Reviewerly/exHarmony huggingface dataset:
Methodological Approaches to the Digital Analysis of Educational Media: Exploring Concepts of Europe and the Nation

Modern educational media, such as textbooks, construct meaning through language in a multi-modal learning environment. An analysis of the construction of the concepts of Europe and the nation in educational media presents a challenge, in that the number of relevant text passages may be considerable, even if the material does not meet the size criteria for larger-scale statistical processes, as required in corpus analysis. To meet this challenge, the chapter presents and discusses methodological approaches to digital analysis, focusing on medium-sized text but also including big data examples. First, we present methodological approaches that may contribute insights into the characteristics of the data. Then, we demonstrate how to explore the constructions of concepts of Europe and the nation by focusing on search terms, pattern identification and the use of grammatical information. We use this as a basis for describing some possibilities for identifying concepts on which identity constructions may draw. We conclude by discussing the potential and the limits of our approach, and identify its functions. Among the methods that we discuss are the analysis of word frequencies, keyword analysis, n-grams, analysis of concordances, collocation analysis and network analysis. We focus on Danish- and German-language data, and provide examples from both seventeenth- to early twentieth-century and current history textbooks, and Wikipedia and social media.
Energy Security: Energy Planning of West Java

The study aims to examine the picture of the condition of energy management in West Java in the future, which is the result of projections of energy supply needs that the Regional Government will carry out concerning current conditions, indicators, and parameters that influence and the vision and mission of regional development.This research method is qualitative with a normative approach.The studies carried out in this research are mainly on Presidential Regulation 22/2017 concerning the National Energy General Plan and West Java Province Regional Regulation Number 2 of 2019 concerning the West Java Province Energy General Plan (RUED-WJP).The RUED-WJP elaborates on the results of modeling energy demand and supply for 2015-2050.Which also includes policies, strategies, energy development programs, and activities that refer to the National Energy Policy's targets, with sustainable and environmentally sound principles for creating energy independence and resilience in West Java Province.
HUBUNGAN ANTARA LITERASI MEDIA ONLINE DAN KINERJA PEMBELAJARAN DITINJAU DARI KEYAKINAN DIRI, MOTIVASI BELAJAR, DAN JENJANG SEMESTER MAHASISWA

This study aimed to determine: (1) the correlation between online media literacy and learning performance in term of self-confidence; (2) the correlation between online media literacy and learning performance in termof learning motivation; and (3) the correlation between online media literacy and learning performance in term of students’ semesters. This research was correlational study which was conducted from April to May 2021. The population of this research were active students of the Accounting Education Study Program, Faculty of Teacher Training and Education, Sanata Dharma University, totaling 204 students. The samples in this study amounted to 135 students who were taken by using proportional random sampling technique. The data were analyzed by product moment correlation. The results of this study indicated that: 1) there was a correlation between online media literacy and learning performance in student respondents with high self-confidence (Sig. (2-tailed) = 0,037), while there was no correlation between online media literacy and learning performance in student respondents with low self-confidence. (Sig. (2-tailed) = 0,095); 2) there was a correlation between online media literacy and learning performance in highly motivated student respondents (Sig. (2-tailed) = 0,038), while there was no relationship between online media literacy and learning performance in respondents low- motivated students (Sig.(2-tailed) = 0,488); and 3) there was a correlation between online media literacy and learning performance in high-semester student respondents (Sig. (2-tailed) = 0,014), while there was no correlation between online media literacy and learning performance in the lower semester tiered student respondents (Sig. (2-tailed) = 0,284).Keywords: Online media literacy, learning performance, self-confidence, learning motivation, and students’ semesters
Tuning Dari Speech Classification Employing Deep Neural Networks

Recently, many researchers have focused on building and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) system has become an important and common tool from games to translation systems, robots, and so on. However, there is still a need for research on speech recognition systems for low-resource languages. This article deals with the recognition of a separate word for Dari language, using Mel-frequency cepstral coefficients (MFCCs) feature extraction method and three different deep neural networks including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Multilayer Perceptron (MLP). We evaluate our models on our built-in isolated Dari words corpus that consists of 1000 utterances for 20 short Dari terms. This study obtained the impressive result of 98.365% average accuracy.
A New Technique to Classification of Bengali News Grounded on ML and DL Models

