Reviewerly/exHarmony huggingface dataset:
A HYBRID DEEP LEARNING BASED CLASSIFICATION FOR SOME BASIC MOVEMENTS IN PHYSICAL REHABILITATION

It is important to perform basic rehabilitation movements such as walking and sitting, which are planned individually, for the follow-up of the patient undergoing physical rehabilitation treatment and to examine the development of the patient over time.In this paper, movement classification based on hybrid deep learning algorithm is proposed to determine if the exercises given by the doctor are performed by the patient accurately.Six basic movements are classified as standing, sitting, laying, walking, walking upstairs, walking downstairs by means of the proposed Alexnet-SVM (Alexnet-Support Vector Machine) hybrid model.During the training and testing stages of the system, 1492 movement signals obtained from smartphone (Samsung Galaxy S II) on the waist are used.For the classification of movement signals by hybrid Alexnet-SVM, at first, the spectrogram images of the signals are obtained by means of Short-time Fourier transform.In order to use the obtained spectrographic images directly in Alexnet-SVM architecture, crop and resize preprocessing are applied.To show the superiority of the proposed Alexnet-SVM, the results are compared with the results of Alexnet, Resnet18 and Resnet18-SVM.The performance parameters of the proposed Hybrid Alexnet-SVM classifier are calculated using accuracy and F1scores, resulting in 87.67% and 93.4%, respectively.The results obtained facilitate the follow-up of the patients who have lost their mobility, whether they are doing the movement correctly and it is possible to determine whether the correct treatment is applied or not.
TEKNOLOGI ALAT BANTU SALAT BAGI DISABILITAS TUNARUNGU BERBASIS GYROSCOPE

A deaf person is someone who has lost the ability to hear so that it hinders the process of language information through his listeners. In Indonesia, most of the population is Muslim and has more than 13,000 people with hearing impairments.In carrying out congregational prayers, it is difficult for the deaf to follow the instructions of the priest's movement. This study aims to make prayer aids technology for the Deaf is to assist in performing congregational prayers. The method used in this tool is to provide notifications in the form of different vibrations at every change in the priest's movement to the Deaf community. The device control system uses Arduino nano FT232RL FTDI FT232. The communication module uses HC-12 SI4463, with 433.4 – 473.0 MHz wireless frequency. The MPU6050 gyroscope sensor is employed for detecting changes in angle or motion, and a micro vibrator motor is used as a vibration indicator. Based on the conducted test, the designed Bracelet performed excellent results. It runs according to its working principle where the Bracelet used by the Imam will send a code, and the code is in the form of a change in the angle to the Bracelet used by the Deaf community in the form of vibration, the vibration at each prayer change will be different. Different vibration notifications on each Shalat movement can help the Deaf know the Imam's movement instructions.Furthermore, the prayer aids technology for the Deaf has a success rate of 89%. Thus, this tool can help the Deaf to perform congregational prayers.
POSSIBILITIES OF USING A CHEMICAL EXPERIMENT ON THE FORMATION OF STUDENTS’ CRITICAL THINKING COMPETENCIES

This paper describes the possibilities of further enhancing the developmental, critical thinking skills of students by making changes to the form and content of the chemical experiment in the general secondary education system. The organization of chemical experiments in accordance with real industrial processes is another tool to ensure that chemistry education is linked to practice.
A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

In recent years, Knowledge Graph (KG) development has attracted significant researches considering the applications in web search, relation prediction, natural language processing, information retrieval, question answering to name a few. However, often KGs are incomplete due to which Knowledge Graph Completion (KGC) has emerged as a sub-domain of research to automatically track down the missing connections in a KG. Numerous strategies have been suggested to work out the KGC dependent on different representation procedures intended to embed triples into a low-dimensional vector space. Given the difficulties related to KGC, researchers around the world are attempting to comprehend the attributes of the problem statement. This study intends to provide an overview of knowledge bases combined with different challenges and their impacts. We discuss existing KGC approaches, including the state-of-the-art Knowledge Graph Embeddings (KGE), not only on static graphs but also for the latest trends such as multimodal, temporal, and uncertain knowledge graphs. In addition, reinforcement learning techniques are reviewed to model complex queries as a link prediction problem. Subsequently, we explored popular software packages for model training and examine open research challenges that can guide future research.
Higher Reciprocity Laws and Ternary Linear Recurrence Sequences

