related words will tend to appear together in the same context, and that they are represented proximately in the vector space. #감성분석(sentiment analysis) #웹 수집(web scraping) #리뷰 말뭉치(review corpus) #의미적 지향성(Semantic Orientation) #워드투벡(Word2Vec) 1. 머리말2. 관련 연구3. 연구 내용4. 요약 및 결론참고문헌
Сетевая коммуникация как фактор эволюции социально-политических отношений

The rapid differentiation of the information space and intensification of attempts to redistribute power and influence increase the risks of conflicting relationships between the social and political forces. However, the phenomenon of network communication goes beyond technical descriptions in quantitative terms as multiple increase of information flows, speeding up of the pace of movement, etc. This new reality immanently develops a new quality of mediation, which objectively and inevitably contributes to the promotion of the contradictory but productive process of new meaning formation. The article substantiates the thesis that the media-based mechanisms of network communication can restrain the risks of chaotic expansion and contain the essential potential of civic self-organization and evolutionary renewal of the principles of social and political relations.
Improving Symbolic Automata Learning with Concolic Execution

Inferring the input grammar accepted by a program is central for a variety of software engineering problems, including parsers verification, grammar-based fuzzing, communication protocol inference, and documentation. Sound and complete active learning techniques have been developed for several classes of languages and the corresponding automaton representation, however there are outstanding challenges that are limiting their effective application to the inference of input grammars. We focus on active learning techniques based on $$L^*$$ and propose two extensions of the Minimally Adequate Teacher framework that allow the efficient learning of the input language of a program in the form of symbolic automata, leveraging the additional information that can extracted from concolic execution. Upon these extensions we develop two learning algorithms that reduce significantly the number of queries required to converge to the correct hypothesis.
Towards a Security Competence of Software Developers

Software growth has been explosive as people depend heavily on software on daily basis. Software development is a human-intensive effort, and developers' competence in software security is essential for secure software development. In addition, ubiquitous computing provides an added complexity to software security. Studies have treated security competences of software developers as a subsidiary of security engineers' competence instead of software engineers' competence, limiting the full knowledge of the security competences of software developers. This presents a crucial challenge for developers, educators, and users to maintain developers' competences in security. As a first step in pushing for the developers' security competence studies, this chapter utilises a literature review to identify the security competences of software developers. Thirteen security competences of software developers were identified and mapped to the common body of knowledge for information security professional framework. Lastly, the implications for, with, and without the competences are analysed and presented.
Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer

Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitational acceleration signal. The signal is divided into several segments. The system will extract features of each signal segment in the time and frequency domain. Each signal segment is then classified according to the type of signal using the classifier through a series of training data processes. The classifier which has the highest accuracy value is exported into the new input signal modeling. As the result, fine K-NN algorithm has the highest level of accuracy in the classification. The fine K-NN algorithm has an accuracy rate of 99.75% in the classification of human activity signals and earthquake signals with a memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms.
Cryptography Core Technology

In this chapter, we describe the analysis of security basis. One is the analysis of elliptic curve discrete logarithm problem (ECDLP). ECDLP is one of the public-key cryptosystems that can achieve a short key size but it is not a post-quantum cryptosystem. Another is analysis to learning with error (LWE), which is a post-quantum cryptosystem and has the functionality of homomorphic encryption. These two security bases have important roles in each protocol described in Sect. 2.2.4.2