Reviewerly/exHarmony huggingface dataset:
Application of Quality in Use Model to Evaluate the User Experience of Online Banking Software

Open source software (OSS) has recently become very important due to the rapid expansion of the software industry. In order to determine whether the quality of the software can achieve the intended purposes, the components of OSS need to be assessed as they are in closed source (conventional) software. Several quality in-use models have been introduced to evaluate software quality in various fields. The banking sector is one of the most critical sectors, as it deals with highly sensitive data; it therefore requires an accurate and effective assessment of software quality. In this article, two pieces of banking software are compared: one open source and one closed source. A new quality in use model, inspired by ISO/IEC 25010, is used to ensure concise results in the comparison. The results obtained show the great potential of OSS, especially in the banking field.
Introduction

This chapter gives the reader a general overview and scope of this habilitation thesis in the development of the research project Participa Inteligente, a Web-based social network to enhance citizens’s participation which uses a dynamic profile generation for the so-called voting advice applications (VAAs).
Health Monitoring using Edge Cognitive Computing Based Smart Health Care

With an enhancement in technology and development of sensors, they have decided to concern the latest technology in particular areas to increases the Quality of their life. One of the important section of research that has seen an agreement of the latest technology is the healthcare. the people who need healthcare services they have to suffer for long process, to reduce the problem As a result, basically this complete technology would try to resole the healthcare issues. The main goal of the project is to develop a remote healthcare system. It have three main parts. The first and most important section in this process is to detecting the patient current health status using this sensors. second important thing is sending data to cloud storage and the last section is to get the detected data for remote viewing. Remote viewing of the data provided to the doctor will able to monitor a patients health progress aboard from hospital places. The Edge-Cognitive- Computing-based (ECC-based) smart healthcare system. This technology is able to check also distinguish and detect the current status of patient using this sensors. It also accommodate the computing resource appropriation of the complete edge computing network expansive to the health different percentage of each user. The experiments show that the ECC-based healthcare system provide a exceed user background and try to improvement the computing resources moderately, as well as consequentially improving in the durability rates of patients in emergency
Open Source Online Learning in Rural Communities

Anyone is free to use open source software without the need to purchase the right to install it. Despite its appeal to school and technology leaders in rural communities, they are less likely to install it than others. In this chapter, three cases in which open source technology was installed to support teaching and learning in three rural communities are described. In each, the systems were deployed and refined using decision-making grounded in educational design research. The projects are detailed, and the method of technology planning is assessed. Unanswered questions are also addressed.
Technical Premise

In this chapter, we briefly describe technical premise on multimedia QoE evaluation. Firstly, we present some mainstream definitions about QoE and introduce how to quantify multimedia QoE. Then, we investigate and classify the influencing factors of multimedia QoE from three aspects. Moreover, we introduce existing mainstream multimedia QoE modeling and prediction algorithms based on machine learning. Finally, we give challenges of multimedia QoE evaluation research.
Application of deep learning to direct and inverse problems in plasmonic coloring

Laser pulses can color noble metals by inducing nanoparticles on their surface. We apply deep learning to solve the direct and inverse problems which link nanoparticle distributions and laser parameters to the produced colour.
Exploration and Exploitation of Developers' Sentimental Variations in Software Engineering

Software development is highly dependent on human efforts and collaborations, which are immensely affected by emotions. This paper presents a quantitative empirical study of the emotional variations in different types of development activities (e.g., bug-fixing tasks), development periods (i.e., days and times) and in projects of different sizes involving teams of variant sizes. The study also includes an in-depth investigation of emotions' impacts on software artifacts (i.e., commit messages) and exploration of scopes for exploiting emotional variations in software engineering activities. This work is based on careful analyses of emotions in more than 490 thousand commit comments across 50 open-source projects. The findings from this work add to our understanding of the role of emotions in software development, and expose scopes for exploitation of emotional awareness in improved task assignments and collaborations.
Improving the Security of Storage Systems

