Reviewerly/exHarmony huggingface dataset:
Smart Door Lock Using Face Recognition

Most doors are controlled by persons using keys, security cards, passwords, or patterns to open the door. This paper aims to help users improve the door security of sensitive locations by using face detection and Recognition. The face is a complex multidimensional structure and needs good computing techniques for detection and Recognition. This paper comprises three subsystems: face detection, face Recognition and automatic door access control. Face detection is the process of detecting the region of the face in an image. The look is seen using the viola jones method, and face recognition is implemented using the Principal Component Analysis (PCA). Face Recognition based on PCA is generally referred to as the use of Eigenfaces. If a face is recognized, it is known, else it is unknown. The door will open automatically for the known person due to the command of the microcontroller. On the other hand, the alarm will ring for the unknown person. Since PCA reduces the dimensions of face images without losing essential features, facial images for many persons can be stored in the database. Although many training images are used, computational efficiency cannot be decreased significantly. Therefore, face recognition using PCA can be more beneficial for door security systems than other face recognition schemes.
IMPLEMENTATION OF E-COMMODITY FOR FARMERS IN ORDER TO FACILITATE THE DISTRIBUTION OF CROPS IN SUKAPURA VILLAGE, KERTASARI DISTRICT, BANDUNG REGENCY

This research was conducted at the Vegetable Farmer Community in Sukapura Village, Kertasari District, Bandung Regency. This study aims to design a distribution system for agricultural products that involves elements of information technology in the form of designing an e-Commodity system. The e-Commodity system can be said to use internet technology to improve and change the main business processes, especially in terms of selling vegetable farm products. The e-Commodity system in addition to cutting distribution channels from farmers to consumers can also provide benefits in managing agricultural products. The approach method used in the distribution system of agricultural products that involves elements of information technology in the form of designing an e-Commodity system in the Vegetable Farmer Community in Sukapura Village, Kertasari District, Bandung Regency uses the Development Live Cycle System (SDLC) as a framework for designing a distribution system for agricultural products that involves information technology elements. The steps/stages are as follows: System Analysis and Design, Design, Application/Coding Development, Testing and Implementation, Maintenance. The findings of this study can be used as reference material in designing a distribution system for agricultural products that involves elements of information technology in the form of designing an e-Commodity system, this aims to collaborate between academia, industry and government in facilitating the distribution of agricultural products and increasing decent work and while at the same time increasing sustainable economic growth by designing and socializing the use of the e-Commodity System that can manage the distribution of agricultural products from upstream to downstream. This research is expected to be a reference in designing a distribution system for agricultural products that involves elements of information technology so that it is hoped that the existence of this e-Commodity System can provide convenience in the distribution of agricultural products and can have an impact on improving welfare for farmers.
Preface

Preface to the 6th International Workshop on Deep Learning in Computational Physics (DLCP-2021), which was held in face to face and online on July 6-8, 2022 in Dubna, Russia.
Pengaruh Motivasi Akademik terhadap Prestasi Matematika Siswa Di SDN 1 Mata Ie Aceh Besar

In learning mathematics, it appears that students are less enthusiastic in participating in learning and their scores are low. This study aims to determine whether there is a significant influence of academic motivation on mathematics achievement at SDN 1 Mata Ie Aceh Besar. This research is a study that looks at the level of students' academic motivation through a scale of elementary students' mathematics learning motivation. The population in this study were all students in grades IV, V, and VI at SDN 1 Mata Ie Aceh Besar totaling 81 students and the samples were taken using a stratified random sampling technique where from each class 10 students were taken randomly, namely 5 students whose scores reached KKM and 5 students whose scores have not reached the KKM so that the total sample in this study amounted to 30 students. Data collection is done in two ways, namely by looking at the scores of students' academic motivation and the value of student report cards, especially in learning mathematics. To see the significant effect of academic motivation on students' mathematical achievement, the product moment correlation test and the F test at a significant level of 0.05 were used. From the results of data analysis, the value of Fcount = 3.029 and Ftable = 2.052, namely Fcount Ftable. Thus HO is rejected, so Ha is accepted and it can be concluded that there is a significant influence of academic motivation on mathematics achievement at SDN 1 Mata Ie Aceh Besar.
Design Principles for Serious Games Authoring Tool

