Reviewerly/exHarmony huggingface dataset:
Quantifying the Effects of Working in VR for One Week

Virtual Reality (VR) provides new possibilities for modern knowledge work. However, the potential advantages of virtual work environments can only be used if it is feasible to work in them for an extended period of time. Until now, there are limited studies of long-term effects when working in VR. This paper addresses the need for understanding such long-term effects. Specifically, we report on a comparative study $i$, in which participants were working in VR for an entire week-for five days, eight hours each day-as well as in a baseline physical desktop environment. This study aims to quantify the effects of exchanging a desktop-based work environment with a VR-based environment. Hence, during this study, we do not present the participants with the best possible VR system but rather a setup delivering a comparable experience to working in the physical desktop environment. The study reveals that, as expected, VR results in significantly worse ratings across most measures. Among other results, we found concerning levels of simulator sickness, below average usability ratings and two participants dropped out on the first day using VR, due to migraine, nausea and anxiety. Nevertheless, there is some indication that participants gradually overcame negative first impressions and initial discomfort. Overall, this study helps lay the groundwork for subsequent research, by clearly highlighting current shortcomings and identifying opportunities for improving the experience of working in VR.
User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network

Recently, user cold-start recommendations have attracted a lot of attention from industry and academia. In user cold-start recommendation systems, the user attribute information is often used by existing approaches to learn user preferences due to the unavailability of user action data. However, most existing recommendation methods often ignore the sparsity of user attributes in cold-start recommendation systems. To tackle this limitation, this article proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to alleviate the sparsity of user attributes. Our model converts new users, items, and associated multimodal information into a Modality-aware Heterogeneous Graph (M-HG) that preserves the rich and heterogeneous relationship information among them. Specifically, to utilize rich and heterogeneous relational information in an M-HG for enriching the sparse attribute information of new users, we design a strategy based on random walk operations to collect associated neighbors of new users by multiple times sampling operation. Then, a well-designed multiple hierarchical attention aggregation model consisting of the intra- and inter-type attention aggregating module is proposed, focusing on useful connected neighbors and neglecting meaningless and noisy connected neighbors to generate high-quality representations for user cold-start recommendations. Experimental results on three real datasets demonstrate that the IHGNN outperforms the state-of-the-art baselines.
Blockchain and AI technology convergence: Applications in transportation systems

The blockchain and artificial intelligence (AI) have been the focal point of innovations and received increasing attention of the community over the last years. The blockchain technology is a distributed ledger of reliable digital records that are shared by the participating networks. AI, on the other hand, and its applications have been used for developing futuristic machines that are capable of having human-like intelligence. The use of both technologies in transportation systems has seen a tremendous growth that resulted in transformation of the transportation industry. Various modes of transportation, such as new transit systems, metro rails, high-speed rails, connected and automated vehicles, autonomous trains, and other emerging transportation modes (hyperloop, e-scooters, hoverboards), still face many challenges, including but not limited to economic issues, energy efficiency, social adoption, data privacy, lack of regulatory standards, reliability, security, and ethical issues. The use of blockchain and AI technologies has the potential to fill the technology gaps in transportation systems and effectively address the existing challenges. The convergence of both technologies is likely to yield significant advantages and provide a common distributed platform for data sharing, reliability, and decision-making. The present study is focused on understanding both technologies and their convergence in various industrial domains with a specific emphasis on transportation systems. A detailed state-of-the-art review of the relevant literature is performed to identify the main advantages and challenges in the applications of blockchain and AI in transportation systems and other related domains along with the ways to overcome these challenges. This study also reveals some critical future research needs for successful development and implementation of these technologies in transportation systems.
FinTech enablers, use cases, and role of future internet of things

In the current global trend, financial organizations strive extensively towards smartening finance to derive the benefits of digitalization. In such an effort, Financial Technology (Fintech) involves usage of several contemporary disruptive technologies such as AI, 5G/6G, Blockchain, Metaverse, IoT, etc., in financial industry to add value to the customer services. By the inception of technology, several major financial services and processes such as lending, verification, fraud detection, quality maintenance, credit scoring, and many more will be simplified and augmented. However, there is a need for research and innovation of disruptive financial products and the enabling ecosystem of technologies. Consequently, several tech-giants have focused their attention on Fintech to introduce Information and Communication Technology (ICT) solutions. In this manuscript, firstly, we envision the future trends and driving applications of Fintech that would emerge during 2030. Further, we attempt to provide a high-level framework of the enablers of Fintech including IoT, 5G, Digital twins and Metaverse for certain use cases. In addition, we provide directions for future Fintech research while anticipating the challenges ahead.
Contourlet and discrete cosine transform based quality guaranteed robust image watermarking method using artificial bee colony algorithm

