Reviewerly/exHarmony huggingface dataset:
Transferrable Contrastive Learning for Visual Domain Adaptation

Self-supervised learning (SSL) has recently become the favorite among feature learning methodologies. It is therefore appealing for domain adaptation approaches to consider incorporating SSL. The intuition is to enforce instance-level feature consistency such that the predictor becomes somehow invariant across domains. However, most existing SSL methods in the regime of domain adaptation usually are treated as standalone auxiliary components, leaving the signatures of domain adaptation unattended. Actually, the optimal region where the domain gap vanishes and the instance level constraint that SSL peruses may not coincide at all. From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i.e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently. We find contrastive learning intrinsically a suitable candidate for domain adaptation, as its instance invariance assumption can be conveniently promoted to cross-domain class-level invariance favored by domain adaptation tasks. Based on particular memory bank constructions and pseudo label strategies, TCL then penalizes cross-domain intra-class domain discrepancy between source and target through a clean and novel contrastive loss. The free lunch is, thanks to the incorporation of contrastive learning, TCL relies on a moving-averaged key encoder that naturally achieves a temporally ensembled version of pseudo labels for target data, which avoids pseudo label error propagation at no extra cost. TCL therefore efficiently reduces cross-domain gaps. Through extensive experiments on benchmarks (Office-Home, VisDA-2017, Digits-five, PACS and DomainNet) for both single-source and multi-source domain adaptation tasks, TCL has demonstrated state-of-the-art performances.
A Feature Cum Intensity Based SSIM Optimised Hybrid Image Registration Technique

The hybrid image registration method is proposed to align two images based on the features or corresponding intensity information present in the images. The motivation behind the proposed work is that there is no one method or algorithm available that is suitable for any kind of images. SURF feature-based algorithm is used to extract, match, and describe the features present in the image. <tex>$1+1$</tex> evolutionary and regular step gradient descent algorithm is used for intensity information present in the images. The performance parameter to evaluate registration accuracy used in the proposed work are SSIM, MSE, PSNR, IQI, PCC and SSD which shows improvement in prior and post registration results.
A secure and lightweight anonymous mutual authentication scheme for wearable devices in Medical Internet of Things

Medical Internet of Things (MIoT) is an emerging area of IoT comprising of wearable sensor nodes having low-powered processors. Wearable nodes gather the physiological data of a patient and transmit this real-time information to the user over an insecure channel. There is a possibility that an attacker might gather the personal or medical information of a user that has been communicated over an insecure channel. Hence, security and privacy become a major concern in MIoT for a user in the last decade. To access the medical data in the resource-constraint environment, in the current manuscript, we propose a secure and lightweight anonymous mutual authentication scheme. Our scheme works utilizing only the basic symmetric cryptosystem techniques that include cryptographic hash functions in one direction and bitwise XOR operation. As a result, the proposed framework is lightweight and also efficient than the state-of-the-art. We have validated our proposed scheme based on BAN-Logic and shown the efficiency concerning functionality, storage requirements, computation complexity, and communication costs.
Intelligent and secure framework for critical infrastructure (CPS): Current trends, challenges, and future scope

Cyber–Physical Systems (CPS) are developed by the integration of computational algorithms and physical components and they exist as a result of technological advancement in embedded systems, distributed systems, and sophisticated networking technologies. Typically, these systems are intended to monitor and manipulate real-environment processes and objects. There had been advancements in CPS that affect various aspects of human lives and enable a wider range of applications and services. However, the interconnectivity between the cyber and physical world in CPS introduces new threats and security challenges and also increases the attack surface. The CPS is more prone to cyber-attacks than physical attacks because of the high accessibility of cyber components than physical ones. If we consider the last decade, it is no longer a myth to see industries relying on CPS being a victim of security attacks. In this paper, considering the applicability and dangers of CPS, we thus presented the key security aspects of CPS by reviewing published literature since 2018. We have examined the CPS requirements, challenges, security methods, security standards, threats, vulnerabilities, and past attacks. Moreover, we have considered the role of machine learning and deep learning in the enhancement of the security of CPS and reviewed the performance of existing works, and analyzed the challenges faced, and possible improvement techniques to enhance performance. We identified some key issues and challenges (such as CPS constraints, the performance of learning models, and security of learning models) in the adaption of learning-based methods for CPS security and accordingly proposed a security framework for CPS. Finally, several suggestions and recommendations are proposed considering the lessons learned from the comprehensive review.
Vertical federated learning-based feature selection with non-overlapping sample utilization

