Reviewerly/exHarmony huggingface dataset:
Digital Watermarking System for Copyright Protection and Authentication of Images Using Cryptographic Techniques

Digital images are transferred with ease through the network. Many users are using the images without the knowledge of the owners. Therefore, a novel watermarking scheme is proposed to ensure copyright protection and authentication of images using cryptography techniques. Here, a quick response (QR) image is generated for a watermark image that contains public and private keys prepared using a cryptosystem. Later, this QR image is scrambled using a chaotic logistic map. The public and private keys are used to cipher and decipher the data. Next, the scrambled QR watermark is embedded into a color image using a single-level discrete wavelet transform followed by singular value decomposition using the key value. Finally, the inverse process is applied to extract the watermark. The proposed method is validated using various image processing attacks. The results are then compared with state-of-the-art watermarking schemes. The experimental results show that the scheme provides good results in terms of robustness and imperceptibility.
Methodical aspects of preparation of educational content on the basis of distance education platforms

The urgency of application of technologies and means of distance learning in educational process of higher educational institutions is designated. The growing frequency of using cloud services and electronic textbooks in mobile and distance learning is noted. The importance of building educational environment is highlighted, where the key element is e-learning resources in digital form, including structure, subject content and metadata about the course. For higher educational institutions, the need for methodical support for the preparation of educational content on the basis of distance education platforms is determined. The experience of using the free distance education platform Moodle within the framework of the higher educational institution Donbass State Engineering Academy is considered. Methodical aspects of training content preparation on the basis of distance education platforms on the example of MoodleDDMA system are given. The General structure of the distance course and an example of evaluation of test tasks of the distance course (module) on topics are considered. An example of the presentation of the course on the basis of distance education platform MoodleDDMA is given. Conclusions about the experience of using the Moodle distance education system at the Donbass State Engineering Academy from the point of view of teachers and students are drawn. The perspective directions of researches and development of the Moodle distance education platform in completion and expansion of educational materials by multimedia elements and links, and also creation of the application for mobile devices for possibility of more effective use of the platform are allocated.
Software Architecture and Non-Fungible Tokens for Digital Twin Decentralized Applications in the Built Environment

Blockchain technology (BCT) can enable distributed collaboration, enhance data sharing, and automate back-end processes for digital twin (DT) decentralized applications (dApps) in the construction industry (CI) 4.0. The aim of this paper was to propose a software architecture and to develop a framework of smart contracts for blockchain-based digital twin (BCDT) dApps throughout the lifecycle of projects in CI 4.0. This paper leveraged the existing literature and action research interviews to identify and validate the critical industry problems, functional requirements (FRs), and non-functional requirements (NFRs) to be addressed by BCDT dApps in CI 4.0. Basic use cases were developed to design a framework of smart contracts for BCDT dApps throughout the lifecycle of projects. The analysis of an online survey was used to identify the key requirements and enablers to propose a software architecture for BCDT applications and to validate the requirements for developing the framework of a smart contract for BCDTs. The findings were: (1) The identification of key problems in CI 4.0 for each BIM/BCDT dimension (3D, 4D, 5D, 6D, 7D, 8D, and contractual (cD)) and the related FRs and NFRs for BCDT applications. Additionally, key use cases were designed to address the problems identified. (2) The proposed BCDT architecture permitted us to narrow gaps in the literature on blockchain-based decentralized digital twins. Moreover, the proposed BCDT architecture and smart-contract framework addressed the main requirements in the literature on BCDTs. (3) The study leveraged the non-fungible token (NFT) standard to develop a framework for smart contracts that addressed the key use cases and the related industry problems and functional requirements that were identified. The study also considered the contractual dimension (cD) as an overarching dimension in relation to the other BCDT dimensions. (4) We also compared the costs of several public blockchains for executing the proposed smart-contract framework throughout the lifecycle of a medium-sized building project. The cost analysis permitted the development of criteria to evaluate the suitability of blockchain networks for BCDT applications in CI 4.0 depending on the principal blockchain networks’ properties (security, decentralization, scalability, and interoperability). Finally, this study resulted in a novel framework that included software architecture, smart-contract use cases, and selection criteria among blockchain networks for BCDT dApps in CI 4.0.
Bibliometric Mapping of Metaverse in Education

This research aims to create a bibliometric map of studies on the use of metaverse in education. We used the bibliometric mapping technique from an international viewpoint to assess trends in the area of metaverse research on education by disclosing the author, publication, keyword, journal, country, and citation factors. Most research on the topic was published in Computers Education. Results indicate that Hwang G.J. is the most relevant author. National Taiwan Normal University and National Taiwan University of Science and Technology are the most relevant universities. USA has the most publications, co-authored publications, single-national publications, and cited articles overall. China is the nation with the greatest number of publications from other countries. Denmark is regarded as being at the top in the field of average citations per article. The terms "augmented reality", "virtual reality" and "second life" is sub-applications of the Metaverse environment, and are often used keywords. Over time, we can observe that prior research on “second life”, “virtual worlds” and “virtual environments” were gradually superseded by studies on “augmented reality”, “virtual reality”, “immersive virtual reality”, and “VR”. Over time, the phrase "metaverse" may become one of these tendencies. The papers in this research were subjected to cluster analysis. All clusters appear to involve “virtual reality”. Additionally, “augmented reality” was included in three clusters. Examining the measured network analysis of the authors reveals that they are clustered into five separate groups.
A deep learning-based diagnostic tool for identifying various diseases via facial images

