Reviewerly/exHarmony huggingface dataset:
AI and Job — Focusing on vocational competency development —

As industrial technology advances in accordance with the 4th industrial revolution, flexibility in the labor market spreads, and at the same time, the skilled skills required in the labor market are expected to change. Therefore, although the importance of vocational competency development is gradually increasing, the vocational competency development system currently implemented in Korea is operated as a suppliercentered system led by the government. It has clear limits. In addition, the demand for manpower and the demand for training are fundamentally inconsistent due to the highly educated young people and long-term unemployment, and the lack of interest in the industry exposes considerable limitations in nurturing field-oriented industrial manpower. Accordingly, it is necessary to establish a manpower training system that meets the demands of the industrial field according to the 4th industrial revolution, and to solve the labor market mismatch by activating the autonomous development of vocational skills of companies and workers. Fundamental changes are also required in the establishment of infrastructure for vocational competency development, such as vocational education facilities, teachers, and vocational training courses to support this.
Towards the development of functional technological units in the academic area of computing and electronics

The objective of this project is to show the feasibility of systematizing some of the administrative activities of the electronics laboratory and computer classrooms in the Academic Area of Computing and Electronics (AACyE), where its main activity is teaching and research, to move towards more functional work units with the use of technology, which we will call Functional Technological Units. In the present work the conditions in which the technological work units are stagnant due to not having adequate information management are described in an introductory manner. The methodologies used for the development of this research were based on the Organizational Development model and the use of agile methodologies such as XP, ERP or WSDM. As a result, two developments of web-oriented systems validated by the ISO 9126 standard and making a qualitative weighting based on the Likert scale are shown. As for the discussion, it focuses on collaborative work and the creation of a synergy between students, teachers and administrative staff. The results that were obtained have allowed administrative processes to be carried out with greater efficiency, impacting on the teaching, administrative work and the training of students in the AACyE.
Multi-population Runge Kutta Optimizer Based on Gaussian Disturbance

To address the lack of development capacity of Runge Kutta Optimizer, we propose the Multi-population Runge Kutta algorithm Based on Gaussian disturbance(MPRUN). In the algorithm, the population is divided into subgroups. The individuals in the subgroups are randomly selected for a global search with decreasing search radius with the number of iterations, which is used to improve the global search ability of the subgroups. In addition, the algorithm introduces a Gaussian disturbance mechanism to generate more uniformly distributed populations, performing random perturbation to the global best individual. Finally, the performance of the optimized algorithm is verified by 30 test set functions.
Unknown Channel End-to-End Learning of Communication System With Residual DCGAN

Conventional communication systems are generally based on modular design, since the modules are optimized separately, the system can not achieve the optimal performance. An end-to-end communication system model can be implemented by deep learning, which can improve the transmission performance. However, the channel environment is changeable and unknown, which make the optimization of the end-to-end communication system impossible. Recently, the birth of the deep convolutional generative adversarial networks (DCGAN) can simulate unknown channels and solve the optimization problem of end-to-end systems. Then, the DCGAN has poor training stability, and the problems of over-fitting and gradient disappearance caused by it will lead to performance degradation. In this paper, we propose a residual-based DCGAN model to alleviate these problems. Specifically, we introduce a residual block structure, which effectively alleviates the over-fitting problem of the gradient. In addition, we introduce the Wasserstein distance to measure the difference between the generated data and the real data distribution, and further solve the problem of model training instability. Simulation results show that our proposed Residual DCGAN-based model effectively improves the block error rate (BLER) performance compared with traditional methods.
A Design of Edge Distributed Video Analysis System Based on Serverless Computing Service

The paper introduces the design of an edge distributed video analysis system based on serverless computing services. Firstly, we briefly describe the challenges faced by the traditional multimedia video intelligent analysis system based on centralize-cloud, and analyze the idea of deploying it as a edge distributed system. Secondly, we proposed a serverless edge distributed video intelligent analysis system, introduced the system architecture and key components. Meanwhile the resource matching scheme between the stateless video analysis function and the actual edge computing device is presented. Finally, by comparing with the small-scale experimental cluster and centralized video analysis system, we verified the efficiency and effectiveness of the system design scheme by this paper.
Energy Saving Architecture based on Android TV in a Smart Home Environment