We describe the set of prime numbers splitting completely in the non-abelian splitting field of certain monic irreducible polynomials of degree three. As an application we establish some divisibility properties of the associated ternary recurrence sequence by primes $p$, thus greatly extending recent work of Evink and Helminck and of Faisant. We also prove some new results on the number of solutions of the characteristic equation of the recurrence sequence modulo $p,$ extending and simplifying earlier work of Zhi-Hong Sun (2003).
Tugas Menulis Esai MPPBI (Bahasa Inggris)_Fitra Aneskhi_20016073

The environment is an influential component in teaching and learning activities. The environment can also support teaching and learning activities in schools.The environment itself can affect the development of students in the learning process, especially the language learning process. Environmental-based learning is known to reduce student saturation in the teaching and learning process. Based on that, the teacher or educator can take advantage of the environment in solving the problem.
Cross-Language Source Code Clone Detection Using Deep Learning with InferCode

Software clones are beneficial to detect security gaps and software maintenance in one programming language or across multiple languages. The existing work on source clone detection performs well but in a single programming language. However, if a piece of code with the same functionality is written in different programming languages, detecting it is harder as different programming languages have a different lexical structure. Moreover, most existing work rely on manual feature engineering. In this paper, we propose a deep neural network model based on source code AST embeddings to detect cross-language clones in an end-to-end fashion of the source code without the need of the manual process to pinpoint similar features across different programming languages. To overcome data shortage and reduce overfitting, a Siamese architecture is employed. The design methodology of our model is twofold -- (a) it accepts AST embeddings as input for two different programming languages, and (b) it uses a deep neural network to learn abstract features from these embeddings to improve the accuracy of cross-language clone detection. The early evaluation of the model observes an average precision, recall and F-measure score of $0.99$, $0.59$ and $0.80$ respectively, which indicates that our model outperforms all available models in cross-language clone detection.
Z-graphic topology on undirected graph

In this work, we define ZG a topology on the vertex set of a graph G which preserves the connectivity of the graph, called Z-graphic topology. We prove that two isomorphic graphs have homeomorphic and symmetric Z-graphic topologies. We show that ZG is an Alexandroff topology and we give a necessary and sufficient condition for a topology to be Z-graphic.
Automatic Data Clustering using Dynamic Crow Search Algorithm

This work proposes Automatic clustering using Dynamic Crow Search Algorithm, which updates its parameters dynamically.Crow Search is a recently proposed algorithm that imitates the working of crow.Clustering is an essential aspect of data analysis whose significance has increased manifold since the advancements of technology which has led to enormous data generation, which need to be analysed in real-time.Automatic clustering detects optimal cluster numbers and produces sustainable cluster centroids.ACDCSA uses Cluster Validity using Nearest Neighbour as an internal validity measure that acts as a fitness function to find the optimal cluster centres.The present work is compared with some well-known other meta-heuristic search algorithms like PSO, DE, WOA and GWO for the automatic clustering task over seven benchmark clustering datasets.Inter-cluster distance, intra-cluster distance and the optimal cluster number produced are used to assess the performance of ACDCSA.
Development of Online-Based Digital Learning Media for Geography Subjects at Madrasah Aliyah Negeri I Wonosobo

&lt;p&gt;Nowadays, learning media is growing in line with the rapid development of information technology. This research was conducted in connection with the need for learning media that can be used anywhere and anytime without any limitation of place and time. This study aims to develop online learning media products for geography learning. The research method used was the Research and Development (R&amp;amp;D) method. The research subjects were students of class X Madrasah Aliyah Negeri 1 Wonosobo. Data collection techniques used the methods of observation, interviews, questionnaires, and documentation. Questionnaires were employed to determine the feasibility of online-based learning media, whereas data analysis techniques utilized percentage techniques. From the study results, it can be concluded that the online-based learning media developed can be said to be good by experts. It can be seen from the testing results on students to determine the level of media feasibility based on predetermined criteria/indicators. Thus, this learning media deserves to be used as an alternative learning media with a good category (88%), namely with details on educational criteria with good results (93%), appearance criteria with good results (85%), and technical criteria with good results (89%). Hence, online-based learning media for geography class X is feasible to use as a choice of digital learning media.&lt;/p&gt;
Detecting Phishing sites Without Visiting them