News classification is the process of categorizing news articles into predefined categories based on their content.It involves utilizing techniques such as machine learning and NLP to analyze the textual features of news articles and assign them to relevant categories.This enables efficient organization and retrieval of news articles, aiding users in accessing information based on their interests.Many topics are covered by news, including local, national, and international news, politics, business, sports, and entertainment.In our native Bengali language, there are numerous classifications for news.The motivation for working in this research is because of the limited research in Bengali text and the scarcity of resources.This study proposed a new technique for the categorization of the news based on a comparison among different machine learning and deep learning model results.First, we have collected more than 20K data.Afterwards, applied several machine learning and deep learning models to pre-processed and cleaned data.Then apply our proposed technique on evaluated results produced by the machine learning and deep learning models to make a final decision to identify the news category.In our proposed technique, we counted the predicted categories from three types of Machine Learning models and two types of Deep Learning models.Finally, we computed the maximum predicted class and assigned the category for the specific news.
A study on needs to design English teaching materials at vocational school

English for Specific Purpose (ESP) instruction in vocational schools necessitates the use of English materials that are tailored to the students' particular requirements. However, the English materials currently available for vocational institutions lack an adequate needs analysis. In order to address this issue, the purpose of this study is to determine the requirements of students in the computer and network engineering department for procedure text teaching materials. Eleventh-grade students majoring in computer and network engineering were selected by simple random sampling and surveyed for the study. The results indicated that the primary objective of the students' explanation-based learning process was to enhance their speaking skills. In addition, instructors favoured interactive, engaging, and simple-to-understand multimedia learning to teach English. This research can be used as a guide for designing English language instructional materials that meet the requirements of vocational students, especially those majoring in computer and network engineering.
The NADI Mathematical Model on the Danger Level of the Bili-Bili Dam

The research discusses the NADI mathematical model due to the overflow of the Bili-Bili dam, using secondary data obtained through online literature review by collecting various information related to the Bili-Bili Dam, starting from the Jeberang River Scheme, the chronology of floods, normal or dry conditions, and dam operation patterns. The aim of this study is to predict the level of danger of Bili-bili dam overflow over time, considering extreme weather factors and standard operating procedures performed by humans. The research uses analytical and computational methods. The study obtained the NADI mathematical model due to the overflow of the Bili-Bili dam, with two equilibrium points: (1) the equilibrium point free of disaster, (2) the disaster equilibrium point, and a basic disaster reproduction number of R0 = 1.219. This indicates that the water discharge from the dam is high and has an impact on the overflowing water for communities around the Jeneberang river. Therefore, it can be concluded that the NADI model can be used to simulate the Bili-bili dam process based on extreme weather and dam SOP, and predict the level of danger of Bili-bili dam overflow, which is also a novelty that has not been done in previous studies.
Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline

Indonesia is one of the countries infected with the covid-19 virus. One of the government's efforts is the covid-19 vaccination. However, the covid-19 vaccination caused controversy for some people because many people refused to be vaccinated. Public perception of the covid-19 vaccine can be categorized into two, namely positive and negative, based on survey from Indonesia ministry of health about acceptance of covid-19 vaccine state that this can be influenced by many factors. These factors are important to know as an effort to increase acceptance of covid-19. Multivariate Adaptive Regression Splines (MARS). The purpose of this study is to determine the classification model of public perception of the covid-19 vaccine and the factors that influence it. The method used in this study is Multivariate Adaptive Regression Splines (MARS). This method is appropriate classification method to be applied to categorical response variable data, The outcomes demonstrate that the optimum mars model is produced by combining BF= 24, MI =3, MO= 1, and GCV=0.07340546. The resulting classification level is 91.5% with influencing factors yaitu gender (x_1), age (x_2), last education (x_4), willingness to vaccinate (x_6), education (x_8). Based on the results obtained, the government can consider these factors for socialization
Online learning environments for the promotion of undergraduate research in pandemic times: A case study in an engineering program

Abstract : This case study explores the use of online learning environments as an alternative tool for the promotion of undergraduate research culture within an engineering program during the COVID-19 pandemic times. This context entailed the transition from face-to-face to online learning environments, which was taken as an opportunity for the diffusion and socialization of outstanding research projects developed by undergraduate students within the classroom, through the launching of an online event and a virtual permanent exhibition. The results of this case study were measured through quantitative indicators related to the number of research projects submitted by the students for participating in this exhibition, the number of courses participating through these projects, and the number of professors involved. Participation of students and professors increased by 85% and 90%, respectively, comparing the online environment to the previous face-to-face one. Consequently, it was concluded that the use of online learning environments supports the promotion of undergraduate research culture, by increasing the interest of students for the socialization of their research results. Keywords: Engineering education; Lifelong learning; Online learning environments; Research competencies; Research culture; COVID-19.
Study of Image Recognition Using Machine Learning