Serious game development involves a multidisciplinary team of teachers and computer scientists. But the difference in computer competencies between the team members is a recurring difficulty in this collaboration. Authoring tools, which provide interfaces adapted to users' competencies, are promising solutions to overcome this difficulty. However, existing authoring tools are either limited in their functionalities (not powerful) or too complex for non-computer scientists (not usable). A comprehensive set of design principles to address this limitation does not yet exist. The objective of this research was to define a set of design principles for the development of powerful and usable authoring tools. To achieve this objective, we first defined a set of design principles. We then developed an authoring tool corresponding to these principles. Finally, we carried out test uses of that tool through the development of twelve serious games. Results show that this authoring tool enabled the development of a wide variety of serious games (powerful) by teams with heterogeneous computer skills (usable). Design principles defined in this research integrate and extend previous works. They allow to overcome the dilemma between the power and usability of authoring tools. This could unlock new possibilities for collaborative approaches in serious games developments.
Interactive Image Segmentation Based on Feature-Aware Attention

Interactive segmentation is a technique for picking objects of interest in images according to users’ input interactions. Some recent works take the users’ interactive input to guide the deep neural network training, where the users’ click information is utilized as weak-supervised information. However, limited by the learning capability of the model, this structure does not accurately represent the user’s interaction intention. In this work, we propose a multi-click interactive segmentation solution for employing human intention to refine the segmentation results. We propose a coarse segmentation network to extract semantic information and generate rough results. Then, we designed a feature-aware attention module according to the symmetry of user intention and image semantic information. Finally, we establish a refinement module to combine the feature-aware results with coarse masks to generate precise intentional segmentation. Furthermore, the feature-aware module is trained as a plug-and-play tool, which can be embedded into most deep image segmentation models for exploiting users’ click information in the training process. We conduct experiments on five common datasets (SBD, GrabCut, DAVIS, Berkeley, MS COCO) and the results prove our attention module can improve the performance of image segmentation networks.
A quantum analytical Adam descent through parameter shift rule using Qibo

In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm.We first describe the gradient evaluation algorithm and its optimization procedure implemented using the Qibo framework.After numerically testing the implementation using quantum simulation on classical hardware, we perform successfully a full quantum hardware optimization exercise using a single superconducting qubit chip controlled by Qibo.We show results for a quantum regression model by comparing simulation to real hardware optimization.
Time-Aware Explainable Recommendation via Updating Enabled Online Prediction

There has been growing attention on explainable recommendation that is able to provide high-quality results as well as intuitive explanations. However, most existing studies use offline prediction strategies where recommender systems are trained once while used forever, which ignores the dynamic and evolving nature of user-item interactions. There are two main issues with these methods. First, their random dataset split setting will result in data leakage that knowledge should not be known at the time of training is utilized. Second, the dynamic characteristics of user preferences are overlooked, resulting in a model aging issue where the model's performance degrades along with time. In this paper, we propose an updating enabled online prediction framework for the time-aware explainable recommendation. Specifically, we propose an online prediction scheme to eliminate the data leakage issue and two novel updating strategies to relieve the model aging issue. Moreover, we conduct extensive experiments on four real-world datasets to evaluate the effectiveness of our proposed methods. Compared with the state-of-the-art, our time-aware approach achieves higher accuracy results and more convincing explanations for the entire lifetime of recommendation systems, i.e., both the initial period and the long-term usage.
Human Development and the Business Model Impact of Bitcoin Transactions

This essay provides characteristics of three emerging business models to comprehend the logic and importance of business model design about the impact of bitcoin transactions on human development. This essay argues that a business model is a social construction that describes how value is produced, and that value production requires human agency. The creation of value through a human agency is demonstrated by the use of bitcoins. Some businesses are changing how they conduct business as a result of the decentralization of financial transactions in a global internet information architecture. The highest volume of cryptocurrency exchanged to date, bitcoin, is supported by this infrastructure. It seems that bitcoin is having an impact on new company models. However, little is known about the development of these models and their implications. This paper provides a theoretical framework for understanding the logic and worth of business models about the use of bitcoin and human agency. It examines the impact of bitcoin transactions on human development. The results show that where people have access to the internet, there is a favorable association between bitcoin transactions and human development. Cluster analysis is used to analyze the logic and value of business model design from the bitcoin effect based on various variables. The evolving paradigms that each of these nations represents are divided into three groups. This paper's contribution is to reveal the bitcoin effect, which is evident in the new business models it makes possible. Based on the logic of its design, this can be used as a lens to analyze the value produced by a bitcoin business model.
BIOMEDICAL IMAGE AUGMENTATION AND TRANSMISSION USING STEGANOGRAPHY