With the current health crisis caused by the COVID-19 pandemic, patients have become more anxious about infection, so they prefer not to have direct contact with doctors or clinicians. Lately, medical scientists have confirmed that several diseases exhibit corresponding specific features on the face the face. Recent studies have indicated that computer-aided facial diagnosis can be a promising tool for the automatic diagnosis and screening of diseases from facial images. However, few of these studies used deep learning (DL) techniques. Most of them focused on detecting a single disease, using handcrafted feature extraction methods and conventional machine learning techniques based on individual classifiers trained on small and private datasets using images taken from a controlled environment. This study proposes a novel computer-aided facial diagnosis system called FaceDisNet that uses a new public dataset based on images taken from an unconstrained environment and could be employed for forthcoming comparisons. It detects single and multiple diseases. FaceDisNet is constructed by integrating several spatial deep features from convolutional neural networks of various architectures. It does not depend only on spatial features but also extracts spatial-spectral features. FaceDisNet searches for the fused spatial-spectral feature set that has the greatest impact on the classification. It employs two feature selection techniques to reduce the large dimension of features resulting from feature fusion. Finally, it builds an ensemble classifier based on stacking to perform classification. The performance of FaceDisNet verifies its ability to diagnose single and multiple diseases. FaceDisNet achieved a maximum accuracy of 98.57% and 98% after the ensemble classification and feature selection steps for binary and multiclass classification categories. These results prove that FaceDisNet is a reliable tool and could be employed to avoid the difficulties and complications of manual diagnosis. Also, it can help physicians achieve accurate diagnoses without the need for physical contact with the patients.
Cyber Security, Cyber Threats, Implications and Future Perspectives: A Review

There is a wealth of information security guidance available in academic and practitioner literature. Although other tactics such as deterrence, deception, detection, and reaction are possible, most of the research focuses on how to prevent security threats using technological countermeasures. The findings of a qualitative study conducted in Korea to determine how businesses use security techniques to protect their information systems are presented in this article. The results show a deeply ingrained preventative mindset, driven by a desire to ensure the availability of technology and services and a general lack of awareness of enterprise security concerns. While other tactics were evident, they were also preventative measures. The article lays out a research agenda for deploying multiple strategies across an enterprise, focusing on how to combine, balance, and optimize systems. This research looked at various topics, including information security and areas where security strategy is likely to be discussed, such as military sources. There are nine security strategies identified. A qualitative focus group approach is used to determine how these security strategies are used in organizations. In focus groups, security managers from eight organizations were asked to discuss their organizations’ security strategies. According to the findings, many organizations use a preventive approach to keep technology services available. Some of the other identified methods were used to support the prevention strategy on an operational level.
Transitions in Entanglement Complexity in Random Circuits

Entanglement is the defining characteristic of quantum mechanics. Bipartite entanglement is characterized by the von Neumann entropy. Entanglement is not just described by a number, however; it is also characterized by its level of complexity. The complexity of entanglement is at the root of the onset of quantum chaos, universal distribution of entanglement spectrum statistics, hardness of a disentangling algorithm and of the quantum machine learning of an unknown random circuit, and universal temporal entanglement fluctuations. In this paper, we numerically show how a crossover from a simple pattern of entanglement to a universal, complex pattern can be driven by doping a random Clifford circuit with $T$ gates. This work shows that quantum complexity and complex entanglement stem from the conjunction of entanglement and non-stabilizer resources, also known as magic.
Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF

In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.
Comparing virtual reality, desktop-based 3D, and 2D versions of a category learning experiment

Virtual reality (VR) has seen increasing application in cognitive psychology in recent years. There is some debate about the impact of VR on both learning outcomes and on patterns of information access behaviors. In this study we compare performance on a category learning task between three groups: one presented with three-dimensional (3D) stimuli while immersed in the HTC Vive VR system (n = 26), another presented with the same 3D stimuli while using a flat-screen desktop computer (n = 26), and a third presented with a two-dimensional projection of the stimuli on a desktop computer while their eye movements were tracked (n = 8). In the VR and 3D conditions, features of the object to be categorized had to be revealed by rotating the object. In the eye tracking control condition (2D), all object features were visible, and participants' gaze was tracked as they examined each feature. Over 240 trials we measured accuracy, reaction times, attentional optimization, time spent on feedback, fixation durations, and fixation counts for each participant as they learned to correctly categorize the stimuli. In the VR condition, participants had increased fixation counts compared to the 3D and 2D conditions. Reaction times for the 2D condition were significantly faster and fixation durations were lower compared to the VR and 3D conditions. We found no significant differences in learning accuracy between the VR, 3D, and 2D conditions. We discuss implications for both researchers interested in using VR to study cognition, and VR developers hoping to use non-VR research to guide their designs and applications.
The Role of Artificial Intelligence in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in early-stage of cancer, from whom all survived cancer. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually that leads to increase the risk of wrong decisions for cancer detection. Thus, utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and availability of datasets obtained from various imaging modalities have opened an opportunity to surpass limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.
Secure Anonymous Conferencing in Quantum Networks

