Reviewerly/exHarmony huggingface dataset:
Effect of Covid-19 on the electronic payment system: usage level trust and competence perspectives

&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Covid-19 has dramatically spread globally and dramatically made several effects in almost all sectors. Electronic commerce and electronic payment systems are important sectors which affected directly by the Covid-19 pandemic. Online markets and electronic payment systems have been recognized as one of the fastest-growing technology in the last decade even within normal situations. However, several factors may influence such growth, which many consider as barriers or enablers of using the electronic payment system. This study aims to shed the light on the influence of Covid-19 on the electronic payment system from trust and competence perspectives. The study employed both qualitative and quantitative methodological approaches for data collection and analysis. The data was collected from 31 semi-structured interviews, 718 surveys, and annual reports. NVivo, Microsoft Visio, and Microsoft power business intelligence were used for the data analysis process. As a part of this study, a proposed framework has been developed which includes both technical and managerial parts.&lt;/span&gt;
Independent Validation of Generic Specimen Design for Inverse Identification of Plastic Anisotropy

Advanced inverse material identification procedures rely on the richness of strain fields generated in a complex specimen. Currently, the design of a complex specimen is mainly based on engineering judgement and experience that are often user-specific. This intuitive approach forms the crux of the problem, addressed in the current research. To this end, the paper embarks on devising a generic and automated method to design mechanical heterogeneous experiments. A notched tensile specimen is optimized to maximize a previously proposed heterogeneity indicator-IT. The effectiveness of this procedure for identifying the anisotropic parameters of the Hill48 yield criterion is validated using two independent methodologies, namely the identifiability method and the Finite Element Model Updating (FEMU) approach to assess the parameter identification quality. The latter approach is based on carefully generated synthetic experiments including the metrological aspects of Digital Image Correlation (DIC) while having access to the ground truth material behavior. For the plane stress Hill48 anisotropic yield criterion, it is shown that the IT-based design procedure correlates with both the identifiability method and the identification accuracy obtained through FEMU.
Entanglement and Non-Locality in Quantum Protocols with Identical Particles

We study the role of entanglement and non-locality in quantum protocols that make use of systems of identical particles. Unlike in the case of distinguishable particles, the notions of entanglement and non-locality for systems whose constituents cannot be distinguished and singly addressed are still debated. We clarify why the only approach that avoids incongruities and paradoxes is the one based on the second quantization formalism, whereby it is the entanglement of the modes that can be populated by the particles that really matters and not the particles themselves. Indeed, by means of a metrological and of a teleportation protocol, we show that inconsistencies arise in formulations that force entanglement and non-locality to be properties of the identical particles rather than of the modes they can occupy. The reason resides in the fact that orthogonal modes can always be addressed while identical particles cannot.
Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models

Given the potential misuse of recent advances in synthetic text generation by language models (LMs), it is important to have the capacity to attribute authorship of synthetic text.While stylometric organic (i.e., human written) authorship attribution has been quite successful, it is unclear whether similar approaches can be used to attribute a synthetic text to its source LM.We address this question with the key insight that synthetic texts carry subtle distinguishing marks inherited from their source LM and that these marks can be leveraged by machine learning (ML) algorithms for attribution.We propose and test several ML-based attribution methods.Our best attributor built using a fine-tuned version of XLNet (XLNet-FT) consistently achieves excellent accuracy scores (91% to near perfect 98%) in terms of attributing the parent pre-trained LM behind a synthetic text.Our experiments show promising results across a range of experiments where the synthetic text may be generated using pretrained LMs, fine-tuned LMs, or by varying text generation parameters.
The technology of formation of teamwork skills in terms of project-based learning students