Developing security systems to protect the storage systems are needed. The main objective of this paper is to study the security of file storage server of an organization. Different kinds of security threats and a number of security techniques used to protect information will be examined. Thus, in this paper, an assessment plan for evaluating cyber security of local storage systems in organizations is proposed. The assessment model is based on the idea of cyber security domains and risk matrix. The proposed assessment model has been implemented on two prestigious and important organizations in the Kingdom of Bahrain. Storage systems of the assessed organizations found to have cyber security risks of different scales. This conclusion gives certainty to the fact that organizations are not capable of following the cyber security evolution and secure their storage systems from cyber security vulnerabilities and breaches. Organizations with local storage systems can improve the cyber security of their storage systems by applying certain techniques.
The Design of Secure Coded Edge Computing for User-Edge Collaborative Computing

In recent years, edge computing (EC), as an emerging technology, has been widely used in various industries. It can meet the needs of industries in real-time business, application intelligence, security and privacy protection. However, edge devices may not always be trustworthy in the edge computing environment. Moreover, traditional edge computing systems have ignored the fact that the computation capability of user device can also be used. In this paper, we propose the Minimum Computation Latency Secure Edge Computing (MCLSEC) scheme to minimize computation latency and provide the security of computing data by utilizing linear coding and the resources of both edge devices and user device. Specifically, we consider the matrix multiplication as a computation task, which is an important module in many application operations, such as machine learning, big data analysis, etc. We firstly theoretically analyze the total computation latency of edge devices and user device in the coded edge computing. We then give the design of the MCLSEC scheme, which includes of the coding scheme and the task allocation scheme. Moreover, we also give theoretical analysis to show the proposed MCLSEC scheme is secure and optimal. Finally, we conduct extensive simulation experiments to show the effectiveness of the proposed scheme. Compared with the existing schemes, MCLSEC scheme significantly reduces the computation latency of edge computing while ensuring data confidentiality.
Text Independent Speaker Identification for Myanmar Speech

Nowadays, speech signal processing is one of the emerg ing applicat ion areas of d igital processing.There are many research areas related to speech processing such as speaker recognition, speech recognition, and speech synthesis.Speaker identificat ion is the task of analy zing the speakers' characteristics in speech to exactly identify individuals.The identification task perfo rms better when there is enough background training data.Mel Frequency Cepstral Coefficient (MFCC), Perceptual Linear Pred iction (PLP) and Filter Bank features are extracted as front-end processing.Constructing Universal Background Model (UBM) is the main co mponent of i-vector system as it is essential for collecting statistics from speech utterances and for clustering the speaker models.Th is paper indicates that the impacts of unlimited speech data in speaker identification by using i-vector method with probabilistic linear discriminative analysis (PLDA) approach and the import role of speaker models in identification process.
Control Architecture Model in Mobile Robots for the Development of Navigation Routes in Structured Environments

Robotic navigation applies to multiple disciplines and industrial environments. Coupled with the application of Artificial Intelligence (AI) with intelligent technologies, it has become significant in the field of cognitive robotics. The capacity of reaction of a robot in unexpected situations is one of the main qualities needed to function effectively in the environment where it should operate, indicating its degree of autonomy. This leads to improved performance in structured environments with obstacles identified by evaluating the performance of the reactive paradigm under the application of the technology of neural networks with supervised learning. The methodology implemented a simulation environment to train different robot trajectories and analyze its behavior in navigation and performance in the operation phase, highlighting the characteristics of the trajectories of training used and its operating environment, the scope and limitations of paradigm applied, and future research.
Decision Graphs

This chapter introduces decision models. Firstly, a brief review of the fundamentals of decision theory is presented. Secondly, we describe decision trees and their evaluation strategy. Thirdly, influence diagrams are introduced, including three alternative evaluation strategies: transformation to a decision tree, variable elimination and transformation to a Bayesian network. The chapter concludes with two application examples: a decision support system for lung cancer and a decision model that acts as a caregiver to guide an elderly or handicapped person in cleaning her hands.
Performance Evaluation of High-tech Innovation Enterprises Based on Grey Chaotic Neural Network