Vertical federated learning (VFL) is a privacy preserving collaborative machine learning technique designed for distributed learning scenarios in which data from different parties have overlap in the sample space. In this paper, a VFL method for feature selection, which is an effective dimensionality reduction technique that selects a subset of informative features from high-dimensional data by eliminating irrelevant and redundant features, is proposed. Because of the potential insufficiency of useful information for learning informative features and the difficulty in sharing raw data among parties due to the increasing awareness of data privacy protection, it is desirable to exploit information from multiple parties without raw data sharing. In this paper, we propose a VFL-based feature selection method that leverages deep learning models as well as complementary information from features in the same samples at multiple parties without data disclosure. In order to further improve feature selection performance, information of samples that do not have features appearing in all parties are also utilized. Promising results in extensive experiments show the effectiveness of the proposed approach in terms of collaborative feature selection without data sharing.
To Talk or to Work: Dynamic Batch Sizes Assisted Time Efficient Federated Learning Over Future Mobile Edge Devices

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the potential to foster numerous applications. However, it poses great challenges to train FL fast enough with limited communication and computing resources of mobile edge devices. Motivated by recent development in ultra fast wireless transmissions and promising advances in artificial intelligence (AI) computing hardware of mobile devices, in this paper, we propose a time efficient FL over future mobile edge devices, called dynamic batch sizes assisted federated learning (DBFL) with convergence guarantee. The DBFL allows batch sizes to increase dynamically during training, which can unleash the computing potential of GPU’s parallelism for on- device training and effectively leverage the fast wireless transmissions (WiFi-6, 5G, 6G, etc.) of mobile edge devices. Furthermore, based on the derived DBFL’s convergence bound, we develop a batch size control scheme to minimize the total time consumption of FL over mobile edge devices, which trade-offs the “talking”, i.e., communication time, and “working”, i.e., computing time, by adjusting the incremental factor appropriately. Extensive simulations are conducted to validate the effectiveness of our proposed DBFL algorithm and demonstrate that our scheme outperforms existing time efficient FL approaches in terms of the total time consumption in various settings.
Offensive Language Detection in Arabic Social Networks Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings

Social networks facilitate communication between people from all over the world.Unfortunately, the excessive use of social networks leads to the rise of antisocial behaviors such as the spread of online offensive language, cyberbullying (CB), and hate speech (HS).Therefore, abusive\offensive and hate detection become a crucial part of cyberharassment.Manual detection of cyberharassment is cumbersome, slow, and not even feasible in rapidly growing data.In this study, we addressed the challenges of automatic detection of the offensive tweets in the Arabic language.The main contribution of this study is to design and implement an intelligent prediction system encompassing a two-stage optimization approach to identify and classify the offensive from the non-offensive text.In the first stage, the proposed approach fine-tuned the pre-trained word embedding models by training them for several epochs on the training dataset.The embeddings of the vocabularies in the new dataset are trained and added to the old embeddings.While in the second stage, it employed a hybrid approach of two classifiers, namely XGBoost and SVM, and a genetic algorithm (GA) to mitigate the drawback of the classifiers in finding the optimal hyperparameter values to run the proposed approach.We tested the proposed approach on Arabic Cyberbullying Corpus (ArCybC), which contains tweets collected from four Twitter domains: gaming, sports, news, and celebrities.The ArCybC dataset has four categories: sexual, racial, intelligence, and appearance.The proposed approach produced superior results, in which the SVM algorithm with the Aravec SkipGram word embedding model achieved an accuracy rate of 88.2% and an F1-score rate of 87.8%.
On the use of evaluation measures for defect prediction studies

Software defect prediction research has adopted various evaluation measures to assess the performance of prediction models. In this paper, we further stress on the importance of the choice of appropriate measures in order to correctly assess strengths and weaknesses of a given defect prediction model, especially given that most of the defect prediction tasks suffer from data imbalance.
Is robot hotel a future trend? Exploring the incentives, barriers and customers' purchase intention for robot hotel stays

