Reviewerly/exHarmony huggingface dataset:
Notice of Retraction: Designing XR into Higher Education using Immersive Learning Environments (ILEs) and Hybrid Education for Innovation in HEIs to attract UN’s Education for Sustainable Development (ESD) Initiative

Numerous studies have shown the efficacy of incorporating extended reality (XR) into higher education. In light of Covid-19 pandemic and the need to improve the virtual presence of remote students and workers, university authorities prioritize the integration of different XR alternatives into education. To choose the most appropriate virtual reality (VR) platforms and apps, instructional designers (IDs) must possess analytical, design, implementation, and evaluation skills. This article shows how unique identifiers may be used to assist in the creation of high-level resources. On the one hand, we offer recommendations for well-established design models, design approaches, and methodologies, and on the other hand, we suggest templates for research and funding leadership, as well as for evaluating XR for optimum usability in higher educational institution&#x2019;s applications.
Fusion of sequential visits and medical ontology for mortality prediction

The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.
Multitarget Domain Adaptation for Remote Sensing Classification Using Graph Neural Network

Remote sensing deals with huge variations in geography, acquisition season, and a plethora of sensors. Considering the difficulty of collecting labeled data uniformly representing all scenarios, data-hungry deep learning models are often trained with labeled data in a source domain that is limited in the above-mentioned aspects. Domain adaptation (DA) methods can adapt such model for applying on target domains with different distributions from the source domain. However, most remote sensing DA methods are designed for single-target, thus requiring a separate target classifier to be trained for each target domain. To mitigate this, we propose multitarget DA in which a single classifier is learned for multiple unlabeled target domains. To build a multitarget classifier, it may be beneficial to effectively aggregate features from the labeled source and different unlabeled target domains. Toward this, we exploit coteaching based on the graph neural network that is capable of leveraging unlabeled data. We use a sequential adaptation strategy that first adapts on the easier target domains assuming that the network finds it easier to adapt to the closest target domain. We validate the proposed method on two different datasets, representing geographical and seasonal variation. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.lrz.de/ai4eo/da-multitarget-gnn/</uri> .
Multi-Chatbot or Single-Chatbot? The Effects of M-Commerce Chatbot Interface on Source Credibility, Social Presence, Trust, and Purchase Intention

Most chatbot interfaces in contemporary m-commerce platforms feature a single chatbot that provides recommendations for all product categories. Nonetheless, there is an emerging research interest in multi-chatbot systems designating multiple chatbots as product/domain-specific advisers. Given the dearth of studies investigating the effects of multi-chatbot versus single-chatbot in the m-commerce context, we addressed this research gap by conducting an online between-subjects experiment to explore how the m-commerce chatbot interface types can differently influence source credibility, social presence, trusting beliefs, and purchase intention. Based on 154 valid responses, the single-chatbot interface led to higher social presence and trusting beliefs toward the m-commerce platform than the multi-chatbot interface. Males attributed the chatbot with higher competence and reported higher purchase intention through the m-commerce platform when engaging with the single-chatbot interface than the multi-chatbot interface. These findings suggest that designating chatbots as product-specific advisers in a multi-chatbot interface without labels to accentuate expertise could not evoke the users to categorize them as product specialists. Moreover, the multi-chatbot interface could have imposed user confusion and unfamiliarity cues, decreasing trust in the m-commerce platform. These findings’ theoretical, design, and managerial implications are discussed through the lens of the computers-are-social-actors paradigm, source credibility theory, source specialization, multiple source effect, and m-commerce behavioral research.
Cost Efficient Scheduling Using Smart Contract Cognizant Ethereum for IoMT

