Reviewerly/exHarmony huggingface dataset:
MID-Crypt: A Cryptographic Algorithm for Advanced Medical Images Protection

Privacy-preserving of medical information (such as medical records and images) is an essential right for patients to ensure security against undesired access parties. This right is typically protected by law through firm regulations set by healthcare authorities. However, sensitive-private data usually requires the application of further security and privacy mechanisms such as encipherment (encryption) techniques. ’Medical images’ is one such example of highly demanding security and privacy standards. This is due to the quality and nature of the information carried among these images, which are usually sensitive-private information with few features and tonal variety. Hence, several state-of-the-art encryption mechanisms for medical images have been proposed and developed; however, only a few were efficient and promising. This paper presents a hybrid crypto-algorithm, MID-Crypt, to secure the medical image communicated between medical laboratories and doctors’ accounts. MID-Crypt is designed to efficiently hide medical image features and provide high-security standards. Specifically, MID-Crypt uses a mix of Elliptic-curve Diffie–Hellman (ECDH) for image masking and Advanced Encryption Standard (AES) with updatable keys for image encryption. Besides, a key management module is used to organize the public and private keys, the patient’s digital signature provides authenticity, and integrity is guaranteed by using the Merkle tree. Also, we evaluated our proposed algorithm in terms of several performance indicators including, peak signal-to-noise ratio (PSNR) analysis, correlation analysis, entropy analysis, histogram analysis, and timing analysis. Consequently, our empirical results revealed the superiority of MID-Crypt scoring the best performance values for PSNR, correlation, entropy, and encryption overhead. Finally, we compared the security measures for the MID-Crypt algorithm with other studies, the comparison revealed the distinguishable security against several common attacks such as side-channel attacks (SCA), differential attacks, man-in-the-middle attacks (MITM), and algebraic attacks.
Visualization of urban development 3D layout plans with augmented reality

In recent times, requirements in urban planning and construction of buildings have dramatically increased with the drastic growth of population in urban areas. Well-designed urban planning helps in the provision of excellent facilities to the public. Furthermore, it transforms a city into a smart city. Urban planning simplifies the provision of resources, i.e. transportation, communication, water provision, garbage disposal system, etc. The existing construction planning visualizations of 2D CAD and 3D BIM renderings work entirely in virtual digital content, which reduces the graphical object's real-time exposure to the construction process. This work explores some software-driven approaches to visualize 3D layout plans for urban development using Augmented Reality (AR). AR environment gives full-scale walkthroughs of designs to workers and designers. The article analyzes two urban planning scenarios (a) creation of new structures, (b) recreation of existing designs. The creation of new structures is done using Building Information Modeling (BIM) with AR to make 3D layout blueprints come alive. Recreation of existing designs develops BIM after merging two environments together, i.e. already existing structures and designs of new structures. Experimental results consisting of photorealistic AR digital visualizations of indoor as well as outdoor scenes of 3D layouts are used to assess the validity of our approach. The article also reports the historical growth of Augmented Reality, and explores the possible ways to architect and design urban planning 3D layout in the AR environment. Furthermore, it discusses certain possible applications of AR in urban planning.
FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things

The wide proliferation of the Internet of Things (IoT) and the unimaginable rapid advance of artificial intelligence (AI) jointly facilitate the Internet of Artificially Intelligent Things (A-IoT). Artificially Intelligent things (AI-T) run machine learning (ML) models locally while interacting and exchanging data with other AI-Ts through A-IoT. However, sensitive data may be abused during transmission by malicious or compromised AI-Ts. Federated learning is thereby proposed to achieve secure communication, where AI-Ts maintain the same ML model by exchanging model parameters instead of raw data. However, there are three significant issues for FL being applied in A-IoT. First, the context of model parameters of each AI-T may leak privacy, resulting from inference attacks. Second, falsified-data-based poisoning attacks may lead to a failure of ML model convergence. Third, exchanging model parameters cost more communication resources than expected in this resource-limited scenario. To address these issues, we propose privacy-preserving decentralized FL for secure and efficient communication (FL-SEC) over A-IoT. A novel blockchain structure is devised to achieve decentralized FL, which prevents single point of failure and poisoning attacks and also provides a personalized incentive mechanism. In addition, improved sign gradient descent is used to replace traditional gradient descent, which preserves the privacy of model parameters while significantly reducing the communication resource consumption. Experiments on real-world benchmark datasets show the superior performance of the proposed model.
Learnable antinoise-receiver algorithm based on a quantum feedforward neural network in optical quantum communication

