Reviewerly/exHarmony huggingface dataset:
Application of Deep Learning and Speech Recognition Technology for Pedestrian Face Recognition in health sectors

Aiming at the problems of traditional face recognition methods, this paper proposes the application of deep learning and speech recognition technology in pedestrian face recognition. Firstly, the pedestrian face image information is collected, and the face image is decomposed by wavelet scale. The improved detail enhanced face image is obtained, and Harris adaptive threshold corner detection is performed on the enhanced face image. The feature points of pedestrian face image is extracted and matched, and the local radial transformation of points and lines and the Epipolar constraint between multiple planes are adopted. Combined with the constraints of the angle and gray approximation measure of the line features of the face image, the line matching of the face close range image is completed. The 3D line features of the pedestrian face image are extracted and fitted by using the principle of face to face intersection. Combined with the pedestrian face image recognition algorithm, the pedestrian face recognition is realized. The experimental results show that the pedestrian face recognition method based on deep learning and speech recognition technology has better performance.
Analysis of Bending Degree of Basilar Artery Using Image Processing

The basilar artery, which is the core of the posterior circulation, supplies blood to the brainstem and cerebellum. When basilar artery blood circulation is impaired, several symptoms can occur. In addition, the bending of the basilar artery causes stroke and infarction. Therefore, an image processing method for analyzing the bending degree of the basilar artery is introduced herein. To analyze the bending degree, the coordinates of the center points of the basilar artery are extracted using image processing techniques such as Canny edge detection, the contour technique, and the distance conversion technique. An image reconstructed using the three-dimensional scatter plot function in MATLAB and vector plots is used to calculate the vectors for each central point of the basilar artery. Meanwhile, the angle of each central point is calculated by selecting the first central point where the basilar artery begins, the central point with the greatest bending degree, and the central point at which the branching ends. The greater the bending degree of the basilar artery is, the larger the magnitude of the vector in the bending direction. The obtained results are verified by experts in the field, and the proposed algorithm demonstrates good performance.
A new image encryption algorithm based on cascaded chaos and Arnold transform

Aiming at the problem that the existing one-dimensional chaotic system has small chaotic interval, Lyapunov exponent is small and the generated chaotic sequence is unevenly distributed, and the correlation is high, a new image encryption algorithm is proposed by this paper. The logistic chaotic mapping and tent chaotic mapping are cascaded by iteration based on Arnold transform, logistic and tent mapping. Experiments show that the algorithm effectively extends the key space of chaotic systems, has good encryption effect and security, and can resist several common attacks.
PENGARUH MINAT BELAJAR MATEMATIKA DALAM PEMBELAJARAN DARING TERHADAP PRESTASI BELAJAR SISWA MAN 1 SRAGEN

Mathematics learning that takes place online causes students' interest and motivation to decrease. This can cause student achievement to also decrease. This study aims to determine the influence of student interest in learning mathematics online on the results of student achievement. The type of this research is quantitative research with the research subjects are the students of class XI MAN 1 Sragen, totaling 22 students. The data collection technique uses questionnaires and documentation of student achievement through the results of the Mid-Semester Examination for the Academic Year 2021/2022. The technical analysis of the data used in this research is the regression correlation test with the help of the SPSS version 24 computer program and the help of Ms. Excel. The results showed that some students who had a high interest in learning were not supported by good learning achievements. Thus, there is no significant effect between student interest in mathematics learning on student achievement in class XI MAN 1 Sragen.
MIRA: Model-Based Imagined Rollouts Augmentation for Non-Stationarity in Multi-Agent Systems

One of the challenges in multi-agent systems comes from the environmental non-stationarity that policies of all agents are evolving individually over time. Many existing multi-agent reinforcement learning (MARL) methods have been proposed to address this problem. However, these methods rely on a large amount of training data and some of them require agents to intensely communicate, which is often impractical in real-world applications. To better tackle the non-stationarity problem, this article combines model-based reinforcement learning (MBRL) and meta-learning and proposes a method called Model-based Imagined Rollouts Augmentation (MIRA). Based on an environment dynamics model, distributed agents can independently perform multi-agent rollouts with opponent models during exploitation and learn to infer the environmental non-stationarity as a latent variable using the rollouts. Based on the world model and latent-variable inference module, we perform multi-agent soft actor-critic implementation for centralized training and decentralized decision making. Empirical results on the Multi-agent Particle Environment (MPE) have proved that the algorithm has a very considerable improvement in sample efficiency as well as better convergent rewards than state-of-the-art MARL methods, including COMA, MAAC, MADDPG, and VDN.
A Novel Method Based on Improved Coyote Optimization Algorithm for Searching the Best Tour for the Travelling Salesman Problem