Users of quantum networks can securely communicate via so-called (quantum) conference key agreement --making their identities publicly known. In certain circumstances, however, communicating users demand anonymity. Here, we introduce a security framework for anonymous conference key agreement with different levels of anonymity, which is inspired by the epsilon-security of quantum key distribution. We present efficient and noise-tolerant protocols exploiting multipartite Greenberger-Horne-Zeilinger (GHZ) states and prove their security in the finite-key regime. We analyze the performance of our protocols in noisy and lossy quantum networks and compare with protocols that only use bipartite entanglement to achieve the same functionalities. Our simulations show that GHZ-based protocols can outperform protocols based on bipartite entanglement and that the advantage increases for protocols with stronger anonymity requirements. Our results strongly advocate the use of multipartite entanglement for cryptographic tasks involving several users.
Contrastive Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships.
Approximating the quantum approximate optimization algorithm with digital-analog interactions

The quantum approximate optimization algorithm was proposed as a heuristic method for solving combinatorial optimization problems on near-term quantum computers and may be among the first algorithms to perform useful computations in the postsupremacy, noisy, intermediate-scale era of quantum computing. In this work we exploit the recently proposed digital-analog quantum computation paradigm, in which the versatility of programmable universal quantum computers and the error resilience of quantum simulators are combined to improve platforms for quantum computation. We show that the digital-analog paradigm is suited to the quantum approximate optimization algorithm due to the algorithm's variational resilience against the coherent errors introduced by the scheme. By performing large-scale simulations and providing analytical bounds for its performance in devices with finite single-qubit operation time we observe regimes of single-qubit operation speed in which the considered variational algorithm provides a significant improvement over nonvariational counterparts in the digital-analog scheme.
A Blockchain-Based Intrusion Detection System Using Viterbi Algorithm and Indirect Trust for IIoT Systems

The industrial internet of things (IIoT) is considered a new paradigm in the era of wireless communication for performing automatic communication in the network. However, automatic computation and data recognition may invite several security and privacy threats into the system during the sharing of information. There exist several intrusion detection systems (IDS) that have been proposed by several researchers. However, none of them is able to maintain accuracy while identifying the threats and give a high false-positive rate in the network. Further, the existing IDS are not able to recognize the new patterns or anomalies in the network. Therefore, it is necessary to propose a new IDS. The aim of this paper is to propose an IDS using the Viterbi algorithm, indirect trust, and blockchain mechanism for IIoT to ensure the required security levels. The Viterbi algorithm and indirect trust mechanism are used to measure the probability of malicious activities occurring in the network while generating, recording, and shipping products in an IIoT environment. Further, the transparency of the system is maintained by integrating blockchain mechanisms with Viterbi and indirect methods. The proposed framework is validated and analyzed against various security measures by comparing it with the existing approaches.
Quantum Error Mitigation via Matrix Product Operators

In the era of noisy intermediate-scale quantum (NISQ) devices, the number of controllable hardware qubits is insufficient to implement quantum error correction (QEC). As an alternative, quantum error mitigation (QEM) can suppress errors in measurement results via repeated experiments and postprocessing of data. Typical techniques for error mitigation, e.g., the quasi-probability decomposition method, ignore correlated errors between different gates. Here, we introduce a QEM method based on the matrix product operator (MPO) representation of a quantum circuit that can characterize the noise channel with polynomial complexity. Our technique is demonstrated on a $\rm{depth}=20$ fully parallel quantum circuit of up to $N_q=20$ qubits undergoing local and global noise. The circuit error is reduced by several times with only a small bond dimension $D^{\prime} = 1$ for the noise channel. The MPO representation increases the accuracy of modeling noise without consuming more experimental resources, which improves the QEM performance and broadens its scope of application. Our method is hopeful of being applied to circuits in higher dimensions with more qubits and deeper depth.
Teachers’ role in digitalizing education: an umbrella review

Abstract As teachers are central to digitalizing education, we summarize 40 years of research on their role in that process within a systematic umbrella review that includes 23 systematic reviews with a total of 1062 primary studies focusing technology integration and aspects of digital literacy. Our findings highlight the international acceptance of the TPACK framework as well as the need for a clear concept of digital literacy. It is unique that we identify and discuss parallels in developing teachers’ digital literacy and integrating digital technologies in the teaching profession as well as barriers to those goals. We conclude by suggesting future directions for research and describing the implications for schools, teacher education, and institutions providing professional development to in-service teachers.Kindly check and confirm whether the corresponding author is correctly identified.Olivia Wohlfart is correctly identified as corresponding author.