Reviewerly/exHarmony huggingface dataset:
Exploring the use of social virtual reality for virtual exchange

Abstract Mentored online intercultural interaction offers foreign language learners the opportunity to develop different competences, including intercultural, linguistic, and digital competence (O’Dowd, 2021). Such virtual exchange (VE) projects typically involve computer-mediated communication via, for example, Zoom. However, the use of high-immersion virtual reality (VR) for synchronous online collaboration in VE projects has received little attention. This study investigated the effect of VR on students’ levels of presence and engagement, on students’ communication and on students’ views on using VR for intercultural encounters compared to traditional videoconferencing tools. Twenty-seven university students from the Netherlands and Germany utilised VR to carry out intercultural learning tasks using English as a lingua franca during a four-week implementation period. Participants responded to pre- and post-intervention questionnaires, completed reflection journals, audio- or video-recorded their VR meetings and participated in interviews. Results showed that the levels of presence and engagement and preferences of social VR compared to videoconferencing for intercultural encounters depended on students individually. A VR immersion experience and comfortability scale was created based on the data which showed mixed experiences. VR influenced participants’ interactions, topics of conversation and communication strategies when they explored their spaces together. The results showed that students’ attitudes towards VR and their subjective experience of VR seem to play an important role in the VE-VR setting. VR provided a safe space for many participants. Positive attitudes towards communicating in the VR environment are highly correlated with positive attitudes towards meeting students from other countries in VR. Implications for language education are provided.
Contrastive Learning Models for Sentence Representations

Sentence representation learning is a crucial task in natural language processing, as the quality of learned representations directly influences downstream tasks, such as sentence classification and sentiment analysis. Transformer-based pretrained language models such as bidirectional encoder representations from transformers (BERT) have been extensively applied to various natural language processing tasks, and have exhibited moderately good performance. However, the anisotropy of the learned embedding space prevents BERT sentence embeddings from achieving good results in the semantic textual similarity tasks. It has been shown that contrastive learning can alleviate the anisotropy problem and significantly improve sentence representation performance. Therefore, there has been a surge in the development of models that utilize contrastive learning to fine-tune BERT-like pretrained language models to learn sentence representations. But no systematic review of contrastive learning models for sentence representations has been conducted. To fill this gap, this article summarizes and categorizes the contrastive learning based sentence representation models, common evaluation tasks for assessing the quality of learned representations, and future research directions. Furthermore, we select several representative models for exhaustive experiments to illustrate the quantitative improvement of various strategies on sentence representations.
Interpretability of Machine Learning: Recent Advances and Future Prospects

The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents such as text, image, audio and video, among others. Consequently, understanding and learning ML-based representations have taken center stage in knowledge discovery in intelligent multimedia research and applications. Nevertheless, the black-box nature of contemporary ML, especially in deep neural networks (DNNs), has posed a primary challenge for ML-based representation learning. To address this black-box problem, the studies on interpretability of ML have attracted tremendous interests in recent years. This paper presents a survey on recent advances and future prospects on interpretability of ML, with several application examples pertinent to multimedia computing, including text-image cross-modal representation learning, face recognition, and the recognition of objects. It is evidently shown that the study of interpretability of ML promises an important research direction, one which is worth further investment in.
ENGA: Elastic Net-Based Genetic Algorithm for human action recognition

Video surveillance and activity monitoring are the practical real-time applications of Human Action Recognition (HAR). A fusion of several Convolutional Neural Network (CNN) architectures has been widely used for effective HAR and achieved impressive results. Feature fusion of multiple pre-trained models also extracts redundant features due to the combinations of identical layers in all CNN architectures. In this study, network-level fusion is proposed, which reduces the possibility of having identical layers throughout the fusion process and helps extract unique features. Three pre-trained models, i.e., NASNetLarge, DenseNet201, and DarkNet53 are selected and analyzed to select the most efficient combinations of layers among these networks. Selected combinations of these networks are fused using five proposed strategies, i.e., sum, max, concatenation, convolutional and bilinear fusion. In the end, a proposed minimized CNN architecture is utilized to extract descriptors, which are optimized using the proposed Elastic Net-based Genetic Algorithm (ENGA) approach. A two-phase hybrid ENGA technique is suggested to pick features using both GA and EN. GA is used in the initial stage to reduce the dimensionality of retrieved features. To eliminate the unnecessary features, EN regularization is put into place in the second phase. The proposed ENGA model is evaluated on four publicly available datasets including UTKinect-Action, MSR-Action3D dataset, Florence3D-Action dataset, and Youtube-8 m, and achieved 99.63%, 99.69%. 98.63% and 91.46% accuracies, respectively.
Novel Time-Distance Parameters Based Hand Gesture Recognition System Using Multi-UWB Radars