The level of an enterprise's innovation performance capability directly determines its position in the international division of labor and industrialization transfer to a large extent. The independent R&D technology of Chinese manufacturing companies has advanced relatively late and the level is relatively low, which has led to the disadvantaged position of manufacturing companies in international competition. In this context, the study of China's innovative performance evaluation, which takes China's innovative enterprises as the main evaluation object, is of great significance for promoting the development of Chinese innovative enterprises and enhancing the status of Chinese enterprises in international competition. This paper aims to study the performance evaluation of high-tech innovation enterprises based on gray chaotic neural network, follow the design principles of high-tech innovation enterprise performance evaluation system, combine the characteristics of high-tech innovation enterprises, establish a performance evaluation index system, and collect sample data on this basis. The application of grey chaotic neural network model for empirical analysis. Through analysis, it can be seen that, except for the error value of the test sample 20, which differs by 0.002, the error values of the other test samples are all within the expected value, indicating that the performance evaluation model of the gray chaotic neural network is available.
A Hierarchy of Ambiguity Aversion

Ambiguity aversion is commonly associated with a preference for mixing among acts. In this paper, we provide an axiomatic hierarchy that characterizes increasingly strong levels of ambiguity aversion. Each level is specified by k, the maximum number of outcomes of the mix that the decision maker is ensured to accept. For the class of invariant biseparable preferences (Ghirardato et al., 2004), we show that each level k of the hierarchy is equivalent to a specific property of the representation functional: Acts with at most k different outcomes are evaluated at the minimum expected utility w.r.t. the set of dominating measures. Furthermore, the extreme levels of the hierarchy correspond to the prominent definitions of ambiguity aversion introduced in Schmeidler (1989) and Ghirardato and Marinacci (2002). We show that in the Choquet expected utility model the hierarchy only has three levels. Furthermore, our level k = 2 axiomatizes exact capacities, providing the first such characterization.
Blockchain-based Crowdsourcing Task Management and Solution Verification Method

Crowdsourcing relies on Internet-wide capability to solve the complicated or large-scale tasks that are difficult to accomplish separately by individuals. However, traditional centralized crowdsourcing systems highly depend on the centralized coordination server to operate, making it extremely vulnerable to the single-point bottleneck and failure. And the whole system lacks verifiable trustworthiness among the participants. This paper proposes a blockchain-based framework for the distributed crowdsourcing without relying solely on any single trusted entity. The solutions for the outsourced tasks are verified with consensus among the participants with a reputation mechanism. We prove by theoretical security analysis that the proposed scheme resists malicious attacks better comparing to other typical crowdsourcing schemes. A prototype system is implemented based on Ethereum to demonstrate the overhead performance in various aspects. Theoretical and experimental evaluations show that the proposed scheme possesses reliability, security, quality, and feasibility.
The Object Detection Method for Pedestrian Video Based on Improved YOLOv3

research-article The Object Detection Method for Pedestrian Video Based on Improved YOLOv3 Share on Authors: Zibo Feng Beijing Institute of Graphic Communication, China Beijing Institute of Graphic Communication, ChinaSearch about this author , Fucheng You Beijing Institute of Graphic Communication, China Beijing Institute of Graphic Communication, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 548–553https://doi.org/10.1145/3495018.3495116Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
International Conference on Humane Innovations in Education - Poster Abstracts

Ultrasonography for the last two decades has become a very important tool in clinical practice all over the world.Some reports suggest that mistakes in determination of the ovarian status range between 30 and 40%, which may be the reason for some reproductive failures.To achieve shorter intercalving period, clinicians have to use any tool available to secure good reproductive performances.Holstein-Friesian cows from the biggest dairy farm in Canton Sarajevo, PD Butmir were randomly selected.The age of cows was between two and five lactations.Cows were at least 60 days postpartum and without any previous anamnestic data about their reproductive status.Device used for clinical examination was Mindray, DP-50 with endorectal linear probe and used frequency between 8 and 10 MHz.Typical ovarian structures such as follicles and the corpus luteum are recognized as dark and compact structures, while pathological structures had similar look, but were greater in size, and had some structural differences.Sonography has proven to be fast, accurate and low effort technique to get the best insight in the ovarian status in live dairy cows, without need for previous anamnestic or ovarian status data.
Study of Query Optimization in Cloud