This paper presents a new method relied on improved coyote optimization algorithm (ICOA) for traveling salesman problem (TSP).COA is a recent recently metaheuristic algorithm that is inspired from the social life of coyote.To improve the performance of ICOA, the 2-opt algorithm is applied to adjust created new solutions.In addition, the swapping technique with varying exchange city number is also equipped to enhance the exploration and exploitation ability of ICOA.The efficiency of the ICOA is compared with COA on the 14-, 30-, 48-, 52-, 76-and 100-city instances.The simulated results show that ICOA reaches the better results than COA for the most instances.The average error of ICOA is 0.0057%, 0.2937%, 0.0613%, 5.8431% and 49.3940% lower than that of COA for the 30-, 48-, 52-, 76-and 100-city instances, respectively.Furthermore, the maximum, average and standard deviation objective values as well as the number of convergence generations of ICOA are also lower than those of COA.Moreover, ICOA has also achieved the optimal solution with higher quality compared to the previous approaches in literature.Consequently, ICOA is one of the methods worth considering for the TSP problem.
Pengaruh Number Head Together dan Motivasi Belajar terhadap Prestasi Siswa

This article aims to determine the effect of the Number Head Together type of cooperative learning model on student achievement, the effect of motivational learning on learning achievement, the effect of NHT model interaction and learning motivation on student achievement. This research was conducted at SDN 112233 Sidorejo, Torgamba District. The population was all 60 fourth grade students. The sample is all students as many as 30 students in class A and as many as 30 students in class B. The research instruments used were learning outcomes tests and learning motivation questionnaires. The research method uses a quasi-experimental method with a 2x2 factorial. The data analysis technique used ANOVA at a significant level of 0.05. The results of the analysis show that the learning achievement of students with the NHT model is higher than those taught by the conventional model (Fcount = 11,619&gt; Ftable = 2.0021), there is a significant difference in the learning outcomes of students who have high motivation and low motivation (Fcount = 15,646&gt; Ftable = 2 0.0021) and there is a significant interaction between the NHT model and students' learning motivation on learning achievement (Fcount = 4.378&gt; Ftable = 2.0021). This study concludes that overall students who are taught with the NHT cooperative learning model and have high motivation have higher learning achievements than students who are taught using the conventional model
Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

This paper proposed a novel adaptive controller of a non-isolated bidirectional coupled inductor DC converter based on an extreme learning machine (ELM) for electric vehicles.MATLAB and SIMULINK are used as the software platform to simulate the proposed method.Different operating conditions are considered to show the efficacy of the extreme learning machine.From the simulation results, it is found that by designing the controller based on an extreme learning machine the controller can automatically adjust the value depending on the operating conditions (motor moving forward, backward, and regenerative braking).In addition, the performance of the motor with DC converter based on ELM is compared with DC converter based on grey wolf optimization and based on conventional PI controller.This is indicated by the overshoot and the settling time is much better when using controller based on an extreme learning machine (overshoot 221.6 and settling time 3.8 second).
An Agent-Based Model for Situational Awareness at Workplace

Self-awareness emotional intelligence symptoms will be unavoidable in the job.These symptoms are generally regarded as acceptable by certain standards, as they are an important part of keeping people informed (or commonly known as situational awareness).However, the most recent research focuses on external components of situational awareness aspects and lacks adaptable methods to deal with the individual's situational awareness dynamics.This study investigated the use of an agent-based modeling strategy for situational awareness in the workplace.It focuses on environmental and personal factors that influence the level of situational awareness at work in a dynamic way.The outcomes included a range of scenarios that corresponded to various personality traits and environmental conditions.Finally, equilibria analysis and automated logical verification were employed to evaluate this computational model in order to check whether there were any conceptual defects, as indicated in the literature.The suggested computational agent-based model has shown rational behavior patterns that are consistent with existing psychology literature on situational awareness in the workplace.
A Proposed Method to Acquire More Geometric Features from Hand-Drawn Sketches

We introduce a novel method for learning geometric features of hand-drawn sketches.The method is based on two models: the first depends on a newly-designed algorithm based on the Decision Tree (DT) technique; while the other is based on the Feed-forward Neural Network (FNN) technique.Each model consists of training and testing phase, we extract features of object and build the knowledge base in training phase.In testing phase, we test the learning ability to appear advantages of using DT with new objects and FNN with large data.The results of our method show desirable performance in learning.Experiments on TU-Berlin, QuickDraw, and our own dataset reveal the effectiveness of the method.We achieve learning accuracy 99.98% on our own dataset, 98.07% on TU-Berlin, and 96.69% on QuickDraw.Experiments signify that geometric feature representation and manipulation by our method brings about a substantial improvement over state-of-the-art methods on sketch classification.
FedCrowd: Towards Promoting the Concurrency of the Server in Asynchronous Federated Learning

