Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Secured Blockchain-Based Patient Health Records Sharing Framework

With the healthcare system’s ongoing digital transformation and the need for patient data sharing to become an essential step to understanding the patient’s health history, cyber security must stay at the forefront and be made a top priority. As a result, most existing data-sharing systems depend on trusted third parties. As a result, these systems lack interoperability, data fragmentation, integrity, security, and privacy. In our previous work, we designed a framework based on Blockchain to secure patient health records exchange(SPHRS) that is fully controlled by the patient in terms of revoking or granting access and creating access policies for care providers. The framework achieves security by using smart contracts for user identity authentication and verification. The distributed IPFS storage is applied to store the encrypted patient health records and ensure immutability. In addition, NuCypher software takes advantage of a proxy re-encryption protocol to store the encryption and decryption keys securely. In this study, we assess the framework’s performance by testing metrics such as blockchain transactions’ gas consumption, throughput, Average response time, and average. Bytes. Furthermore, the security of the framework is discussed. SPHRS demonstrates how we can establish a novel approach to efficiently secure patient health record sharing. However, it shows a promising result that can potentially transform the digital patient healthcare system.
Attack and Defense on Aircraft Trajectory Prediction Algorithms

The aviation industry needs lead to an increase in the number of aircraft and their flights. When the number of flights within an airspace increases, the chance of a mid-air collision (or collision) increases. Collision Avoidance Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms are discussed by using existing aircraft trajectory data containing multiple different aircraft types with different flight patterns. With this dataset, the future aircraft heading is predicted using the latitude, longitude, altitude, velocity and time. The three algorithms’ performances were evaluated in terms of their accuracy and robustness. These trajectory prediction algorithms were as well evaluated in the case of adversarial samples. Although algorithms can find reliable ATPs, earlier research has shown that they are also vulnerable against adversarial attacks that produce adversarial samples. Adversarial samples are similar to original samples from the dataset. These perturbations can cause trained algorithms to predict unreliable trajectories, which cause a security threat for learning-based trajectory algorithms, as adversarial attacks can result in intentional collisions. Adversarial training is applied as defense method in order to increase the robustness ATPs algorithms against the adversarial samples. The adversarial samples are included in the training data in order to make the algorithm more robust in the case of an adversarial attack. The findings in this research show that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm is most robust against adversarial samples and adversarial training will benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and mainly the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.
Predicting pre-service English language teachers’ intentions to use augmented reality

Augmented Reality (AR) is a new-generation technology that shows video, picture, and animated 3 D objects over real-life scenes. Despite AR’s prevalence around the world, language teacher education programs are not prompt in the integration of digital educational tools. The present study explored the factors that predict pre-service English teachers’ intentions to use AR in their future language classes. For this purpose, a correlational research design was applied. The participants were exposed to an implementation process which included training on creating AR experiences as well as developing a stand-alone AR application and a discussion session on AR in language classes. Next, a 5-point Likert-type scale of Decomposed Theory of Planned Behavior (DTPB) was administered to pre-service English teachers (n = 141). Results indicated that attitude with a very high impact from perceived usefulness was the most significant predictor of participants’ intentions to use AR. On the other hand, facilitating conditions and ease of use were the least impactful factors. The findings offer implications for teacher educators, curriculum designers, teacher education program developers and teachers.
Long-Term Evolution Experiment with Genetic Programming

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.
A Dual-Generator Translation Network Fusing Texture and Structure Features for SAR and Optical Image Matching

The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%.
Real-time Machine Learning for Symbol Detection in MIMO-OFDM Systems

Recently, there have been renewed interests in applying machine learning (ML) techniques to wireless systems. Nevertheless, ML-based approaches often require a large amount of data in training, and prior ML-based MIMO symbol detectors usually adopt offline learning approaches, which are not applicable to real-time signal processing. This paper adopts echo state network (ESN), a prominent type of reservoir computing (RC), to the real-time symbol detection task in MIMO-OFDM systems. Two novel ESN training methods, namely recursive-least-square and generalized adaptive weighted recursive-least-square, are introduced to enhance the performance of ESN training. Furthermore, a decision feedback mechanism is adopted to improve training efficiency and BER performance. Simulation studies show that the proposed methods perform better than previous conventional and ML-based MIMO symbol detectors. Finally, the effectiveness of our RC-based approach is validated with a software-defined radio (SDR) transceiver and extensive field tests in various real-world scenarios. To the best of our knowledge, this is the first real-time SDR implementation for ML-based MIMO-OFDM symbol detectors. Our work strongly indicates that ML-based signal processing could be a promising and critical approach for future wireless networks.
Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections

3D models of manufactured objects are important for populating virtual worlds and for synthetic data generation for vision and robotics. To be most useful, such objects should be articulated: their parts should move when interacted with. While articulated object datasets exist, creating them is labor-intensive. Learning-based prediction of part motions can help, but all existing methods require annotated training data. In this paper, we present an unsupervised approach for discovering articulated motions in a part-segmented 3D shape collection. Our approach is based on a concept we call category closure: any valid articulation of an object’s parts should keep the object in the same semantic category (e.g. a chair stays a chair). We operationalize this concept with an algorithm that optimizes a shape’s part motion parameters such that it can transform into other shapes in the collection. We evaluate our approach by using it to re-discover part motions from the PartNet-Mobility dataset. For almost all shape categories, our method’s predicted motion parameters have low error with respect to ground truth annotations, outperforming two supervised motion prediction methods.
Continuous User Authentication using Keystroke Dynamics for Touch Devices

An authenticate users have increased due to failures in traditional authentication systems. Keystroke dynamics-based authentication is one of the most secure behavioral biometric authentication systems. This study aims to research and implement a non-fool proof, low-cost continuous authentication system for touch devices based on keystroke dynamics. A custom-developed mobile application was used to collect users’ keystroke dynamics. Bigrams were used as input parameters. 2 artificial neural networks were used in this study. The first network was used to identify users’ handedness, while the second one decided to use validity. Also, input was not limited, and users could type free text. As a result, overall accuracy was above 83.74%. Based on the results, we concluded that keystroke dynamics could be used for continuous user authentication purposes even with freely typed tests.
Smart Attendance Monitoring Technology for Industry 4.0

Keeping track of employee attendance in academic settings can be a difficult task. It frequently wastes a significant percentage of the category’s productive time when done manually. In this study, the OpenCV open-source image processing library presents an effective Raspberry Pi-based methodology that reduces product cost and aids in connecting to heterogeneous devices for attendance. When teaching and testing and collecting employee photos and taking attendance, the system delivers a user-friendly interface that maximizes the user experience. Face detection and recognition are done with LBP histograms, and the database is updated with SQLite (a lightweight version of SQL for the Raspberry Pi) rather than MySQL.
Swin Transformer Based on Two-Fold Loss and Background Adaptation Re-Ranking for Person Re-Identification

Person re-identification (Re-ID) aims to identify the same pedestrian from a surveillance video in various scenarios. Existing Re-ID models are biased to learn background appearances when there are many background variations in the pedestrian training set. Thus, pedestrians with the same identity will appear with different backgrounds, which interferes with the Re-ID performance. This paper proposes a swin transformer based on two-fold loss (TL-TransNet) to pay more attention to the semantic information of a pedestrian’s body and preserve valuable background information, thereby reducing the interference of corresponding background appearance. TL-TransNet is supervised by two types of losses (i.e., circle loss and instance loss) during the training phase. In the retrieval phase, DeepLabV3+ as a pedestrian background segmentation model is applied to generate body masks in terms of query and gallery set. The background removal results are generated according to the mask and are used to filter out interfering background information. Subsequently, a background adaptation re-ranking is designed to combine the original information with the background-removed information, which digs out more positive samples with large background deviation. Extensive experiments on two public person Re-ID datasets testify that the proposed method achieves competitive robustness performance in terms of the background variation problem.
Motion Perception Investigated Inside and Outside of the Laboratory