Today, advances in photo editing and manipulation software and widespread access to social media have made it easier for unauthorized persons to manipulate and copy digital images. Therefore, studies on protecting the copyright of images have been increasing in recent years. This paper proposes a Contourlet, discrete cosine transform, and singular value decomposition based robust image watermarking method using Artificial Bee Colony (ABC) algorithm to obtain high robustness with a predetermined quality threshold. In the proposed method, the strength factor and embedding positions are optimally determined using the ABC algorithm for each image. The predetermined quality threshold defined in the optimization procedure guarantees a PSNR value higher than 40 dB to ensure high imperceptibility. The robustness of the proposed method was evaluated by applying a total of thirteen attacks from five different attack groups. Results confirmed that the presented method is robust to the compression, noise, enhancement, geometric, and filtering attacks.
A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology

Healthcare fraud is a global problem affecting both developing and developed countries. It is the deliberate attempt of the perpetrators to take undue advantage of the inefficiencies in current healthcare systems. Fraud tends to deny legitimate beneficiaries of universal health coverage, especially those under health insurance protection. In this work, we propose using machine learning techniques and blockchain technology to detect and prevent fraud in healthcare, especially in claims processing. A decision tree classification algorithm is adopted to classify the original claims dataset. The extracted knowledge is programmed in the Ethereum blockchain smart contract to detect and prevent healthcare fraud. The comparative experimental results show that the best performing tool achieves a classification accuracy of 97.96% and a sensitivity of 98.09%. This means that the proposed system enhances the blockchain smart contract’s ability to detect fraud with an accuracy of 97.96%.
Visually meaningful image encryption scheme based on new-designed chaotic map and random scrambling diffusion strategy

Since most of the existing image encryption algorithms hide the plaintext information by encrypting the image into a noise like image, the encrypted image is very different from the original image, and the visual security of the encrypted image is ignored. In view of this defect, a visually meaningful image encryption scheme combining new chaotic mapping and parallel compressed sensing is proposed, which makes the image content and vision get double protection. Firstly, a newly designed one-dimensional chaotic map derived from the fractional form of trigonometric function is used for constructing the controlled measurement matrix and disturb the strong correlation of images. Secondly, a bidirectional random scrambling algorithm based on chaotic magic transformation (CMT) is designed to disturb the wavelet packet coefficient matrix of plain image. Then, the scrambled secret image is encrypted and compressed with the help of chaotic measurement matrix and the newly designed random chaotic pixel diffusion strategy. Finally, the secret image is hidden in another large carrier image such as plain image through the wavelet packet transform embedding algorithm. As a result, an image is protected by both visual and information security. Simulation results and security analysis make clear that the proposed encryption scheme is feasible and effective under the premise of guaranteeing visual security and decryption quality.
Multi-modal Masked Autoencoders for Medical Vision-and-Language Pre-training

Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies have been dedicated to this field to facilitate medical vision-and-language understanding. In this paper, we propose a self-supervised learning paradigm with multi-modal masked autoencoders (M $$^3$$ AE), which learn cross-modal domain knowledge by reconstructing missing pixels and tokens from randomly masked images and texts. There are three key designs to make this simple approach work. First, considering the different information densities of vision and language, we adopt different masking ratios for the input image and text, where a considerably larger masking ratio is used for images. Second, we use visual and textual features from different layers to perform the reconstruction to deal with different levels of abstraction in visual and language. Third, we develop different designs for vision and language decoders (i.e., a Transformer for vision and a multi-layer perceptron for language). To perform a comprehensive evaluation and facilitate further research, we construct a medical vision-and-language benchmark including three tasks. Experimental results demonstrate the effectiveness of our approach, where state-of-the-art results are achieved on all downstream tasks. Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training. The source code is available at https://github.com/zhjohnchan/M3AE .
Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma

The next major evolutionary stage for voice assistants will be their capability to initiate interactions by themselves. However, to design proactive interactions, it is crucial to understand whether and when this behaviour is considered useful and how desirable it is perceived for different social contexts or ongoing activities. To investigate people’s perspectives on proactivity and appropriate circumstances for it, we designed a set of storyboards depicting a variety of proactive actions in everyday situations and social settings and presented them to 15 participants in interactive interviews. Our findings suggest that, although many participants see benefits in agent proactivity, such as for urgent or critical issues, there are concerns about interference with social activities in multi-party settings, potential loss of agency, and intrusiveness. We discuss our implications for designing voice assistants with desirable proactive features.
Action recognition based on RGB and skeleton data sets: A survey

Action recognition is a major branch of computer vision research. As a widely used technology, action recognition has been applied to human–computer interaction, intelligent pension, and intelligent transportation system. Because of the explosive growth of action recognition related methods, the performance of action recognition on many difficult data sets has improved significantly. In terms of the different data sets used for action recognition, action recognition can mainly be divided into RGB-based action recognition method and skeleton-based action recognition method. The former method can take advantage of the prior knowledge of image recognition. However, it has high requirements for computing power and storage ability, and it is difficult to avoid the influence of irrelevant background and illumination. In contrast, the latter method’s calculation amount and required storage space are reduced significantly. However, it lacks context information that is useful for action recognition. This review provides a comprehensive description of these two methods, covering the milestone algorithms, the state-of-the-art algorithms, the commonly used data sets, evaluation metrics, challenges, and promising future directions. So far as we know, this work is the first survey covering traditional methods of action recognition, RGB-based end-to-end action recognition method, pose estimation, and skeleton-based action recognition in one review. This survey aims to help scholars who study action recognition technology to systematically learn action recognition technology, select data sets, understand current challenges, and choose promising future research directions.
Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options

