Reviewerly/exHarmony huggingface dataset:
Subformula Linking for Intuitionistic Logic with Application to Type Theory

Abstract Subformula linking is an interactive theorem proving technique that was initially proposed for (classical) linear logic. It is based on truth and context preserving rewrites of a conjecture that are triggered by a user indicating links between subformulas, which can be done by direct manipulation, without the need of tactics or proof languages. The system guarantees that a true conjecture can always be rewritten to a known, usually trivial, theorem. In this work, we extend subformula linking to intuitionistic first-order logic with simply typed lambda-terms as the term language of this logic. We then use a well known embedding of intuitionistic type theory into this logic to demonstrate one way to extend linking to type theory.
Agile Entrepreneurship Innovation in Fashion Design Thinking During COVID-19 and Beyond: Reimagine Education to Create Skills for Fashion Business

Purpose: The COVID-19 crisis had a severe impact on University education (on-line learning, off-campus examinations). In addition, the COVID-19 pandemic raises questions about the quality of education and training in a number of disciplines, like fashion design, where social entrepreneurship opportunities and in-situ functionalities are essential for a quality curriculum. Hence, to remain relevant and innovative, fashion design thinking will need to reimagine education in order to create skills for e-entrepreneurship and prepare fashion entrepreneurs for e-business. Therefore, new concepts for fashion design thinking for innovation and e-entrepreneurship have to introduce in an e-classrooms curriculum. &#x0D; &#x0D; Methodology: This study employed the use of questionnaires to collect quantitative data and structured interviews to compile qualitative data (opinions) of two main groups of fashion design professionals: (a) clothing merchants and manufacturers, (b) fashion design green entrepreneurs. In particular, linear regression used to analyze the quantitative data (SPSS functionality) and data analytics software (QSR NVivo) adopted to encode the answers from the interviews.&#x0D; &#x0D; Findings: Findings obtained in this study show that &amp;ndash;with the admission of agile entrepreneurship superiority and functional solution in crises like COVID-19- the green entrepreneurs are better positioning are better prepared to withstand the current COVID-19 or future crisis. Hence the requirement to integrate green entrepreneurship courses into the fashion d esign curriculum will be proposed to create innovation and value in fashion design thinking. &#x0D; &#x0D; Originality and value: This study inserts itself in a multidisciplinary field, mainly composed of four disciplinary areas: &amp;ldquo;fashion design thinking and education&amp;rdquo;, &amp;ldquo;digital transformation&amp;rdquo;, &amp;rdquo;green entrepreneurship&amp;rdquo;, and &amp;ldquo;work-from-home entrepreneurship&amp;rdquo;. The introduction of the new term &amp;ldquo;Agile entrepreneurship&amp;rdquo; as a new disciplinary concept in fashion design curriculum is also crucial.&amp;nbsp; &#x0D; &#x0D; Research limitations: The main limitation of this study is related to the definition of &amp;ldquo;Agile entrepreneurship&amp;rdquo; itself in COVID-19 and beyond work-from-home era, as bibliography still diverges on this subject.
SUPERVISED MACHINE LEARNING ALGORITHMS FOR DETECTING CREDIT CARD FRAUD

One of the most serious ethical challenges in the credit card industry is fraud. Our paper’s major goal is to identify credit card theft and offer a reasonable solution to the problem. Credit card fraud has cost customers and banks billions of dollars around the world. Fraudsters are constantly attempting to come up with new ways and tricks to commit fraud, despite the fact that there are several measures in place to prevent it. Fraud detection is extremely important in the banking and finance industries. For detection purposes, we will use an artificial neural network. As a result, in order to prevent it, we will develop a system that will not only detect fraud, but will also detect it before it occurs. In order to detect new scams, our system will learn from previous frauds. Mining algorithms were used to detect fraud, but they failed miserably. We use machine learning methods to detect fraud in credit card transactions in our paper. The research employs supervised learning methods that are applied to a kaggle dataset that is severely skewed and imbalanced. We used robust scalar to balance the set, resulting in 51 percent non-fraud cases and 49 percent fraud ones. Logistic regression, random forest, decision tree, and KNN have all been implemented, with additional learning curves displaying which algorithm performs best. Accuracy, specificity, precision, and sensitivity are the evaluation criteria, and a comparative chart is created to show the comparative analysis of various supervised learning algorithms. KEYWORDS: KNN,Neural network,Logistic regression,Random forest,Decision tree
Modeling scenarios of extreme situations in the energy sector caused by cyber threats

