Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Background Subtraction and CNN Algorithms for Mid-Block Traffic Data Collection and Classification

Classification of vehicles in the traffic stream is a pre-requisite for planning and designing the facilities for road-users. Considering the importance and gaining popularity of automated systems in this field, the aim of this article is to compare two algorithms- one using the Background Subtraction (BS) technique and the other using Convolutional Neural Network (CNN) with a primary focus on an increased number of vehicle classifications. To check the reliability of these algorithms, the outputs produced were validated against the data obtained from Kachkoot Toll Plaza, India. The results were analyzed using drop-line diagrams and confusion matrices. The overall efficiency of the CNN-based algorithm (0.98) was found to be better than the BS-based algorithm (0.95). The comparison presented in this paper will be useful for transportation professionals and agencies.
TEACHER TALK IN YOUNG LEARNERS� CLASSROOM INTERACTION

In English Foreign Language (EFL) class, learners are not familiar with English in daily conversation, except several learners who have background in using English in their home or environment. Therefore, teacher plays a significant role as facilitator to introduce them into the language. This study aims to describe teacher talk and students talk as well as the way teacher talk is realized in young learners� classroom interaction by applying a descriptive method. The fifth grade classroom interaction at a primary school in Majalengka was recorded as the sample of the analysis. The data gained through observation was in the form of transcribed interaction. Besides, interview was also used to collect the data. To analyze the data, this study employed Flanders Interaction Analyzed Categories. Upon analyzing the data, the following findings are identified; 1) Accepting feeings (2 occuraces); 2) Encouragement (17 occuraces); 3) Accepting or using ideas of learners (54 occuraces); 4) Asking questions (142 occuraces); 5) lecturing (49 occuraces); 6) Giving directions (53 occuraces); 7) Criticizing learners behaviour (9 occuraces); 8) Learners-response (151 occuraces); 9) Learners-initiation (7 occuraces); and 10) Period of silence or confusion (8 occuraces). Finally, the use of similar activity for too long will make young learners feel bored, therefore, teacher needs to provide variety and more meaningful activities. In this case, teacher can use various techniques to stimulate learners� response. Besides, creating group discussion will give more opportunity to the learners to talk and learn from their peers.Keywords: teaching speaking; teacher talk; students talk; young learners.
Using 3D Hologram Technology (3DHT) in the Distance Learning Program to Enhance the Professional Skills of Tour Guidance Undergraduate Students

Vocational training is the cornerstone of many industries especially those that depend mainly on the human element like tourism. The tour guide is considered one of the most vital employees in tourism. Depending on his performance and interaction with the tourists, the destination image is positively or negatively formed. Accordingly, it is necessary to pay great attention to tour guides' education and training through formal specialized institutes to enhance the professional skills of tour guides. The training of the tour guidance undergraduate students is usually carried out through field trips in the monumental sites and museums in order to gain professional experience. However, because of the recent COVID19 Pandemic that resulted in full-scale quarantine, it became necessary to search for another technological approach through distance learning educational platform to achieve the educational objectives of the training courses. The recent study's aim is to detect the possibility of using 3DHT in distance learning programs to reinforce the professional skills of students and achieve the maximum benefits for them beside considering this innovative method as an alternative approach of education that can be adopted in similar circumstances.
A tale of two studies: TEL research and the theory-practice nexus

This paper explores the role of theory in two research studies conducted through a design-based methodological lens.Rejecting the binary distinction between interpretivist and positivist positions characterised by the paradigm wars, the authors ensured both that practical interventions were guided by relevant theoretical perspectives, and that the evaluation of these interventions subsequently informed the development of theory.In the first of these cases, a series of interventions introducing video-enhanced practice to two cohorts of undergraduate students was situated against a backdrop of both social and cognitive constructivist theories of learning, leading to the development of an integrated model of video-enhanced assessment and feedback.In the second case, a broad range of theoretical developments in the fields of social-constructivist, constructionist, contextualised, experiential and placebased learning informed the design of a series of outdoor mobile learning activities intended to enhance learner engagement of children with science topic work, leading to theoretical developments around digital capital and the digital disconnect.The paper argues that through embracing a pragmatist epistemology, design-based research offers a methodological approach underpinned by a symbiotic relationship between theory and practice in technology-enhanced learning research.
User space transformation in deep learning based recommendation

Deep learning based recommendation methods, such as the recurrent neural network based recommendation method (RNNRec) and the gated recurrent unit (GRU) based recommendation method (GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.
Visual Analytic Method for Metro Anomaly Detection and Diffusion