Federated learning is a privacy-preserving machine learning paradigm where a global model is trained collaboratively across resource-limited devices such as mobile phones, wearable devices, and sensors while the training data remains on these devices. Recently proposed asynchronous federated learning framework FedAsync has addressed the problems occurred in synchronous federated learning such as wasting time waiting for all nodes to finish uploading or losing parts of the contribution of nodes due to some unexpected reasons. However, on the server-side, it is not suitable for concurrency due to the data race on the global model. We proposed a method named FedCrowd, where the server&#x0027;s concurrency is improved by elaborately coordinating the working threads to relieve the data race on the global model. Several experiments are performed to show that our method could effectively increase concurrency of the server and reduce the training time.
Specifics of Self-Organization of Student’s Educational Activity During the COVID-19 Pandemic

Education is one of the most severely impacted sectors by Covid-19. One of the problems in the context of the organization and implementation of distance learning is the readiness of students to self-organize their learning activities. The issue of self-organization has been the focus of this article. Analysis and generalization of various studies show that in the conditions of quarantine, the effectiveness of learning directly depends on the processes of self-organization. To activate the process of self-organization, the student should initiate the process of the activity itself, and then update personal priorities to achieve the goal according to value dominants, motives, meanings, and prospects. The basic psychological and pedagogical concepts of self-organization have been analyzed. The role of the self-organization of educational activity in the student's academic success has been discussed. It was proved that formed self-organization of educational activity is a system-forming characteristic of the individual, which determines the value readiness of a student for independent lifelong learning, provides a high level of adaptability in the conditions of social and economic changes, and the ability to develop new technologies and new models of social interaction and behavior. The analysis has provided key insights into phenomena of self-organization in general and student’s self-organization of educational activity in particular. Based on the study and the practical experience of the authors, the model of self-organization of independent work of students has been presented. It comprises seven main blocks: introductory; actualization of knowledge; reflexive; competencies; diagnostic-reflexive; programming; self-education.
Mastery of Pre-Reading Skills (Vowels) of A Preschool Student Using the VOKAD Method

Reading skills are highly emphasized in the MOE Preschool Curriculum and Assessment Standard Document as they are crucial in developing students' oral communication and basic literacy throughout their schooling years.However, some preschool students struggle in mastering pre-reading skills (vowel letters) even after the first three months of instruction.As a result, they are also likely to lag behind their peers in mastering reading.Thus, an action research using the VoKad method was conducted to help improve the mastery of pre-reading skills (vowel letters) among preschool students who are struggling in reading as well as to help teachers in supporting students with these challenges.The researcher utilized the "edutainment" approach in this study which combines reading and singing techniques using the VoKad method.All instruction was carried out in the Malay language.Three teaching and learning activities were conducted over a period of one month.The findings of the study showed a very encouraging improvement in the first two weeks of intervention and the students successfully mastered all the vowel letters and sounds after four weeks of the study implementation.It is important to note that pre-reading skills do not only concern the mastery of vowels but also the skills of recognising and pronouncing consonants and segmenting open syllables.Therefore, the researchers suggest that future studies should also investigate and address students difficulty in mastering other pre-reading skills such as recognising and pronouncing consonants.
Artificial Intelligence based Chatbot: A Case Study

This paper shows the implementation of an artificial intelligent chatterbot with human interaction. Our paper will now tap into the aspects of artificial intelligence because we are designing a smart chatbot and of course, using machine learning to do so and deep learning which is involving more in-depth concepts of artificial intelligence and tapping into the territory of unsupervised learning from data more with the help of neural networks and more complicated models which help with studying better the unstructured or the unlabeled form of data. The name of our Chabot will be finance manager. It will interact with the user efficiently and come up with reasonable responses for their queries, we will also have a module to help people with their wellness in our chatbot apart from it just being a plain financial aspect talker for a more refined personal touch to our paper.
Upaya Meningkatkan Kemampuan Guru Sma Negeri 1 Tapung Hilir, Kabupaten Kampar Provinsi Riau Dalam Memanfaatkan Akun Belajar.Id Yang Terintegrasi Dengan Google Workspace For Education Melalui Workshop Penggunaan Teknologi Informasi Pada Kegiatan Pembelajar

