Reviewerly/exHarmony huggingface dataset:
SHOPPYVERSE: THE METAVERSE FUTURE

Technology is widely spreading and capturing every field as well as every sector with great advancement and efficiency. Technologies like artificial intelligence, machine learning, blockchain, the metaverse, and devops are evolving day by day and adding value to human life by solving some problems or increasing the efficiency of current solutions. This study aims to introduce and explain how to develop an AI-powered, metaverse-based immersive platform that provides services like gaming, events, a workspace, and shopping in the metaverse. The project consists of the following seven tasks: The first task is to create real-life cities and immersive content that will allow users to interact with various assets and channels as well as use metaverse services via mobile and VR in dual mode. The second task is to create an interactive event platform for virtual events, which helps users make events more attractive with great reach. The third task is to create a platform for gamers that allows both gamers and non-gamers to play games in new ways with excellent AI integration. The fourth task is to create a metaverse workspace for various start-ups that are adding the most value to today's society but are struggling with employee communication and high office costs. The fifth task is to create a shopping platform that allows users to interact and try on clothes virtually, bridging the gap between customers and vendors. The sixth task is to develop a business model and, lastly, promote the prototype stage of the platform with a beta version. Key Words: Metaverse, Web3.0, Immersive, Future, Unreal engine, Blender.
Regionalized models for Spanish language variations based on Twitter

Spanish is one of the most spoken languages in the world. Its proliferation comes with variations in written and spoken communication among different regions. Understanding language variations can help improve model performances on regional tasks, such as those involving figurative language and local context information. This manuscript presents and describes a set of regionalized resources for the Spanish language built on 4-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities and examples of using regional resources on message classification tasks.
Multi-book Label Detection Model using Object Detection and OCR

본 논문에서는 객체 탐지와 광학 문자 인식(이하 OCR)을 복합적으로 사용하여 책장 내의 다수의 책이 꽂혀 있을 때 책등을 탐지하여 책 제목을 추출하고 이를 통해 책에 대한 추가적인 정보를 확인할 수 있는 모델을 구현한 결과에 대해 다루고 있다. 객체 탐지 과정은 책등과 책 제목 영역 탐지로 단계를 분리하였고 2개의 Yolov5 모델을 사용하여 객체 탐지에 대한 성능을 높였다. OCR 단계에서는 출력되는 텍스트의 정확도를 높이기 위하여 이미지가 OCR 모델에 입력되기 전 단계에서 흑백 변환, 크기 변환, 밝기 조절과 같은 전처리를 수행하였다. 느린 OCR 처리 시간에 대한 문제를 보완하기 위해 설계한 모델을 웹 애플리케이션에 적용하는 과정에서 사용자가 원하는 책을 선택하는 기능을 추가하였다. 추가적으로 객체 탐지와 광학 문자 인식을 통해 추출된 제목을 이용해 저자, 출판사, 가격, 후기와 같은 정보를 확인할 수 있도록 하였다. In this paper, we discuss the results of implementing a model that uses a combination of object detection and Optical Character Recognition(OCR) to detect book spine and extract book titles when multiple books in a bookcase are plugged in, thereby confirming additional information about the book. The object detection process was divided into book spine detection and book title area detection, and two Yolov5 models were used to increase the performance of object detection. As an OCR result, pre-processing such as grayscale, resizing, and brightness adjustment was performed before entering images into OCR model to increase the accuracy of text output. In the process of applying a model designed to compensate for the problem of slow OCR processing time to web applications, it has added a function that allows users to select the book they want. In addition, information such as author, publisher, price, and reviews can be confirmed through book titles extracted through object detection and OCR.
Quantum gates between mesoscopic spin ensembles

Quantum algorithmics with single spins poses serious technological challenges such as precision fabrication, rapid decoherence, atomic-scale addressing, and readout. To circumvent atomic-scale challenges, we examine the case of fully polarized mesoscopic spin ensembles (spin-coherent states) whose total angular momenta states map to qudit submanifolds. We show that in the limit where the size of the ensembles is small compared to their separation, it is possible to treat them as qubits with an effective coupling strength that scales with the number of spins. If the spins within each ensemble are decoupled (e.g., via control fields, spinning or diffusional averaging, or materials engineering), one- and two-qubit gate operations can be implemented with high fidelities.
Structural Equation Modeling to Investigate the Impact of Strategic Planning on Crisis Management: Empirical Evidence from Jordanian Telecommunication Companies