Recently internet of medical things (IoMT) act as a smart doctor using sensor wearable's device in human body.This smart doctor device senses necessary medical data from human and transfer via network immediately to physician.It is important to transfer sensitive data very securely.Blockchain becomes trending technology to provide high security to both end users in the network.Traditionally security structure is relying on cryptographic techniques which is very expensive and takes more time in securely transmitting data.To overcome this issue, this paper builds a cost effective, blockchain with IoMT using fog-cloud computing.The aim of research is to provide cost effective healthcare services in the present system.This study develops an IoMT systems using fusion of scheduling techniques in blockchain.We propose a Smart Contract with Function Based Cost Efficient Task Scheduling (FTS-SCON) algorithm in blockchain framework.The proposed algorithm uses functions to schedule the task in the blockchain which is cost effective.With the help of cryptography based Blockchain schemes with smart contracts enables consistency and validation of data with symmetric cryptography.Simulation results shows the proposed outperform all existing elements regarding data security, validation by 10%, and cost of application execution by 30% in IoMT.
Fast Expansion-Bins-Determination for Multiple Histograms Modification Based Reversible Data Hiding

Reversible data hiding (RDH) is a research hotspot nowadays. By RDH, after data extraction, the cover image can be restored without information loss. Among numerous existing RDH techniques, multiple histograms modification (MHM) is a general reversible embedding framework, and it is experimentally verified better than the traditional single histogram based methods. However, the expansion-bins-determination process for MHM is conducted through naive exhaustive search, which is time consuming. Based on this consideration, a fast expansion-bins-determination method for MHM is proposed in this paper. Specifically, to determine the optimal expansion bins, instead of solving the optimization problem of discrete variables, we consider a general form of this problem with differentiable objective function and real variables, so that advanced analysis tools such as Lagrange multiplier can be utilized. By the proposed approach, compared with the original MHM, the expansion bins can be determined quickly with only a tiny performance loss, and thus the practicality of MHM is improved.
Secure Computation for Threshold Functions with Physical Cards: Power of Private Permutations

Abstract Card-based cryptography is a variant of multi-party computation using physical cards like playing cards. There are two models on card-based cryptography, called public and private models. The public model assumes that all operations are executed publicly, while the private model allows the players private operations called private permutations (PP, for short). Much of the existing card-based protocols were developed under the public model. Under the public model, 2 n cards are necessary for every protocol with n -bit input since at least two cards are required to express a bit. In this paper, we propose n -bit input protocols with fewer than 2 n cards by utilizing PP, which shows the power of PP. In particular, we show that a protocol for ( n -bit input) threshold function can be realized with only $$n+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> cards by reducing the threshold function to the majority voting. Toward this end, we first offer that two-bit input protocols for logic gates can be realized with fewer than four cards. Furthermore, we construct a new protocol for three-input majority voting with only four cards by observing the relationship between AND/OR operations. This protocol can be easily extended to more participants, and to the protocol for threshold functions.
Bots in software engineering: a systematic mapping study

Bots have emerged from research prototypes to deployable systems due to the recent developments in machine learning, natural language processing and understanding techniques. In software engineering, bots range from simple automated scripts to decision-making autonomous systems. The spectrum of applications of bots in software engineering is so wide and diverse, that a comprehensive overview and categorization of such bots is needed. Existing works considered selective bots to be analyzed and failed to provide the overall picture. Hence it is significant to categorize bots in software engineering through analyzing why, what and how the bots are applied in software engineering. We approach the problem with a systematic mapping study based on the research articles published in this topic. This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community. This study aims to provide an introduction and a broad overview of bots used in software engineering. Discussions of the feedback and results from several studies provide interesting insights and prospective future directions.
Exploring factors and metrics to select open source software components for integration: An empirical study

Open Source Software (OSS) is nowadays used and integrated in most of the commercial products. However, the selection of OSS projects for integration is not a simple process, mainly due to a of lack of clear selection models and lack of information from the OSS portals. We investigate the factors and metrics that practitioners currently consider when selecting OSS. We also investigate the source of information and portals that can be used to assess the factors, as well as the possibility to automatically extract such information with APIs. We elicited the factors and the metrics adopted to assess and compare OSS performing a survey among 23 experienced developers who often integrate OSS in the software they develop. Moreover, we investigated the APIs of the portals adopted to assess OSS extracting information for the most starred 100K projects in GitHub. We identified a set consisting of 8 main factors and 74 sub-factors, together with 170 related metrics that companies can use to select OSS to be integrated in their software projects. Unexpectedly, only a small part of the factors can be evaluated automatically, and out of 170 metrics, only 40 are available, of which only 22 returned information for all the 100K projects. Therefore, we recommend project maintainers and project repositories to pay attention to provide information for the project they are hosting, so as to increase the likelihood of being adopted. OSS selection can be partially automated, by extracting the information needed for the selection from portal APIs. OSS producers can benefit from our results by checking if they are providing all the information commonly required by potential adopters. Developers can benefit from our results, using the list of factors we selected as a checklist during the selection of OSS, or using the APIs we developed to automatically extract the data from OSS projects.
User behavior-based and graph-based hybrid approach for detection of Sybil Attack in online social networks