With the rapid development of urbanization in recent years, thousands of people have flooded into the city, which has brought tremendous pressure on the supervision and operation of relevant traffic management departments. In particular, the unexpected events in the urban rail transit system have caused great troubles for city managers. Aiming at the problem of abnormal passenger flow in the metro, this paper proposes a visual analytic method to support the abnormal passenger flow detection, verification, and diffusion analysis in the metro system. The method provides an intuitive visual metaphor and allows users to perform simple interactive operations to verify abnormal passenger flow. In addition, the method reveals the diffusion law of abnormal passenger flow in time and space in a two-dimensional diffusion view. The Beijing Rail Transit AFC data are used to validate the developed system, and two reliable analysis cases are presented. The system can help users quickly grasp the abnormal propagation rules and help them to develop different scheduling strategies for different anomalous propagation paths.
Improved Method for Parallelization of Evolutionary Metaheuristics

This paper introduces a method for the distribution of any and all population-based metaheuristics. It improves on the naive approach, independent multiple runs, while adding negligible overhead. Existing methods that coordinate instances across a cluster typically require some compromise of more complex design, higher communication loads, and solution propagation rate, requiring more work to develop and more resources to run. The aim of the new method is not to achieve state-of-the-art results, but rather to provide a better baseline method than multiple independent runs. The main concept of the method is that one of the instances receives updates with the current best solution of all other instances. This work describes the general approach and its particularization to both genetic algorithms and ant colony optimization for solving Traveling Salesman Problems (TSPs). It also includes extensive tests on the TSPLIB benchmark problems of resulting quality of the solutions and anytime performance (solution quality versus time to reach it). These tests show that the new method yields better solutions for about two thirds of the problems and equivalent solutions in the remaining third, and consistently exhibits better anytime performance.
Butler-Volmer current equation and fractal nature correction in electrochemical energy

The Global Energy Crisis necessitated improving research into new, renewable and alternative energy sources. Due to that, our focus is on the area of some phenomena and applications where different synthetic methods and micro-structure property optimization achieved significant improvement in the electro physical properties of output materials and components. This is especially important for higher energy efficiency and electricity production (batteries and battery systems, fuel cells, and hydrogen energy).The improvement of energy storage tank capacity is one of the most important development issues in the energy sphere too. It is because of this very promising research and application area that we are expanding the knowledge on these phenomena through fractal nature analysis. So, the results obtained in the field of electrochemical energy sources, especially in electrolyte development, are taken into account the analysis of fractal nature optimization. Based on the research field of fractal material science, particularly electronic materials, we conducted research in micro-structure fractal influence in the area of electrochemistry. We investigated the consolidation parameters of Fe2O3 redox processes. The influence of activation energy, fundamental thermodynamic parameters, and also the fractal correction of electrode surface area through complex fractal dimension with recognized grains and pores, and the Brownian motion of particles is introduced. Finally, the electrochemical Butler-Volmer equation fractalization is obtained. These results practically open new frontiers in electrochemical energy processes performed through the Arrhenius equation within electrolyte bulk and electrode relations and more complete and precise energy generation.
Identification and Analysis of Accident Scenarios in the Hospital Central Oxygen Unit by Fault Tree Analysis Method and Evaluation of the Adequacy of Control Devices Using Layer of Protection Analysis

Identification and Analysis of Accident Scenarios in the Hospital Central Oxygen Unit by Fault Tree Analysis Method and Evaluation of the Adequacy of Control Devices Using Layer of Protection Analysis
Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung

Globally, the number one cause of death each year is cardiovascular disease. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels, such as coronary heart disease, heart failure or heart failure, hypertension and stroke. The purpose of this study was to measure the performance of accuracy, precision, recall and f-measure of the K-NN and Crossvalidation methods on a dataset of cardiovascular patients. The dataset used was 1000 records consisting of 11 attributes (age, gender, height, etc.) cardiovascular and non cardiovascular patient data, the dataset was obtained from the UCI Machine Learning Repository managed by the Hungarian Institute of Cardiology Budapest: Andras Janosi, MD, University Hospital, Zurich, Switzerland. The steps taken are: dividing the simulation ratio of the dataset to 20:80, 50:50 and 80:20, applying crossvalidation (k-fold = 10) and classification using the K-NN method (k = 2 to K = 900). The research results from the simulation of the dataset ratio 50:50 obtained an accuracy value of 82%, 82% precision, 82% recall and 80% f-measure at a value of K = 13, then the research results from the simulation of the dataset ratio 20:80 obtained an accuracy value of 87%, 87% precision, 97% recall and 92% f-measure at the value of K = 3, and the results of research from the simulation of the dataset ratio 80:20 obtained an accuracy value of 91%, 92% precision, 60% recall and 72% f-measure at the value K = 5.
Attendance System using Machine Learning-based Face Detection for Meeting Room Application

