Reviewerly/exHarmony huggingface dataset:
Involving Students in Developing Material in Speaking Class: Students’ Perceptions

Speaking skills is important especially for future English teachers since it is the most widely used means of communication used in the teaching and learning process. In order to be able to deliver the material seamlessly, the teachers should have high competence in speaking with other competencies as well. This study aims to identify the students’ speaking circumstances and their perceptions of the involvement of students in developing material in speaking class. This study was a descriptive qualitative study employing observation, questionnaires, and interviews as the means of collecting data. The participants of this study were 7 second-semester students of the English education department at Universitas Nasional Karangturi Semarang joining in a transactional conversation speaking class. Inductive data analysis in this study was based on Burns (2010) and Hatch (2002). The results showed that the majority of the students still find it difficult to find a company in learning speaking from native English, however, it shifted into the native English that wanted to be a conversation buddy with them continuously, not only one-time conversation. The second was that the students were glad to be involved in the material development of the speaking class. Since they have different likings to native English videos to refer to. Introducing a topic at the beginning of the class really helped the students to stay on the right track in searching for the native English video in the speaking class.
Literacy Lessons and a Reading Contest to Improve Students’ Reading Comprehension (A Case of Students in an Indonesian Senior High School, SMAN 1 Soe)

Teaching and learning is a good way to reach the educational goals. This is a classroom action research. The objective of this study was finding out how reading literacy improve students’ comprehension. This study used mix method design. The instruments used were test. The data of this study were collected through spreading questionnaires and doing interviews. The result of the study showed reading literacy could improve students’ comprehension. The data analysis showed that there was a significant improvement of students’ comprehension in cycle I compared to cycle II. It was because reading literacy could facilitate students in identifying problems, brain storming, analyzing, formulating problems, and presenting well.
TPR: The Implementation of Speech Acts (Directives) in Classrooms

One of the key factors to win global competition is mastering international language, English. In Indonesia, this language is considered as a foreign language and whenever a language has status as foreign language, it is certainly that teaching this language is more challenging. Teaching foreign language particularly English must involve two related field of study, language teaching (pedagogy) and applied linguistics. These two fields of study have made collaboration in order to solve the problem coming English language teaching. This article will describe about the implementation of one of speech acts, (directives), in children classrooms to teach tenses and aspects in the form Total Physical Response (TPR), method in children based on one of SLA grand theories, behaviorism.
Position-based Prompting for Health Outcome Generation

Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just data of a specific domain, but also to the style or linguistic pattern of the prompts themselves. We observe that, satisfying a particular linguistic pattern in prompts is an unsustainable constraint that unnecessarily lengthens the probing task, especially because, they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting objective and domain. We therefore explore an idea of using a position-attention mechanism to capture positional information of each word in a prompt relative to the mask to be filled, hence avoiding the need to re-construct prompts when the prompts linguistic pattern changes. Using our approach, we demonstrate the ability of eliciting answers to rare prompt templates (in a case study on health outcome generation) such as Postfix and Mixed patterns whose missing information is respectively at the start and in multiple random places of the prompt. More so, using various biomedical PLMs, our approach consistently outperforms a baseline in which the default mask language model (MLM) representation is used to predict masked tokens.
Fake News, Disinformation, and Deepfakes: Leveraging Distributed Ledger Technologies and Blockchain to Combat Digital Deception and Counterfeit Reality

The rise of ubiquitous deepfakes, misinformation, disinformation, propaganda and post-truth, often referred to as fake news, raises concerns over the role of Internet and social media in modern democratic societies. Due to its rapid and widespread diffusion, digital deception has not only an individual or societal cost (e.g., to hamper the integrity of elections), but it can lead to significant economic losses (e.g., to affect stock market performance) or to risks to national security. Blockchain and other Distributed Ledger Technologies (DLTs) guarantee the provenance, authenticity and traceability of data by providing a transparent, immutable and verifiable record of transactions while creating a peer-to-peer secure platform for storing and exchanging information. This overview aims to explore the potential of DLTs and blockchain to combat digital deception, reviewing initiatives that are currently under development and identifying their main current challenges. Moreover, some recommendations are enumerated to guide future researchers on issues that will have to be tackled to face fake news, disinformation and deepfakes, as an integral part of strengthening the resilience against cyber-threats on today's online media.
Interleaving Loidreau's Rank-Metric Cryptosystem

