Reviewerly/exHarmony huggingface dataset:
Threat Object-based anomaly detection in X-ray images using GAN-based ensembles

The problem of detecting dangerous or prohibited objects in luggage is a very important step during the implementation of Security setup at Airports, Banks, Government buildings, etc. At present, the most common techniques for detecting such dangerous objects are by using intelligent data analysis algorithms such as deep learning techniques on X-ray imaging or employing a human workforce for inferring the presence of these threat objects in the obtained X-ray images. One of the major challenges while using deep-learning methods to detect such objects is the lack of high-quality threat image data containing the "dangerous" objects (objects of interest) versus the non-threat image data in practical scenarios. So, to tackle this data scarcity problem, anomaly detection techniques using normal data samples have shown great promise. Also, among the available Deep Learning Strategies for anomaly detection for computer vision applications, generative adversarial networks have achieved state-of-the-art results. Considering these insights, we adopted a newly proposed architecture known as Skip-GANomaly and devised a modified version of it by using a UNet++ style generator which performed better than Skip-GANomaly, getting an AUC of 94.94% on Compass-XP, a public X-ray dataset. Finally, for targeting better latent space exploration, we combine these two architectures into an Ensemble, which gives another boost to the performance, getting an AUC of 96.8% on the same Compass-XP, a public X-ray dataset. To further validate the effectiveness of ensemble-based architecture, its performance was tested on patch-based training data on a subset of randomly chosen images of another huge public X-ray dataset named as SIXray, and obtained an AUC of 75.3% on this reduced dataset. To demonstrate the prowess of the discriminator and to bring some explainability to the working of our ensemble, we have used Uniform Manifold Approximation and Projection to plot the latent-space vectors for the dangerous and non-dangerous objects of the test-set; this analysis indicates that the Ensemble learns better features for separating the anomalous class from non-anomalous with respect to the individual architectures. Thus, our proposed architecture provides state-of-the-art results for threat object detection. Most importantly, our models are able to detect threat objects without ever being trained on images containing threat objects.
Identifying Early Predictors of Learning in VR-based Drone Training

The use of small unmanned aerial systems, or drones, has grown rapidly in recent years, but the FAA has no formal requirement for hands-on training. Physical drone training has major limitations, and virtual reality (VR) offers a promising alternative. This study sought to identify early performance markers of learning in VR-based drone training using a physical controller. 14 participants completed a customized VR-based drone training curriculum, while performance metrics, perceptions of workload, and physiological data were collected. Participants were clustered into high and low performers based on a final evaluation task, and separate analyses of variance were conducted to test performance differences between the two groups over time in each training level. We found significant differences in the performance metrics and subjective workload of low and high performers throughout training, suggesting that performance can be predicted early in training and opening the door to future adaptive training systems.
Pixel-Level and Feature-Level Domain Adaptation for Heterogeneous SAR Target Recognition

The performance of synthetic aperture radar (SAR) target recognition has been substantially enhanced by the deep learning technology. It is still difficult to get strong recognition performance due to the distribution differences across heterogeneous SAR images. To enhance target recognition performance in heterogeneous SAR situations, a pixel-level and feature-level domain adaptation (PFDA) approach is proposed in this letter to deal with this problem. The pixel-level translation module in the first step generates images with high visual similarity with another distributed images from one distributed images. The second step involves introducing feature alignment into the network to lower the probability distribution divergence of heterologous SAR images in the feature space and enhance recognition performance. We evaluated our method on the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset and a self-built airplane dataset to ensure its effectiveness. Compared to existing domain adaption approaches, experimental results reveal that our strategy greatly improves recognition performance and model stability for heterogeneous SAR target recognition.
Cyber Threat Analysis Using Natural Language Processing for a Secure Healthcare System

