Reviewerly/exHarmony huggingface dataset:
Professionalitet — new essence and old meanings

Research is devoted to a new strategic initiative for the development of secondary vocational education "Professionalitet". This initiative is declared as a new level of education in colleges and contains approaches and principles for updating the content of vocational education, intensifying the development of professional educational organizations and reducing the duration of training. A retrospective analysis of the history of secondary vocational education revealed the predisposition of this level of education to rejection and change in the semantic connotations of the levels of vocational education in different historical periods. The process of development is often presented as a struggle of emerging new entities with old meanings, which inevitably continue to function in the minds of people. Historical retrospective knowledge gives us the opportunity to optimize our views on the inevitable new and the obsolete old. Presents the characteristics of the project "Professionalitet". The analysis of the socio-economic reasons for the creation of professionalism allows us to speak about its objective necessity, and the presented structure of the new level of education gives an answer to the question-due to what the training period will be reduced. Special attention is paid to the risks and problems that will have to be faced when implementing this project.
IBOOST: A Lightweight Provably Secure Identity-Based Online/Offline Signature Technique Based on FCM for Massive Devices in 5G Wireless Sensor Networks

The fifth-generation (5G) wireless network is commercialized.The need to integrate massive devices in 5G and wireless sensor networks (WSN) to provide several convenient services for network users becomes imperative.However, there are growing concerns that 5G-WSNs pose new security threats to sensitive user information.User authentication and key agreement have been provided for secure end-to-end communication.However, stricter security techniques are required as billions of massive devices are being networked to collect and process complex user data in real-time.Therefore, anonymous authentication and authorization are highly coveted for privacy preservation and prevention of unlawful exploitation of user data.However, guaranteeing data integrity, authentication, and non-repudiation require special-purpose identity-based signature techniques that are quite difficult to design in practice.In order to address this problem, this paper proposes a lightweight, provably secure identity-based online/offline signature technique (IBOOST) and its extension for massive devices in 5G-WSNs using fractional chaotic maps.The IBOOST scheme achieves multi-time use of offline storage at a lower processing time.Therefore, the signer can reuse the offline pre-stored information in a polynomial time.This makes our IBOOST superior to the existing online/offline signature techniques that allow only a single signature.Additionally, the new technique enables the pre-registration process with a secret key, and no secret key is required in the offline stage.Also, the proposed IBOOST proves to be secure in the random oracle unforgeability under the chosen message attack (UF-IBS-CMA).Finally, the IBOOST and its enhanced version (A-IBOOST) give the lowest computational costs compared to several contending techniques.Therefore, the proposed IBOOST shows superior security and performance with better computational overhead than the preliminary techniques.
Bundle Recommendation and Generation with Graph Neural Networks

Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Related work can be divided into two categories: 1) to recommend the platforms prebuilt bundles to users; 2) generate personalized bundles for users. These two problems are not well solved. In this work, we propose two graph neural network models, a BGCN model for prebuilt bundle recommendation, and a BGGN model for personalized bundle generation. First, BGCN unifies the user-item interaction, the user-bundle interaction and the bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item-level semantics. Second, BGGN re-constructs bundles into graphs based on the item co-occurrence pattern and the users supervision signal. The complex and high-order item-item relationships in the bundle graph are explicitly modeled through graph generation. Empirical results demonstrate the substantial performance gains of BGCN and BGGN. We have released the datasets and codes at this link: https://github.com/cjx0525/BGCN.
An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems

Heterogeneities in data representation and care processes create interoperability complexity among Electronic Health Record systems (EHRs). We can resolve such data and process level heterogeneities by following consistent healthcare standards like Clinical Document Architecture (CDA), OpenEHR, and HL7 FHIR. However, these standards also differ at the structural and implementation level, making interoperability more complex. Hence, there is a need to investigate mechanisms that can resolve data level heterogeneity to achieve semantic data interoperability between heterogeneous systems. As a solution to this, we offer an architecture that utilizes a resource server based on GraphQL and HL7 FHIR that establishes communication between two heterogeneous EHRs. This paper describes how the proposed architecture is implemented to achieve interoperability between two heterogeneous EHRs, HL7 FHIR and OpenMRS. The presented approach establishes secure communication between the EHRs and provides accurate mappings that enable timely health information exchange between EHRs.
Privacy-Preserving Asynchronous Grouped Federated Learning for IoT