Objective: The aim of the study was to embed sensitive patient information and image data in a cover image to enable secure transmission of confidential data. Methods: Image steganography is a process used to embed (hide with encryption) data which includes text, images or audio, and video files inside the main image file. This embedding is done by altering the values of some pixels which are chosen by the encryption algorithm. The algorithm used in this case is the Discrete Wavelet Transformation Algorithm using MATLAB. Results: Statistical features such as signal to noise ratio (SNR), Peak SNR, and (mean squared error) were extracted from the medical images to test the loss during steganography and transmission. Conclusion: A technique that enables secure and swift image data transmission using image steganography technique is proposed.
Research on the Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network

Visual tracking is one of the key research fields in computer vision. Based on the combination of correlation filter tracking (CFT) model and deep convolutional neural networks (DCNNs), deep correlation filter tracking (DCFT) has recently become a critical issue in visual tracking because of CFT’s rapidity and DCNN’s better feature representation. However, DCNNs are often complex in structure, which most possibly results in the conflict between the rapidity and accuracy of DCFT. To reduce such conflict, this paper proposes a model mainly including: (1) Based on the pre-pruning network obtained by feature channel importance, an optimal global tracking pruning rate (GTPR) is determined in terms of the contribution of filter channels to tracking response. (2) Based on (GTPR), an alternative convolutional kernel is defined to replace non-important channel kernels, which leads to the further pruning of the feature network. (3) An online updating pruned feature network with a structural similarity index is employed to adapt the model to tracking scene changes. (4) The proposed model was performed on OTB2013; experimental results demonstrate the model can effectively enhance speed with a 45% increment while guaranteeing tracking accuracy, and improve tracking accuracy with a 4% increment when tracking scene changes take place.
Potential of Bluetooth Wireless Technology as a Tool for Agricultural Extension

Information and Communication Technology (ICT) provides opportunities to improve farmers&amp;rsquo; livelihoods. Bluetooth wireless technology (BWT) is a simple cellphone-based innovation that can significantly reduce the cost of disseminating information to farmers. A two-stage survey was conducted in Ghana in 2011 and 2012 to: (i) appraise cellphone ownership, cost, and the existence of BWT, and (ii) assess the potential of using BWT to disseminate information to farmers on grain storage technology. The survey was conducted in four districts in the Northern and Ashanti regions of Ghana. Results of the appraisal study show that there were 27 cellphones for about 1,000 people, with 20% owned by women. The majority (78%) of phones were purchased new for $20 or less. About half (48%) of the cellphones had BWT. The follow-up study showed that most respondents learned (91.7%) and received (88.3%) the videos on grain storage (Purdue Improved Crop Storage-PICS) technology via BWT from extension agents. Three-fourths of the respondents watched the PICS videos on their cellphones six times or more. Among those who received the PICS videos, each viewed and shared it with nine and seven more people, respectively. The overall results support the effective use of BWT embedded in basic phones as a tool for conveying agricultural extension messages to farmers. Development partners and extension services should take advantage of BTW embedded in basic phones to improve access to agricultural and health information.
A Review on Breast Cancer Prediction Using Machine Learning

One of the most in demand research topic in today's technology world is medical area and cancer is one of them.The second main cause of death in the world is cancer.In 2015 about 8.8 million people have died due to cancer.For the early detection of breast cancer several types of research have been done to start the treatment and increase the survivability.Breast cancer affects the women mentally as well as emotionally.The goal of this challenge is to provide a framework that uses a cancer medical dataset as input and then analyses the dataset to produce findings that help medical experts better understand the state of the disease.The majority of studies concentrate on mammography results.However, incorrect detection in mammography pictures can occasionally result, endangering the patient's health.Cancer that forms in the cells of breast is said to be breast cancer.The cancer should be cured if it is diagnosed in early stage.So, with the help of machine learning algorithm the cancer should be diagnosed early.Most of the women lives are affected by the breast cancer in all over the world.There are two types of tumors that can be found in breast cancer i.e. malignant or benign.If a person is having a cancer disease, then it will be categorized as malignant otherwise it is known as benign.In 2020, 685000 deaths and 2.3 million women diagnosed with breast cancer globally, somewhere in world in every 14 seconds, a woman is diagnosed with breast cancer [1].Patient life from the breast cancer can be saved only if it is found in early stage; if it is diagnosed later then the chances of survival are less.If the cancer is diagnosed early then the patient will get a better treatment.This study will concentrate on a few machine learning methods for identifying if a breast cancer is malignant or benign.The Wisconsin Breast Cancer Dataset, which was acquired via Kaggle, was used in this study.Our goal is to evaluate how accurately various machine learning algorithms can detect breast cancer.These include Random Forest Classifier, Decision Tree Classifier, Support Vector Machine, and K-Nearest Neighbors.All the experiments are conducted on a Jupiter platform.After analyzing the accuracy of each algorithm the most suitable one is Support Vector Machine that gives the better accuracy among all i.e., 98%.
Improving Student’s Listening Skill through Bingo Game: A Classroom Action Research at Fifth Grade Students in Sidomulyo