The immense popularity of social networks has rendered them vulnerable to security threats such as Sybil attacks. In Sybil attacks, attackers send several friend requests from fake identities, and some of the genuine users might accept them unknowingly and become victims of Sybil attacks. Once genuine users accept friend requests from Sybil identities, attackers can spam, phish, and conduct other harmful actions in the target networks. Though genuine users have links to Sybil users unknowingly, they do less interaction and have low strength of relationship with them. Thus, the relationship strength measure can be useful to detect a Sybil attack. Considering the relationship strength measure, we propose a behavior-based and graph-based hybrid approach to detect a Sybil attack in Online Social Networks (OSNs). We identify the behavior features, use them to measure the relationship strength, and identify attack edges. We use the graph-based feature(betweenness-centrality) to leverage attack-edge identification and detect Sybil nodes. We have evaluated our scheme using real-world datasets, and experimental results validate the proposed scheme’s effectiveness.
Tailoring the Scrum framework for software development: Literature mapping and feature-based support

Literature faces the lack of studies relating which characteristics of the Scrum framework are adapted. Understanding such variations is useful for prospective software development projects and guiding teams at conducting Scrum customizations. We aimed at identifying how the Scrum framework has been adapted to the context of Agile software development projects and how adaptations might be represented to aid researchers and practitioner at analyzing Scrum processes deployed or to be deployed. We carried out a systematic mapping study in five electronic sources, 11 journals and 15 conferences/workshops. We submitted the 281 returned studies to various filters, which resulted in 50 studies with data extracted, analyzed, and quality evaluated. SMS provides a panorama on the Scrum characteristics adapted to roles, events, and artifacts. We decided to adopt feature models for hierarchically accommodating found Scrum adaptations as it supports adaptations in the form of variability. We evaluated the resulting feature model with practitioners from different companies in the perspective of Perceived Usefulness and Perceived Ease of Use considering the Technology Acceptance Model (TAM). Therefore, we demonstrated the produced feature model aids users to better visualize and understand the documented Scrum adaptations. The panorama on Scrum adaptations and the problems during Scrum adoption are discussed to providing a means to practically understand and tailor (configure) such adaptations. Such adaptations are an essential source of information on the variety of Scrum elements, thus researchers and practitioners may take the results of this work as a guide to understand how different adaptations occur in different contexts during software development. In addition, the conceived feature model is an important asset to guide such users at selecting Scrum characteristics and respective adaptations to perform. The feature model also promotes reuse of knowledge gathered up from several different information sources.
HAREDNet: A deep learning based architecture for autonomous video surveillance by recognizing human actions

Human Action Recognition (HAR) is still considered as a significant research area due to its emerging real-time applications like video surveillance, automated surveillance, real-time tracking and resecue missions. HAR domain still have gaps to cover, i.e., random changes in human variations, clothes, illumination, and backgrounds. Different camera settings, viewpoints and inter-class similarities have increased the complexity of this domain. The above-mentioned challenges in uncontrolled environment have ultimately reduced the performances of many well-designed models. The primary objective of this research is to propose and design an automated recognition system by overcoming these afore-mentioned issues. Redundant features and excessive computational time for the training and prediction process has also been a noteworthy problem. In this article, a hybrid recognition technique called HAREDNet is proposed, which has a) Encoder-Decoder Network (EDNet) to extract deep features; b) improved Scale-Invariant Feature Transform (iSIFT), improved Gabor (iGabor) and Local Maximal Occurrence (LOMO) techniques to extract local features; c) Cross-view Quadratic Discriminant Analysis (CvQDA) algorithm to reduce the feature redundancy; and d) weighted fusion strategy to merge properties of different essential features. The proposed technique is evaluated on three (3) publicly available datasets, including NTU RGB+D, HMDB51, and UCF-101, and achieved average recognition accuracy of 97.45%, 80.58%, and 97.48%, respectively, which is better than previously proposed methods.
Bag dissimilarity regularized multi-instance learning