Federated learning (FL), a cooperative distributed learning framework, has been employed in various intelligent Internet of Things (IoT) applications (e.g., smart health-care, smart home, and smart industry). However, there may be malicious devices in these IoT applications inferring other devices’ privacy or destroying the uploaded model parameters. Besides, due to the heterogeneity of IoT devices, it is difficult for the existing synchronized FL to effectively train models through non-identical independently distributed (non-IID) local data sets. To address these issues, we propose an asynchronous grouped federated learning framework (PAG-FL) for IoT, enabling multiple devices and the server to collaboratively and efficiently train models without revealing privacy. PAG-FL framework consists of an adaptive Rényi Differential Privacy-based privacy budget allocation (ARB) protocol and an asynchronous weight-based grouped update (AWGU) algorithm. In particular, our ARB protocol applies Rényi Differential Privacy and adaptively adjusts the privacy budget to obtain an efficient local model. The AWGU algorithm can defend against the poisoning attack on non-IID data set by weighing grouped local models to generate a global model. Meanwhile, it also realizes the asynchronous optimized update by adopting a lazy loading strategy. Theoretically, the proposed framework has a convergence guarantee and a privacy guarantee when training over the non-IID data set in an asynchronous FL. Our empirical experiments validate the effectiveness of the theoretical design and demonstrate the improved utility and robustness of PAG-FL in heterogeneous IoT.
Effects of Avatar Appearance and Locomotion on Co-Presence in Virtual Reality Collaborations

In this paper we share the results of a user study designed to investigate the impact of locomotion techniques and avatar appearances in multi-user virtual reality (VR) settings. In our study we compared continuous joystick-based and non-continuous teleportation-based locomotion with regards to the observer's perceived co-presence, cybersickness, fairness and enjoyment in a competitive game environment. Likewise, we compared the effects of humanoid and abstract avatar appearances.
Tips for Effective Implementation of Virtual Reality Exposure Therapy in Phobias—A Systematic Review

Background: The high incidence of phobias and the limited accessibility of psychotherapy are the reasons for the search for alternative treatments that increase the availability of effective treatment. The use of virtual reality (VR) technology is an option with the potential to overcome the barriers in obtaining an effective treatment. VR exposure therapy (VRET) is based on a very similar rationale for in vivo exposure therapy. The study aimed to answer the question of how to perform exposure therapy in a virtual reality environment so that it is effective. Methods: A systematic review of the literature, using PRISMA guidelines, was performed. After analysis of 362 records, 11 research papers on agoraphobia, 28 papers on social phobia and 10 about specific phobias were selected for this review. Results: VRET in agoraphobia and social phobia is effective when performed from 8 to 12 sessions, on average once a week for at least 15 min. In turn, the treatment of specific phobias is effective even in the form of one longer session, lasting 45–180 min. Head mounted displays are an effective technology for VRET. Increasing the frequency of sessions and adding drug therapy may shorten the overall treatment duration. The effectiveness of VRET in phobias is greater without concomitant psychiatric comorbidity and on the condition of inducing and maintaining in the patient an experience of immersion in the VR environment. Long-term studies show a sustained effect of VRET in the treatment of phobias. Conclusion: A large number of studies on in VR exposure therapy in phobias allows for the formulation of some recommendations on how to perform VRET, enabling the effective treatment. The review also indicates the directions of further VRET research in the treatment of phobias.
Real-Time Text Classification of User-Generated Content on Social Media: Systematic Review