Hand gesture recognition (HGR) has a vital role to develop an intelligent interface for human–computer interaction. This letter proposed an automated system for HGR using three ultrawideband radars. In this system, the received radar reflection is measured for each hand gesture action which is recorded with respect to time duration and distance of hand from radar during hand motion. A time-distance matrix is formed for each gesture action, and reflection density is obtained for time-distance analysis. Furthermore, mean density function-based parameters are suggested, namely, reflection density per unit time, reflection density per unit distance, and mixed moments of time and distance with higher order functions. Features are computed in entire time-distance region and further limited to local time-distance ranges to extract the gesture distinguishable information. Computed features are ranked and given to the classification model. The achieved high accuracy justifies the primacy of features and the developed system.
Information complementary attention-based multidimension feature learning for person re-identification

With the need for criminal investigation technology and the development of deep learning, the task of person re-identification has gradually become a research hotspot. Recently, various neural network-based person re-identification technologies designed by researchers have shown excellent results. However, most of the frameworks focus on complex structural design or redundant networks to guide model construction, which hugely increases the cost of train and application cost. In addition, the correlation between the channel information and spatial information on the pedestrian feature map is also relatively lacking. Therefore, we design a lightweight attention module to address the lack of correlation question response. The proposed module sequentially extracts person images’ channel and spatial features and effectively associates the two kinds of information through sequential connections. The proposed attention module has a simple structure, and the parameter increase in the backbone network is tiny. We place the fuse module in each feature extraction layer to focus on the pedestrian information extracted by each layer. To solve the problem of complex model structure, we choose the residual network as the backbone network and the attention mechanism to extract person features without using pose point estimation or additional network assistance to reduce model complexity. We adjust the drop rate of the person classification layer to improve the model’s generalization ability. We estimate the performance of our method on three public datasets: Market-1501, DukeMTMC-reID, and CUHK03 (both detected and labeled) demonstrate the proposed method’s effectiveness and obtain highly competitive performance on the three datasets.
Probabilistic-constrained control of interval type-2 T–S fuzzy systems under the multi-node round-robin scheduling protocol

This work concentrates on the control design of interval type-2 (IT2) T–S fuzzy systems under probabilistic saturation constraints. The actual control signals are allowed to exceed some preset thresholds with a certain frequency. Meanwhile, the sensors are governed by the multi-node round-robin scheduling protocol, which permits more than one sensors to transmit their information at every moment. The main objective is to synthesize a fuzzy controller such that the closed-loop system is locally stochastically stable under probabilistic saturated constraints and the multi-node round-robin scheduling protocol. To this end, the probabilistic saturation constraints are characterized by a Bernoulli-distributed stochastic process, and the received state at the controller side is formulated based on an updating rule and a compensation strategy. By constructing new membership functions, a token-dependent control law is subsequently designed. The stability analysis is facilitated by a modified sector condition dealing with the saturation nonlinearities. With suitable selection of initial states, sufficient conditions are derived to achieve the local stochastic stability of the closed-loop IT2 T–S fuzzy system. A larger domain of stochastic stability can be obtained via a searching algorithm. Finally, the proposed method is illustrated via a simulation example.
Efficient Random Forest Algorithm for Multi-objective Optimization in Software Defect Prediction