The paper considers the violation of cybersecurity as a possibility of a real impact (intentional or accidental) from cyberspace on the physical infrastructure of a digital energy facility. In energy security studies, such impacts are considered as extreme situations, including critical and emergency situations. A model of scenarios of extreme situations in the energy sector caused by cyber threats using Bayesian Belief Network and the stages of modeling are considered in more detail. The five main stages are i) modeling cyber threats vectors of intrusion and advance towards the target asset; ii) modeling an attack on a target system in the technological segment of the local area network; iii) modeling technogenic threats to energy security caused by cyber threats; iv) modeling consequences at the level of the facility system; v) modeling consequences at the level of the infrastructure. This approach allows one to build cause and effect relationships from vulnerabilities in the cyber environment to the consequences. Modeling stages are aimed at increasing the level of cyber situational awareness, which, in turn, related with energy security issues.
Improving Privacy in Data Service Composition

Today, the de-facto standard mechanism for data service providers to share their services is through web-service interfaces; clients invoke the service through request messages and receive the data as payloads in the corresponding response messages. Typically, clients need information beyond what any single provider offers; in such cases, multiple data services must be composed to provide a complete solution meeting the client needs. The term “data-service composition” refers to a unified interface that delivers to the client the data it needs in response to a single request message, as if it were available withing a single source. Data-service composition is useful and convenient for the requesting client, but raises privacy concerns since a participating data-service provider potentially can infer information about the data held by other providers. In this paper, we propose a data-service composition method that relies on a mediator for the communication between any two service providers, ensures that the mediator is strictly following the data-composition plan, and maintains privacy between the mediator and service providers. The data service provider first authenticates that the input data is coming from the correct source as per the composition plan, and this is done whilst ensuring complete privacy between the mediator and other service providers. Similarly, data service providers also authenticate the destination of their output data. The approach is validated and its performance evaluated using a real world online retail dataset.
KEKELIRUAN PELAFALAN FONEM DALAM KOSAKATA BAHASA INDONESIA OLEH VLOGGER ASING BERBAHASA INDONESIA

This research was conducted to describe the phonemic pronunciation errors which include vocals and consonants, and consonant accompaniment sounds in Indonesian vocabulary conducted by Indonesian-speaking foreign vloggers. This research is a descriptive qualitative research with data collection method using refer and note method with tapping technique. The data source of this research is the speech of two BIPA speakers from Korea namely Akang Daniel and Hari Jisun and two BIPA speakers from Russia namely Irina WS and Kseniia and Josef on their respective Youtube channels. Analysis of research data using the referential equivalent method with the determinant element technique. Presentation of data uses phonetic symbols based on The International Phonetic Alphabet or IPA. The results showed that the phonemic pronunciation error in the Indonesian vocabulary by Indonesian-speaking foreign vloggers occurred at the vowel and consonant levels. Vocal pronunciation errors include phonemes /a/, /e/, /i/, /ə/, /ε/, and /u/, while consonant pronunciation errors include phonemes /r/, /s/, /d/, /k/, /h/, /t/, /y/, /x/, and /ŋ/. There are four sounds accompanying the mistake of consonant pronunciation, namely gemination, vocalization, aspiration, and glottalization.
Kesesuaian Muatan Pelajaran Bahasa Indonesia Kelas IV Tema Indahnya Kebersamaan SD/MI

This study aims to analyze the content of Indonesian in terms of the use of sound and correct language and the suitability of image illustrations with the material in Theme 1 Beautiful Togetherness. This research uses a descriptive qualitative approach. The type of study chosen is in library research, which means that the researcher analyzes the teacher's book and student's book Theme 1, The Beauty of Togetherness. The method of quoting the data used is Forum Group Discussion (FGD). The result is Indonesian language learning material in Theme 1, The Beauty of Togetherness. It follows the EYD, and the illustrations used are apparent; however, some need correction not to misinterpret when the learning takes place
Memory Trick Game Towards Students’ Enthusiasm In Learning Grammar

This was a qualitative research and aimed to investigate whether how memory trick game could improve students in learning grammar at SMPN 18 Mataram in the academic year of 2019/2020. The subject of this research was the second grade students of SMPN 18 Mataram that only one class consist of 15 students. This research was limited by the grammatical role only. The research instruments consisted of questionners, field note, and documentation. The data analyzed in transcript for the observation and taking note for shows that the students at SMPN 18 Mataram still low enthusiasm and comprehend the Memory Trick Game. The researcher took the research and has the provident which the questioner was looking for the students’ surface knowledge about the what is grammar and how enthusiasm student in learning grammar. The result of cycling process also shows that in the first cycle consist of three meetings, and the students were still need to be more concentrate in order to make them more understand in expanding their ideas into a good grammar. Then the cycle two was also consist of three meetings, the students’ score in this cycle was better than the cycle one. The memory trick game as the solution from the researchers also proved that there was an improvement from the first cycle to the second cycle.
THE ROLE OF WHATSAPP IN TEACHING VOCABULARY TO THE STAFFS OF UMBUL PONGGOK KLATEN