We propose and analyze an interleaved variant of Loidreau's rank-metric cryptosystem based on rank multipliers. We analyze and adapt several attacks on the system, propose design rules, and study weak keys. Finding secure instances requires near-MRD rank-metric codes which are not investigated in the literature. Thus, we propose a random code construction that makes use of the fact that short random codes over large fields are MRD with high probability. We derive an upper bound on the decryption failure rate and give example parameters for potential key size reduction.
Exploring the Use of Various Board Games to Enhance Speaking Skills Viewed from Students’ Phonology Awareness

Speaking skill is classified as a productive skill after writing skills. It is related to communicative performance dealing with phonetic, fluency, accuracy, pronunciation, grammatical competence, intonation, and vocabulary mastery. Besides, students’ phonology knowledge is crucial in understanding speakers’ utterances in speaking activities. Therefore, students’ phonology awareness in speaking classes determine the students are success or not in having speaking skills. This study aims to elaborate the use of speaking board games to improve students’ speaking skills viewed from phonology awareness. To attain the research aims, this study employed classroom action research. The problems are focused on students’ speaking skills in relation to phonology awareness. The research data are classified into qualitative and quantitative data. The qualitative data were gained using interview and observation sheets, while the quantitative data were collected using speaking tests. Based on the research findings, this study can be concluded that the use of speaking board games in teaching speaking classes which was combined with other various materials was able to improve students’ confidence and critical thinking skills to speak. It helped students practice their fluency, practice their accuracy, and gave smooth correction to the grammatical errors being made. Providing students with various speaking practices, the use of board games also facilitated students to improve their pronunciation inside and outside classes, vocabulary, and engage them to learn the English daily expressions in various topics.
Furnace Temperature Prediction Based on Optimized Kernel Extreme Learning Machine

In power plants, theFurnace furnace temperature directly affects the combustion efficiency and safe operation of the boiler combustion system, which is of great significance for the boiler combustion control system [1]. However, furnaceFurnace combustion is an extremely complex process, and its temperature is affected by many related factors.
Research Challenges and Prospective Business Impacts of Cloud Computing: A Survey

In today's information technology (IT) era, a major part of the costs is being spent on computational needs. Enterprises are in efforts to increase their Return on Investment (ROI) and individuals are trying to reduce their costs. In this regard, cloud computing which emerges as a fifth utility can reduce costs and enhance performance of IT solutions. A large number of companies and institutions are dealing with cloud related issues as a provider or user. Due to the fact that cloud computing services have been proposed in recent years, organizations and individuals face with various challenges and problems such as how to migrate applications and software platforms into cloud and how to ensure security of migrated applications and etc. Given that many different definitions of cloud computing is presented in many publications and projects, a concrete and clear definition for cloud computing considering its characteristics, models and services is provided in this paper. In addition, current challenges and open issues in cloud computing is discussed in details and further recommendations and roadmaps for scientific activities and researches as well as potentials for improvements in this area from scientific and commercial points of view are given in this paper.
The Effectiveness of Teaching Speaking by Using Drilling Method through YouTube

The purpose of this study is to improve students’ speaking skills by using the drilling method through YouTube. The problem of this study is Does the use of the drilling method on YouTube enhance students'' speaking skills. In this study, the researcher will try to analyze students'' speaking skills. Based on the independent sample t-test and paired sample t-test, it is found out that there is a significant difference between the experimental group using YouTube in the drilling learning method and the control group using conventional media in the drilling learning method. The researcher utilizes Quasi-experimental as a method in this study. The result of paired sample t-test shows the positive correlation between the results in the experimental group. Based on the results of the research described it can be concluded that there is a significant positive influence in the use of YouTube on learning speaking using the drilling method for class XI students of SMA YPI Darussalam. Students will have pretest and posttest to check their skill. Especially in their fluency, vocabulary, grammar, and pronunciation
Distributed Peer-to-Peer Energy Trading in Virtual Microgrids: A Blockchain Survey for Future Smart Grid