Multi-instance learning (MIL) is able to cope with the weakly supervised problems where the training data is represented by labeled bags consisting of multiple unlabeled instances. Due to its practical significance, MIL has recently drawn increasing attention. Introducing bag representations is an attractive way to learn MIL data. However, it is difficult for the existing MIL methods to utilize both implicit and explicit bag representations simultaneously. In this paper, we propose a bag dissimilarity regularized (BDR) framework that incorporates multiple bag representations regardless of explicitness or implicitness. Here, the implicit bag representations are incorporated into a regularization term that contains the intrinsic geometric information provided by the bag dissimilarities. The regularization term can be added to the objective function of supervised classifiers. An effective method for explicit bag embedding is also proposed, which exploits the Fisher score derived from factor analysis. Finally, we propose two specific BDR methods based on support vector machine and broad learning system. The proposed BDR methods are evaluated on 14 datasets, and have achieved competitive results with limited computation consumption. We also discuss the effectiveness and the characteristics of BDR framework.
An improved temporal phase unwrapping based on super-grayscale multi-frequency grating projection

Multi-frequency temporal phase unwrapping (TPU) is an essential algorithm in phase-shifting profilometry, especially when measuring surface discontinuities and isolated objects. The unit-frequency phase with primary contour information guides the high-frequency wrapped phase with measured objects' detail information unwrapping. Ordinary, the projected digital grating is of 256-grayscale at most for the 256-grayscale standard of the projector. The digital error will inevitably be introduced into the projection grating, leading to the phase error in each wrapped phase calculation. Using unit-frequency phase with error will directly affect the low-frequency and high-frequency wrapped phases unwrapping, thus limiting the phase unwrapping accuracy to some extent. In this work, by skillfully using the time-division multiplexing characteristic of projector and the integral characteristic of CCD camera, a high accuracy temporal phase unwrapping method based on super-grayscale multi-frequency grating projection is proposed. The expected super-grayscale rather than traditional 256-grayscale multi-frequency gratings are purposefully designed to reduce the digital error initially. The captured realistic super-grayscale patterns can calculate the high-resolution phase information, so that the reliable unit-frequency phase can guide the low-frequency and high-frequency fringe order calculation more precisely, and improve the measurement accuracy initially. The experimental results verify the validity and practicability of the proposed method.
Cross attention redistribution with contrastive learning for few shot object detection

Few-shot object detection aims to learn to detect novel objects from only a few annotated samples. Most training frameworks adopt the fusing of high-dimensional features with semantic information on the support images to learn the recognition and localization process of novel objects on the query images. Most prior works directly use a cross-correlation mechanism to integrate semantic information from support features. However, such operations will introduce noise to the query features, confusing the generation of region proposals and affecting the final localization precision. In this paper, we focus on sufficient mining and integrating the support features conducive to generating regional proposals to improve further the stability and accuracy of the few-shot object detector. We propose a cross-attention redistribution (CAReD) module to adaptively integrate support features into query features, effectively removing harmful support features and enhancing the regional features of novel categories. In addition, to classify the novel instances accurately, it is necessary to minimize the intra-class distance while maximizing the inter-class distance. To this end, this paper proposes a network training strategy based on contrastive learning, which can better supervise the training process of CAReD and, more importantly, can effectively improve the classification precision for bounding boxes. Experiments on Pascal VOC and MS-COCO datasets show that CAReD significantly improves upon two baseline detectors (+ 3.6% on Pascal VOC benchmark and + 4.4% on MS-COCO benchmark), achieving state-of-the-art results under few-shot detection settings.