Reviewerly/exHarmony huggingface dataset:
Cloud-Based Intelligent Virtual Try-On Using Augmented Reality

Advancement of technology had a significant impact on various industries, with innovative solutions like cloud computing, IoT, augmented reality (AR), and virtual reality (VR) changing the game in many ways. Here is a system known as “virtual try-ons” that leverages IoT devices like mobile cameras, cloud storage for data, and an intelligent interface for user interaction. Many people are opting for online shopping, and various challenges arise with this transition, one of which is the issue of “try-on.” VR solves this challenge by introducing “virtual try-on,” which replaces traditional try-on methods. It enables an individual to preview and virtually try on their desired products like clothes, watches, shoes, etc. from the comfort of their own homes, making the shopping experience easier and smoother. It also adds an element of fun and excitement to the shopping experience, increasing the hedonic value for consumers and allowing consumers to experiment and play with different products, styles, and colors in a way that is not possible with traditional shopping methods.
The Influence of Students' Satisfaction in Online Learning and Its Implications on Students' Motivation and Learning Achievement

This study aims to describe the process of lectures conducted online, namely how the influence of online learning service satisfaction on learning motivation and student achievement of a state Islamic university in Riau. The data collection method in this study was a survey in the form of a questionnaire which was distributed to students with a total sample of 246 people who were distributed to all faculties. Data analysis using SEM Path Analysis 3.0 and SPSS 21. The results showed that satisfaction in online learning was in the satisfied category, motivation was quite high, and achievement was very high, and the effect of satisfaction in online learning on learning motivation was significant in the 2-sided test (t table = 1.97) with a t-statistic value of 9.525 or greater than t table, and the p-value is smaller than alpha 5% (0.000 0.05). The effect of satisfaction in online learning on learning achievement is significant in the 2-sided test (t table = 1.97) with a t-statistic value of 0.856 which is smaller than the t table, and the p-value is smaller than alpha 5% (0.392 0.05. The effect of learning motivation on learning achievement is significant in the 2-sided test (t table = 1.97) with a t-statistic value of 3.090 greater than the t table, and the p-value is less than 5% alpha (0.002 0.05). The effect of satisfaction in online learning on achievement is significant in the 2-sided test (t table = 1.97) with a t-statistic value of 2.999 greater than the t table, and nil ai p is smaller than alpha 5% (0.003 0.05). The results of the research above indicate that satisfaction in online learning has a significant effect on learning achievement through student learning motivation at university.
Applications and Challenges of Quantum Image Processing – A Comprehensive Review

The tremendous parallel processing capability of quantum computers allow quantum image processing, a multidisciplinary field combining image processing and quantum computing, to expand the potential outcomes for image processing. The problem of quantum computation is to create effective quantum algorithms since quantum computers need extremely effective algorithms than classical algorithm. Additionally, information storage, communication, and computing power are increasing in relevance with the number and importance of processing digital images. Some of these issues might be resolved by encoding the visual data in quantum-mechanical systems as opposed to classical methods and by switching from classical to quantum information processing. Moreover, the quantum image processing is analysed based on the recent research works and is summarized. This study projects the application of quantum image processing and challenges.
Recent development in Extended Reality technologies

This study discusses the recent trends in Extended Reality (XR) technologies. Extended Reality is a group of three technologies i.e., virtual reality, augmented reality, and mixed reality. It has been used in many high-performance applications like military, gaming and medicine. In general, all immersive technologies increase the sense of realism, by fusing the virtual and real worlds. The same underlying technologies that enable AR and VR also power XR to deliver a wide range of innovative user experiences. According to Qualcomm, the future of XR is to develop sleek headsets to revolutionize user experiences on a daily basis and also in many different market segments, including education, retail, and healthcare. This study summarizes the different types of extended reality technologies, XR standardization work, XR and its multi-interactive technologies, and implementation of XR in real life applications.
Location based Augmented Reality – GeoAR