This study was conducted with the aim of knowing the significant role of WhatsApp in teaching vocabulary, especially staff learning achievement which was limited to vocabulary achievement as measured by a vocabulary test at Umbul Ponggok Klaten. Participants in this study amounted to 30 people consisting of 15 men and 15 women. All participants will learn English as a foreign language using WhatsApp to learn new vocabulary items via their mobile, tablet or laptop. Participants' English level will be measured. Random sampling procedure was carried out. To carry out the research, a true experimental design was used. The participants were assigned to two experimental and control groups. The assessment instrument in this study used a pre-test and post-test. The results showed that the use of the WhatsApp application in teaching vocabulary to the Umbul Ponggok Klaten staff was able to improve the achievement of new English vocabulary mastery of the Umbul Ponggok Klaten staff which was getting better.
Evil Twin Attack Mitigation Techniques in 802.11 Networks

Evil Twin Wi-Fi attack is an attack on the 802.11IEEE standard.The attack poses a threat to wireless connections.Evil Twin Wi-Fi attack is one of the attacks which has been there for a long time.Once the Evil Twin Wi-Fi attack is performed this acts as a gateway to many other attacks such as DNS spoofing, SSL Strip, IP Spoofing, and many more attacks.Thus, preventing the attack is essential for privacy and data security.This paper will be going through in detail how the attack is performed and different measures to prevent the attack.The proposed algorithm sniffs for fake AP using the whitelist in all the channels, once an unauthorized AP is detected the user has an option to de-authenticate any user in the unauthorized network in case any clients do connect to it by accident also the algorithm will be checking if any de-authentication frame is being sent to any of the AP to know which of the AP is being compromised.The efficiency of proposed approach is verified by simulating and mitigating the evil-twin attack.
Timing Black-Box Attacks: Crafting Adversarial Examples through Timing Leaks against DNNs on Embedded Devices

Deep neural networks (DNNs) have been applied to various industries. In particular, DNNs on embedded devices have attracted considerable interest because they allow real-time and distributed processing on site. However, adversarial examples (AEs), which add small perturbations to the input data of DNNs to cause misclassification, are serious threats to DNNs. In this paper, a novel black-box attack is proposed to craft AEs based only on processing time, i.e., the side-channel leaks from DNNs on embedded devices. Unlike several existing black-box attacks that utilize output probability, the proposed attack exploits the relationship between the number of activated nodes and processing time without using training data, model architecture, parameters, substitute models, or output probability. The perturbations for AEs are determined by the differential processing time based on the input data of the DNNs in the proposed attack. The experimental results show that the AEs of the proposed attack effectively cause an increase in the number of activated nodes and the misclassification of one of the incorrect labels against the DNNs on a microcontroller unit. Moreover, these results indicate that the attack can evade gradient-masking and confidence reduction countermeasures, which conceal the output probability, to prevent the crafting of AEs against several black-box attacks. Finally, the countermeasures against the attack are implemented and evaluated to clarify that the implementation of an activation function with data-dependent timing leaks is the cause of the proposed attack.
On-line process decisions using convolutional neural network for centering high-precision short-focus lens

This study integrated the use of a centering machine with an automatic optical axis measuring technique to improve the centering process for short-focus lenses, which are widely used in interferometric inspection, microscopy, and spectrometry. A major concern of the centering process is coma aberrations during the axis centering of a lens, which leads to deformation of the image system. Because of the small size and high curvature of short-focus lenses, high optical axis error and unstable grinding quality are highly problematic within the high-precision centering process. To reduce optical axis error and improve manufacturing quality, an on-line optical axis measuring system that applies convolutional neural network (CNN) machine learning for the evaluation of centering stability was developed. According to experimental results, the CNN achieved 95% accuracy. With the use of trace classification and optical axis measurements, the optical axis error was controlled to <150 μrad, the range of cracks to <E0.1, and the circularity error to <0.1 mm.
U-TAPIS: AUTOMATIC SPELLING FILTER AS AN EFFORT TO IMPROVE INDONESIAN LANGUAGE COMPETENCIES OF JOURNALISTIC STUDENTS