Due to the apparent demands and constraints faced by energy systems operating in the market world, the Prosumer Energy trade strategy was selected as a potential opportunity for research and industries. Energy trading has expanded due to the availability of dispersed energy sources and power users who produce more electricity than they would otherwise and can profitably export their excess fuel. The energy trading system blends energy from various sources and effectively coordinates it to ensure stable and optimal usage of available resources and better facilities for energy users. Peer-to-peer (P2P) energy trading is a joint research topic that involves various managerial and technical challenges. This paper provides an overview of peer-to-peer energy exchange and how blockchain can be used to increase transparency and overall performance, including the degree of decentralization, scalability, and device reliability. A thorough examination of the Prosumer Smart Grid environment is explored and clarified. The energy sharing mechanism among consumers comprises two major components: information/digital technology and optimization techniques. Three blockchain-based energy sharing models have been proposed to overcome technical and market barriers to adopt this revolutionary technology. The paper further discusses open topics and possible future paths for peer-to-peer blockchain-based energy sharing.
Influence of deformed cavity field and atomic dipole interaction on the quantum correlations of two-qubit system

Abstract The possibility of generating of some quantum correlations of entangled or separable initial two-qubit system interacts with a deformed cavity mode on the presence of dipole-dipole interaction is discussed. These quantum correlations are the coherence, non-separability, and steering degrees, which are quantifiers by using l 1 norm of coherence, Entanglement of Formation (EoF), and Einstein-Podolsky-Rosen (EPR)-steering. It is shown that, maximizing or minimizing the quantum correlations depend on the initial settings states of the two-qubit system. To maximize the quantum correlations on the presences of deformation, one has to prepare the initial two-qubit system in an entangled state, setting large values of the coupling dipole and decreasing the effect of deformation parameter.
Finding The Potential Accepted Answer On Stack Overflow: a Text Mining Approach

Abstract Background: Stack Overflow has been the first website developers look for to find their answers. Many questions asked on Stack Overflow has several answers but doesn’t have an accepted answer. This leaves all the answers untrustworthy and makes people doubt their correctness. In these scenarios, mostly the questioner hasn’t found their desired answer. But, in some cases, the questioner either somehow forgets to mark an answer as the accepted answer or just doesn’t know how to do so. This causes people with the same question to lose their trust in the existing unaccepted answers and spend more time searching online for the solution. Aim: We aim to improve Stack Overflow by detecting the potential solution to questions with no accepted answers. Specifically, our proposed method looks through all submitted answers and marks one as the potential solution. This helps the questioner find and Stack Overflow to suggest the best answer. Method: In this paper, we used text mining to extract 13 different features from 15464 questions, 37275 answers, and 72025 comments of Stack Overflow posts and then learned them by machine learning models and predicted if an answer is the accepted answer or not. This approach only used English texts from a post. Results: Our method resulted best with the Adaboost ensemble model with 71% accuracy and 70% F1-score. The results show this problem can be addressed and Stack Overflow posts can be learned by machine learning and lead us to find the best answer.
XOR Chain and Perfect Encryption at the Dawn of the Quantum Era

In this work we present a new algorithm that achieves the perfect Shannon secret by means of the XOR function and a method that we call multiple key reuse. The algorithm has two execution modes: message authentication and data encryption. The XOR encryption scheme allows for batch encryption and exhibits Perfect Forward Secrecy (PFS). Furthermore, based on our fundamental algorithm, we have developed a new strategy for blockchain implementation that does not require Proof of Work (PoW), but defines a fair mechanism for miner selection and secure addition of blocks to the chain.&#x0D; Since our method is mainly based on the Boolean XOR function, the strength of the cryptosystem can be directly established thanks to its mathematical properties. Due to the risk that quantum computers represent for current cryptosystems based on prime factorization or discrete logarithm, we postulate that our method represents a promising alternative in the quantum era for the security of communications between Internet of Things devices as well as Blockchain technology.
Leveraging Machine Learning and Model-Agnostic Explanations to Understand Automated Diagnosis of Cardiovascular Disease