To explore whether robot hotels will be a future trend, the main purpose of this study is to increase understanding of the incentives and barriers that influence customers' purchase intention (PI) for robot hotel stays. This study also explores how customers' tech-savviness moderates the causal relationships between incentives, barriers, and PI. Hotel customers in Taiwan were surveyed with a 6-part questionnaire; 408 responses were collected using convenience sampling. The data is analyzed with structural equation modeling (SEM). The research results indicate that incentives and barriers impact respondents' PI for robot hotel stays, with tech-savviness serving as moderators. This indicates that particular types of individuals (e.g., individuals more comfortable with technology and/or change) are more susceptible to the idea of robot hotels and are more willing to pay to stay at robot hotels. Other individual types may require additional incentives before choosing to patronize robot hotels.
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning

Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud computing.Some prior works proposed coded computing strategies to jointly address all three challenges.They require either a large number of workers, a significant communication cost or a significant computational complexity to tolerate Byzantine workers.Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework.In this paper, we propose Adaptive Verifiable Coded Computing (AVCC) framework that decouples the Byzantine node detection challenge from the straggler tolerance.AVCC leverages coded computing just for handling stragglers and privacy, and then uses an orthogonal approach that leverages verifiable computing to mitigate Byzantine workers.Furthermore, AVCC dynamically adapts its coding scheme to trade-off straggler tolerance with Byzantine protection.We evaluate AVCC on a compute-intensive distributed logistic regression application.Our experiments show that AVCC achieves up to 4.2× speedup and up to 5.1% accuracy improvement over the state-of-the-art Lagrange coded computing approach (LCC).AVCC also speeds up the conventional uncoded implementation of distributed logistic regression by up to 7.6×, and improves the test accuracy by up to 12.1%.
Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation

We introduce similarity weighted aggregation, a principled and efficient method for regularized weight aggregation in federated learning. Our method is adapted to non-IID collaborators and is simultaneously cost-efficient. This is the first method to propose a sliding-window to select the collaborators, to the best of our knowledge. We demonstrate our method on the federate training task of the FeTS 2021 challenge. We proposed two variations coined Similarity Weighted Aggregation (SimAgg) and Regularized Aggregation (RegAgg). SimAgg results on internal validation data demonstrate that the proposed method outperforms the baseline FedAvg. The method SimAgg by our team HT-TUAS won 2nd position on both leaderboards in FeTS2021 challenge. SimAgg is the only method to be among the top performing methods on both the leaderboards, making it robust and reliable to data variations. Our solution is open sourced at: https://github.com/dskhanirfan/FeTS2021
Do an Organization’s Digital Transformation and Employees’ Digital Competence Catalyze the Use of Telepresence?

Rapid changes in the external environment are increasing interest in digital transformation. In particular, the recent breakout of infectious diseases such as COVID-19 has required certain companies to restrict physical exchanges among their members. As a result, companies must strive to maintain productivity and performance by supporting business exchange activities through online platforms. A metaverse technology that supports individuals’ exchange activities in a virtual space based on 3D technology has recently attracted attention from companies. However, as previous studies related to the metaverse have focused on strengthening personalized content and services, additional research is needed in order to understand the metaverse’s effects at the organizational level. This study aims to present the conditions for strengthening the motivation to use the metaverse from the perspective of organizations and members who have applied the metaverse in the workplace on a trial basis. In this study, an online questionnaire was conducted targeting workers of organizations with a history of using the metaverse for business, and the hypotheses were tested using 304 valid samples. As a result of the analysis, it was found that telepresence, a characteristic of the metaverse, influences the intention to continue using the metaverse through informativeness, interactivity, and enjoyment. In addition, the shared goal of the organization’s digital transformation moderated the influence relationship between telepresence and an individual’s motivations, and an individual’s digital competence moderated the influence relationship between motivation and the intention of continuous use. This study suggests a digital transformation strategy for an organization in terms of suggesting ways to strengthen the motivations for the use of the metaverse of employees in the organization.
Leveraging ShuffleNet transfer learning to enhance handwritten character recognition

Handwritten character recognition has continually been a fascinating field of study in pattern recognition due to its numerous real-life applications, such as the reading tools for blind people and the reading tools for handwritten bank cheques. Therefore, the proper and accurate conversion of handwriting into organized digital files that can be easily recognized and processed by computer algorithms is required for various applications and systems. This paper proposes an accurate and precise autonomous structure for handwriting recognition using a ShuffleNet convolutional neural network to produce a multi-class recognition for the offline handwritten characters and numbers. The developed system utilizes the transfer learning of the powerful ShuffleNet CNN to train, validate, recognize, and categorize the handwritten character/digit images dataset into 26 classes for the English characters and ten categories for the digit characters. The experimental outcomes exhibited that the proposed recognition system achieves extraordinary overall recognition accuracy peaking at 99.50% outperforming other contrasted character recognition systems reported in the state-of-art. Besides, a low computational cost has been observed for the proposed model recording an average of 2.7 (ms) for the single sample inferencing.
Survey on sign language recognition in context of vision-based and deep learning

