Reviewerly/exHarmony huggingface dataset:
The Realization of Formulaic Expressions in EFL Teacher-Student Classroom Interactions

Formulaic expressions are essential for developing students' communicative competence to speak English naturally and fluently. This research aimed to determine how the EFL teachers and students used the five types of formulaic expressions in their classroom interactions. This study is discourse analysis, particularly conversation analysis, using a qualitative research approach. The research subjects were two teachers and thirty-one students from two different EFL classes in a private high school in Semarang. The data were obtained by recording their 5x40 minutes' classroom interactions. The video recordings were transcribed and then observed. After all of the data were collected, they were classified and analyzed based on Biber et al.'s (1999) framework and then described qualitatively. The study results showed that the teachers and the students realized all five types of formulaic expressions; lexical bundles, idiomatic phrases, free combinations of verb+particle, binomial expressions, and inserts. It shows that the teachers and the students are able to speak relatively natural English. By doing the further analysis, it was also found that even though the teachers used the formulaic expressions a lot more than the students, the orders of the frequency of formulaic expressions used are similar, as well as some expressions used. Surprisingly, it was revealed that there were some expressions which were not used by the teachers but were realized by the students.&#x0D;
The Usability of Training Management System for Teachers’ Professional Development: A Preliminary Study

The usability study of the Ministry of Education’s Training Management System (SPLKPM) is a preliminary study conducted by the researcher to determine the rationale and analysis of research needs regarding system usage behaviour among teachers. The purpose of this study is to assess the system's usability in the management of in-service teacher training. SPLKPM serves as a platform that records and stores training data and credit point reporting for all Ministry of Education (MoE) staff members based on 13 activities outlined in continuous professional development credit points (MyPBB). The respondents for this study were in-service teachers who are also regular users of SPLKPM. A total of 32 respondents provided feedback that was randomly distributed through several mediums; emails and mobile messaging apps. The study found that the existing system has a usability score of 45.9, compared to the average of 68, indicating that it requires improvement and that a comprehensive analysis of the factors that influence system use should be done. In conclusion, most teachers believe that the system needs to be improved to facilitate more frequent recording and use.
FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)

Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variables and seasonal elements, namely the SARIMAX model (Seasonal ARIMA with eXogenous input). Based on the analysis that has been carried out, the prediction of rainfall in Pangkalpinang City, Bangka Belitung Islands Province can be modeled with the SARIMAX model (0,1,3)(0,1,1){12} for monthly rainfall and SARIMAX (0,1,2 )(0,1,3){12} for maximum daily rainfall. When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. If viewed based on the AIC value of the SARIMA model, the SARIMAX model is also more suitable to be used to predict rainfall in Pangkalpinang City.
RoViST: Learning Robust Metrics for Visual Storytelling

Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence.Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr.However, such metrics based on n-gram matching tend to have poor correlation with human evaluation scores and do not explicitly consider other criteria necessary for storytelling such as sentence structure or topic coherence.Moreover, a single score is not enough to assess a story as it does not inform us about what specific errors were made by the model.In this paper, we propose 3 evaluation metrics sets that analyses which aspects we would look for in a good story: 1) visual grounding, 2) coherence, and 3) nonredundancy.We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST).Our metric sets outperforms other metrics on human correlation, and could be served as a learning based evaluation metric set that is complementary to existing rule-based metrics.
The use of information and communication technologies in the activities of a modern teacher: a view from the inside

Information and communication technologies occupy an important place in the life of a modern teacher. But their use is unsystematic. The purpose of this work is the specified area of information technology, the specified area of responsibility, and the space of tools and techniques that can be used by the teacher. To analyze the activities of teachers, we based on the principles of a unified model language, methods of systems analysis, and elements of the statistical information processing. As a result, the basic actors of the teacher’s activity are detemined: the state, the administration of the higher education institution, the management of the structural unit and the teacher himself. In the work "student" is considered not as an actor, but as a living environment for teaching, as the main goal for any teacher. Then we considered a teacher as the class with its required and optional attributes. These attributes are determined by state and social requirements for the modern teacher and have different states. Further the complex of the factors influencing on these attributes’ states formation are defined. Objective and subjective reasons that hinder the formation of attributes are analyzed. The resulting significant factors are distributed to the spheres of certain actors’ existence. The range of responsibilities and influence of actors on significant factors are determined. In analyzing the attributes of the teacher and the factors that contribute to or hinder their formation, open information from the reports of the Ministry of Education of Ukraine and state institutions for the collection of statistical information was used. Among the attributes are common to all teachers, regardless of the level of education they teaching. The study examined data on teachers of higher education and general secondary education (with an emphasis on teachers in grades 5-11). As a result, we emphasize the responsibilities of a teacher of higher education. We determine the range of tasks that teacher must solve in their daily work. We choose the tasks of teacher’s pedagogical activity, subjected to automation and unification. As a result, from a practical point of view, a range of elementary problems has been identified, the solution of ones can be accelerated and optimized. This work is the beginning of an article’s series devoted to solving each elementary problem from a defined list.
A Scalable Microarchitecture for Efficient Instruction-Driven Signal Synthesis and Coherent Qubit Control

Execution of quantum algorithms requires a quantum computer architecture with a dedicated quantum instruction set that is capable of supporting translation of workloads into actual quantum operations acting on the qubits. State-of-the-art qubit control setups typically utilize general purpose test instruments such as arbitrary waveform generators (AWGs) to generate a limited set of waveforms or pulses. These waveforms are precomputed and stored prior to execution, and then used to produce control pulses during execution. Besides their prohibitive cost and limited scalability, such instruments suffer from poor programmability due to the absence of an instruction set architecture (ISA). Limited memory for pulse storage ultimately determines the total number of supported quantum operations. In this work, we present a scalable qubit control system that enables efficient qubit control using a flexible ISA to drive a direct digital synthesis (DDS) pipeline producing nanosecond-accurate qubit control signals dynamically. The designed qubit controller provides a higher density of control channels, a scalable design, better programmability, and lower cost compared to state-of-the-art systems. In this work, we discuss the new qubit controller's capabilities, its architecture and instruction set, and present experimental results for coherent qubit control.
role of the Wikidata librarian in a renewed Bibliographical Universe: “next generation metadata”, next generation librarians

Starting from a brief analysis of the OCLC Research report "Next generation metadata", the contribution proposes a reflection on the librarian metadata and the new centrality of its role in the LIS; among the various declinations that can assume such professional figure, One of the most interesting is the Wikidata librarian, of which a first definition is outlined. Following some international experiences of the use of Wikidata as a tool for new working methods, already consolidated in many library institutions, and as a particularly suitable environment for experimentation, thanks to its features of open, free, collaborative, easy to understand and use system. While information professionals and a large part of the academic and scientific environment have understood the potential of Wikidata, some weaknesses of this instrument have been highlighted, which must be corrected and recalibrated in the name of universally accessible and reusable knowledge, also taking into account the requirements contained in the Semantic Web Manifesto by the Cataloging and Indexing Study Group of the Italian Association of Libraries.
SRE_BBC: A Self-Adaptive Security Enabled Requirements Engineering Approach for SLA Smart Contracts in Blockchain-Based Cloud Systems

Current blockchain-based cloud (BBC) systems have several security vulnerabilities regarding smart contracts (SC), and several attacks have been reported recently. The SC development lacks standard design processes that follow software lifecycle principles to model secure SC. Secondly, the security mechanisms in the SC are not constantly evolved to resist evolving adversary attacks. BBC systems lack self-adaptive security capability to make spontaneous decisions when adversarial attacks are encountered. To build a self-adaptive secure BBC system that follows standard software development lifecycle principles to model secure SC, we propose the so-called self-adaptive security RE_BBC framework. The framework would utilize the MAPE-BBC adaptation loop to make decisions internally based on the threat models, goal models, and service level agreement (SLA) SC security specifications. The framework identifies vulnerabilities and threats and takes precautionary measures using self-adaptive SC agents. We validated the proposed methodology theoretically and empirically, and statistically proved the research questions and hypothesis using the t-test and Mann-Whitney U test. Subsequently, we compare our proposed approach with the Security Quality Requirements Engineering approach (SQUARE). The feasibility results and the replicated study results indicate that the proposed approach outperformed the SQUARE approach in terms of artifacts quality, self-adaptive security evaluation quality, efficiency in response time, complexity, and usefulness of the proposed approach for the Healthcare Data Management (HDM) system. SC security developers can immensely benefit from our proposed methodology. They need not reengineer SC from scratch; depending on their security needs and plan, the contract can be adapted to execute a new plan.
Prediction on the Combine Effect of Population, Education and Unemployment on Criminal Activity Using Machine Learning

Criminology is a complex subject.There are various factors which affect rate of crime in a particular society.Few of the main aspects are population, unemployment and education.In this article we would explore the correlation of rate of violent crimes in areas where majority of the inhabitants are afro Americans with the aforesaid aspect.Following are the effects in brief of mentioned reasons influencing rate of crime.Unemployment is one of the multiple causes contributing higher rate of crimes in contemporary society.Relation of crime with unemployment is manifested well through the offensive conducted by the unemployed.At a time when there is a death of suitable jobs or opportunities with crime is undermine by the promise of expect rewards in the mind of an individual.Good education is essential for development of professional skill / abilities in individuals.Members of community with higher level of education can avail rewarding jobs resulting in higher gross capita income.Consequently, risk associated with crime outweighs lure of benefits.Apart from the above good education tunes psychological, moral and social upbringing of an individual and thereby contributes the most to make her a better person.A person with rewarding jobs and high moral value would most likely endeavor to avoid activities which is not ethical and/or legal in the eyes of law.Rapid population growth, if not properly managed can have negative impact on crime rate.This is especially evident in areas such as those inhibited by impoverished afro American population.Reason could be deteriorated standard of living as available resources become scarce.Unfavorable living and economic condition can be conducive environment for criminal activities.
Body Calibration: Automatic Inter-Task Mapping between Multi-Legged Robots with Different Embodiments in Transfer Reinforcement Learning

Machine learning algorithms are effective in realizing the programming of robots that behave autonomously for various tasks. For example, reinforcement learning (RL) does not require supervision or data sets; the RL agent explores solutions by itself. However, RL requires a long learning time, particularly for actual robot learning situations. Transfer learning (TL) in RL has been proposed to address this limitation. TL realizes fast adaptation and decreases the problem-solving time by utilizing the knowledge of the policy, value function, and Q-function from RL. Taylor proposed TL using inter-task mapping that defines the correspondence between the state and action between the source and target domains. Inter-task mapping is defined based on human intuition and experience; therefore, the effect of TL may not be obtained. The difference in robot shapes for TL is similar to the cognition in the modification of human body composition, and automatic inter-task mapping can be performed by referring to the body representation that is assumed to be stored in the human brain. In this paper, body calibration is proposed, which refers to the physical expression in the human brain. It realizes automatic inter-task mapping by acquiring data modeled on a body diagram that illustrates human body composition and posture. The proposed method is evaluated in a TL situation from a computer simulation of RL to actual robot control with a multi-legged robot.
Comparison of Inter-Rater Reliability Techniques in Performance-Based Assessment

The aim of this study is to analyse the importance of the number of raters and compare the results obtained by techniques based on Classical Test Theory (CTT) and Generalizability (G) Theory. The Kappa and Krippendorff alpha techniques based on CTT were used to determine the inter-rater reliability. In this descriptive research data consists of twenty individual investigation performance reports prepared by the learners of the International Baccalaureate Diploma Programme (IBDP) and also five raters who rated these reports. Raters used an analytical rubric developed by the International Baccalaureate Organization (IBO) as a scoring tool. The results of the CTT study show that Kappa and Krippendorff alpha statistical techniques failed to provide information about the sources of the errors causing incompatibility in the criteria. The studies based on G Theory provided comprehensive data about the sources of the errors and increasing the number of raters would also increase the reliability of the values. However, the raters raised the idea that it is important to develop descriptors in the criteria in the rubric.
brief survey on different methods employed in each module for diagnosing alzheimer disease in the early stage

Numerous individuals are afflicted with disorders of the brain all around the globe. There is a need to research and discover an approach to these disorders is crucial currently. Dementia is perhaps an occurrence of brain disease. Injuries or typical elderly might lead to a decline in cognitive processes including thinking, remembering, and some other psychological attributes. Alzheimer's disease (AD) accounted for 60 to 80 percent of all cases of mental ailment and is among the most common forms of dementia. "Magnetic Resonance Imaging (MRI)" could be used to diagnose this condition at a preliminary phase and allow individuals to have a long and healthy life. An intellectual system widely recognized as "Machine Learning (ML)" utilizes many probabilities and optimizing approaches that provide PCs the ability to benefit from massive and complicated information. As a consequence, experimenters are increasingly relying on ML for the early detection of AD.
AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages

Dialogue generation is an important NLP task fraught with many challenges. The challenges become more daunting for low-resource African languages. To enable the creation of dialogue agents for African languages, we contribute the first high-quality dialogue datasets for 6 African languages: Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yor\`ub\'a. These datasets consist of 1,500 turns each, which we translate from a portion of the English multi-domain MultiWOZ dataset. Subsequently, we investigate & analyze the effectiveness of modelling through transfer learning by utilziing state-of-the-art (SoTA) deep monolingual models: DialoGPT and BlenderBot. We compare the models with a simple seq2seq baseline using perplexity. Besides this, we conduct human evaluation of single-turn conversations by using majority votes and measure inter-annotator agreement (IAA). We find that the hypothesis that deep monolingual models learn some abstractions that generalize across languages holds. We observe human-like conversations, to different degrees, in 5 out of the 6 languages. The language with the most transferable properties is the Nigerian Pidgin English, with a human-likeness score of 78.1%, of which 34.4% are unanimous. We freely provide the datasets and host the model checkpoints/demos on the HuggingFace hub for public access.
Classification of Scatter Plot Images Using Deep Learning

Scatter plot is one of the well-known charts and is frequently embedded in different types of documents such as articles, books, and dissertations.However, the information given in the scatter plots can't be directly noticed by visually impaired individuals, because they are usually in an image format, and so they are not naturally readable by machines.To solve this problem, this paper proposes a system that can extract visual properties from scatter plot images using deep learning and image processing techniques.It is the first study that automatically classifies scatter plots in terms of two aspects: degree of correlation (strong or weak) and types of correlation (positive, negative, or neutral).In the experimental studies, alternative convolutional neural network (CNN) architectures were compared on both synthetic and real-world datasets in terms of accuracy, including Residual Networks (ResNet), Alex Networks (AlexNet), and Visual Geometry Group (VGG) Networks.The experimental results showed that the proposed system successfully (93.90%) classified scatter plot images to help visually impaired users understand the information given in the graph.
Evonne: Interactive Proof Visualization for Description Logics (System Description) -- Extended Version

Explanations for description logic (DL) entailments provide important support for the maintenance of large ontologies. The "justifications" usually employed for this purpose in ontology editors pinpoint the parts of the ontology responsible for a given entailment. Proofs for entailments make the intermediate reasoning steps explicit, and thus explain how a consequence can actually be derived. We present an interactive system for exploring description logic proofs, called Evonne, which visualizes proofs of consequences for ontologies written in expressive DLs. We describe the methods used for computing those proofs, together with a feature called signature-based proof condensation. Moreover, we evaluate the quality of generated proofs using real ontologies.
Application of Intelligent Speech Synthesis Technology Assisted by Mobile Intelligent Terminal in Foreign Language Teaching

In today’s increasingly frequent international exchanges, language ability has been paid more and more attention. However, the current foreign language teaching in some areas lacks a standard external input environment, and the gap between learners’ own ability and the new requirements has not been effectively solved. At present, intelligent speech synthesis technology is mature. It is found that this technology can play a unique advantage in language teaching. Based on this, this study studies the feasibility of the application of intelligent speech synthesis technology in foreign language teaching from the perspective of technology. On this basis, the application mode of intelligent speech synthesis technology in foreign language teaching is constructed to verify its application effect in foreign language teaching. The main research work of this paper is as follows: firstly, it analyzes the feasibility of using clever speech synthesis technologies to teach foreign languages, and it lists in detail the application conditions of intelligent speech synthesis technology in different teaching activities and course types. Then, it constructs the application mode of intelligent speech synthesis technology in foreign language teaching. Finally, the experimental research on the application mode of intelligent speech synthesis technology in listening teaching is carried out. The experimental results show that intelligent speech synthesis technology has achieved good results in foreign language teaching and laid a foundation for foreign language teaching research.