Abstract: This article presents a survey of machine learning for image recognition. Image Recognition is the task of identifying objects of interest within an image and recognizing which category the image belongs to. The common goal of image recognition is the classification of detected objects into different categories. Image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition and is a widely used in various fields. In this paper use of machine learning for image recognition is discussed.
Availability, accessibility and use of library information resources for control of criminality of inmates in correctional centres in South-West, Nigeria

The act of relapsing to crime after release has become a recurrent decimal to inmate and it poses a security threat to the society. Persistence on incarceration of offenders without meaningful engagement is like remaking criminality rather than reformation. Extant literature suggests educational opportunities to reduce criminal involvement of inmates. This research investigated the availability, accessibility and use of library information resources for control of criminality among inmate in custodial centres in Southwestern Nigeria. Descriptive Survey Design was used and the population was 455 inmates who affirmed that tapes, radio sets, television sets, sound recording gadgets, video and audio cassette were available for use by inmates in their custodial centre (mean = 3.20), books were adequately available for use by inmates in their custodial centre (mean = 3.06) and pamphlets were parts of reading materials available for use by inmates in their custodial centre (mean = 2.62). The mean score range of 2.50 to 3.24 indicated a high level of availability while 3.25 to 4.00 indicated a very high level of accessibility of library information resources in the custodial centres in Southwestern Nigeria. It was concluded that with the availability and accessibility of library information resources to the inmates, the maximum use of the resources is capable of controlling criminality tendencies among inmates in custodial centres in Southwestern Nigeria.
Graph Theoretic Approach to Randomness Test Based on the Overlapping Blocks

Cryptographic parameters such as secret keys, should be chosen randomly and at the same time it should not be&#x0D; so difficult to reproduced them when necessary. Because of this, pseudorandom bit (or number) generators take the role of true random generators. Outputs of pseudorandom generators, although they are produced through some deterministic process, should be random looking, that is not distinguishable from true random sequences. In other word they should not follow any pattern. In this paper we propose a new approach using graph theory, to determine when to expected a fixed pattern to appear in a random sequence for the fist time. Using these expected values and comparing them with the observed values a randomness test can be defined. In this work patters are traced through the sequence in an overlapping manner.
DETECTION OF HUMAN PSYCHOLOGICAL STRESS WITH DEEP CONVOLUTIONAL NEURAL NETWORK USING DIFFERENT CRITERIAS FOR FEATURE SELECTION ON BASIS OF CONFIDENCE VALUE OF PAIRED t-TEST

Psychological stress has begun to be a social concern and is a major source of dysfunction in day-to-day life.Many neuropsychology ailments are implicated due to chronic stress.Sadness, seizures, cardiac arrest, and stroke are all increased by stress.The key prey of psychological stress is the human cerebrum, as revealed by a recent study of neuroscience, as the human cerebrum has the ability to recognize whether circumstances are terrifying or stressful.As an example, if an objective method for recognizing stress is used while considering the human cerebrum, the associated negative effect could be significantly reduced.As a result, a deep convolutional neural network involving raw EEG signals and a study of stressed subjects is suggested in this paper.Stress is induced in the experimental setting by using a mental arithmetic task (MAT) tool based on an established prototype, the Montreal imaging stress task (MIST).Task performance and subjective feedback confirmed the initiation of stress.EEG feature extraction, feature selection (paired t-test and four criteria), and classification (Deep Convolutional Neural Network) are all part of the proposed system.The proposed work gives good results when criteria 1, 2, and 3 are chosen as compared to criteria 4. Maximum accuracy is obtained from the Change in Power feature through the AF7 channel.
Research on English Learning Strategies in Junior High Schools Based on Cognitive Load Theory

In the case of advocating autonomous learning and lifelong learning, learners need to master English learning strategies and use them effectively. How to exert functions of English learning strategies is a key issue that needs to be solved. In this study, the revised The Strategy Inventory for Language Learning (SILL) to design a questionnaire and interviews were conducted to understand the current situation of English learning strategies used by junior high school students. Then, in order to augment students’ initiative in using learning strategies, based on the cognitive load theory, this study proposed six strategies. First, decrease internal cognitive load: reducing elements and linking old and new knowledge. Second, decrease external cognitive load: setting goals and reducing redundancy. Third, increase germane cognitive load: stimulating learning motivation and multi-modal training.
Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network