In this study, the impact of strategic planning (SP) and crisis management (CM) in the Jordanian telecommunication companies was investigated after COVID 19 pandemic.The sample of the study consisted of (180) employees working in the Jordanian telecommunication companies selected randomly.In order to achieve the objectives of the study, a questionnaire was used.Data were analyzed and study hypotheses were tested using SPSS and Amos.The results showed that strategic planning plays a vital role in the ability to manage crisis and that there is a statistically positive significant impact for SP and CM, in light of the results, recommendations were provided.
Geochemical Features and Zoning of the Filizchay PyritePolymetallic Deposit (Southern Slope of the Greater Caucasus)

The article discusses the geochemical features and zoning of the ore deposit of the Filizchay deposit on the southern slope of the Greater Caucasus. The spatial distribution of valuable (Cu, Zn, Pb) components, their sum, as well as the distribution of Ag, which have a high content for the Filizchay deposit, are considered. A field model was built on the basis of the Leapfrog GEO program. In order to quantify zoning, the index of geochemical zoning was calculated based on the ratios of the concentrations of elements accumulated in the upper part of the ore deposit (Zn, Pb, Ag) and elements accumulated in the lower part of the deposit (Cu, Co, Mn). It has been established that in the Azerbaijan part of the southern slope of the Greater Caucasus, as we move from the northwest to the southeast, there is a regional ore zonality, expressed in the change of chalcopyrite-pyrrhotite ores by sulfide-polymetallic ones, as a result of which there is a decrease in the concentration of copper and, accordingly, an increase in the amount of zinc and especially lead. According to the degree of intensity of manifestation of zinc, lead and silver mineralization in the deposit, two parts can be distinguished: the western one is richer, where almost all the maxima are concentrated, and the eastern one is poorer, which accounts for most of the minima. Additional exploration work is recommended in the eastern part. The distribution of elements in individual wells established a clear zoning in terms of the thick ness of the ore deposit, manifested in a regular change in the mineral and chemical composition of ores from a lying to a hanging side. This leads to the accumulation of copper, cobalt, tin and manganese in the foot, and the concentration of zinc, lead, cadmium, thallium, mercury, antimony, etc. in the hanging side of the ore deposit.
Pengembangan Sistem Absensi Karyawan di Institut Informatika dan Bisnis Darmajaya Dengan Menggunakan Teknologi Barcode

There are so many ways to process employee attendance, one of which is to use the manual method. So far, there are still many large companies that still implement attendance manually, but this causes a lot of time leaks or other violations, which of course makes it less effective and efficient and causes attendance information to be inaccurate. In a company with quite a lot of employees, it is very necessary to have proper, fast, accurate attendance management. An accurate methodology for solving problems in this modern era with the use of QR barcodes because it will really help companies to attend to employees in real time. The system is made with the PHP programming language and uses the MYSQL database. The purpose of QR Code (Quick Response Code) technology in companies is as a tool in processing employee attendance data, employee identity cards and also processing employee data which is beneficial for employees because they can carry out computerized attendance activities. The results to be obtained from the research and implementation of this system are by entering several examples of employee data as an experimental form of attendance transactions, and the attendance application program is made to run properly. The system created produces several features in the form of user features, checking QR codes for attendance, generating QR codes from each employee card, recapitulation and attendance reports on the system, and employee data in the form of employee names, positions, work shifts and work location placements
The Characteristics of Passionate Educators and Their Role in Education

In this research, it was investigated what passion is, who is considered a passionate teacher and the characteristics of a passionate teacher.According to the findings of the study, the genuine definition of passion includes enthusiasm, desire, effort, dedication, and commitment.These are the most significant contributors to the success of education and teachers.These attributes play a significant effect in the academic success of kids.The teaching profession has been characterized differently by different authors and academics.Among these include being loyal, shaping the destiny of a nation, being the parent of a student, being patient, being productive, and setting an example.They are the fundamental factors that affect the fate of societies.The results of the studies show, the component that influences the future of a nation is the teacher's passion.He is the individual who prepares all professions for life.They are the military, police, politicians, cleaners, and attorneys; in short, everyone.All of these concepts will be brought to life by passionate educators.
Translating Polysemous Emojis into Kurdish: An Empirical Study

Due to the polysemous and ambiguous nature of the emojis, translators encounter difficulties in rendering them into Kurdish.This paper is an attempt to find out the nature and the frequency of the problems related to emojis and suggest more appropriate ways for dealing with them when they are translated into Kurdish.The paper takes up a descriptive-analytic approach.The data of the study is collected primarily from the 'Emoji movie' produced in 2017.The data are then categorized and analyzed thoroughly to explore the underlying factors of these problems and suggest effective strategies for translating them with minimum ambiguity.The results of this study show that polysemous emojis could be disambiguated through the context and other extralinguistic factors such as the setting and the technological background of the translators.
Local triangular patterns: novel handcrafted feature descriptors for facial expression recognition