The quantum communication process usually consists of three stages: the sender who prepares encoded carriers, the transmission in noisy channels, and the quantum receivers. The transmitted quantum information can be inevitably affected by kinds of quantum noise in the environment. Thus, quantum protocols are extensively studied to improve communication efficiency and accuracy under the influence of quantum noise. The optimization strategies usually occur in these three stages. In this paper, we focus on the optimization strategy of quantum receivers in the third stage. In quantum receiver algorithms, the key to distinguish received non-orthogonal coherent states in free-space optical quantum communication is to construct an optimum displacement operator for transforming the current coherent state into a state that is easier to distinguish than before. To improve the antinoise ability and accuracy of quantum communication, this paper proposes a universal optimization strategy of quantum receivers called learnable antinoise receiver (LAN receiver). In this strategy, a parametrized quantum circuit is constructed as a quantum feedforward neural network as the displacement operator to improve the antinoise ability. The parameters used in the quantum circuit are updated by gradient descent continuously to find the best parameter combination of the quantum circuit that minimizes the error rate and the qubits affected by quantum noise are used as training and testing data. The simulation of the proposed algorithm shows that the LAN receiver can resist different kinds of strong quantum noise. The average error rate of the proposed algorithm LAN receiver under the strong noise channel is 0.18, which has better performance than other type of receivers under the influence of strong quantum noise.
Enforced block diagonal subspace clustering with closed form solution

Subspace clustering aims to fit each category of data points by learning an underlying subspace and then conduct clustering according to the learned subspace. Ideally, the learned subspace is expected to be block diagonal such that the similarities between clusters are zeros. In this paper, we provide the explicit theoretical connection between spectral clustering and the subspace clustering based on block diagonal representation. We propose Enforced Block Diagonal Subspace Clustering (EBDSC) and show that the spectral clustering with the Radial Basis Function kernel can be regarded as EBDSC. Compared with the exiting subspace clustering methods, an analytical, nonnegative and symmetrical solution can be obtained by EBDSC. An important difference with respect to the existing ones is that our model is a more general case. EBDSC directly uses the obtained solution as the similarity matrix, which can avoid the complex computation of the optimization program. Then the solution obtained by the proposed method can be used for the final clustering. Finally, we provide the experimental analysis to show the efficiency and effectiveness of our method on the synthetic data and several benchmark data sets in terms of different metrics.
A ranking-based adaptive cuckoo search algorithm for unconstrained optimization