Cloud computing in very simple terms, is basically where a company uses someone else’s computing services (usually over the internet) instead of having to run that software on their own computers. Today, cloud computing plays an important role in service-oriented technologies. The main purpose of cloud computing is, it allows consumers and businesses to use applications without installation and access their personal files at any computer with internet access and it allow users to easily and efficiently calculate and save resources. The recent approach is to process data expression and search. To improve cloud performance, it is necessary to optimize the processing time. Our research provides a comprehensive overview of the different models and methods used to optimize queries to reduce execution time and improve resource utilization. We conducted various query optimization research activities for the classic SQL and Map Reduce platforms.
Improving Collaborative Filtering Algorithms

This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.
A Small Step in DTJournal’s Evolution

Do small things with great love.-Mother Theresa
Contemporary Research Challenges and Applications of Service Oriented Architecture

Service Oriented Architecture (SOA) is distributed architectural framework that provides service-based solutions for improving the effectiveness of enterprise’s IT infrastructure. In this framework, technical and business processes are implemented as services. A service is an independent software application that has been designed to perform a specific function with emphasis on loose coupling between interacting services and their components. SOA permits developers to utilize many of the resources from existing services to form the distributed applications. This study has investigated to highlight the emerging issues of SOA such as service structures advancement, requirements of evolution for current age applications like mobile-cloud, medical and mechanism for interoperable operations. The paper also uncovers the practical application domains of SOA. Ithas identified research attentions in these domains with detection of issues to carry further research to overcome constraints in current scenarios.
Demography of Open Source Software Prediction Models and Techniques

Open source software (OSS) is currently a widely adopted approach to developing and distributing software. Many commercial companies are using OSS components as part of their product development. For instance, more than 58% of web servers are using an OSS web server, Apache. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This chapter reports on a systematic literature survey aimed at the identification and structuring of research that offers prediction models and techniques in analysing OSS projects. The study outcome provides insight into what constitutes the main contributions of the field, identifies gaps and opportunities, and distils several important future research directions. This chapter extends the authors' earlier journal article and offers the following improvements: broader study period, enhanced discussion, and synthesis of reported results.
Cloud Computing, Smart Technology, and Library Automation

As technology continues to change, the landscape of the work of librarians and libraries continue to adapt and adopt innovations that support their services. Technology also continues to be an essential tool for dissemination, retrieving, storing, and accessing the resources and information. Cloud computing is an essential component employed to carry out these tasks. The concept of cloud computing has long been a tool utilized in libraries. Many libraries use OCLC to catalog and manage resources and share resources, WorldCat, and other library applications that are cloud-based services. Cloud computing services are used in the library automation process. Using cloud-based services can streamline library services, minimize cost, and the need to have designated space for servers, software, or other hardware to perform library operations. Cloud computing systems with the library consolidate, unify, and optimize library operations such as acquisitions, cataloging, circulation, discovery, and retrieval of information.
Improving the Performance of Pre-trained Systems in Sentence Retrieval

Information Retrieval is the task of satisfying an information need by retrieving relevant information from large collections. Recently, contextualised pre-trained systems have surpassed traditional systems on benchmarks that provided vast amounts of context. In contrast, pre-trained systems underperform in environments where context is insufficient. Hence, this work aims to improve the performance of pre-trained systems under such resource constraints. To this aim, we turned to context augmentation. Since sentences typically consist of few terms, we recognised sentence retrieval as a context-constrained environment. However, most context augmentation methods were either ineffective in sentence retrieval or computationally expensive, while those methods that were effective in sentence retrieval and computationally cheap were unable to capture contextual information. Hence, we developed a novel context augmentation method for sentence retrieval that preserves contextual information while maintaining low latency. The proposed method allowed the pre-trained system to achieve a 19% performance gain in a context-constrained environment. Its effectiveness was also validated by participation in the EPIC-QA challenge. Specifically, it allowed the pre-trained system to rank third among 55 competing systems, with less than a 1% performance gap to first place.