In a modern meeting room, a smart system to make attendance quickly is mandatory.Most of the existing systems perform manual attendance, such as registration and fingerprint.Despite the fingerprint method can reject the Unknown person and give the grant access to the Known person, it will take time to register first a person one-by-one.Moreover, it is possible to create long queues for fingerprint checking before entering the meeting room.Machine learning, along with the Internet of Things (IoT) technology is the best solution; it offers many advantages when applied in the meeting rooms.Generally, the method used is to create a presence by detecting faces.In this paper, we present a facial recognition authentication based on machine learning technology for connection to the meeting rooms.Furthermore, specific website to display the detection result and data storage design testing is developed.The method uses 1) the Dlib library for deep learning purposes, 2) OpenCV for video camera processing, and 3) Face Recognition for Dlib processing.The proposed system allows placing the multiple cameras in a meeting room as needed.However, in this work, we only used one camera as the main system.Tests conducted include identification of one Known person, identification of one Unknown person, identification of two people, and three people.The parameter to be focused is the required time in detecting the number of faces recorded by the camera.The results reveal that the face can be recognized or not recognized, then it will be displayed on the website.
A Complete Axiomatisation for Probabilistic Trace Equivalence

We provide an axiomatisation for = pTr , a variant of probabilistic trace equivalence as formulated by Bernardo et al., 2014, in the setting of the alternating model of Hansson.The equivalence considers traces individually instead of trace distributions.We show that our axiomatisation is sound and also complete for recursion-free sequential processes.Due to the nature of the trace equivalence, the axiomatisation is particularly complex.
Beyond-Birthday-Bound Secure Cryptographic Permutations from Ideal Ciphers with Long Keys

Coron et al. showed a construction of a 3-round 2n-bit cryptographic permutation from three independent n-bit ideal ciphers with n-bit keys (TCC 2010). Guo and Lin showed a construction of a (2d − 1)-round dn-bit cryptographic permutation from 2d − 1 independent n-bit ideal ciphers with kn-bit keys, where d = k + 1 (Cryptography and Communications, 2015). These constructions have an indifferentiability security bound of O(q2/2n) against adversaries that make at most q queries. The bound is commonly referred to as birthday-bound security. In this paper, we show that a 5-round version of Coron et al.’s construction and (2d+1)-round version of Guo and Lin’s construction yield a cryptographic permutation with an indifferentiability security bound of O(q2/22n), i.e., by adding two more rounds, these constructions have beyond-birthday-bound security. Furthermore, under the assumption that q ≤ 2n, we show that Guo and Lin’s construction with 2d+2l−1 rounds yields a cryptographic permutation with a security bound of O(q2/2(l+1)n), where 1 ≤ l ≤ d − 1, i.e., the security bound exponentially improves by adding every two more rounds, up to 4d − 3 rounds. To the best of our knowledge, our result gives the first cryptographic permutation that is built from n-bit ideal ciphers and has a full n-bit indifferentiability security bound.
Cyber Security in the Age of COVID-19: An Analysis of Cyber-Crime and Attacks

The world has witnessed unprecedented change in all aspects of operations during this COVID-19 pandemic. The pandemic has led to many gullible individuals and organizations being exploited by cybercriminals. The hackers take advantage of heightened anxiety among many internet users to launch attacks and breach data. The current paper adopted a systematic literature review as a suitable method for data collection. The findings have revealed that since the pandemic outbreak, there has been an increased incidence of cyber-crimes and attacks.Education and more sensitization about adequate cybersecurity are recommended to mitigate the COVID-19 threats on data security.
SMARTPHONE VIDEO RECORDING AS A LEARNING TOOL TO IMPROVE INDONESIAN EFL STUDENTS� SPEAKING PERFORMANCE