The Software Defect Prediction (SDP) process provides reliable software by identifying defect-prone modules before the testing stage. It efficiently and effectively utilizes quality assurance resources. Most predictive models are trained on historical data which belong to the same project or comparable project. These models show satisfactory performance as they utilize similar settings to the considered projects. But the limitation of these models is that they are effective only if there are adequate historical data to train a predictive model. In reality, however, such historical data are minimal for some projects and absent for new projects. The defect prediction in such projects which lack historical data can be accomplished by training prediction models on different project data. This process is known as Cross-Project Defect Prediction (CPDP). Software defect datasets also suffered from class imbalance issues which further degrades the model’s performance. In this research work, the authors have proposed a Multi-Objective Random Forest (MO-RF) algorithm with a data resampling technique to minimize the probability of false alarms, to maximize the probability of detection and to overcome the class imbalance problem. The study also evaluates the performance of other prediction models. The proposed method has shown percentage improvement (in terms of AUC) of 2.78 and 3.46 over MONB and MONBNN, respectively.
hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialized platforms and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA-sequencing techniques to predict the expression of 138 genes (incorporated from six commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E) stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n=335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06), p < 5 x 10-3), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index = 0.65, hazard ratio = 1.85 (95% CI 1.30-2.68), p < 5 x 10-3).
e₹—The digital currency in India: Challenges and prospects

The Reserve Bank of India (RBI) has recently launched the country's first pilot project for the digital currency known as the digital rupee or e-Rupee (e₹). The launch of the digital rupee represents a significant advancement in the "Digital India" revolution. It will be a fantastic opportunity for India since it might make conducting business easier while enhancing the security and resilience of the overall payments system. Digital currency attempts to rapidly progress monetary policy to disrupt physical money, lower the cost of financial transactions, and reshape how the money will circulate. Although the effects of digital currency cannot be foreseen, it is extremely important to thoroughly research digital currency and its effects on the operational stage. The development of a digital currency infrastructure has some challenges in terms of performance, scalability, and different usage scenarios. The article clarifies what e₹ is. How does it work? What makes it different from cryptocurrencies? What are the major challenges and prospects for it in India?
Genetic algorithm for Traveling Salesman Problem

Traveling Salesman Problem (TSP) is one of the most famous NP-hard problems which is hard to find an optimal solution. Many heuristic algorithms are applied to find a suboptimal solution in a limited time. In this paper, we employ a Genetic Algorithm (GA) to solve the TSP, and a further study is conducted by evaluating the performance of different crossover and mutation methods with a heuristic strategy. Four experiments with different parameters are designed, which apply instances from benchmark TSPLIB. Partial-mapped crossover and rotate mutation with offspring-parent competition strategy has shown efficient gets the best results.
Admission control policy and key agreement based on anonymous identity in cloud computing

Abstract Cloud computing has completely revolutionized the concept of computing by providing users with always-accessible resources. In terms of computational, storage, bandwidth, and transmission costs, cloud technology offers its users an entirely new set of advantages and cost savings. Cross-cloud data migration, required whenever a user switches providers, is one of the most common issues the users encounter. Due to smartphones’ limited local storage and computational power, it is often difficult for users to back up all data from the original cloud servers to their mobile phones to upload and download the data to the new cloud provider. Additionally, the user must remember numerous tokens and passwords for different applications. In many instances, the anonymity of users who access any or all services provided by this architecture must be ensured. Outsourcing IT resources carries risks, particularly regarding security and privacy, because cloud service providers manage and control all data and resources stored in the cloud. However, cloud users would prefer that cloud service providers not know the services they employ or the frequency of their use. Consequently, developing privacy protections takes a lot of work. We devised a system of binding agreements and anonymous identities to address this problem. Based on a binding contract and admission control policy (ACP), the proposed model facilitates cross-cloud data migration by fostering cloud provider trust. Finally, Multi-Agent Reinforcement Learning Algorithm (MARL) is applied to identify and classify anonymity in the cloud by conducting various pre-processing techniques, feature selection, and dimensionality reduction.
How does service robot anthropomorphism affect human co-workers?