Teachers at SMA Negeri 1 Tapung Hilir rarely know how to use a learning.id account with Google Workspace for Education. Researchers often find teachers struggling to use this information technology devices and asking for support from other teachers. Thus, a teacher's inability to use technology negatively impacts the learning process. To improve teachers' ability to use a learning.id account that is integrated with Google workspace for education, a workshop on the use of information technology in activities and learning will be held in 2020/2021. After the action research, data revealed an increase in teachers' ability to use their learning.id accounts. In cycle 1, instructors' capacity to use the learning.id account, which was connected with Google Workspace for Education, increased significantly. The findings of teacher-led exams on the usage of information technology show this trend. In the test, 26 (86.67%) got the predicate "Completed." The average teacher score was 81.15, with the lowest being 67. The top score was 89. Cycle II results indicated the greatest growth in instructors' capacity to use the learning.id account with the Google workspace for education. The findings of teacher-led exams on the usage of information technology show this trend. On the test, 100% of teachers got the predicate “Completed”. The average teacher score was 88.48, with the lowest being 78. While 9 was the highest
A note on entanglement classification for tripartite mixed states

We study the classification of entanglement in tripartite systems by using Bell-type inequalities and principal basis. By using Bell unctions and the generalized three dimensional Pauli operators, we present a set of Bell inequalities which classifies the entanglement of triqutrit fully separable and bi-separable mixed states. By using the correlation tensors in the principal basis representation of density matrices, we obtain separability criteria for fully separable and bi-separable 2 ⊗ 2 ⊗ 3 quantum mixed states. Detailed example is given to illustrate our criteria in classifying the tripartite entanglement.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH STORY GRAMMAR STRATEGY AT SMP NEGERI 08 SATAP DODA BUNTA

The objective this research is to improve students’ reading comprehension through story grammar strategy at SMP Negeri 08 Satap Doda Bunta. This research used a pre-experimental research design with one group pretest and posttest design. The population of this research was the students of VIII grade of SMP Negeri 8 Satap Doda Bunta consisted of twenty-eight students. The sample of this research was all the population of the research. In collecting data, the researcher used test. The test consisted of pretest and posttest. Data were analyzed statistically. After being analyzed, it was found that the result of the t-counted was 9,245. With 27 degree of freedom, and a the significance level of 0,05, the t-table was 1,703.Therefore, the result of t-counted is greater than t-table. It is clear that the research hypothesis is accepted. In other words, story grammar strategy can effectively improve the reading comprehension of VIII grade students at SMP Negeri 8 Satap Doda Bunta.
Architectural Drawing of G+10 Building By 3ds Max+V-Ray Lumion

This work aims to complete the planning and architectural drawing of the G+10 building. The planning is carried out in AutoCAD followed by architectural drawing in Auto-desk 3DS MAX 2015. The rendering of this work has been done in LUMION 10. The building design is a multi-story residential building i.e., G+10. The building is rectangular in shape. The ground floor is left as a parking space for 40 flats of the building. Every floor has 4 flats, two 2bhk and two 3bhk. Plans for all the floors are identical. The orientation of the building is in such a way that the front is facing towards the south. The building has been designed as an RCC framed structure and the type of wall is a brick wall.
It's online, but is it learning? - A reflection on the acceleration towards digital distance learning (DDL) in mainstream higher education.

The Coronavirus pandemic during the past two years (March 2020 – March 2022) has accelerated investment in, and the use of, digital distance learning (DDL). The transition towards DDL in mainstream higher education during this period has expanded both in terms of depth and breadth. Few academic practitioners, observers, managers, or students could have foreseen this rapid pace of change. Unprecedented government interventions in social contact and mobility presented little choice to universities than to adapt in response. The emergency pandemic conditions created an opportunity to implement changes to delivery methods with far less consideration or scrutiny than would have otherwise in more normal times. In this position paper, I provide a brief history of distance learning as a backdrop then to explore some of ‘deeper’ pedagogical implications of these rapid change transition to DDL in mainstream higher education.
FrBMedQA: the first French biomedical question answering dataset

FrBMedQA is the first French biomedical question answering dataset, containing 41k+ passage-question instances. It was automatically constructed in a cloze-style manner, from biomedical French Wikipedia articles. To test the validity and difficulty of the dataset, we experimented with four statistical baseline models, a biomedical bidirectional encoder representation from transformers (BERT)-based model, and two French BERT-based language model. We also did human evaluation on a subset of the test set. All the three tested models were not able to surpass the best performing baseline model. Human performance at 61.11% is leading the leaderboard with more than 8% from the best performing model. We made available the dataset and the code to reproduce our results.