This study aims to research on immersive and interactive experience to users by using augmented reality and GPS technology. The system will be built using the Vuforia engine or ARcore support, and will be able to detect the user's location and device location using GPS. Once the system is initiated, it will check for GPS support in the user's device and ask for camera access. Then, using Vuforia's markerless tool and AR+GPS package, the system will detect the user's location and search for the rendered scene for the respective location. Each building will be labelled with AR objects for better understanding of the place and tracking where the user is. The AR objects will provide information about the building, such as its name, purpose, and history. This will enhance the user's experience and enable them to navigate the campus more easily. The application will create a scene that renders the real environment image and the rendered AR scene image, providing an interactive output. Users will be able to interact with the AR objects by clicking on them to access more information, such as photos and videos. Overall, the proposed system will be a useful tool for navigating the campus, providing users with a unique and immersive experience.
Smart Identification and Detection of Living and Non-Living things for Enhanced Security System using IoT

The increasing need for advanced security systems has led to the development of various intelligent identification and detection techniques. This research proposes a smart identification and detection system that distinguishes between living and non-living things for enhanced security. The proposed system uses deep learning techniques for feature extraction and classification of images. The security system trains a deep Convolutional Neural Network on the ImageNet dataset, which is a large collection of labeled images of various objects and living beings. The trained model is then used to identify and detect living and non-living things in real-world scenarios. The proposed system shows promising results in accurately distinguishing between living and non-living objects, which can be used to enhance security in various domains such as surveillance, border control, and access control. This system implements and simulates the proposed model using the TensorFlow framework, which is a widely used open-source library for building and training deep learning models. The model is trained on a large dataset of images, using various optimization techniques to improve the accuracy of the predictions. The trained model is then deployed in a real-world scenario to detect living and non-living objects with high accuracy. The proposed system can provide enhanced security in various domains where the detection of living and non-living objects is crucial. The system can be used to detect intruders, unauthorized access, and other security threats. Additionally, the proposed system can be integrated with other security systems to provide a comprehensive security solution. Overall, the proposed smart identification and detection system can significantly improve security systems in various domains, making them more efficient and reliable.
Interval Methods in Knowledge Representation

International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 31, No. 03, pp. 531-532 (2023) No AccessInterval Methods in Knowledge Representationhttps://doi.org/10.1142/S0218488523970036Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetails Recommended Vol. 31, No. 03 Metrics History PDF download
Can a Single Neuron Learn Predictive Uncertainty?

Uncertainty estimation methods using deep learning approaches strive against separating how uncertain the state of the world manifests to us via measurement (objective end) from the way this gets scrambled with the model specification and training procedure used to predict such state (subjective means) — e.g., number of neurons, depth, connections, priors (if the model is bayesian), weight initialization, etc. This poses the question of the extent to which one can eliminate the degrees of freedom associated with these specifications and still being able to capture the objective end. Here, a novel non-parametric quantile estimation method for continuous random variables is introduced, based on the simplest neural network architecture with one degree of freedom: a single neuron. Its advantage is first shown in synthetic experiments comparing with the quantile estimation achieved from ranking the order statistics (specifically for small sample size) and with quantile regression. In real-world applications, the method can be used to quantify predictive uncertainty under the split conformal prediction setting, whereby prediction intervals are estimated from the residuals of a pre-trained model on a held-out validation set and then used to quantify the uncertainty in future predictions — the single neuron used here as a structureless “thermometer” that measures how uncertain the pre-trained model is. Benchmarking regression and classification experiments demonstrate that the method is competitive in quality and coverage with state-of-the-art solutions, with the added benefit of being more computationally efficient.
Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial network (I-GAN) is proposed to overcome the class imbalance problem. Here, the proposed method is the consolidation of deep convolutional generative adversarial network (DCIGAN) and modified convolutional neural network, (MCNN), therefore it is known as DCIGAN-MCNN method. First, the DCIGAN is utilized for balancing the dataset by generating more samples in the training dataset. Then, this training dataset based the classification of Breast cancer is developed using the modified convolutional neural network. The proposed method is executed in MATLAB. The performance analysis are carried out in Breast Cancer Wisconsin (Prognostic) Data Set provides Maximum Geometry Mean (MGM) as 24.058%, 9.582%, Matthews’s correlation coefficient (MCC) as 78.623%, 30.357% higher than the existing methods, like CI-BC-RK-SVM, CI-BC-GA, CI-BC-DC-CNN, CI-BC-RF and CI-BC-BMIC-Net respectively. Finally, the simulation results prove that the proposed method can be able to find the optimal solutions efficiently and accurately.
Optimization design of remotely operated vehicle (ROV) for Madura Strait Area