Now-a-days, cyberattacks are increasing at an unprecedented rate. Phishing is a social engineering attack which has a massive global impact, destroying the financial and economic value of corporations, government sectors and individuals. In phishing, attackers steal users personal information such as username, passwords, debit card information and so on. In order to detect zero-hour attacks and protect end-users from these attacks, various anti-phishing techniques are developed, but the end-users have to visit the websites to know whether they are safe or not, which may lead to infecting their system. In this paper, we propose a method where end-users can detect the genuineness of the sites without visiting them. The proposed method collects legitimate and phishing URLs and extract features from them. The extracted features are given as input to six different classifiers for training and constructing the model. The classifiers used are Naive-Bayes, Logistic Regression, Random Forest,CatBoost, XGBoost and Multilayer perceptron. The method is tested by developing into an extension so that the end-users can use it when browsing. In the browser extension when the user takes the cursor over any link, a pop-up appears showing the nature of the website i.e., safe site or deceptive site and then a confirm box shows up asking the user whether they want to visit or not. The performance of the approach is tested using a dataset consisting of 2000 phishing and legitimate website URLs and the method is able to detect the sites correctly in very little time. Random-Forest is chosen for constructing the model as it gives the highest accuracy of 95%.
Effect of College English Education Reform Strategy From the Perspective of Multiculturalism

The traditional analysis method based on BP neural network cannot effectively analyze the effect of college English education reform at different levels. To this end, an analysis method for the effect of English education reform strategy from the perspective of multiculturalism is proposed. Through diversified network teaching mode of teacher-student interaction, students-machine interaction and student-student interaction, a diversified teaching mode of college English with interactive and dynamic openness is constructed. For the teaching mode, in this paper, AHP and fuzzy theory are used in the constructing of the fuzzy evaluation index system for the effect of college English education reform from six aspects: teaching plan, teaching method, teaching ability, classroom setting, teaching attitude and classroom performance, to realize the analysis of the effect of college English education reform from the perspective of multiculturalism. The experimental results show that the evaluation results of each reform effect by using the proposed method are higher than 95 points, and the analysis effect is good.
Use of Immersive Virtual Reality to validate the Frith-Happé Animations

Autism has been previously linked to differences in social cognition and sensory processing.A classical measure of low-level social cognition is the Frith-Happé animations task which measures perceived animacy and "mentalizing" ability (mentalizing is the attribution of thoughts and feelings to others).We have adapted this classic psychological paradigm to Virtual Reality (VR) and evaluated its feasibility.3D versions of the Frith-Happé animations were constructed using the software package Unity.The animations had the appearance of solid triangles of different sizes moving on a ground plane.Participants viewed these animations in our VR lab wearing a standard Head-Mounted Display.White et al.'s (2011) multiple choice scoring system was used to categorize animations based on the perceived interaction of 3D geometrical shapes.The current study aims to validate the Frith-Happé animations within a Virtual Reality (VR) context utilizing a qualitative approach to emphasize a participant's subjective interpretation of the animation and personal experience of VR.Four themes were discovered using inductive thematic analysis.Participants' verbal feedback indicated that the increased level of immersion available in VR helped them appreciate the animacy more.VR has thus been found to be an appropriate tool for investigating animacy and mentalizing ability in neurotypical participants.These findings are discussed primarily regarding the refinement of the existing animations and how these may be employed in future research.
Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos

Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language query to video segments, but estimating precise boundaries is still under-explored. In this paper, we propose entity-aware and motion-aware Transformers that progressively localizes actions in videos by first coarsely locating clips with entity queries and then finely predicting exact boundaries in a shrunken temporal region with motion queries. The entity-aware Transformer incorporates the textual entities into visual representation learning via cross-modal and cross-frame attentions to facilitate attending action-related video clips. The motion-aware Transformer captures fine-grained motion changes at multiple temporal scales via integrating long short-term memory into the self-attention module to further improve the precision of action boundary prediction. Extensive experiments on the Charades-STA and TACoS datasets demonstrate that our method achieves better performance than existing methods.
Online Voting System

Abstract: The ‘Online Voting System’ is a web based voting platform for conducting elections online. This system seeks to use face recognition algorithm for voter identity authentication to enhance the security of the electioneering process and ultimately providing an online platform which enables all eligible voters to exercise this activity from any location. The user must sign in/login using their respective credentials and they will be logged in into the system only after the face recognition authentication is successful. Thereafter, the voter can cast their vote securely and logout of the system. Hence, this project based on Online Voting System could be used for conducting secure and fair elections online. Keywords: online voting, face detection, face recognition, authentication, django, tensorflow.js, deepface
Emotion classification of human facial images by a neural network