Facial expressions are common across cultures and are used universally by humans to express their internal emotions, intentions and thoughts. The main task in facial expression recognition (FER) systems is to develop efficient feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting significant features, local triangular patterns (LTrP) named mini triangular pattern (mTP) and mega triangular pattern (MTP) have been proposed in order to minimise the feature vector length and to maximise the recognition accuracy. The proposed mTP method extracts two features in a 3 × 3 circular neighbourhood, whereas, MTP method extracts two features in a 5 × 5 circular neighbourhood. The proposed methods (mTP and MTP) are implemented on three benchmark FER datasets namely TFEID, MUG and KDEF. The experiments have been performed with respect to both six and seven expressions in person independent setup to simulate a real-world scenario. The experimental results demonstrated the efficiency of the proposed methods when compared to the standard existing methods.
The human voice recognition algorithm in determining gender classification

This study aims to determine the type of person's voice to group them based on the type of voice. So far, determining the type of person's voice manually requires an expert such as a vocal trainer. To determine only one or two persons' voice will be quick, but to determine more person's voices such as a choir group will be difficult because it takes time and costs. This manual method is not effective to determine the type of sound in a large group. Therefore, we need a system to determine the type of voices. This study use Mel Frequency Cepstral Coefficient (MFCC) and K-Nearest Neighbor (K-NN). MFCC extracts voice features and K-NN classifies voice types.
Strategi Pengembangan Kerjasama Perpustakaan Uinsu dengan Perpustakaan Umsu Dalam Upaya Meningkatkan Layanan Perpustakaan

Library services need to always be improved so that visitors feel comfortable and their information needs are met by visiting the library. In order to realize excellent library services, library collaboration is important to do in improving service quality. The North Sumatra State Islamic University Library is a university library that has collaborated with several university libraries, both university libraries in North Sumatra and those outside North Sumatra. However, the one that most often carries out collaboration activities with the UINSU library is the University of Muhammadiyah Sumatra Utara (UMSU) library. The strategy for developing cooperation between the library of the State Islamic University of North Sumatra and the University of Muhammadiyah Sumatra Library consists of developing human resources, resource sharing, preparing for accreditation, collections, and information technology.&#x0D; Keywords: Library Collaboration, Library service
A preliminary empirical study of react library related questions shared on stack overflow

React is a JavaScript library to develop user interfaces for single-page applications. Developers utilize react to build large web apps that allow users to update data without refreshing the page. Despite its benefits, many developers face react-related issues in the implementation. To find a solution, developers commonly shared and discussed their issues on stack overflow (SO). Although recent studies have demonstrated the benefits of utilizing react in web development, the trends of the users’ attentions remain unknown. In this study, we conducted a preliminary empirical study of react library-related questions shared on SO. We applied an exploratory data analysis technique to investigate the distribution of problems shared by the developers. The findings reveal that although the quantity of react-related topics on SO has risen over time, community interest is beginning to decrease. This is shown by the increase of the unsolved questions and the decrease of the number of views per year. Regarding the react users’ activity, most of them are more active in providing answers rather than commenting and providing scores. The findings of this study might point to future research that recommends approaches to assist the react community in overcoming issues while using react in the early phases.
A proposed scenario to improve the Ncut algorithm in segmentation

In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel computing in GPU. The Ncut is improved in four steps: determination of number of clusters in segmentation, computing the similarity matrix W, computing the similarity matrix's eigenvalues, and grouping on the Fuzzy C-Means (FCM) clustering algorithm. Some experimental results are shown to prove that our scenario is 20 times faster than the Ncut algorithm while keeping the same accuracy.
Design of Audio-Based Accident and Crime Detection and Its Optimization

