Reviewerly/exHarmony huggingface dataset:
TODO: A Core Ontology for Task-Oriented Dialogue Systems in Industry 4.0

Nowadays, the demand in industry of dialogue systems to be able to naturally communicate with industrial systems is increasing, as they allow to enhance productivity and security in these scenarios. However, adapting these systems to different use cases is a costly process, due to the complexity of the scenarios and the lack of available data. This work presents the Task-Oriented Dialogue management Ontology (TODO), which aims to provide a core and complete base for semantic-based task-oriented dialogue systems in the context of industrial scenarios in terms of, on the one hand, domain and dialogue modelling and, on the other hand, dialogue management and tracing support. Furthermore, its modular structure, besides grouping specific knowledge in independent components, allows to easily extend each of the modules, attending the necessities of the different use cases. These characteristics allow an easy adaptation of the ontology to different use cases, with a considerable reduction of time and costs. So as to demonstrate the capabilities of the the ontology by integrating it in a task-oriented dialogue system, TODO has been validated in real-world use cases. Finally, an evaluation is also presented, covering different relevant aspects of the ontology.
Traffic Sign Detection Based on Convolutional Neural Network

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.
GAMBARAN MOTIVASI BERPRESTASI SISWA KELAS VIII MTs ALBIDAYAH

The purpose of this study is to find out the picture of the motivation of achievement students of class VIII MTs Al Bidayah.The method used in this study is a quantitative approach with this type of quantitative deriftive research. The data collection method used in this study is a questionnaire that is adjusted from the indicators of Mc Clleland's achievement motivation theory (1961). The results showed that the achievement motivation of MTs. Albidayah students belonged to the high category with an average score (mean) of 86.3 and a percentage of achievement of 86.3%. The three dimensions of student motivation also showed high results, namely N-ach (mean = 57.37; achievement = 88.26%), N-aff (mean = 16.43; achievement = 82.17%), and N-pow (mean = 13.13; achievement = 87.56%). This means that students have a good enough motivation to perform when participating in learning activities in school. Keywords: : motivation, achievement, students
Analisis Hidrograf DAS Poso dengan Metode Hidrograf Satuan Sintetis Snyder dan Hidrograf Satuan Sintetis Soil Conversation Service (SCS)

The Poso River is a river located in Poso Regency, Central Sulawesi Province, which has a length of 74.58 km, and watershed area of 1092.810 km2. Energy in the Poso River is used for hydroelectric power plant (PLTA). With the construction of the Poso hydropower plant, maximum flood discharge data is needed for the prevention of Poso hydro power plant safety. In calculating the flood discharge, the method used is a synthetic unit hydrograph. Synthetic unit hydrograph is a graph of the relationship between flow rate (Q) and time (t). In this study, the method used to calculate the designed flood discharge is the Snyder synthetic unit hydrograph method and the Soil Conversation Service (SCS) synthetic unit hydrograph. The aims of this study are to determine the largest flood discharge value and to determine the hydrograph shapes of the two methods. The parameters that will be obtained from both methods are peak time (Tp), base time (Tb) and peak discharge (Qp). From the analysis it can be found that in the Snyder SUH method, the peak time (Tp) is 12.616 hours, the base time (Tb) is 67.276 hours with a peak discharge (Qp) of 21.672 m3sec. Whereas in the SCS SUH method, the peak time (Tp) is 10.954 hours, the base time (Tb) is 57.268 hours with a peak discharge (Qp) of 20.751 m3/sec. The result demonstrates the result that the largest flood discharge has occurred in the Snyder SUH method.
The use of personal pronoun in Donald Trump’s speech

This study concerns with the use of personal pronouns in political speech. The aims of this study are to find out personal pronouns used in Donald Trump’s speech and their functions. The source of data of this research is the speech by Donald trump at 74th session of the united nation general assembly. This study used descriptive qualitative method. The result shows that Donald Trump used personal pronoun I to emphasize his authority and his good qualities such as confident, share experience, and opinion. Personal pronoun you is used to refer to a specific group when he wants to separate one group from other groups. He also used you to refer to general to avoid conflict. He used personal pronoun inclusive we to involve himself with the united nation member. He used exclusive we to show his good relation with other groups in sharing responsibility. He used personal pronouns they to make him and the united nation members less responsible for other groups activities.
Decision Support System Design Model for Choosing Effective Learning Method in Higher Education Institution