The aim of this systematic review is to determine the current state of the art in the real-time classification of user-generated content from social media. Focus is on the identification of the main characteristics of data used for training and testing, the types of text processing and normalization that are required, the machine learning methods used most commonly, and how these methods compare to one another in terms of classification performance. Relevant studies were selected from subscription-based digital libraries, free-to-access bibliographies, and self-curated repositories and then screened for relevance with key information extracted and structured against the following facets: natural language processing (NLP) methods, data characteristics, classification methods, and evaluation results. A total of 25 studies published between 2014 and 2018 covering 15 types of classification algorithms were included in this review. Support vector machines (SVMs), Bayesian classifiers, and decision trees were the most commonly employed algorithms with recent emergence of neural network approaches. Domain-specific, application programming interface (API)-driven collection is the most prevalent origin of datasets. The reuse of previously published datasets as a means of benchmarking algorithms against other studies is also prevalent. In conclusion, there are consistent approaches taken when normalizing social media data for text mining and traditional text mining techniques are suited to the task of real-time analysis of social media.
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification

Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior that the lesions on one side of breasts rarely appear in the corresponding areas on the other side, we explore to answer a counterfactual question to identify the lesion areas. This counterfactual question means: given an image with lesions, how would the features have behaved if there were no lesions in the image? To answer this question, we derive a new theoretical result based on the symmetric prior. Specifically, by building a causal model that entails such a prior for bilateral images, we identify to optimize the distances in distribution between i) the counterfactual features and the target side's features in lesion-free areas; and ii) the counterfactual features and the reference side's features in lesion areas. To realize these optimizations for better benign/malignant classification, we propose a counterfactual generative network, which is mainly composed of Generator Adversarial Network and a prediction feedback mechanism, they are optimized jointly and prompt each other. Specifically, the former can further improve the classi?cation performance by generating counterfactual features to calculate lesion areas. On the other hand, the latter helps counterfactual generation by the supervision of classification loss. The utility of our method and the effectiveness of each module in our model can be verified by state-of-the-art performance on INBreast and an in-house dataset and ablation studies.
Unsupervised Continual Learning in Streaming Environments

A deep clustering network (DCN) is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This article presents an unsupervised approach of DCN construction on the fly via simultaneous deep learning and clustering termed autonomous DCN (ADCN). It combines the feature extraction layer and autonomous fully connected layer in which both network width and depth are self-evolved from data streams based on the bias-variance decomposition of reconstruction loss. The self-clustering mechanism is performed in the deep embedding space of every fully connected layer, while the final output is inferred via the summation of cluster prediction score. Furthermore, a latent-based regularization is incorporated to resolve the catastrophic forgetting issue. A rigorous numerical study has shown that ADCN produces better performance compared with its counterparts while offering fully autonomous construction of ADCN structure in streaming environments in the absence of any labeled samples for model updates. To support the reproducible research initiative, codes, supplementary material, and raw results of ADCN are made available in https://github.com/andriash001/AutonomousDCN.git.
TEST: Triplet Ensemble Student-Teacher Model for Unsupervised Person Re-Identification

The self-ensembling methods have achieved amazing performance for semi-supervised representation learning and domain adaptation. However, the disadvantage of these methods is that the teacher network is tightly coupled with the student network, which limits the descriptive ability of the self-ensembling model. To overcome the coupling effect between the teacher network and the student network, we propose a novel Triplet Ensemble Student-Teacher (TEST) model for unsupervised person re-identification, which consists of one teacher network T and two student networks S1 and S2. Similar to the traditional self-ensembling model, the student network S1 is applied to update the teacher network T. Furthermore, a closed-loop learning mechanism is built in the TEST model by imposing an ensemble consistent constraint between T and S2, and performing a heterogeneous co-teaching procedure between S1 and S2. With the closed-loop learning mechanism, the TEST model can loosen the constraint between the teacher T and the student S1, and enhance the descriptive ability of S1. Besides, the knowledge exchange between S1 and S2 can ensure that the two student networks can elegantly deal with the noisy labels and avoid coupling. By training the TEST model with the clustering-generated pseudo labels, we can achieve effective and robust representation learning for unsupervised person re-identification. The evaluations on three widely-used benchmarks show that our approach can achieve significant performance compared with state-of-the-art methods.
Improved Raft Consensus Algorithm in High Real-Time and Highly Adversarial Environment