Digitalization is a megatrend that affects and transforms societal, economic, and environmental processes on a global scale. Driven by a combination of technological advances as well as shifting societal demands, digitalization also affects the operation and planning of the electricity sector. This paper uses megatrend analysis framework to analyze digitalization phenomena, its regional differences, technologies, use cases and challenges. It highlights potential system-level benefits (e.g., increased efficiency, transparency, consumer participation) and challenges (e.g., electricity demand growth, autonomy loss, increasing cyber risks) currently reported in the literature. Eventually, building on the thorough analysis, we present a menu of policy options to exploit its full potential of digitalized electricity systems while mitigating adverse effects on decarbonization goals and consumers.
An Efficient and Secure Big Data Storage in Cloud Environment by Using Triple Data Encryption Standard

In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method.
Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

Biometric security is a growing trend, as it supports the authentication of persons using confidential biometric data.Most of the transmitted data in multimedia systems are susceptible to attacks, which affect the security of these systems.Biometric systems provide sufficient protection and privacy for users.The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks.In addition, they have not been studied on different and large biometric datasets.Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems.This paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic cryptosystem.The rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates, and hence enhance the security and privacy of users.The suggested cryptosystem adds significant permutation and diffusion to the encrypted biometric templates.We introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against attacks.In addition, a Field Programmable Gate Array (FPGA) implementation of the proposed system is introduced.The obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes, such as Double Random Phase Encoding (DRPE) to reveal superiority, and hence high recognition performance of the proposed cancellable biometric recognition system.The obtained results prove that the proposed cryptosystem enhances the security and leads to better efficiency of the cancellable biometric recognition system in the presence of different types of attacks.
CRCCN-Net: Automated framework for classification of colorectal tissue using histopathological images

Colorectal cancer has a high mortality rate that continuously affects human life globally. Early detection of it extends human life and helps in preventing disease. Histopathological inspection is a frequently used approach to diagnose and detect colorectal cancer. Visual inspection of histopathological diagnosis requires more inspection time and the decision depends on the subjective perception of clinicians. This work proposed lightweight, less complex convolutional neural network-based architecture for automated classification of multi-class colorectal tissue histopathological images using two publicly available datasets, colorectal histology, and NCT-CRC-HE-100K, respectively. Histopathological images are provided as input to pre-trained models Xception, InceptionResNetV2, DenseNet121, VGG16, and the proposed network colorectal cancer classification convolutional neural network. This is the first study that compares the computational time of different deep learning architectures for the classification of colorectal tissue. The developed network requires less computational time for training compared to other pre-trained models. Accuracy, sensitivity, precision, false-positive rate, false-negative rate, specificity, F-1 score, and area under the curve have been used to evaluate the performance of the proposed architecture. The proposed network attained an accuracy of 93.50%, and 96.26% on the colorectal histology dataset, and NCT-CRC-HE-100K dataset, respectively. On the merged dataset, an accuracy of 99.21% is achieved by the newly developed network. The comparative analysis shows that the proposed framework outperformed existing state-of-the-art approaches. Clinicians may install the presented CRCCN-Net to confirm the diagnosis in the hospitals.
Set-CNN: A text convolutional neural network based on semantic extension for short text classification

A semantic extension-based classification algorithm for short texts, i.e., Set-CNN, is proposed in this paper. The proposed Set-CNN features three aspects. First, a semantic extension mechanism based on the fast clustering algorithm is applied to enrich the features of short texts. Second, a multiple-channel convolutional framework is proposed to capture semantic features at different levels. More specifically, both ordinary 1D convolution and atrous convolution are performed on the original texts to capture local and global semantic information. Ordinary 1D convolution convolves words one by one to capture original semantic information at the word level, which can be considered local semantic information. Atrous convolution convolves an entire short text to capture the context-level information of the original text, i.e., the global semantic information. This information can offset the noise incurred by semantic extension. The convolution channel equipped with an evolved GLU takes extended short texts as the object of convolution to capture semantic information at the extended context level. In addition, it functions to mitigate vanishing of the gradient. Third, we design a multiple-channel version of Text-CNN to generate different feature maps, which capture semantic features on different scales, and provide useful information to improve the classification performance of short texts. Finally, the performance of Set-CNN is assessed extensively over 4 datasets, namely, Subj, TREC, SST-2 and the Sogou corpus. The experimental results show that Set-CNN is more effective than state-of-the-art alternatives, including CNN-VE, multichannel CNN, BERT, etc. In particular, Set-CNN exhibits excellent performance as a lightweight text classifier, with lower computational complexity than BERTbase.