Representational Momentum and Representational Gravity describe systematic perceptual biases, occurring for the localization of the final location of a moving stimulus. While Representational Momentum describes the systematic overestimation along the motion trajectory (forward shift), Representational Gravity refers to a systematic localization bias in line with gravitational force (downward shift). Those phenomena are typically investigated in a laboratory setting, and while previous research has shown that online studies perform well for different task, motion perception outside of the laboratory was not focused to date. Therefore, one experiment was conducted in two different settings: in a typical, highly controlled laboratory setting and in an online setting of the participants' choosing. In both experiments, the two most common trial types, implied motion stimuli and continuously moving stimuli, were used, and the influence of classical velocity manipulations (by varying stimulus timing and distance) was assessed. The data pattern across both experiments was very similar, indicating a robustness of both phenomena and indicating that motion perception can very well be studied outside the classical laboratory setting, opening a feasible possibility to diversify access to motion perception experiments everywhere.
Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network

In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hyper-parameters of BiGRU neural network. Finally, a network security situation prediction model based on EMD-ELPSO-BiGRU is constructed to predict each intrinsic mode function, respectively, and the prediction results are superimposed to obtain the final network security situation prediction value. Simulation results show that ELPSO has better optimization performance, and EMD-ELPSO-BiGRU model has higher prediction accuracy and significantly improved convergence speed compared with other traditional prediction methods.
Experimental preparation of generalized cat states for itinerant microwave photons

Generalized cat states represent arbitrary superpositions of coherent states, which are of great importance in various quantum information processing protocols. Here we demonstrate a versatile approach to creating generalized itinerant cat states in the microwave domain, by reflecting coherent state photons from a microwave cavity containing a superconducting qubit. We show that, with a coherent control of the qubit state, a full control over the coherent state superposition can be realized. The prepared cat states are verified through quantum state tomography of the qubit state dependent reflection photon field. We further quantify quantum coherence in the prepared cat states based on the resource theory, revealing a good experimental control on the coherent state superpositions. The photon number statistic and the squeezing properties are also analyzed. Remarkably, fourth-order squeezing is observed in the experimental states. Those results open up new possibilities of applying generalized cat states for the purpose of quantum information processing.
High-Capacity Reversible Data Hiding in Encrypted Images with Flexible Restoration

In this paper, we propose a novel reversible data hiding in encrypted images (RDH-EI) method that achieves the highest hiding capacity in the RDH-EI research field and full flexibility in the processing order without restrictions. In the previous work in this field, there exist two representative methods; one provides flexible processing with a high hiding capacity of 2.17 bpp, and the other achieves the highest hiding capacity of 2.46 bpp by using the BOWS-2 dataset. The latter method has critical restrictions on the processing order. We focus on the advantage of the former method and introduce two efficient algorithms for maximizing the hiding capacity. With these algorithms, the proposed method can predict each pixel value with higher accuracy and refine the embedding algorithm. Consequently, the hiding capacity is effectively enhanced to 2.50 bpp using the BOWS-2 dataset, and a series of processes can be freely conducted without considering any restrictions on the order between data hiding and encryption. In the same way, there are no restrictions on the processing order in the restoration process. Thus, the proposed method provides flexibility in the privileges requested by users. Experimental results show the effectiveness of the proposed method in terms of hiding capacity and marked-image quality.
A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10-16% from 10-15 SNR.
Blockchain design in health data management

Delays in communication and diverse workflows impact healthcare organizations, due to a lack of interoperability and data management issues. Patients generate a large amount of health data to be acquired, retained and distributed. In recent years, electronic health records (EHRs) are becoming increasingly popular to manage patients’ data, enhancing the overall quality of healthcare services. However, EHRs face several challenges, including privacy concerns, economic constraints, and technological limitations. This paper aims to provide a blockchain-based platform to enhance EHR interoperability, allowing for the secure and fast sharing of patient medical data. We used the design science research methodology to define the stages of the research. As a result, our innovative blockchain platform can monitor patients by listing their therapies and treatment history. Furthermore, leveraging on blockchain technology, data becomes part of a distributed database where medical professionals maintain patient privacy and eliminate transparency risks. The paper contributes to the description of merging the medical value chain, the design of the system architectural model, the construction of system components, and the validation of the system components, demonstrating the feasibility of data exchange between different healthcare organizations.
Conflicting Aims and Values in the Application of Smart Sensors in Geriatric Rehabilitation: Ethical Analysis

Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns.This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities.On the basis of a systematic literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles.We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients.The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access.