The problem of Indonesian language errors among students is of particular observation. This problem becomes an important concern for students majoring in journalism because one day the graduates will become journalists. A language error filtering application has been developed that can be used quickly and accurately in journalists’ work. This application, which involves statistical analysis, computational language, and artificial intelligence, is named U-Tapis. This study was aimed at finding out the feasibility and effectiveness measures of the U-Tapis model by focusing on the language of students’ journalistic works such as opinions, news items, and news articles. The study involved 30 students majoring in Journalism, a private university in Jakarta, Indonesia. It was found that the students’ error rate decreased after the use of the model. It can be concluded that, in addition to eligibility which reaches 92.31%, the U-Tapis application can help effectively increase students’ proficiency in the use of the Indonesian language.
Formation of digital competence of teachers in the conditions of distance learning

The purpose of this article was to identify the features and relationships between different stages of formation of digital competence of teachers in terms of distance learning. The research methodology is based on a statistical analysis of data from a survey of 38081 teachers using a structured OECD interview (2018a) on the development of digital competencies. The study found that courses on the use of ICT for teaching were included in the formal education of teachers in 63.1% of cases. 58% of teachers are well prepared for the use of ICT in education, while 42% of teachers answered a low level of preparation for the use of ICT in education. The practical value of the study lies in identifying the features of the formation of digital competence: the importance of training programs for the development of digital skills.
Perception of EFL Students on the Use Grammarly Application in Writing Class

As EFL students, we cannot separate from writing assignments and articles in English. For some students, it is not easy to express thoughts with good grammar directly. They often require several repetitions of works to avoid making mistakes. Even so, they still make a mistake in grammar. Grammatical errors in the writing class often make it difficult for lecturers to understand student writing content so that students get unsatisfactory feedback. One solution to this problem is implementing the Grammarly application in writing assignments and articles. This study aims to determine students' views on using the Grammarly application in writing assignments and articles. This study uses a qualitative approach with descriptive analysis methods. The research subjects were students of the fifth semester of English Language Education, FKIP Tidar University. The research data used an automatic grammar checker in the learning process of writing namely Grammarly.
Evolving reservoir weights in the frequency domain

Reservoir Computing models are a class of recurrent neural networks that have enjoyed recent attention, in particular, their main family, Echo State Networks (ESNs). These models have a large number of hidden-hidden weights (in the so-called reservoir) forming a recurrent topology. The reservoir is randomly connected with fixed weights during learning: only readout parameters (from reservoir to output neurons) are trained; the reservoir weights are frozen after initialized. Since the reservoir structure is fixed during learning, only its initialization process has an impact on the model's performance.
InfoInternet for education in the Global South: A study of applications enabled by free information-only internet access in technologically disadvantaged areas

This paper summarizes our work on studying educational applications enabled by the introduction of a new information layer called InfoInternet. This is an initiative to facilitate affordable access to internet-based information in communities with network scarcity or economic problems from the Global South. InfoInternet develops both networking solutions as well as business and social models, together with actors like mobile operators and government organizations. In this paper we identify and describe characteristics of educational applications, their specific users, and learning environment. We are interested in applications that make the adoption of Internet faster, cheaper, and wider in such communities. When developing new applications (or adopting existing ones) for such constrained environments, this work acts as initial guidelines prior to field studies.
A Recursive and Parallelized Dynamic Programming Implementation of Hard Merkle-Hellman Knapsack System for Public Key Cryptography

Abstract Merkle-Hellman public key cryptosystem is a long-age old algorithm used in cryptography. Despite being computationally fast, for very large input sizes it may operate slower due to thread creation overhead or reaching a deadlock situation. In this paper, we discuss the working principles of the Traditional Merkle-Hellman knapsack cryptosystem, which is an Easy knapsack. The challenges of Hard Knapsack and how it overcomes the shortcomings of the Traditional Easy Knapsack, are also discussed. The Hard knapsack variant of Merkle-Hellman is solved first using plain recursion and then improvised using a dynamic programming approach to the problem. Parallelism and Concurrency has been achieved on the dynamic programming implementation using OpenMP API which further has enhanced the performance time. A comparative study of both variants of Hard Knapsack for messages of different lengths has shown that the latter is faster.
Professional training of future teachers of labor education and technology by means of information and communication technologies

The purpose of this article was to identify the features of professional training of future teachers of labor education and technology by means of information and communication technologies. The research methodology is based on a statistical correlation analysis of the results of the structured survey the OECD Teaching and Learning International Survey (TALIS), conducted in 2018 in 48 countries. Sample of research - teachers and school leaders working in schools providing lower secondary education. The results make it possible to state the following main features of professional training of future teachers of labor training. Identified lack or inadequacy of digital technologies for teacher instruction, which increases the risk of insufficient professional development, mastering digital skills.