Cuckoo search (CS) has been proven to be one of the most efficient metaheuristic algorithms in solving global optimization problems. However, it suffers from a slow convergence speed and premature convergence, especially when the complexity of the problem increases. To address these shortcomings, a ranking-based adaptive cuckoo search algorithm, called RACS, is proposed in this paper. Specifically, a novel ranking-based mutation strategy is designed at first, which is inspired by the natural phenomenon that good species or individuals always contain good information and thus have better odds of guiding others. In the proposed ranking-based mutation strategy, the global search equation is modified in combination with a ranking-based vector selection method, where some of the parent vectors are proportionally selected according to their rankings. The higher ranking a parent vector obtains, the more opportunity it will be chosen. Secondly, a crossover operation with parameter adaptation is employed after the Lévy flights random walk to preserve some good elements of the current solutions from being changed. Furthermore, a replacement strategy is designed to update the solutions not improved through pre-determined cycles by exploiting the beneficial information from the discarded solutions saved in the external archive. To evaluate the comprehensive performance of RACS, extensive experiments are conducted on three well-known test suites and an application problem of identifying unknown parameters of fractional-order nonlinear systems. Simulation results demonstrate that the presented strategies bring a significant improvement in effectiveness and efficiency on CS. Besides, RACS is verified to be superior or at least comparable to other CS variants and state-of-the-art algorithms on most of the benchmark problems, and thus, can be regarded as a useful and promising technique for solving real-world complex optimization problems.
Augmented Multimodality Fusion for Generalized Zero-Shot Sketch-Based Visual Retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) has attracted great attention recently, due to the potential application of sketch-based retrieval under zero-shot scenarios, where the categories of query sketches and gallery photos are not observed in the training stage. However, it is still under insufficient exploration for the general and practical scenario when the query sketches and gallery photos contain both seen and unseen categories. Such a problem is defined as generalized zero-shot sketch-based image retrieval (GZS-SBIR), which is the focus of this work. To this end, we propose a novel Augmented Multi-modality Fusion (AMF) framework to generalize seen concepts to unobserved ones efficiently. Specifically, a novel knowledge discovery module named cross-domain augmentation is designed in both visual and semantic space to mimic novel knowledge unseen from the training stage, which is the key to handling the GZS-SBIR challenge. Moreover, a triplet domain alignment module is proposed to couple the cross-domain distribution between photo and sketch in visual space. To enhance the robustness of our model, we explore embedding propagation to refine both visual and semantic features by removing undesired noise. Eventually, visual-semantic fusion representations are concatenated for further domain discrimination and task-specific recognition, which tend to trigger the cross-domain alignment in both visual and semantic feature space. Experimental evaluations are conducted on popular ZS-SBIR benchmarks as well as a new evaluation protocol designed for GZS-SBIR from DomainNet dataset with more diverse sub-domains, and the promising results demonstrate the superiority of the proposed solution over other baselines. The source code is available at https://github.com/scottjingtt/AMF_GZS_SBIR.git.
Parameter estimation and system identification for continuously-observed quantum systems

This paper gives an overview of parameter estimation and system identification for quantum input-output systems by continuous observation of the output field. We present recent results on the quantum Fisher information of the output with respect to unknown dynamical parameters. We discuss the structure of continuous-time measurements as solutions of the quantum Zakai equation, and their relationship to parameter estimation methods. Proceeding beyond parameter estimation, the paper also gives an overview of the emerging topic of quantum system identification for black-box modeling of quantum systems by continuous observation of a traveling wave probe, for the case of ergodic quantum input-output systems and linear quantum systems. Empirical methods for such black-box modeling are also discussed.
NANO-MOOCs to train university professors in digital competences

Rapid changes in technology force Higher Education Institutions (HEIs) to generate policies and permanent digital adaptations in their exercise of forming professionals through university professors. HEIs -in their permanent desire to qualify teaching faculty and graduate high-level professionals-develop continuous training events to strengthen and update techno-pedagogical skills that allow giving concrete responses to the needs of a globalized society during a human-educational crisis that arises from the COVID-19 pandemic. This study aims at analyzing whether nano-MOOCs improve digital teaching competences in university professors since in the scientific literature, this topic does not show with certainty the effectiveness of these types of courses in teacher training. By conducting a quantitative descriptive-inferential, comparative quasi-experimental research (pre-test and post-test) and with a sample made up of 297 faculty members from Universidad Técnica del Norte (UTN, Ibarra-Ecuador) belonging to the five academic units that compose it, it was identified that the teaching staff has limitations in two of the areas of competence that are articulated by INTEF Common Framework: creation of digital content and security; nevertheless, they did show optimal skills in the areas of information and information literacy, communication and collaboration, and problem solving. The findings also determined that online training based on a nano-MOOC format becomes a successful alternative for university faculty training, 83.84% of the participants under study improved their level of digital competence. These results show that an efficient customizable training can be achieved in less time and adjusted to the needs and characteristics of the professors. The criteria of various authors in this field are ratified with this research, it is, therefore, relevant to evaluate the level of digital competence of teachers and, based on that, be able to plan a personalized training program.
Gender-based multi-aspect sentiment detection using multilabel learning