Abstract The pervasiveness of cardiovascular disease and physician misdiagnosis creates the need for artificial intelligence models to improve diagnosis accuracy. The study trains machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompts the creation of an automated open-source diagnosis tool powered by the MLP. Local Interpretable Model-Agnostic Explanations (LIME) are employed to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering segments patients into ten clusters, after which each example is passed through LIME. The resulting histograms depict a complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows nuances in the MLP's understanding of patients from different clusters. LIME analysis reveals that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and ST segment slope. Future experiments should replicate this study's LIME methodology on data sets containing more specialized features in order to gain practical medical insights about the different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models adaptable to other diseases as well.
N-gram-based Content Indexing: Semiautomated Analysis of Holocaust Testimonies

Holocaust interview collections are key resources to preserve and share the memories of genocide survivors.As the size of testimonial collections makes manual analysis unwieldy, Digital Humanities (DH) methods can help develop a comprehensive view of the collections' contents and narratives.Developing nuanced and comprehensive methods to index and summarize the content of these interviews is central to ensuring survivor testimonies are accessible and searchable even in large-scale archives.This chapter develops a content indexing system that is not limited to the identification of keywords to summarize content, but rather includes longer phrases and emotional expressions.In particular, the chapter presents a semiautomated DH approach based on N-grams, i.e. frequent sentence chunks of 'N' words in a row, tested on David Boder's corpus of Holocaust testimonies.The results show that this approach allows us to identify speech patterns and narrative structures that go unseen in traditional keyword-based indexing of the survivors' testimonies, including structural, non-verbal categories like uncertainty, reticence, emotional insistence on time references.This method also helps shift our perspective from the narrative we expect survivors to use to the actual narrative they use, which may go unnoticed if we are not actively searching for it or if we only focus on major keywords.
Automatic Identification of Biblical Citations and Allusions in Hebrew Texts

The Hebrew text of scripture forms a foundational piece of shared culture for Hebrew writers. Hebrew texts through the ages, whether medieval or modern, virtually all draw upon the Hebrew Biblical text in one way or another. The identification of these Biblical citations and allusions is generally a primary prerequisite for the analysis or publication of the text. The automation of this process has long been a desideratum among scholars; however, for many years, an effective solution proved elusive. To be sure, exact citations of Biblical prooftexts are trivial to identify: they are generally introduced with a stock phrase such as “as it is written”, and the exact reproduction of the Biblical texts allows for a quick and easy lookup to determine the source of the quote. However, the overwhelming majority of Biblical text reuse is not in the form of exact citations, but is rather found as a reworking and adaptation of the Biblical text, adjusting the text to fit the new context, while still maintaining a set of linguistic cues in order to keep the connection to the original verse. For instance, a case of Biblical allusion will often be based upon the reuse of a set of lexemes from a given verse, even as those lexemes are adjusted in terms of their tense, person and gender. Additionally, prefixes and suffixes may be added, dropped or altered; additional words may be interpolated; and the order of the lexemes may be altered as well. In this chapter, I present a new algorithm for the identification of Biblical allusions, designed to address all of these challenges.
Modeling Task Effects in Human Reading with Neural Network-based Attention

Research on human reading has long documented that reading behavior shows task-specific effects, but it has been challenging to build general models predicting what reading behavior humans will show in a given task. We introduce NEAT, a computational model of the allocation of attention in human reading, based on the hypothesis that human reading optimizes a tradeoff between economy of attention and success at a task. Our model is implemented using contemporary neural network modeling techniques, and makes explicit and testable predictions about how the allocation of attention varies across different tasks. We test this in an eyetracking study comparing two versions of a reading comprehension task, finding that our model successfully accounts for reading behavior across the tasks. Our work thus provides evidence that task effects can be modeled as optimal adaptation to task demands.