In an educational institution, especially higher education, producing qualified graduates is the highest priority achievement. The quality of the graduates is highly dependent on the applied learning method. However, sometimes universities have difficulty in determining appropriate learning for their students. Determination of this decision requires a tool that can facilitate decisionmakers in analyzing all the considerations in choosing decisions. Another challenge is when the decision is shared among multiple stakeholders with equal contribution. It consequently creates a need for a mechanism that can provide an equal contribution for each stakeholder. The research tries to create a model to design a tool that can determine the decisions that need to be taken. This model is built with the Multi-Criteria Decision Analysis (MCDA) approach. MCDA is selected because it can be implemented in a collaborative ecosystem where multiple stakeholders are involved. Then, a literature study is conducted to determine the attributes and decision-making parameters. In addition, the primary data derived from interviews to a total of 40 respondents consisting of students, lecturers, and staff are taken into consideration to complete the literature study. The literature review and interview output are then translated to some parameters that influence an effective learning system. The result shows that this model can be used as a reference in developing a web-based application as an implementation of MCDA.
Towards a companion system for collision avoidance during robot-assisted needle placement

Abstract Robotic assistance systems for surgery enable fast and precise interventions with reduced complication rates. However, these benefits are accompanied by a more complex operating room (OR) and the risk of collision with robotic assistance systems. Current strategies for collision avoidance and minimizing possible injuries require the adaptation of robotic trajectories and a computational model of the surroundings. In contrast, this work presents a novel companion system for collision avoidance without influencing robotic trajectories. The companion system consists of a preoperative planning application and an augmented reality application for intraoperative support. The companion system visualizes the workflow within the OR and allows robot movements to be seen virtually, before they are executed by the actual robotic assistance system. Preliminary experiments with users imply that the companion system leads to a positive user experience, enables users to follow a predefined workflow in the OR, but requires further refinement to improve accuracy for practical collision avoidance.
Simplified Coherent Synaptic Receptor for Filterless Optical Neural Networks

Neuromorphic computing is touted to be a game changer for existing and emerging data processing applications. Towards this direction, artificial neural network implementations have moved into the focus of research. Advancing neural networks to the optical domain offers several advantages, such as high data throughput at time-of-flight inference latency. The present work proposes coherent synaptic interconnects as a path towards filterless neural networks with increased routing flexibility. A coherent synaptic receptor with integrated weighing functionality is experimentally investigated and evaluated for a 1-GHz train of 130-ps wide spikes. Homodyne detection is accomplished through use of an optically injection locked local oscillator, while its phase and the responsivity of the co-integrated photodiode are exploited to realize a tunable weight upon reception of the incoming optical spikes. Moreover, the switching of the sign is shown for the detection weight, underpinning the feasibility to extend the allocation of synapses toward both, wavelength and time dimensions.
Low Resource German ASR with Untranscribed Data Spoken by Non-Native Children &amp;#8212; INTERSPEECH 2021 Shared Task SPAPL System

This paper describes the SPAPL system for the INTERSPEECH 2021 Challenge: Shared Task on Automatic Speech Recognition for Non-Native Children's Speech in German. ~ 5 hours of transcribed data and ~ 60 hours of untranscribed data are provided to develop a German ASR system for children. For the training of the transcribed data, we propose a non-speech state discriminative loss (NSDL) to mitigate the influence of long-duration non-speech segments within speech utterances. In order to explore the use of the untranscribed data, various approaches are implemented and combined together to incrementally improve the system performance. First, bidirectional autoregressive predictive coding (Bi-APC) is used to learn initial parameters for acoustic modelling using the provided untranscribed data. Second, incremental semi-supervised learning is further used to iteratively generate pseudo-transcribed data. Third, different data augmentation schemes are used at different training stages to increase the variability and size of the training data. Finally, a recurrent neural network language model (RNNLM) is used for rescoring. Our system achieves a word error rate (WER) of 39.68% on the evaluation data, an approximately 12% relative improvement over the official baseline (45.21%).
Development of digital literacy and competence of future sports teachers in the physical and educational environment