The development of transportation technology is increasing every day; it impacts the number of transportation and their users. The increase positively impacts the economy's growth but also has a negative impact, such as accidents and crime on the highway. In 2018, the number of accidents in Indonesia reached 109,215 cases, with a death rate of 29,472 people, which was mostly caused by the late treatment of the casualties. On the other hand, in the same year, there were 8,423 mugs, and 90,757 snitches cases in Indonesia, with only 23.99% of cases reported. This low reporting rate is mostly caused by the lack of awareness and knowledge about where to report. Therefore, a quick response surveillance system is needed. In this study, an audio-based accident and crime detection system was built using a neural network. To improve the system's robustness, we enhance our dataset by mixing it with certain noises which likely to occur on the road. The system was tested with several parameters of segment duration, bandpass filter cut-off frequency, feature extraction, architecture, and threshold values to obtain optimal accuracy and performance. Based on the test, the best accuracy was obtained by convolutional neural network architecture using 200ms segment duration, 0.5 overlap ratio, 100Hz and 12000Hz as bandpass cut-off frequency, and a threshold value of 0.9. By using mentioned parameters, our system gives 93.337% accuracy. In the future, we hope to implement this system in a real environment.
Design and Development of an Intelligent Ground Vehicle for constrained environments

The Intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International in the 1990s. The IGVC challenges engineering student teams to develop, test, and compete with their intelligent vehicles, focusing on a series of autonomous mobility objectives pertaining to advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. The IGVC offers a design experience that is at the very cutting edge of engineering education. Autonomous robots are becoming prevalent in many aspects of modern society and the IGVC, while being in a controlled environment, provides a great example of how self-navigating vehicles could be used, such as going into areas that may pose risk to human life. Our goal is to design and construct a ground vehicle with autonomous capabilities. LIDAR, camera (computer vision), IMU sensor, and GPS enable the vehicle to function autonomously. The vehicle is capable of detecting and following lanes, avoiding obstacles, and tracking GPS coordinates. The entire system is run on the ROS (Robot Operating System), with sensors and actuators acting as nodes that interact with one another.
Neural Network Panning: Screening the Optimal Sparse Network Before Training

Pruning on neural networks before training not only compresses the original models, but also accelerates the network training phase, which has substantial application value. The current work focuses on fine-grained pruning, which uses metrics to calculate weight scores for weight screening, and extends from the initial single-order pruning to iterative pruning. Through these works, we argue that network pruning can be summarized as an expressive force transfer process of weights, where the reserved weights will take on the expressive force from the removed ones for the purpose of maintaining the performance of original networks. In order to achieve optimal expressive force scheduling, we propose a pruning scheme before training called Neural Network Panning which guides expressive force transfer through multi-index and multi-process steps, and designs a kind of panning agent based on reinforcement learning to automate processes. Experimental results show that Panning performs better than various available pruning before training methods. Our code is made public at: https://github.com/kangxiatao/RLPanning.
Neighborhood Region Smoothing Regularization for Finding Flat Minima in Deep Neural Networks

Due to diverse architectures in deep neural networks (DNNs) with severe overparameterization, regularization techniques are critical for finding optimal solutions in the huge hypothesis space. In this paper, we propose an effective regularization technique, called Neighborhood Region Smoothing (NRS). NRS leverages the finding that models would benefit from converging to flat minima, and tries to regularize the neighborhood region in weight space to yield approximate outputs. Specifically, gap between outputs of models in the neighborhood region is gauged by a defined metric based on Kullback-Leibler divergence. This metric could provide insights in accordance with the minimum description length principle on interpreting flat minima. By minimizing both this divergence and empirical loss, NRS could explicitly drive the optimizer towards converging to flat minima, and meanwhile could be compatible with other common regularizations. We confirm the effectiveness of NRS by performing image classification tasks across a wide range of model architectures on commonly-used datasets such as CIFAR and ImageNet, where generalization ability could be universally improved. Also, we empirically show that the minima found by NRS would have relatively smaller Hessian eigenvalues compared to the conventional method, which is considered as the evidence of flat minima (Code is available at https://github.com/zhaoyang-0204/nrs ).
Network Pruning via Feature Shift Minimization

Channel pruning is widely used to reduce the complexity of deep network models. Recent pruning methods usually identify which parts of the network to discard by proposing a channel importance criterion. However, recent studies have shown that these criteria do not work well in all conditions. In this paper, we propose a novel Feature Shift Minimization (FSM) method to compress CNN models, which evaluates the feature shift by converging the information of both features and filters. Specifically, we first investigate the compression efficiency with some prevalent methods in different layer-depths and then propose the feature shift concept. Then, we introduce an approximation method to estimate the magnitude of the feature shift, since it is difficult to compute it directly. Besides, we present a distribution-optimization algorithm to compensate for the accuracy loss and improve the network compression efficiency. The proposed method yields state-of-the-art performance on various benchmark networks and datasets, verified by extensive experiments. Our codes are available at: https://github.com/lscgx/FSM .