Purpose Service robots are gradually becoming more anthropomorphic and intelligent. This research aims to investigate how anthropomorphic service robots with different levels of intelligence affect their human counterparts. Design/methodology/approach Two between-subject experimental studies were used to test whether different levels of service robot anthropomorphism with different levels of intelligence influence employees' morale and resistance to service robots. Findings Study 1 shows that the effect of service robot anthropomorphism (low vs. high) on employees' resistance and morale is mediated by perceived job-security threat. Study 2 validates this mediating effect and shows that it is moderated by the type of AI (mechanical vs. analytical). Specifically, when exposed to mechanical AI-powered service robots, employees exhibit a higher perceived job-security threat toward robots with a high (vs. low) degree of anthropomorphism. This moderating effect is not observed when employees are exposed to analytical AI-powered service robots. This moderated mediation effect is also found for the signing of a petition as the behavioral outcome. Practical implications Service firms considering the adoption of mechanical AI-powered service robots should choose a low (vs. high) anthropomorphic robot to reduce the sense of job-security threat felt by human employees, which subsequently increases their acceptance. However, if analytical AI-powered service robots with are to replace their human employees, the degree of anthropomorphism becomes irrelevant. Originality/value This is the first empirical study to explore how anthropomorphic service robots can influence human employees' evaluations and behaviors.
How blockchain technology hyperautomatically affects corporate performance by green supply chain?

ABSTRACTABSTRACTHyperautomation is a true digital transformation using advanced techniques and automates complicated business processes. Blockchain-based networks have hyper-automation turbo-boost power as they create tamper-proof, real-time data exchange networks. The study aims to examine the impact of blockchain on the performance of Chinese electronics manufacturers through the green supply chain and circular economy in the hyper-automation environment. Respondents (n = 376) in the present study were made up of employees from 300 China-based electronics companies. The results show that blockchain technology with hyper-automation can impact business performance through a circular economy and green supply chain. In addition, they have direct practical implications for Chinese electronics manufacturers moving towards a green supply chain by applying blockchain in the hyper-automation environment.KEYWORDS: Blockchain technologyhyperautomationgreen supply chaincorporate performancecircular economy Disclosure statementNo potential conflict of interest was reported by the authors.Ethics of Empirical ResearchNo ethics issues in this studyInformed consentAny participants (or their guardians if unable to give informed consent, or next of kin, if deceased) who may be identifiable through the manuscript (such as a case report), have been given an opportunity to review the final manuscript and have provided written consent to publish.Authorship contributionsFei Jiang: Conceptualization, Methodology, Validation, Investigation, Writing, Funding acquisition. Khong Kok Wei: Formal analysis, Software, Resources, Visualization, Funding acquisition. Sin-Pei Ng: Editing and Review, Data Curation, Formal analysis, Validation. Jie Liu: Writing, Funding acquisition. Muslim Amin : Review & Editing, Supervision. Halimin Herjanto: Review & Editing, Supervision.Additional informationFundingThis paper is the midterm research result of the 2022 Guangxi University Teachers Basic Research Ability Improvement Project' Construction of multimodal transport smart logistics system based on the new western land-sea corridor under the infuence of RCEP (code:2022KY1252); Special fund for science and technology innovation strategy of Guangdong Province in 2021 (special fund for climbing plan) (pdjh2021a0944).
Forecasting day-ahead 1-minute irradiance variability from numerical weather predictions

Accurate forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore reliable forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly, missing crucial information on meteorological variability such as clouds. In this work we present a methodology to forecast minute-to-minute irradiance variability in terms of its probability density function (PDF) based on hourly NWP results, by applying statistical postprocessing using machine learning. The algorithm is tested using the 2.5 × 2.5 km2 HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 meteorological stations throughout the Netherlands used as a ground truth. The applicability of the algorithm to 31 × 31 km2 global-scale models is investigated using ERA5 reanalysis data, which yields comparable accuracies. We find that almost half of the inaccuracy of the postprocessed result is due to errors in the radiation forecast of the NWP model used as input. Finally, the proposed post-processing algorithm is compared to the next generation weather models based on high resolution Large Eddy Simulation (LES), at 75 m horizontal grid spacing, on a case study spanning four days. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of cloud and irradiance dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.