High real-time and highly adversarial environment put forward higher requirements for the performance of blockchain consensus algorithm. To improve Raft’s consensus efficiency and safety, we propose an improved Raft algorithm called “hhRaft” to optimize Raft consensus process by introducing a new role of monitor. In the leader election phase, monitor nodes supervise the candidate nodes by identifying the malicious node’s forged Requestvote message. In the log replication phase, monitor nodes supervise the leader node by comparing the computing results of transactions. Through the performance test on the Consortium Blockchain -- Hyperledger Fabric, it is proved that hhRaft is superior to the original Raft algorithm in terms of transaction throughput, consensus latency, and anti-Byzantine Fault capabilities, making it suitable for use in high real-time and highly adversarial environment.
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images

Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large-scale variation, complex background interference, and nonuniform density distribution greatly limit the counting accuracy, particularly striking in remote-sensing imagery. To mitigate the above issues, this article proposes a novel framework for dense object counting in remote-sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM. Moreover, a reliable supervision manner improved from Bayesian and counting loss (BCL) is utilized to learn the density probability and then compute the count expectation at each annotation. It can relieve nonuniform density distribution to a certain extent. Extensive experiments on four remote-sensing counting datasets demonstrate the effectiveness of the proposed method and its superiority compared with state of the arts. Additionally, experiments extended on four commonly used crowd counting datasets further validate the generalization ability of the model. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gaoguangshuai/psgcnet</uri> .
The Utilization of Augmented Reality Technology for Sustainable Skill Development for People with Special Needs: A Systematic Literature Review

New technologies such as Augmented Reality can be used to enhance the possibility of obtaining new experiences to assist people with special needs. However, in the literature, there are not enough studies conducted on the use of Augmented Reality as an assistive technology, especially for people with special needs. The purpose of this study is to highlight the use of Augmented Reality technology on people with special needs for skill development. This systematic literature review includes recent and high-quality articles from chosen prestige databases between the years 2010 and 2020. The selected studies which fitted the eligibility selection criteria have been analyzed and synthesized. The study findings reveal the importance of using AR technology to assist individuals with special needs in their skill development process, to help them become more independent. We hope this study will enlighten researchers and the developers of AR tools. It has been recommended that more studies be done on the sustainable use of AR as an assistive technology, particularly for children with special needs, to make their life easier.
Predictors for distributed ledger technology adoption: integrating three traditional adoption theories for manufacturing and service operations

It is expected that blockchain technology will bring a disruptive paradigm shift in the manner in which transactions are conducted in the manufacturing and service enterprises. By eliminating the drawbacks of trust-related issues in a business chain, the distributed database of blockchain can bring transparency with pseudonymity and irreversibility of records. In this paper, we advance the limited literature on DLT and its adoption in the manufacturing and service enterprises. The proposed model is based on the integration of three traditional adoption theories namely Technology Acceptance Model (TAM), Technology Readiness Index (TRI) and Theory of Planned Behavior (TPB). Based on a survey of 211 experts of Pakistan, the proposed model was tested using structural equation modelling. The study result confirms that Theory of Planned Behavior and TAM play a key role in the disruptive technology implementation. It is one of the early studies on blockchain technology adoption in the manufacturing and service enterprises and the study results indicate that more manufacturing and service industries are transforming to intelligent operations. Smart manufacturing system through blockchain applications has become the focus of attention of businesses.
Underwater image recovery utilizing polarimetric imaging based on neural networks

Underwater imaging faces challenges due to complex optical properties in water. Our purpose is to explore the application of polarimetric imaging in image recovery under turbid water based on deep learning. A polarization camera is used to capture the polarization images of objects under water as datasets. The method used in our study aims to explore a structure and loss function that is suitable for the model. In terms of the model structure, four pairs of models consisting of polarized version and gray version based on the idea of dense U-Net and information flow were proposed. In the aspect of loss function, the method of combining weighted mean squared error with perceptual loss was proposed and a proper set of loss weights was selected through comparison experiments. Comparing the model outputs, it is found that adding polarized information along with the light intensity information to the model at the very front of the model structure brings about better recovering image. The model structure proposed can be used for image recovery in turbid water or other scattering environments. Since the polarization characteristics are considered, the recovered image has more detailed features than that where only intensity is considered. The results of comparison with other methods show the effectiveness of the proposed method.