Each era has its own requirements for the level of competence formation of employees. The fourth industrial revolution focuses on the development of human potential. One of the forms that allow us to reveal the potential capabilities of the individual is project activity.&#x0D; This paper presents the experience of developing team work skills among university students. The paper analyzes the possibility of using project activities for the development and improvement of team work skills in order to implement a modern competence-based approach in higher education. The paper describes the technology of forming team work skills. On the basis of the conducted research, promising opportunities for the implementation of teamwork in the educational environment were identified, as well as problematic aspects of this type of activity were identified.&#x0D; The purpose of this paper is to describe the experience of implementing the technology of forming students teamwork skills in the format of project training.&#x0D; Methodology. The study was conducted using the test method of V. Stefanson Q-sorting, a formative experiment in the format of students project activities and the authors questionnaire Students attitude to teamwork in the conditions of project activities, mathematical methods of analysis.&#x0D; Results. The technology of forming team work skills in the framework of project training of university students has been tested. As a result, it was revealed that project training has a complex impact on the formation of teamwork skills, this was noted by 83.5% of respondents.&#x0D; Conclusion. Project activity in the educational environment not only forms the skills of teamwork, but also is a tool for creating positive attitudes of students to team activities, which indicates the wide resource possibilities of this form of work.
Traversability mapping in off-road environment using semantic segmentation

Autonomous driving in off-road environments is challenging as it does not have a definite terrain structure. Assessment of terrain traversability is the main factor in deciding the autonomous driving capability of the ground vehicle. Traversability in off-road environments is defined as the drivable track on the trails by different vehicles used in autonomous driving. It is very crucial for the autonomous ground vehicle (AGV) to avoid obstacles such as trees, boulders etc. while traversing through the trails. The goal of this research has three main objectives: a) collection of 2D camera data in the off-road / unstructured environment, b) annotation of 2D camera data depending on the vehicles’ ability to drive through the trails , and c) application of semantic segmentation algorithm on the labeled dataset to predict the trajectory based on the type of ground vehicle. Our models and labeled datasets will be publicly available.
Does cloud computing improve team performance and employees' creativity?

Purpose The present investigation goals to empirically test the role of expert cloud on team performance and employee creativity. Here, the expert cloud comprises cloud application, cloud management, cloud infrastructure and cloud resources. The present study aims to identify important and key criteria and examine the relationships among them. In other words, the purpose of this study to find out the impact and relationship between cloud application, cloud management, cloud infrastructure and cloud resources and team performance and employee creativity. Design/methodology/approach Today, human societies’ rapid growth and the environmental changes that surround us every day are clearly visible. They highly affect our activities. In today’s highly complex organizations, people alone cannot handle all the issues that have arisen. As organizational managers are faced with diverse cultures in the governance of organizations, the need to use work teams with different abilities and specializations to achieve the goals of organizations leads managers to use teamwork and focus on employee creativity. On the other hand, the expert cloud makes it possible for human societies like universities, firms, industries, institutes, businesses and colleges to pool and share their human resources’ skills, knowledge and experiences to meet the competitive era’s demands. Therefore, the current investigation examines the impact of expert cloud on team performance and employee creativity. The research information is collected using an online questionnaire. The data collected is analyzed using AMOS and SMART PLS software. Findings All of the formulated hypotheses are supported. The results have shown that cloud application, cloud management, cloud infrastructure and cloud resources positively and significantly affect team performance and employee creativity. Practical implications Managers must be conscious of the vital role that the professional cloud plays in team performance and the innovation of workers. This paper would also make executives more conscious of the powerful tools in the field of cloud computing. Firms can use the outcomes of this paper investigation to improve team performance and employee creativity. Originality/value It is considered one of the initial efforts to demonstrate the impacts of expert cloud on team performance and employee creativity. This study’s value relies on that practitioners and academics may have supporting evidence on the role played by novel technology such as an expert cloud.
A Review of Fuzzy Logic and Artificial Neural Network Technologies Used for MPPT

Solar electric power generating stations play a major role in meeting the growing demand for electric power. These generating stations make use of solar photovoltaic (PV) panels to perform the conversion of solar energy to electric energy. However, the solar panel output is highly unpredictable because the output is a function of number of factors; some of which are not in the control of humans like the weather conditions, and the output is also a function of the age of PV panel, dust and other debris collected on the panel, direction and angle of elevation and so on. The solar panels exhibit a low efficiency. Currently, a lot of research is going on to overcome these issues. This paper represents a review of two modern techniques used in solar photovoltaic systems which enhance the extraction of maximum output power in an efficient manner. The Artificial Intelligence Based MPPT Techniques for PV Applications, and, a Forecasting System of Solar PV Power Generation using Wavelet Decomposition and Bias- compensated Random Forest are reviewed and compared in this paper.
Sentiment Analysis Using Deep Learning for Recommendation in E-Learning Domain