Cyber threats in the healthcare sector have increased significantly in recent years. Attackers are now using sophisticated techniques to launch multi-phase cyber attacks to compromise the system and leak patient healthcare data. Healthcare organisations need to protect IT infrastructures and understand the threats and possible attack surface for a secure healthcare service delivery. Hence, threat analysis is one of the key activities for tackling the potential risks and ensuring security of a system context. This work presents a threat analysis approach that allows to identify and assess the possible threats within healthcare information infrastructure. The approach considers the existing threat data from widely used repositories and uses Natural Language Processing to identify threats among cyber security news, also evaluating their corresponding level. The preliminary experimental assessment shows promising results, providing a realistic manner to assess the threats, allowing to adopt the proposed approach in real-world contexts.
Blockchain Based Precision Agriculture Model Using Machine Learning Algorithms

Precision Agriculture (PA) can be considered one of the modern innovations in the field of agriculture where the latest technologies can be used to improve the quantity and quality of agriculture products. Whether it comes to farming or livestock, the consumer community always prefer better products. This paper looks into the models in which IoT, Machine Learning and blockchain technology can be integrated to form a network through which the concept of precision agriculture can be obtained. Deployment of feasible blockchain networks is done and transactions between the other two elements are also studied.
Advances in Portable X-ray Fluorescence Spectrometry

Over the last two decades, advances in the design, miniaturization, and analytical capabilities of portable X-ray fluorescence (pXRF) instrumentation have led to its rapid and widespread adoption in a remarkably diverse range of applications in research and industrial fields. The impetus for this volume was that, as pXRF continues to grow into mainstream use, analysts should be increasingly empowered with the right information to safely and effectively employ pXRF as part of their analytical toolkit. This volume provides introductory and advanced-level users alike with readings on topics ranging from basic principles of pXRF and qualitative and quantitative approaches, through to machine learning and artificial intelligence for enhanced applications. It also includes fundamental guidance on calibrations, the mathematics of calculating uncertainties, and an extensive reference index of all elements and their interactions with X-rays. Contributing authors have provided a wealth of information and case studies in industry-specific chapters. These sections delve into detail on current standard practices in industry and research, including examples from agricultural and geo-exploration sectors, research in art and archaeology, and metals industrial and regulatory applications. As pXRF continues to grow in use in industrial and academic settings, it is essential that practitioners continue to learn, share, and implement informed and effective use of this technique. This volume serves as an accessible guidebook and go-to reference manual for new and experienced users in pXRF to achieve this goal.
Alternate Iterative Least-Squares Algorithm Based on Nonuniform Phase Shifting for Suppressing Nonlinearity Errors in Fringe Projection Profilometry

In fringe projection technique, nonlinearities of used devices are recognized as major factors inducing phase errors, especially when the used camera and projector simultaneously have nonlinear features. In this case, ripple artifacts appear to be unevenly distributed over the reconstructed surface depending on the local reflectivity. For suppressing these errors, this paper suggests a phase-measuring technique which is characterized by two critical steps including anti-aliasing phase-shifting and iterative least-squares phase estimation. Anti-aliasing phase-shifting requires the selected phase shifts to be nonuniform and to have no common divisors. It provides a possibility of accurately recovering phases from few to four fringe patterns and eliminating influence of high order harmonics on the measurement results. By expanding the fringe signals from projector into harmonics and modeling the camera response as a polynomial, we derive a least-squares algorithm based on alternate iterations to calculate the fringe phase map and the related nonlinearity coefficients simultaneously. Through practical experiments, it has been demonstrated that this technique allows us to decrease artifacts caused by the simultaneous nonlinearities of the camera and the projector, thus improving measurement accuracy significantly.
The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance

Automatic speaker verification is susceptible to various manipulations and spoofing, such as text-to-speech synthesis, voice conversion, replay, tampering, adversarial attacks, and so on. We consider a new spoofing scenario called "Partial Spoof" (PS) in which synthesized or transformed speech segments are embedded into a bona fide utterance. While existing countermeasures (CMs) can detect fully spoofed utterances, there is a need for their adaptation or extension to the PS scenario. We propose various improvements to construct a significantly more accurate CM that can detect and locate short-generated spoofed speech segments at finer temporal resolutions. First, we introduce newly developed self-supervised pre-trained models as enhanced feature extractors. Second, we extend our PartialSpoof database by adding segment labels for various temporal resolutions. Since the short spoofed speech segments to be embedded by attackers are of variable length, six different temporal resolutions are considered, ranging from as short as 20 ms to as large as 640 ms. Third, we propose a new CM that enables the simultaneous use of the segment-level labels at different temporal resolutions as well as utterance-level labels to execute utterance- and segment-level detection at the same time. We also show that the proposed CM is capable of detecting spoofing at the utterance level with low error rates in the PS scenario as well as in a related logical access (LA) scenario. The equal error rates of utterance-level detection on the PartialSpoof database and ASVspoof 2019 LA database were 0.77 and 0.90%, respectively.
EMRShareChain: A Privacy-Preserving EMR Sharing System Model Based on the Consortium Blockchain

Electronic medical record (EMR) Sharing can reduce duplicate medical tests, reduce treatment costs and improve the quality of medical services. However, most medical organizations currently use the internal network to track patients but don’t implement data sharing with other medical organizations. Meanwhile, the EMR is also highly vulnerable to theft and tampering by malicious attackers. To solve the above problems, we design a privacy-preserving EMR sharing system model based on consortium blockchain, called EMRShareChain. In EMRShareChain, multiple medical organizations firstly spontaneously build up a medical consortium. The EMR generated by a patient’s treatment at a medical organization is encrypted and stored securely in the cloud, and the index of the EMR is stored in the blockchain. With the above storage method, not only the risk of EMR leakage is reduced, but also the EMR cannot be tampered with arbitrarily. Then, other organizations in the medical consortium can find the patient’s relevant EMR in the blockchain by keywords in the index and keyword searchable encryption, and obtain the EMR key and EMR storage address generated by the conditional proxy re-encryption after the patient’s authorization to achieve access to the EMR and data sharing. In addition, an improved PBFT consensus algorithm (I-PBFT) based on comprehensive scoring mechanism is proposed to improve the blockchain block-out efficiency and stability, making it more suitable for medical scenarios. Finally, a prototype implementation of the above model is carried out based on the Hyperledger Fabric framework. The Hyperledger fabric chaincode is accessible at https://github.com/xl13455/EMRShareChain and code can be downloaded.
Are Software Updates Useless against Advanced Persistent Threats?

Considering the conundrum of software updates.
Measurement outlier-resistant mobile robot localization using multiple Doppler-azimuth radars under round-robin protocol

This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used. For mitigating the influence of outliers, a time-varying state estimator is constructed which contains a saturation function with variable saturation levels. This paper aims at seeking out a practicable yet effective solution to the addressed robot localization problem by devising the constructed estimator which can assure that, over a finite horizon, the localization error satisfies the given H∞ performance index. By constructing an appropriate Lyapunov function, the sufficient condition, which can guarantee the localization error to fulfill the given H∞ performance, is established. Then, by resorting to the solution to a set of linear matrix inequalities, the constructed estimator can be devised. In the light of the estimator design strategy proposed in this paper, the corresponding robot localization algorithm is developed. At last, some simulations are conducted to testify the usefulness of the developed robot localization algorithm.
Communication-Efficient Device Scheduling via Over-the-Air Computation for Federated Learning

Artificial intelligence (AI) is expected as a revo-lutionary technology to be widely used in Internet-of- Things (IoT) networks for computationally intensive tasks. However, the traditional centralized training framework imposes large latency, network burdens and high risk of privacy disclosure. As a promising distributed solution, federated learning involves the collaborative model training among edge devices, with the orchestration of a server to carry the capacities of low-latency and privacy preservation for AI -driven networks. To further improve the communication efficiency, over-the-air computation (AirComp) is capable of computing while transmitting data by exploiting the superposition property of wireless channels to harness the interference. However, gradient aggregation suffers from channel distortion induced by channel fading and noise, which may degrade the training performance. Moreover, it is beneficial to schedule the informative edge devices in federated learning under limited energy resources. In this work, we propose a dynamic device scheduling scheme for AirComp enabled federated learning systems. In this scheme, a proper number of qualified edge devices with channel inversion based power control are scheduled to participate the model training, where local updates diversity, channel condition and energy consumption are exploited jointly. Inspired by the Lyapunov drift-plus-penalty method, we formulate the optimization problem to attain the device selection strategy. Simulation results validate that the proposed scheme can achieve a close-to-optimal test accuracy with fast convergence rate, and present good performance of robustness under different channel conditions.
ICT efficacy and response to different needs in university classrooms: effects on attitudes and active behaviour towards technology