Facial recognition using artificial neural networks is a biometric technology currently being used in fields such as cybersecurity and criminal investigation. We sought to automatically distinguish between an image of a happy human face and an image of a sad human face with predictions that are better than random guesses. We trained a machine learning model (VGG16, a type of convolutional neural network) on a public image dataset of 12,000 human faces. The resulting model predicted the emotion label 93% of time when shown a test set of 200 images that the model had never seen during training. The results show that the VGG16 convolutional neural network learned features from our data that produced an output which was sufficient to train the additional layers of the model to perform our task at 93% accuracy. We suspect this is because VGG16 was already familiar with features that it learned from ImageNet (a separate dataset of over 2 million images). It is currently unknown whether our model can predict labels for new images outside of the FER13 dataset, but preliminary tests show promising results. Our model was trained using a cloud computing service and a relatively small amount of data, indicating that these kinds of results are easily obtainable by all.
Content-Based Image Retrieval for Songket Motifs using Graph Matching

Indonesia is a country that has abundant cultural wealth. One of the characteristics of Indonesian culture is Songket. Songket is a typical Malay woven cloth that has many variants of motifs, each of which represents a different meaning and philosophy. Songket is often found in Sumatra Island with different motifs in each region. With so many types of songket motifs, not everyone can recognize and distinguish between one songket motif and another, even Indonesian citizens themselves. With the help of computers, it is easier to find information about a songket motif or to find a similar songket motif. The field that can play a role in solving this problem is Content-Based Image Retrieval (CBIR). This study aims to carry out a content retrieval process on the songket core motif using graph matching-based processing. In this study, the method used is felzenzswalb segmentation, and graph matching through the VF2 isomorphism algorithm and graph edit distance. The number of songket core motif images used as data is 180 data in the form of color images measuring 64 x 64 pixels. Based on the results of the study, it was found that the optimal graph matching algorithm and parameters in this study were the VF2 algorithm for artificial images with an f-1 score of 91.05%, and Graph Edit Distance with GED≤8 parameters for songket motif images with an f1-score. by 53.36%.
Tinjauan Kelengkapan Pengisian Formulir Assesment Awal Poli Klinik Pasien Rawat Jalan Menggunakan Metode IAR

The hospital is one of the health care facilities with various services helping people who have health probems. Medical record is a file that contains records and data related to patient identity, examination results, treatment history that has been given, other actions and services that have been carried out to patients. This study aimed to describe the completeness of filling the initial polyclinic assessment form. The methode of this study was descriptive analysis. The population of this study were the initial assessment of the physiotherapy clinic 312 medical records, surgery poly 177 medical records, and internal medicine clinic 457 medical records. And the sample of this research is 91 medical records. The results showed that from 91 medical records, 70 (77%) of forms is complete and 21 (23%) of forms is incomplete, which consisted of complete patient identification 80 (88%) and incomplete 11 (12%), completeness of important reports/notes 54 (59%) and incomplete 37 (41%), completeness of author authentication 73 (80%) and incomplete 18 (20%), and completeness of good records 72 (79%) and incomplete 19 ( 21%).
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction

Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate camera poses as input, which could be difficult to obtain in realistic settings. In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses. The core of our approach is a fast and robust multi-view reconstruction algorithm to jointly refine 3D geometry and camera pose estimation using learnable neural network modules. We provide a thorough benchmark of state-of-the-art approaches for this problem on ShapeNet. Our approach achieves best-in-class results. It is also two orders of magnitude faster than the recent optimization-based approach IDR. Our code is released at \url{https://github.com/zhenpeiyang/FvOR/}
Facial Expression Based Music Recommendation System

Facial expression is a non-verbal communication gizmo which helps to understand the overall presence of the user. The cues which are observed by cognizance of facial expression of the individual are based upon capturing the image in real time. Therefore, facial expression recognition, since it excerpt and analyzes information from an image or video, it is capable to deliver unfiltered and unbiased data as its responses. Facial expression recognisation is a vital instrument used in distinct fields for improving the product by enhancing the performance through monitoring user’s behavior. This technology is made by using machine learning (ML) concept. It first captures an image then detects the face from the captured image. It then uses biometric markers and landmarks of the face from captured image with the help of haarsascade classifier. More precisely, this technology automatically detects the six fundamental and universal expressions i.e. happy, fear, anger, sad, surprise, neutral and then it will show the playlist of music on youtube accordingly. Keywords- Facial expression, Unbiased data, Playlist, Haar sascade classifier, Music, Landmarks, Biometric.