The massive increase in Internet data traffic caused by video-streaming applications has quickly raised energy consumption and carbon dioxide emissions (CO2). Therefore, there is a certain amount of research effort for lowering energy consumption related to streaming. In this paper, we first provide an overview of the studies measuring the energy consumed by the elements of the video streaming chain and then propose an HBBTv - based architecture for reducing the energy consumption based on the user behaviour in a Smart Home (SH) environment. The architecture is discussed in terms of sustainability for the end user's Quality of Experience (QoE).
Researching on Cloud Service Architecture of Data Products

In order to make full use of data resources and enhance the transaction frequency of data trading market, we propose a cloud service architecture of data products, make data products available to the public as a cloud service, moving from a procurement system to a procurement data service. Data cloud services have several advantages. 1) It comprehensively integrates the data of government, enterprises and individuals, and promotes the integration and application of multiple data. 2) Data cloud services are inexpensive, easy to use and maintenance-free, which can greatly reduce the procurement costs for those who require data products. Since the issue of data right confirmation and data pricing is involved in the process of data circulation and fusion, we also propose a method of data right confirmation based on blockchain. This enables the transfer and trading of different rights to data by NFTs such as data ownership, data use right, and data revenue right. In addition, we analysis the data pricing ideas that should be adopted in the data trading market at different times and under different circumstances, and give the corresponding methods for calculating the price of data products. Finally, we introduced the actual application scenario of data products cloud service architecture.
Generalized Net Model of Quality of Service Estimation of Overall Telecommunication System Including a Security Stage

Generalized Nets (GNs) have been successfully used in the modeling of service systems. In recent years, GN models of various compositions of services with intuitionistic fuzzy estimations of uncertainty have been developed. In the present paper, a GN model of Quality of Service (QoS) estimation of an overall telecommunication system including users and a security stage is described. A special naming system for the places and the transitions of the GN is proposed which makes the derivation of analytical expressions easier. The values of some QoS indicators on service stage level and on overall network and system levels are evaluated through the characteristic functions of some special places of the GN. The proposed GN model can be used to define new QoS indicators in overall telecommunication systems. It can also be used for dynamic QoS management of next generation service networks and for development of a framework to assess the QoS and security features of next generation service networks.
Data Driven Positioning Analysis of Music Streaming Platforms

This study investigates the market position of music streaming platforms by analyzing user sentiment and topics expressed in customer reviews. In contrast to traditional methods, this study employs machine learning techniques to extract less biased and more authentic comments from user review data. Sentiment and topic analysis are utilized to identify the emotional tone of the language used and distinct topics discussed within customer reviews of the four most popular music streaming platforms, namely Spotify, Amazon Music, Apple Music, and YouTube Music. The study comprises four main steps, including data collection, cleaning and pre-processing, sentiment analysis, and topic modeling. The results reveal that Amazon Music is prominent in functionality aspects, while Spotify ranks highest across all topics. Apple and YouTube Music have the highest scores in reviews related to customization. The proposed approach provides valuable insights into user perceptions and preferences, which can assist brands in improving their market position. The paper concludes with a summary of the findings, marketing implications, and suggestions for future research.
Artificial Intelligence in Breast Pathology

As the most common cancer and second leading cause of cancer-related death worldwide, efficient and accurate diagnosis of breast cancer are foundational in improving outcomes. Breast cancer diagnosis, and subsequent therapy and prognosis, is based on manual histopathological examination. Traditional methods of pathology slide-interpretation are both time-consuming and subjective, with inevitable interobserver variability. Advancements at the intersection of technology and research have provided an opportunity to make the diagnostic process more streamlined and precise. From digitized whole-slide imaging, to artificial intelligence that can detect, stage, and predict therapeutic response, there are opportunities to improve clinical workflow efficiency, diagnostic quality, and ultimately optimize patient care and accessibility globally.
3-D Face Reconstruction Method Using Deep Learning Based Simulated Annealing

Face reconstruction becomes more accurate and popular in the age of artificial intelligence and deep learning. Particularly, 3-D face reconstruction can be one of the key technologies in meta-world and virtual reality applications. In recent years, studies on realistic 3-D face reconstruction draw much attention among the researchers. For realizing accurate shape and facial textures, the neural network models for deep learning are under investigation and several schemes are present-ed. In this paper, a deep learning based simulated annealing algorithm is proposed for 3-D face reconstruction. Face labeling, feature extraction, and 3-D reconstruction are three major elements investigated in this study. A set of computer simulation is performed by using the CelebFaces Attributes and Labeled Faces in the Wild data sets. The system performance is evaluated in terms of reconstruction accuracy and the results show us that the proposed method can be a successful alternative for providing accurate and robust 3-D face reconstruction.
Analysis of the Factors Behind the Value of an Agency for an Airline