This research aims to find out and describe the improvement of students’ listening skill through bingo game. Classroom Action Research (CAR) designed by Kemmis and Mc Taggart which consisting of four phase in each cycle: planning, acting, observing, and reflecting was used as the research method. In obtaining the result, there were two types of data collection: the qualitative data (observation and interview) and the quantitative data (pre-test and post-test). In analyzing the data, descriptive analysis and statistic analysis were used to find out the result of bingo game implementation. The result showed that the use of bingo game improved the students’ score and their responses were good. For the students’ responses, it can be seen through the observation and interview taken. In addition, the improvement of the students’ achievement were proved after the tests in each cycle (pre-test, post-test I, and post-test II) taken: the students’ mean score in pre-test was 57.25 and the students’ mean score in post-test II was 75 which the improvement of the students’ mean score in pre-test and post-test II was 17.75. Then, the percentage of the students who met the KKM in pre-test was 35%, post-test I was 65% and post-test II was 85%. In summary, the result showed that the implementation of the Classroom Action Research met the target CAR (75%) which means that the Classroom Action Research was successful.&#x0D;
Evaluating image encryption algorithms for the hyperchaotic system and fibonacci q-matrix, secure internet of things, and advanced encryption standard

In the era of information technology, users had to send millions of images back and forth daily. It's crucial to secure these photos. It is important to secure image content using digital image encryption. Using secret keys, digital images are transformed into noisy images in image encryption techniques, and the same keys are needed to restore the images to their original form. The majority of image encryption methods rely on two processes: confusion and diffusion. However, previous studies didn’t compare recent techniques in the image encryption field.This research presents an evaluation of three types of image encryption algorithms includinga Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and AES techniques. The Fibonacci Q-matrix in the hyperchaotic technique makes use of a six-dimension hyperchaotic system's randomly generated numbers and confuses the original image to dilute the permuted image. The objectives here areto analyze the image encryption process for the Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and Advanced Encryption Standard (AES), and compare their encryption robustness. The discussed image encryption techniques were examined through histograms, entropy, Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and correlation coefficients. Since the values of the Chi-squared test were less than (293) for the Hyperchaotic System &amp; Fibonacci Q-matrix method, this indicates that this technique has a uniform distribution and is more efficient. The obtained results provide important confirmation that the image encryption using Fibonacci Q-matrix in hyperchaotic algorithm performed better than both the AES and SIT based on the image values of UACI and NPCR.
A Reliable Block-Chain Based Biometric Authentication Solution for Aadhar

Objectives: To propose a reliable Block-chain based Biometric Authentication Solution (BBAS) for the Aadhar biometric authentication system.Methods:We have used Sokoto Coventry Fingerprint Dataset (SOCOFing) data set for biometrics.The presented model was implemented using the Ethereum network Geth (v.1.9.25) and Solidity (v.0.6.0).Python 3.8 and Web3py were used at the client side.Findings: From the proposed solution, it is inferred that the BBAS avoids the single point of failure problem as the biometrics are distributed throughout the block-chain.Novelty: This research proposes a new hybrid scheme that uses a block-chain that stores the hash value of the biometric files and a trusted third party (Aadhar) to store the biometric files, thereby avoiding storing the same bio-metric files throughout the block-chain.
Application of Computer-Assisted Instruction in Hypermedia Learning

Various types of applications of computer-aided technology have begun to be used in the learning process. The application when viewed from the way it is presented and the goals to be achieved include tutorials, namely the presentation of the material in stages, a drill that aims to help students master the material that has been learned before, as well as simulations and games, namely exercises in applying the knowledge and skills that have just been learned. One form of computer-based learning media that can be used is hypermedia. Hypermedia refers to computer software that uses elements of text, graphics, video, and audio that are connected, which can make it easier for users to switch to information. Communication between students and computers in Computer Assisted Instruction includes several stages, namely: (1) The computer presents the subject matter, (2) The learner studies the material, (3) the computer asks questions, (4) The learner gives a response, (5) The computer checks the response, if it is judged to be correct, the computer presents the next material, but if it is judged to be wrong, the computer gives the correct answer along with the explanation. The application of Computer Assisted Instruction in the development of learning hypermedia produces a product, namely learning hypermedia on digital image material. In testing the effectiveness of hypermedia products, students are carried out through post-testing. The results of the post-test obtained 75% of the results stated that the digital image learning hypermedia product was effective for use.