This research attempts to reveal the effectiveness of using smartphone video recording as an English learning tool to improve the students� speaking performance and the students� interest in using the tool in speaking learning process. All participants were second-grade students of private boarding school in Makassar, Indonesia. This research was conducted through quasi experimental study design with two groups comprises of 25 students in the experimental group and 25 students in the control group. The instruments were speaking test to collect the data about the students� speaking performance and questionnaire to find out the students� interest. The data collections were then analyzed through descriptive and inferential statistics through SPSS Program Version 23.0. The result of data analysis showed that (1) using smartphone video recording was more effective than in-class monologue presentation to improve the students� performance in speaking. It is seen from the significance t test result that reveals the significance value is lower than the significance level; 0.005 &lt;0.05. Furthermore, in relation to the students� interest in using smartphone video recording to learn speaking and (2) it was found that the students� interest in using smartphone video recording was categorized as high. Keywords: Smartphone video recording; speaking performance; interest.
An Epistemic Bridge for Presupposition Projection in Questions

It is often assumed that presuppositions in wh-questions project universally. However, Schwarz &amp; Simonenko (2018) note examples of such questions where universal projection is absent. I discuss their account and propose an alternative: by reasoning about the pragmatics of question-answer discourse, I arrive at a version of Stalnaker's bridge principle that is sensitive to what the speaker already knows about the true answer to the question. This Epistemic Bridge predicts universal projection for canonical information-seeking questions, but less than universal projection for certain non-canonical question uses such as quiz questions.
Measuring Memorization Effect in Word-Level Neural Networks Probing

Multiple studies have probed representations emerging in neural networks trained for end-to-end NLP tasks and examined what word-level linguistic information may be encoded in the representations. In classical probing, a classifier is trained on the representations to extract the target linguistic information. However, there is a threat of the classifier simply memorizing the linguistic labels for individual words, instead of extracting the linguistic abstractions from the representations, thus reporting false positive results. While considerable efforts have been made to minimize the memorization problem, the task of actually measuring the amount of memorization happening in the classifier has been understudied so far. In our work, we propose a simple general method for measuring the memorization effect, based on a symmetric selection of comparable sets of test words seen versus unseen in training. Our method can be used to explicitly quantify the amount of memorization happening in a probing setup, so that an adequate setup can be chosen and the results of the probing can be interpreted with a reliability estimate. We exemplify this by showcasing our method on a case study of probing for part of speech in a trained neural machine translation encoder.
A Convolutional Neural Network for Regional Photovoltaic Generation Point Forecast

As the rapid growth of photovoltaic (PV) generation capacity, the form of regional PV power integrated by multiple PV plants is becoming more and more common. The changing law of regional PV power is of great significance to control the operation of the power system. This paper presents a novel regional PV power point forecast method that uses the convolutional neural network (CNN) model. In the method, the structure of CNN is applied to extract the nonlinear features between the input data and regional PV power. The forecast of regional PV power in a real power grid is carried out to illustrate the validity of the proposed method. Verification results show that the CNN model can provide more accurate point forecast for regional PV power results than the traditional regional PV power forecast methods.
Information-Geometric Optimization with Natural Selection

Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between classical population genetics of quantitative traits and evolutionary optimization, and formulate a new evolutionary algorithm. Optimization of a continuous objective function is analogous to searching for high fitness phenotypes on a fitness landscape. We describe how natural selection moves a population along the non-Euclidean gradient that is induced by the population on the fitness landscape (the natural gradient). We show how selection is related to Newton’s method in optimization under quadratic fitness landscapes, and how selection increases fitness at the cost of reducing diversity. We describe the generation of new phenotypes and introduce an operator that recombines the whole population to generate variants. Finally, we introduce a proof-of-principle algorithm that combines natural selection, our recombination operator, and an adaptive method to increase selection and find the optimum. The algorithm is extremely simple in implementation; it has no matrix inversion or factorization, does not require storing a covariance matrix, and may form the basis of more general model-based optimization algorithms with natural gradient updates.
Designing in virtual environments: The integration of virtual reality tools into industrial design research and education

industrial design research and education
Understanding the Experiences of Familiar Identity Theft Victims When a Parent is the Perpetrator: A Pilot Study

Incidents of familiar identity theft are becoming more common, yet limited research has explored the experiences of such victims, particularly those who had their identity stolen by a parent.In this qualitative pilot study, six participants shared their experiences during interviews.Data were analyzed using interpretive content analysis.The following preliminary themes emerged from the data: Not Filing a Police Report, Negative Impacts, Positive Impacts, Social and Demographic Factors, and Helpful Resources.Lessons learned regarding methods and suggestions for future research are provided.