Sentiment analysis (SA) is one of the methods that can assist in extracting information from a large amount of data. It is considered one of the research fields in text mining, which has become vital to employ within recommendation systems, as well as in e-learning environments. In the current work, we present a new method of recommendation model utilizing sentiment analysis based on convolutional neural network (SABCNN) and natural language processing (NLP) techniques. Starting from collecting and analyzing the learners’ sentiments of reviews for the e-content with their corresponding rating within e-platforms, a sentence or a specific text is classified to multi-levels by determining what semantics of feelings it holds. Our research aims towards recommending learning resources that are relevant to the learners’ preferences with the aid of the previous reviews of other learners, sharing him/her the top preferences.
Exploring semantic differences between the Indonesian prefixes<i>PE-</i>and<i>PEN-</i>using a vector space model

Abstract Indonesian has two prefixes, PE- and PEN- , that are similar in form and meaning, but are probably not allomorphs. In this study, we applied a distributional vector space model to clarify whether these prefixes have discriminable semantics. Comparisons of pairs of words within and across morphologically defined sets of words revealed that cosine similarities of pairs consisting of a word with PE- and a word with PEN- were reduced compared to pairs of only PE- words, or of only PEN- words. Furthermore, nouns with PE- were more similar to their base words than was the case for words with PEN- . The specialized use of PE- for words denoting agents, and the specialized use of PEN- for denoting instruments, was also visible in the semantic vector space. These differences in the semantics of PE- and PEN- thus provide further quantitative support for the independent status of PE- as opposed to PEN- .
Protocols for estimating multiple functions with quantum sensor networks: Geometry and performance

We consider the problem of estimating multiple analytic functions of a set of local parameters via qubit sensors in a quantum sensor network. To address this problem, we highlight a generalization of the sensor symmetric performance bounds of Rubio et al., [J. Phys. A 53, 344001 (2020)] and develop an optimized sequential protocol for measuring such functions. We compare the performance of both approaches to one another and to local protocols that do not utilize quantum entanglement, emphasizing the geometric significance of the coefficient vectors of the measured functions in determining the best choice of measurement protocol. We show that, in many cases, especially for a large number of sensors, the optimized sequential protocol results in more accurate measurements than the other strategies. In addition, in contrast to the sensor symmetric approach, the sequential protocol is known to always be explicitly implementable. The sequential protocol is very general and has a wide range of metrological applications.
Linguistic analysis of suspected child sexual offenders’ interactions in a dark web image exchange chatroom

Child sexual offenders convene in dark web spaces to exchange indecent imagery, advice and support. In response, law enforcement agencies deploy undercover agents to pose as offenders online to gather intelligence on these offending communities. Currently, however, little is known about how offenders interact online, which raises significant questions around how undercover officers should ‘authentically’ portray the persona of a child sexual offender. This article presents the first linguistic description of authentic offender–offender interactions taking place on a dark web image exchange chatroom. Using move analysis, we analyse chatroom users’ rhetorical strategies. We then model the move sequences of different users and user types using Markov chains, to make comparisons between their linguistic behaviours. We find the predominant moves characterising this chatroom are Offering Indecent Images, Greetings, Image Appreciation, General Rapport and Image Discussion, and that rhetorical strategies differ between users of different levels of offending and dark web image-sharing experience.
Mobile Attendance based on Face Detection and Recognition using OpenVINO