Deep learning is a neural network that creates innovations that give computer-implanted problem-solving expertise. One of the principles of computer vision is a detection system with a vision framework that can identify things encountered in the same manner as a human vision system. Using an artificial intelligence-based Convolutional Neural Network (CNN) model with deep learning techniques, we present a face emotion identification system. The categorization of facial expressions will be utilized as the basis for a face recognition system trained using CNN. The applications are intended to use the OpenCV, Keras, and TensorFlow libraries as the backend. We were discussing the study on the best use of xception architectural models in facial expression recognition systems. Based on the results of these tests, the study obtained an increased accuracy value in training and data testing on an xception architecture model trained for facial expressions using the FER-2013 dataset, resulting in an accuracy value of 66% as well as the value of each average for precision (76%), recall (65%), and F1 score (63%).
Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.

Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, we leverage real-world electronic health records in the observational medical outcomes partnership (OMOP) data model to develop machine learning models to predict MCI up to a year in advance of recorded diagnosis. Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68.2±0.7. We identify the clinical factors mentioned in clinician notes that are most predictive of MCI. Using similar association mining techniques, we develop a data-driven list of clinical procedures commonly ordered in the workup of MCI cases, that could be used as a basis for guidelines and clinical order set templates.
Assisting the Assistant: A Cobot for Voice Customer Support

Despite recent advances in automation, customer support still requires a substantial amount of human intervention through voice channels. With the aim of improving the work of human assistants, we developed a collaborative bot (cobot) to help them in the process of handling customer voice interactions. The cobot is a reasoning agent that starts from loading background customer data into a dynamic knowledge graph. Then it captures the audio stream of the conversation, converts it to text in real time, analyzes the blocks of conversation with neural technologies and “thinks” about the results. Assistants can also supply data to the cobot, based on the information they gather from the ongoing conversation. The reasoning agent provides information and action suggestions to the human assistant by applying heuristics on data collected from both automatic and human sources, based on a task and domain-specific conceptual models (ontologies). While designing a prototypical solution for utility services in Italy, we are faced with many problems, including spontaneous speech understanding, factual and linguistic knowledge representation, and efficient heuristic reasoning. We adopted a standards-based approach and experimented with open source reasoners and publicly available language models. The paper presents preliminary findings and outlines the system design, with focus on the interplay of neural language processing and logic reasoning.
BitTorrent over SCION: Leveraging Unused Network Capacities in the Internet

In the current Internet, BGP is used by network operators to determine the inter-domain forwarding paths for traffic based on their local view of the network. For endhosts however, there is no way to a) influence this path and b) benefit from additional paths. We hypothesize that with path-aware networking as enabled by SCION, endhosts can overcome these limitations and aggregate otherwise unused capacities in the network. Consequently, we present our BitTorrent over SCION implementation, which enables multipath communication and intelligent path selection for endhosts in global torrent networks. Comparing our implementation against BitTorrent over BGP and BGP-M in a small-scale Internet topology, we observe an increase in goodput of 48% through multipathing compared to BitTorrent over BGP and 33% compared to the BGP-M candidate, which confirms our hypothesis.
Detecting and Mitigating Cache Side Channel Threats on Intel SGX

Intel Software Guard Extensions (SGX) protect sensitive content of applications on the cloud platform by creating an isolated environment on an untrusted operating system. However, resent works have shown that the SGX is vulnerable to a variety of side channel attacks which could be severely damage the data confidentiality provided by SGX, such as the cache side channel attack. Unfortunately, existing defense mechanisms either provide an incomplete protection or incur too much performance costs. In this paper, we propose a defense countermeasure against cache side channel attacks for SGX by detecting abnormal each level cache use behaviors. We create auxiliary threads for each enclave thread and detect when asynchronous enclave exits (AEX) occur, which defeats the condition of L1/L2 cache side channel attacks that attacker and victim threads execute in the same physical core. We put some guard data to the cache lines and inspect access time, which detects last level cache eviction set behaviors. More importantly, we utilize optimizations to reduce the performance overhead caused by AEX detection. In comparison to existing approaches, our design is secure against any cache level side channel attacks and its performance loss increases less.