Generally, discounted fares are offered for agencies requesting group reservations compared to individual fares. This is because group bookings are seen as a more stable source of revenue for airlines. Especially in the Covid-19 period, the agencies whose business was seriously damaged requested the lowest prices they can get, to be able to find passengers and make a profit. The airlines were also in a financial crisis and must have balanced the prices to make a profit while attracting the agencies. From this perspective, the need for customer segmentation and defining the value of agencies for airlines arises. The purpose of this study is to define “which dynamics, that the agency creates while making business with airlines, will be more important to form its value” by using Fuzzy Cognitive Mapping (FCM), which is one of the decision-making techniques. This study examines which variables might affect the value of agencies to an airline through the weight of these variables in order to create insights into the pricing strategies of airlines to travel agencies.
Social Spider Optimization for Text Classification Enhancement

The task of classifying text documents into the various categories for which they have been previously defined is known as text classification. The number of text documents available online exhibits exponential growth, so it is essential that a model be implemented to better organize these documents to improve the search process. The curse of dimensionality challenge is the most significant obstacles encountered during text document classification, severely affecting its performance. Identifying and reducing to a few key characteristics is a necessary first step. This study aims, first and foremost, to explore a Social Spider Optimization (SSO) method for selecting and reducing feature dimensions to enhance the text classification capabilities of machine learning models. The SSO strategy is a nature-inspired algorithm for optimization that mimics the collaborative actions of social spiders to find optimal solutions to optimization challenges. The research makes use of a standard open-source dataset to evaluate how successful the SSO algorithm is at improving the overall performance of various classifiers. These classifiers include Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Stochastic Gradient Descent (SGD) among others. The findings demonstrate that the SSO algorithm results in a substantial improvement in the classification performance of these classifiers. This research sheds light on the potential of algorithms that are inspired by natural phenomena, such as SSO, to strengthen the productivity of text classification across a multitude of domains, which includes natural language processing and information retrieval.
Generating High-Quality Prediction Intervals for Regression Tasks via Fuzzy C-Means Clustering-Based Conformal Prediction

Accurately assessing uncertainty and prediction of a regression model is essential for making informed decisions, especially in high-risk tasks. Conformal Prediction (CP) is a powerful distribution-free uncertainty quantification framework for building such models as it is capable to transform a single-point prediction of any machine learning model into a Prediction Interval (PI) with a guarantee of encompassing the true value for specified levels of confidence. On the other hand, to generate high-quality PIs, the PIs should be as narrow as possible while enveloping a certain amount of uncertainty (i.e. confidence level). The generated width of the PIs mainly depends on the nonconformity measure used within the CP. In this study, we propose two novel Fuzzy c-Means Clustering (FCM) based nonconformity measures for CP with nearest neighbors to learn distribution-free and high-quality PIs for regression. The proposed approach generates tight PIs by evaluating the degree of nonconformity of a new data point compared to the so-called calibration points via Fuzzy Sets (FSs). From the calibration dataset, we extract representative FSs via FCM and assign every test point alongside the nearest neighbors within the calibration dataset with membership grades to adapt the nonconformity measure. To evaluate the performance, we present statistical comparisons and demonstrate that the proposed FCM-based nonconformity measures result in high-quality PIs.
Fuzzy Color Computing Based on Optical Logical Architecture

This paper is dedicated to the development of intelligent techniques of optical computing for real-time decision support systems (DSS) with a large array of fuzzy input data. Currently offered as an alternative to the binary systems, fuzzy optical computing devices are very complex. To increase the efficiency of logical systems in the formation and processing of an array of input fuzzy data, we propose to use a light emitter of a certain color as a fuzzy set (FS) that is a carrier of logical information. This allows building of logical solutions and conclusions based on the additive and subtractive conversion of light radiation by appropriate color filters, measuring illumination in optical channels, and switching light emitters. The main algorithmic logical procedures based on optical gates that perform the basic logical operations of disjunction and conjunction, negation, and search for a new solution are considered. To solve a problem with a large amount of fuzzy data, we propose the architecture of networks of logical structures and inference procedures. Also, we implement optimization of the optical circuit solutions to increase the reliability of decisions and estimates. When describing color sets using a fuzzy sequence, we propose to implement a non-linear rank scale. This increases the accuracy of solutions, as shown by the decrease in root-means-square estimation error.
Approach to Architectural Distortion and Asymmetry