Digital competence is considered to be a crucial learning outcome in education in the 21st century. In this context, research highlights the fact that the perceptions that instructors manifest about different aspects of Information and Communication Technologies (ICTs) condition these professionals' behaviour towards these learning resources. In the same line, this study aims to analyse the effects that exist between a series of dimensions related to the perception of university teaching staff on the capacity of ICTs to respond to different needs of students, on perceived efficacy and attitudes towards these tools and, lastly, on active behaviour towards their use. To do so, 345 university instructors from the Spanish educational system filled in an online questionnaire. The application of a Structural Equation Model underscores the fact that the ability of ICTs to respond to the different needs of students in the university classroom and their perceived efficacy in the teaching-learning process both exert a positive effect on attitudes in favour of their incorporation into the classroom. In turn, these attitudes also have a significant effect on active behaviour with ICT resources. In addition, various mediating effects are seen to influence an active behaviour. All this gives rise to a discussion on the implications of these results to encourage the training of university teaching staff in the knowledge and management of ICTs. Increasing confidence in ICTs as effective tools to respond to different needs could significantly favour positive attitudes and behaviour so that these resources are actively integrated into the classroom.
AR Point &amp;Click: An Interface for Setting Robot Navigation Goals

This paper considers the problem of designating navigation goal locations for interactive mobile robots. We investigate a point-and-click interface, implemented with an Augmented Reality (AR) headset. The cameras on the AR headset are used to detect natural pointing gestures performed by the user. The selected goal is visualized through the AR headset, allowing the users to adjust the goal location if desired. We conduct a user study in which participants set consecutive navigation goals for the robot using three different interfaces: AR Point &Click, Person Following and Tablet (birdeye map view). Results show that the proposed AR Point &Click interface improved the perceived accuracy, efficiency and reduced mental load compared to the baseline tablet interface, and it performed on-par to the Person Following method. These results show that the AR Point &Click is a feasible interaction model for setting navigation goals.
Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.
A Humane Paradigm as the Conceptual Core of Education in the Age of Information Society

The article, based on a systematic analysis of the problems of national education in the age of information society and digitalised education, reveals the significance of the human paradigm, which is the conceptual core and the major platform for the educational process under the regime of aggravation. The convergence of education and information technology produces not only new areas of knowledge, pedagogical tools and teaching methods but also large-scale transformations in social practice. Like any social change, the digitalisation of education has a variety of consequences - from positive to negative ones. It is shown that the digitalisation of education, which meets great hopes not only in Ukraine and Poland, can be productive only if an anthropocentric algorithm is dealt with. Along with the huge and not much-studied possibilities of digitalisation of education, it is proved that many problems and risks are associated with the total introduction of the “digital” in the educational system. These are social, psychological, health, organisational and methodological risks, and some problems with the reduced quality of education. However, the ethics-related risks are perhaps the most dangerous among them. As regards this, it is proved that the humane paradigm, as a paradigm of the integrity of knowledge that provides values and goals of education, is a guarantor of the harmonisation in many processes related to translating information and knowledge into a digital form. The humane paradigm is the basis of the genetic connection of knowledge, skills, digital literacy, moral imperatives, and life values, which in a synergetic algorithm contribute to students’ effective development, systematic thinking, and socialisation. The three most important blocks of educational problems in the discourse on modern cyber-socialisation (transformation of the worldview component of education, reconstruction of the content of education, imbalance of educational and didactic processes) are outlined, and the ways and mechanisms of solving them are suggested. Implementing integrated strategies within the humane paradigm (which is the nourishing source of each of these blocks) might result in the idea of the so-called “new humanism” in education.