Every day we see many people with disabilities like the deaf, the dumb and the blind, etc. Sign language is one of the communication tools for the hard-of-hearing people community and common people community. But, normal people find it hard to understand the sign language and gestures of the deaf and dumb. Many tools can be used to translate the sign language created by the disabled into a form that normal people can understand. The studies are based on various image acquisition, preprocessing, hand gesture segmentation, extraction of features, and classification methods. This paper aims to research and examine the methods employed within the SLR systems, and the classification methods used, and to propose the most promising technique for future research. Due to the latest advancement in classification methods, a few of the currently proposed works specifically contribute to classification methods, together with hybrid techniques and deep learning. This paper specializes in the classification strategies utilized in earlier Sign Language Recognition. Based on our review, HMM-based techniques were explored significantly in previous studies, which include modifications. Deep learning consisting of convolutional neural networks has become popular over the past five years.
Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator

Contact tracing applications are technological solutions that can quickly trace and notify users of their potential exposure to the Covid-19 virus and help contain the spread of the disease. However, extant research delineating the various factors predicting the adoption of contact tracing apps is scant. The study's primary objective is to develop and validate a research model based on the unified theory of acceptance and use of technology (UTAUT), health belief model (HBM), perceived privacy risk and perceived security risk to understand the adoption of contact tracing application.An online survey was carried out among users of the 'Aarogya Setu' contact tracing app in India. The partial least squares structural equation modelling (PLS-SEM) tool was employed to analyze data from 307 respondents.The results showed that performance expectancy, social influence, and facilitating conditions positively influenced users' intention to adopt the app. In contrast, perceived privacy and security risks were significant barriers to app adoption. Perceived disease threat as a moderator mitigated the adverse impact of perceived privacy risk on users' intention to adopt contact tracing apps.The current study gives insights on both drivers and barriers to the adoption of contract tracing applications. Various theoretical and practical implications of significance are provided for academicians and practitioners to effectively promote app adoption to tackle the Covid-19 pandemic.
The effect of online learning in modern history education

Interactive digital technologies have become relevant in modern education. The use of these technologies in the classroom contributes to the development of professional competencies and skills. The purpose of the study is to analyze the effectiveness of interactive learning environments and online platforms in learning history. The features of modern online learning platforms and the possibilities of their use in the study of history have been analyzed. The research was carried out at the Department of National History of Moscow City University (Moscow, the Russian Federation). The experiment involved 623 history students of different years of study of the following training profiles: "History" (124 students of 1-4 years of study) and "Pedagogical education" ("History and a foreign language", "History and social studies" - 399 students of 1-5 years of study). Also, the survey was addressed to 29 faculty members from the university under consideration. The results of the first survey show that the majority of students (62%) noted the use of traditional methods and 38% of respondents indicated the use of the modern ones. The majority of students (52%) also answered that digital tools are rarely used in the study of history while 71% of respondents reported that digital technology affects their motivation to learn. Therefore, the majority of students rated their level of motivation as average (43%) or low (47%). The most frequent professional competencies and skills are "technical competence (modern technologies)" (62%), "creativity" (65%), "critical thinking" (60%), "strategic thinking" (55%), and "practical skills" (78%). Based on the results obtained in the first survey, a program was developed to increase the motivation of students to study history and archeology, as well as to familiarize themselves with modern educational technologies that can be used in the learning process. The second survey showed an increase in the indicators of students' motivation to learn, which indicates the effectiveness of the developed program for the study of history. The majority of students (85%) answered that digital tools affect their motivation to learn. Thus, they rated it as medium (50%), high (20%) and low (30%). Accordingly, the indicators of professional competencies and skills increased by 10%: "technical competence (modern technologies)" (72%), "creativity" (75%), "critical thinking" (71%), "strategic thinking" (65%), and "practical skills" (88%). No less significant were the shifts in students' creative thinking development indicators - they changed from 18% (before the experiment) to 58% (after the experiment). The practical significance and prospects for further research are explained by the possibility of using the developed program for the study of history in other universities located in different countries. Also, the methodological basis of the proposed program can be used to study not only history but also other subjects as digital technologies are relevant in the modern education system.