The OpenVINO toolkit enables versatile computer vision with an Intel® Movidius <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> Neural Compute Stick 2 connected to a Raspberry Pi. This small portable platform provides new opportunities for innovative solutions in computer vision applications and beyond. This paper investigates its feasibility for mobile attendance systems for settings such as classrooms or other scenarios that require headcount or roll call. Related studies of face-based systems are explored, while the advantages of the proposed system are highlighted. Although there are some positioning constraints, the proof-of-concept system processes an approximate average of five faces per second. That means it can take attendance in a lecture room of 90 students in about 18 seconds. A recognition accuracy of 98.1% with an f-score of 96.9% was yielded on a private classroom dataset captured with a modest RPi camera. These promising results were achieved using a tiny ResNet-18 architecture, producing significantly better results than MobileNet. Furthermore, it outperformed the recognition accuracy of other `lightweight' methods used in the literature that do not run off embedded devices on publicly available datasets.
Prospect-theoretic Q-learning

We consider a prospect theoretic version of the classical Q-learning algorithm for discounted reward Markov decision processes, wherein the controller perceives a distorted and noisy future reward, modeled by a nonlinearity that accentuates gains and under-represents losses relative to a reference point. We analyze the asymptotic behavior of the scheme by analyzing its limiting differential equation and using the theory of monotone dynamical systems to infer its asymptotic behavior. Specifically, we show convergence to equilibria, and establish some qualitative facts about the equilibria themselves.
Processing presence: how users develop spatial presence through an immersive virtual reality game

A primary affordance of virtual reality (VR) headsets is to give the user spatial presence or the illusion of being in the virtual environment. Although considerable research connects VR to spatial presence, spatial awareness, and spatial ability, little is known about how users develop spatial presence in VR learning environments. This study addresses that gap by exploring spatial presence experience construction in a VR educational game and investigating whether users' knowledge, game experience, and VR experience impact the establishment of spatial presence. In this study, 56 high school students played an immersive 3D VR cell biology game where players search for clues within a virtual cell to diagnose the cell. Findings suggest that players' perceptions of spatial presence are linked to how they allocate their attention during the game, their level of interest in cellular biology, and their visual-spatial acuity, but are not linked to their game experience, VR experience, or prior knowledge of the content area. These results indicate that well-scaffolded, engaging virtual environments can foster spatial presence among users, regardless of prior knowledge or experience, and gives practitioners clues about how to design VR learning environments.
Online learning effects on students with learning disabilities: Parents’ perspectives

The study aimed to investigate the parent’s perspectives toward the effect of online learning on their child’s learning, particularly, the ones with learning disabilities. This descriptive study randomly selected one hundred and ninety-three parents with students with learning difficulties. All participants received an online designed form of survey to achieve study objectives. The results carryout that: the parents has medium perspectives toward the effects of online learning, while they have high perspectives toward factors related to their children . Furthermore, there is a statistically significant impact of online learning on the learning of students with learning difficulties and the effects of online learning on students learning disabilities differ based on their disability and are higher in case of multiple learning difficulties.&#x0D; &#x0D; Keywords: Online learning; Parents perspectives; Students; learning disabilities; Dyslexia; Dyscalculia; Dyspraxia.
A new standard error based artificial bee colony algorithm and its applications in feature selection

Feature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other well-known algorithms.
Extracting Structured Supervision From Captions for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) methods have received significant attention in recent years, since they can dramatically reduce the annotation costs of fully supervised alternatives. While most previous studies focused on leveraging classification labels, we explore instead the use of image captions, which can be obtained easily from the web and contain richer visual information. Existing methods for this task assigned text snippets to relevant semantic labels by simply matching class names, and then employed a model trained to localize arbitrary text in images to generate pseudo-ground truth segmentation masks. Instead, we propose a dedicated caption processing module to extract structured supervision from captions, consisting of improved relevant object labels, their visual attributes, and additional background categories, all of which are useful for improving segmentation quality. This module uses syntactic structures learned from text data, and semantic relations retrieved from a knowledge database, without requiring additional annotations on the specific image domain, and consequently can be extended immediately to new object categories. We then present a novel localization network, which is trained to localize only these structured labels. This strategy simplifies model design, while focusing training signals on relevant visual information. Finally, we describe a method for leveraging all types of localization maps to obtain high-quality segmentation masks, which are used to train a supervised model. On the challenging MS-COCO dataset, our method moves the state-of-the-art forward significantly for WSSS with image-level supervision by a margin of 7.6% absolute (26.7% relative) mean Intersection-over-Union, achieving 54.5% precision and 50.9% recall.