Breasts are generally symmetric with a similar distribution of density and architecture. Asymmetry of breast tissue is an area with increased density compared to the contralateral breast. It is essential to rule out underlying malignancy in such asymmetric densities. Architectural distortion refers to distortion of normal parenchyma, which necessitates differentiating from a malignant mass causing architectural distortion.
POSTURE DETECTION AND ALERT SYSTEM USING FLEX AND ACCELEROMETER SENSORS

The main objective is to detect thecorrect or incorrect posture by detecting the changes occurring in human spinal cord movement using sensors i.e., flex sensor. Device also contains the accelerometer sensorto detect the angle of the neck movement of the user. The posture is analysed from the data sent from both the sensors and if bad posture is detected then the buzzer sensor in the device alerts the user to correct the posture. The device is made up of a flex sensor, accelerometer module, buzzer, and a microcontroller. The flex sensor is used todetect the bending movement of the spinal cord, and the accelerometer module is used todetect the angle of the neck. Both sensors are connected to the microcontroller, which processes the data and determines if the posture is correct or incorrect. If the posture is incorrect, the buzzer alerts the user to correct their posture. The data is transferred to cloud to store the data and use it as a reportfor doctor. The cloud application allows us toremotely monitor and store our real-time datafrom anywhere in the world.
A Bottom-Up 2-Stage Approach for Constructing Arabic Knowledge Graph

Since Google has coined the term ‘knowledge graph’ (KG) in 2012, it has garnered much attention from the commercial and scientific domains. This term has been applied in numerous applications, such as automated fraud detection, supply chain management, semantic search, and intelligent question-answering (QA). The KG organises and represents strands of information as a semantic graph with higher usage efficiency, better modification, and clearer comprehension. However, KG has faced multiple challenges during the construction process particularly for Arabic language. As such, this study identified the Arabic KG (AKG) construction difficulties from Arabic text in order to propose a bottom-up 2-stage approach to construct AKG from Arabic texts. This proposed approach has dual stages: linguistic-based and machine learning-based (ML-based) stages. The linguistic-based stage is based on Natural Language Processing (NLP) techniques and three main tasks are deployed: corpus analysis, term extraction, and concept conceptualisation. As for the ML-based stage, it is based on semi-supervised ML techniques and three main tasks are applied: taxonomic classification, semantic mapping, and knowledge visualisation. The proposed methodology could successfully address the complex nature of the Arabic language in an effective manner to construct AKG.
Performance Analysis of Scheduling Algorithms for Virtual Machines and Tasks in Cloud Computing

Scheduling virtual machines (VMs) and tasks in the appropriate location is a fundamental challenge integral to the consolidation process in cloud data centers. Therefore, many optimization methods have been developed as optimal solutions to assign VMs to host or tasks to VM. The most popular algorithms are SJF (Short Job First), FCFS (First Come First Served), MinMin, and MaxMin. This work introduces a comparative study by evaluating those algorithms with PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) using the CloudSim toolkit. The performance metrics used are degree of imbalance and makespan. The simulation results showed that ACO outperforms the evaluated scheduling algorithms in terms of the degree of imbalance and VM makespan.
A Recent Review of Video Anomaly Detection for Smart Cities

According to Maslow’s hierarchy of needs, safety is a crucial component that must be satisfied before individuals can attend to higher up needs. Therefore, video surveillance systems have become more popular and heavily used in urban areas, expressing people's need for the safety of their lives and goods. However, video surveillance cameras alone cannot serve efficiently this purpose. Thusly, the urge for an automated video surveillance system has emerged to timely detect anomalies and raise early alarms to ensure public safety and security. In this paper, we will be focusing mainly on safety and security in smart cities, as we will be reviewing more precisely recent works related to video anomaly detection in public spaces, we will also provide the reader with a brief dataset benchmarking along with a comparative analysis of state-of-the-art literature.