Abstract Nowadays, the development of unmanned vehicles is significantly increased. Since the remotely operated vehicle (ROV) has more advantages than Diver. Then, ROV has an important role in underwater inspection and research in various sectors, such as offshore infrastructure development. ROV will give some critical information to the operator about an underwater inspection like bathymetry, pipeline inspection, and the other crucial subsea structure that the divers can’t reach. The optimization design has been carried out to determine ROV operational requirements in Madura Strait by Total-Based Method. ROV will be divided into 3 leading function groups, they are Features, Survive and Operate. Every group will be divided into Function Breakdown Structure (FBS). FBS will be divided again into Work Breakdown Structure (WBS). The optimum design will be determined by linking every breakdown function. The results are the ROV has 2MP 1080P resolution for camera, 2000 lumens for lighting, 50 m detection area for sonar and echosounder, 100 N grip force for Gripper, equipped with stainless steel frame, 2 hours long for operational duration, 5kg.f for thruster, operated with cable 300 m long and equipped with 6-unit thruster.
E-Business Trends and Challenges in the Modern Digital Enterprises in Asia

During the last two decades, there have been many shifts in how e-business is conducted. The term “e-business” in this era of industry 5.0 can apply to any commercial or business activity incorporating data exchange over the internet. In industry 5.0, with technological advancements, companies now have access to new clients and markets thanks to the proliferation of the internet. The IBM Internet marketing division coined the phrase “e-business” in 1996, and since then, this word has become more familiar in digitalized enterprises worldwide. Every business performs trade, or the purchasing and selling of products and services among different organizations, as one of its most basic tasks. The adoption and acceptance of digital technology in everyday life affect our societal values, society, and future business prospects, along with its use in industrial production. This chapter will highlight how e-business growth put Asian business as the industrial warehouse of the world and how it impacts social, cultural, and technological sustainability in both the short and long terms.
Post-pandemic Resilience of Hybrid Software Teams

Background. The COVID-19 pandemic triggered a widespread transition to hybrid work models (combinations of co-located and remote work) as software professionals’ demanded more flexibility and improved work-life balance. However, hybrid work models reduce the spontaneous, informal face-to-face interactions that promote group maturation, cohesion, and resilience. Little is known about how software companies can successfully transition to a hybrid workforce or the factors that influence the resilience of hybrid software development teams. Goal. The purpose of this study is to explore the relationship between hybrid work and team resilience in the context of software development. Method. Constructivist Grounded Theory was used, based on interviews of 26 software professionals. This sample included professionals of different genders, ethnicities, sexual orientations, and levels of experience. Interviewees came from eight different companies, 22 different projects, and four different countries. Consistent with grounded theory methodology, data collection, and analysis were conducted iteratively, in waves, using theoretical sampling, constant comparison, and initial, focused, and theoretical coding. Results. Software Team Resilience is the ability of a group of software professionals to continue working together effectively under adverse conditions. Resilience depends on the group’s maturity. The configuration of a hybrid team (who works where and when) can promote or hinder group maturity depending on the level of intra-group interaction it supports. Conclusion. This paper presents the first study on the resilience of hybrid software teams. Software teams need resilience to maintain their performance in the face of disruptions and crises. Software professionals strongly value hybrid work; therefore, team resilience is a key factor to be considered in the software industry.
On the perceived relevance of critical internal quality attributes when evolving software features

Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Indeed, internal attributes can become critical if their measurements assume anomalous values. Yet, current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. This qualitative study investigates the developers’ perception of the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industrial case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant when adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, the reasons behind the relevance of each critical attribute, and the interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.
Novice Programmers Strategies for Online Resource Use and Their Impact on Source Code

Websites are frequently used by programmers to support the development process. This paper investigates programmer-Web interactions when coding, and combines observations of behaviour with assessments of the resulting source code. We report on an online observational study with ten undergraduate student programmers as they engaged in programming tasks of varying complexity. Screens were recorded of participants’ activities, and each participated in an interview. Videos and interviews were thematically analysed. Novice programmers employed various strategies for seeking and utilising online knowledge. The resulting source code was examined to determine the extent to which it met requirements and whether it contained errors. The source code analysis revealed that coding with the websites involved more coding time and effort, but increased the possibility of producing correct code. However, coding with websites also introduced instances of either incorrect or non-executable source code.
Perceptions of Task Interdependence in Software Development: An Industrial Case Study

Context: Task interdependence is a work design factor that expresses the mutual dependency between tasks that compose a whole work. In software development, task interdependencies are created by the technical dependencies between the components of the software system and by how the development tasks are allocated to individuals in a teamwork context. Despite its importance for individual and team effectiveness, we still do not have studies about how software engineers perceive task interdependence in practice. Goal: To understand the perceptions of software engineers about the interdependence in their work and how these perceptions interact with other human and technical factors in the development process. Method: We performed an exploratory qualitative case study of a single software development team in a Brazilian software company that developed solutions for the financial market. We interviewed all 10 team members and used standard coding techniques from qualitative research to code, categorize, and synthesize data. Results: Individuals are consistent in their understanding of task interdependence and how it happens in practice. However, there are asymmetries between the individual perceptions in an interdependence relationship, which seem to exacerbate expressed feelings of anxiety and dissatisfaction. Conclusion: Our results suggest that the perception of task interdependence in software development is often not symmetrical with potential negative effects on emotional states that are related to motivation and satisfaction in the workplace.
The “applied poetics” of Instagram: the Greek Instapoetry landscape

This study maps the Greek Instapoetry landscape by exploring a) the central formats and themes through which Greek Instapoetry becomes communicated, the most common hashtag sequences used and the predominant types of elicited responses, as well as b) the basic perceptions and experiences of Instapoetry practitioners. Findings evidenced how multiple individual forms of labour are involved in practices of content production, rendering thereby Instapoetry as a system of “applied poetics,” structured around the application of distinctive and individualised types of technological affordances for textual composition. Traditional understandings of poetry were found to be strongly upheld amongst practitioners, while the value of print was shown to be preserved as holding an additional merit. Poetic hashtags were found to be more related with visibility, in order to generate algorithmic classifications and were used mostly with strategic intent. “Ideal” instapoems were construed as short and comprehensible texts that address issues of love or point towards inspirational messages, able to elicit intense emotions in readers. Under this light, there is evidence to suggest that Instapoetry could be approached as a digitally distributed cognition ecosystem, in which affect plays a central role in understanding the cognitive processes underpinning interactions with Instapoetry-related content.
Background

In this chapter, we introduce the background knowledge about fine-grained image analysis including both fine-grained recognition and retrieval. We first formulate the definition of fine-grained image analysis, then describe its major challenges, followed by the connection between fine-grained recognition with fine-grained retrieval, and finally discuss domain-specific applications related to fine-grained image analysis.
Fine-Grained Image Recognition

In this chapter, we systematically sort out deep learning based approaches of fine-grained image recognition with different learning paradigms, including recognition by localization-classification subnetworks, recognition by end-to-end feature encoding and recognition with external information.