Thanks to the rapidly evolving technology, today's students are improving along with the digital world.The aim of this research was to study the level of students' knowledge of digital technology (computer, tablet computer, laptop, smartphones, interactive whiteboard and other digital devices).The authors used the following approachesobservation, comparative investigations and synthesis of modern techniques to the formation of digital competencies of students, analysis of science, sociological, pedagogical and methodological literature; the study and implementation of various approaches, programs, mobile devices for the development of digital competence of students, the modelling of various approaches and pedagogical situations, the comprehension of their own experience.The study involved 37 students, future teachers of biology and English language of Kazan (Volga region) Federal University.The pedagogical essence and content of the concept "digital competence of the students" has been revealed, which consists in the fact that its development encourages students to constantly improve themselves, to personal and professional development.Defined pedagogical conditions aimed at improving the quality of knowledge of students using digital technologies in the educational space of Kazan (Volga region) Federal University.Developed a model for improving the quality of knowledge of students using various digital technologies in the educational process of the university.Criteria for the dynamics of the quality of knowledge of students using various digital technologies, tools and devices when studying at the university are determined.The results of the study can be used to formulate initiatives to develop the skills of the digital economy in the population.
Implementation of Haversine Formula to Determine the Shortest Path Using Web Based Application for a Case Study of High School Zoning in Depok

The zoning system that regulates regional zones for new students is basically carried out to bring the student's domicile closer to school, the application of the zoning system is carried out at every level by the Regional Government in accordance with its authority.In the application of zoning system, it is necessary to pay attention on the availability of the school capacity which is adjusted to the availability of the number of school-age children at each level in the area.Depok only has 13 Public Senior High School spread across 11 sub-district which can accommodates 3,858 Junior High School graduates.This becomes disproportionate to the number of junior high school graduates which are more than the school available capacity.The condition that occurs is that there is no form of calculating the distance from student's location to the school based on a straight line so that an accurate distance is obtained.In this study, a mapping of the distribution of high school in Depok was carried out, then the distance was calculated using the Haversine formula.Implementation of distance measurement in this study in the form of a website.The results of the study can provide distance information to get school recommendations that are in accordance with the zone criteria, and this study has a good accuracy value of 97.22%.
Sistem Pakar dalam Mengidentifikasi Gejala Stroke Menggunakan Metode Naive Bayes

Stroke is a disease caused by brain damage caused by disruption of the blood supply to the brain. At this time in general, people are still not very familiar with how this stroke disease or do not realize the symptoms that may have appeared from the start. People also tend to be hesitant to visit the hospital to check their symptoms and feel they are delaying further examinations. This is certainly a scourge that continues to make the number of strokes increase. In assisting the community in identifying stroke disease, an expert system is needed that is able to identify the type of stroke based on the symptoms felt. The data used in this study were obtained from Brain Hospital. Dr. Drs. M. Hatta Bukittinggi which was later developed into a website-based system using the PHP Framework Laravel programming language and MySQL as the database. The system is built based on the Naive Bayes method which is one of the Expert System methods that has a high accuracy value. The use of this system is expected to be able to provide knowledge to the public about the symptoms that might lead to what type of stroke the user might suffer, so that the user can use the results of the system as a reference to visit the hospital and immediately get more targeted help. This system can perform calculations that match the results of the doctor's diagnosis with an accuracy value of 100% in identifying the type of stroke from 10 data samples used.
Learning-Based Synthesis of Social Laws in STRIPS

In a multi-agent environment, each agent must take into account not only the actions it must perform to achieve its goals, but also the behavior of other agents in the system, which usually requires some sort of coordination between the agents. One way to avoid the complexity of centralized planning and online negotiation between agents is to design an artificial social system. This system enacts a social law that restricts the behavior of the agents. A robust social law enables the agents to reach their goals while keeping them from interfering with each other. However, the problem of efficient synthesis of such laws is computationally hard, and previously proposed search techniques do not scale well. In this paper, we propose the use of graph neural networks to predict social laws from a graph-based representation of multi-agent systems. However, as this prediction can be wrong, we use heuristic search to correct possible mistakes in the network's prediction ensuring that the produced social law is indeed robust. Our empirical evaluation shows that this approach beat the previous state-of-the-art in social law synthesis, and that is can learn from an imperfect expert, even in the presence of noise.
Deep Learning-Based Correct Answer Prediction for Developer Forums