Sentiment analysis is an important task in the field of natural language processing that aims to gauge and predict people’s opinions from large amounts of data. In particular, gender-based sentiment analysis can influence stakeholders and drug developers in real-world markets. In this work, we present a gender-based multi-aspect sentiment detection model using multilabel learning algorithms. We divide Abilify and Celebrex datasets into three groups based on gender information, namely: male, female, and mixed. We then represent bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), and global vectors for word representation (GloVe) based features for each group. Next, we apply problem transformation approaches and multichannel recurrent neural networks with attention mechanism. Results show that traditional multilabel transformation methods achieve better performance for small amounts of data and long-range sequence in terms of samples and labels, and that deep learning models achieve better performance in terms of mean test accuracy, AUC Score, RL, and average precision using GloVe word embedding features in both datasets.
Joint Device Selection and Power Control for Wireless Federated Learning

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</i> scheme with error-free model exchange and full device participation.
Blockchain in Secure Healthcare Systems: State of the Art, Limitations, and Future Directions

Modern healthcare is a data-intensive domain representing an amalgamation of long-term electronic medical records, real-time patient monitoring data, and more recently sensor data from wearable computing. Blockchain in healthcare can address a multitude of challenges in healthcare, including care coordination, data security, and interoperability concerns, as technology advances. Technical challenges such as processing speed and massive data duplication will be resolved as improved technology. This data needs to be accessed seamlessly by a multitude of players from the general physicians to hospitals, medical service providers to insurance companies. Thus, healthcare-related data needs to be verified, securely stored, and shared while maintaining patient privacy and control over what portion of the data is shared, with whom it is shared, and how it is consumed. Blockchain has emerged as a technology stack of choice for distributed authentication, secure storage, and automated analysis of stored data in diverse domains including healthcare. Its distributed nature is a natural fit to the healthcare ecosystem with multiple participating entities and patients in different geographic locations. In this paper, we review the technology of blockchain to the healthcare domain analyzing and classifying work done in the field. Open challenges are identified and future directions for research are also presented.
Bio-physiological-signals-based VR cybersickness detection

With the gradual maturity of virtual reality (VR) technology in recent years, VR industry is in a trend of rapid growth, providing new possibilities for content design. Although VR technology has been able to provide users with excellent immersive experience, side effects that affect the user experience still exist, especially the cybersickness. It would cause extreme physical discomfort to the users and the discontinuation of use. Many researchers have tried to find the inducement of cybersickness and to detect and limit the occurrence of this symptom, but most of the current detection and analysis methods rely on subjective questionnaires to collect users' posterior states, such as dizziness, nausea, cold sweats, disorientation, eyestrain and so on. There is no mature real-time cybersickness detection system for VR developers to evaluate the susceptibility of their products to cybersickness so far, which has hindered the adoption of VR to some extent. The purpose of this study is to implement the real-time monitoring of cybersickness using physiological sensors to measure data and quantify the influence factors of cybersickness through deep learning model. Besides, we have developed a VR experimental platform and passive navigation task to induce user cybersickness. During the experiment, to train the LSTM Attention neural network model, we collected the user's real-time physiological signals, including skin electrical activity (EDA) and electrocardiogram (ECG), as well as the position and bone rotation data of the users' virtual avatar. The model can detect the level of users' cybersickness in real-time during VR experience. And the model has been verified by the fivefold cross-validation that the average accuracy of 96.85% was achieved for classification of cybersickness level, showing great performance compared with other relevant studies. The results show the feasibility of accurate classification of cybersickness using the model we proposed. Also the model can provide reference for VR researchers and developers to improve the user experience.