Developer forums are essential for software engineers to solve their problems with the assistance of experts on such forums. However, sometimes the solutions (answers) of a problem are not satisfactory or challenging to select the potential answer. Information seekers usually browse all the answers within the question thread to get the potential answer. The manual selection of correct answers is a tedious and time-consuming task. In this paper, we propose an automatic classification approach to predict the correct answers for developer forums. We first extract the metadata and combination of Q/A for each thread of the developer community (Stack Overflow). Then, the natural language processing techniques are applied to preprocess the Q/A combinations of the given dataset. After that, a keyword ranking algorithm is leveraged to extract keywords and their ranking scores for each Q/A combination. Based on keywords and their ranking scores for each Q/A combination, a keywords-based feature vector is constructed. Subsequently, word embedding is leveraged to convert each preprocessed Q/A combination into a text-based feature vector. Finally, we pass the metadata, keywords-based features, and text-based features to the ensemble deep learning model for training to predict correct answers. The results of 10-fold cross-validation specify that the proposed approach is accurate and surpasses the state-of-the-art. On average, it improves the accuracy, precision, recall, and f-measure up to 1.72%, 24.96%, 6.57%, and 16.62%, respectively.
Auditor's Perception on Technology Transformation: Blockchain and CAATs on Audit Quality in Indonesia

The purpose of this study is to analyze the auditor's perception of the implementation of technology transformation such as: blockchain and CAATs that can affect audit quality at the Big Four Public Accounting Firm in Jakarta.This study uses quantitative research methods using a combination of primary and secondary data.The data collection techniques used in this study was questionnaires.The sample was taken using purposive sampling method, which resulted in 60 respondents.Data analysis was carried out with SmartPLS 3.0 and IBM SPSS 26 software which resulted in the conclusion that the auditor's perception of the implementation of blockchain had a significant positive effect on audit quality, while the auditor's perception of the implementation of CAATs had no significant positive effect on audit quality.
Integrating Eye- and Mouse-Tracking with Assistant Based Speech Recognition for Interaction at Controller Working Positions

Assistant based speech recognition (ABSR) prototypes for air traffic controllers have demonstrated to reduce controller workload and aircraft flight times as a result. However, two aspects of ABSR could enhance benefits, i.e., (1) the predicted controller commands that speech recognition engines use can be more accurate, and (2) the confirmation process of ABSR recognition output, such as callsigns, command types, and values by the controller, can be less intrusive. Both tasks can be supported by unobtrusive eye- and mouse-tracking when using operators’ gaze and interaction data. First, probabilities for predicted commands should consider controllers’ visual focus on the situation data display. Controllers will more likely give commands to aircraft that they focus on or where there was a mouse interaction on the display. Furthermore, they will more likely give certain command types depending on the characteristics of multiple aircraft being scanned. Second, it can be determined via eye-tracking instead of additional mouse clicks if the displayed ABSR output has been checked by the controller and remains uncorrected for a certain amount of time. Then, the output is assumed to be correct and is usable by other air traffic control systems, e.g., short-term conflict alert. If the ABSR output remains unchecked, an attention guidance functionality triggers different escalation levels to display visual cues. In a one-shot experimental case study with two controllers for the two implemented techniques, (1) command prediction probabilities improved by a factor of four, (2) prediction error rates based on an accuracy metric for three most-probable aircraft decreased by a factor of 25 when combining eye- and mouse-tracking data, and (3) visual confirmation of ABSR output promises to be an alternative for manual confirmation.
Predicting Class-Imbalanced Business Risk Using Resampling, Regularization, and Model Emsembling Algorithms

We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cross validation. Two undersampling strategies including random undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two oversampling methods including random oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and Boosting, are applied on the DT classifier for further model improvement. The results show that, Boosting on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively.