Reviewerly/exHarmony huggingface dataset:
Contrastive-ACE: Domain Generalization Through Alignment of Causal Mechanisms

Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains. The fundamental objective is to understand the underlying "invariance" behind these observational distributions and such invariance has been shown to have a close connection to causality. While many existing approaches make use of the property that causal features are invariant across domains, we consider the invariance of the average causal effect of the features to the labels. This invariance regularizes our training approach in which interventions are performed on features to enforce stability of the causal prediction by the classifier across domains. Our work thus sheds some light on the domain generalization problem by introducing invariance of the mechanisms into the learning process. Experiments on several benchmark datasets demonstrate the performance of the proposed method against SOTAs.
Query-Efficient Adversarial Attack With Low Perturbation Against End-to-End Speech Recognition Systems

With the widespread use of automated speech recognition (ASR) systems in modern consumer devices, attack against ASR systems have become an attractive topic in recent years. Although related white-box attack methods have achieved remarkable success in fooling neural networks, they rely heavily on obtaining full access to the details of the target models. Due to the lack of prior knowledge of the victim model and the inefficiency in utilizing query results, most of the existing black-box attack methods for ASR systems are query-intensive. In this paper, we propose a new black-box attack called the Monte Carlo gradient sign attack (MGSA) to generate adversarial audio samples with substantially fewer queries. It updates an original sample based on the elements obtained by a Monte Carlo tree search. We attribute its high query efficiency to the effective utilization of the dominant gradient phenomenon, which refers to the fact that only a few elements of each origin sample have significant effect on the output of ASR systems. Extensive experiments are performed to evaluate the efficiency of MGSA and the stealthiness of the generated adversarial examples on the DeepSpeech system. The experimental results show that MGSA achieves 98% and 99% attack success rates on the LibriSpeech and Mozilla Common Voice datasets, respectively. Compared with the state-of-the-art methods, the average number of queries is reduced by 27% and the signal-to-noise ratio is increased by 31%.
Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT

The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.
Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame Information

Nowadays, according to the World Health Organization (WHO), of the world's population suffers from a hearing disorder that makes oral communication with other people challenging. At the same time, in an era of technological evolution and digitization, designing tools that could help these people to communicate daily is the base of much scientific research such as that discussed herein. This article describes one of the techniques designed to transcribe Spanish Sign Language (SSL). A Leap Motion volumetric sensor has been used in this research due to its capacity to recognize hand movements in 3 dimensions. In order to carry out this research project, an impaired hearing subject has collaborated in the recording of 176 dynamic words. Finally, for the development of the research, Dynamic Time Warping (DTW) has been used to compare the samples and predict the input with an accuracy of 95.17%.
Virtual collaboration as co-enacting intercorporeality

This paper draws on Merleau-Ponty's perspective on intercorporeality to inquire into the role of the body in virtual collaboration. Merleau-Ponty redefines the human body beyond pure physicality, as material-semiotic processes between self and other. From this perspective, meaningful engagements are based on the intertwinement of bodily activities including gestures and displays. Drawing on a rich case of an exclusively virtual collaboration, this paper illuminates how bodily expressivity is rematerialised through digital technology. The findings show how distributed bodies pattern an intercorporeal armature constituted of processes of co-presencing, co-orienting, and co-investing. The paper problematises claims of virtual disembodiment and advances current understanding of how body and technology merge to make virtual collaboration possible. The rich empirical insights are synthesised into four main theoretical propositions that constitute a novel perspective on virtual intercorporeality. This perspective advances our understanding of virtual embodiment and elaborates the role of technology and language in the process of mutual attunement between bodies.
Janus: Hierarchical Multi-Blockchain-Based Access Control (HMBAC) for Multi-Authority and Multi-Domain Environments

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability and scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at the domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute-Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.
Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models

The application of Quantum Computing (QC) to fluid dynamics simulation has developed into a dynamic research topic in recent years. With many flow problems of scientific and engineering interest requiring large computational resources, the potential of QC to speed-up simulations and facilitate more detailed modeling forms the main motivation for this growing research interest. Despite notable progress, many important challenges to creating quantum algorithms for fluid modeling remain. The key challenge of non-linearity of the governing equations in fluid modeling is investigated here in the context of lattice-based modeling of fluids. Quantum circuits for the D1Q3 (one-dimensional, three discrete velocities) Lattice Boltzmann model are detailed along with design trade-offs involving circuit width and depth. Then, the design is extended to a one-dimensional lattice model for the non-linear Burgers equation. To facilitate the evaluation of non-linear terms, the presented quantum circuits employ quantum computational basis encoding. The second part of this work introduces a novel, modular quantum-circuit implementation for non-linear terms in multi-dimensional lattice models. In particular, the evaluation of kinetic energy in two-dimensional models is detailed as the first step toward quantum circuits for the collision term of two- and three-dimensional Lattice Boltzmann methods. The quantum circuit analysis shows that with O(100) fault-tolerant qubits, meaningful proof-of-concept experiments could be performed in the near future.
Meta-heuristic algorithms-based feature selection for breast cancer diagnosis: A systematic review

The process of choosing a subset of significant features to be used in developing predictive models is known as feature selection. Recently, a feature selection method has been required since it is challenging to mine and convert large amounts of data into useful information. Finding victims of the feature selection approach is extremely challenging. Using the feature selection method effectively may greatly increase the classification prediction rate of various classifiers. In addition, by decreasing data size and selecting the optimal feature set in data mining and pattern recognition applications, the usage of feature selection decreases computational time and speeds up the data mining process. Therefore, feature selection algorithms have recently been used for breast cancer detection where early-stage diagnosis and treatment increase patient safety. In this paper, three meta-heuristic optimization techniques that are used in feature selection methods for breast cancer detection, namely Genetic algorithm (GA), Particle swarm optimization (PSO), and Artificial Bee Colony (ABC), will be presented. Finally, several research papers will be summarized and compared regarding the number of selected features and classification accuracy based on each algorithm.
Three-dimensional reconstruction of moving HDR object based on PSP

High quality 3D reconstruction of HDR object can be achieved by projecting extra fringe patterns with different intensity. However, it requires the object to be kept static. The object motion not only introduces phase shift among the fringe patterns, but also changes the position of the overexposure pixels. This paper proposes a new method to reconstruct the moving HDR object by utilizing the features of motion. For the specific point on the object, overexposure will not happen among all the fringe patterns as the overexposure position is changed with the motion. The unsaturation fringe patterns are selected from the captured fringe patterns and the correct phase information is retrieved based on unequal phase shift algorithm. The effectiveness of the proposed algorithm is verified by the experiments.
Training with scaled logits to alleviate class-level over-fitting in few-shot learning

Deep learning methods are criticized for the requirement of large set of labeled training data. Therefore, few-shot learning (FSL), which enables fast learning with only a few labeled examples, draws increasing attention. In FSL, the model is trained on base classes to learn the meta-knowledge, which is then applied to novel classes. The generalization from base classes to novel classes in FSL suffers from the class-level over-fitting problem. Generally, in traditional classification tasks, training samples and test samples are from the same class set, where over-fitting happens at the sample level. However, in FSL, the model is evaluated with classification tasks on novel classes. Thus, good fitting on base classes does not guarantee a good generalization to novel classes. In this paper, we reveal the class-level over-fitting problem in FSL and provide an explanation of the cause of this problem. Based on the explanation, we argue that simply scaling the logits of classifier during training can alleviate the class-level over-fitting problem, and analyze how scaling logits (SL) alleviates class-level over-fitting based on gradient back-propagation. Extensive experiments show that SL boosts the performance to the extent of 14% on four popular benchmark datasets. Further, SL also demonstrates its effectiveness on confidence calibration.
Unsupervised dimension-contribution-aware embeddings transformation for anomaly detection

Deep-learning-based anomaly detection methods achieve excellent performance with the help of powerful feature extraction capabilities. However, the existing methods ignore the fine-grained dimension contributions of low-dimensional embeddings to anomaly analysis from different perspectives. Therefore, we propose an unsupervised Dimension-Contribution-aware Embeddings Transformation method for Anomaly Detection (DCETAD), focusing on the different contributions of the crucial dimensional information of low-dimensional embeddings to anomaly detection from different perspectives. Specifically, we design the Dimension-Contribution-aware Sparse AutoEncoder (DC-SAE) to learn more discriminative embeddings on the basis of suppressing the sub-optimal and useless dimensions and highlight the dimension contributions of low-dimensional embeddings; then, perform the affine transformation to analyze the dimension contributions of low-dimensional embeddings in different transformation subspaces; finally, construct these transformation subspaces as multiple hyperspheres to comprehensively evaluate the anomaly score. Experimental results demonstrate that DCETAD not only achieves excellent detection performance but also outperforms recent state-of-the-art models.
Augmented Reality Uses and Applications in Aerospace and Aviation

Augmented reality's (AR) first aerospace application came around at the end of the First World War in the form of the aircraft gunsight. Over the last century, this technology evolved to serve different areas of need, including engineering, training, space operations, and flight crew support. The most notable AR applications are the head-up display (HUD) and the helmet-mounted display (HMD), which are used to provide navigation assistance to pilots flying high-profile missions. In recent years, various aerospace corporations have turned to AR technology to enhance their prototyping, manufacturing, and maintenance operations. Similarly, airlines and airports are investigating the use of AR to support daily tasks of operating crew and personnel. These efforts are supported by research groups studying the potential of AR in air traffic control and management, airport operations and security, crew training, aircraft cabin visualization, and In-Flight Entertainment and Communication (IFEC). This chapter presents an overview of AR's history and applications in the aerospace industry throughout the years.
A Cybersecurity Knowledge Graph Completion Method Based on Ensemble Learning and Adversarial Training

The application of cybersecurity knowledge graphs is attracting increasing attention. However, many cybersecurity knowledge graphs are incomplete due to the sparsity of cybersecurity knowledge. Existing knowledge graph completion methods do not perform well in domain knowledge, and they are not robust enough relative to noise data. To address these challenges, in this paper we develop a new knowledge graph completion method called CSEA based on ensemble learning and adversarial training. Specifically, we integrate a variety of projection and rotation operations to model the relationships between entities, and use angular information to distinguish entities. A cooperative adversarial training method is designed to enhance the generalization and robustness of the model. We combine the method of generating perturbations for the embedding layers with the self-adversarial training method. The UCB (upper confidence bound) multi-armed bandit method is used to select the perturbations of the embedding layer. This achieves a balance between perturbation diversity and maximum loss. To this end, we build a cybersecurity knowledge graph based on the CVE, CWE, and CAPEC cybersecurity databases. Our experimental results demonstrate the superiority of our proposed model for completing cybersecurity knowledge graphs.
Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum

This investigation presents a recurrent paraconsistent neural network (RPNN), as the main element of the model reference control (MRC) strategy for the rotary inverted pendulum (RIP). The RIP characteristics, such as nonlinearity, two-degree-of-freedom (2DoF) motion, and under-actuated system, make it an ideal device to apply and test the RPNN. The designed paraconsistent neural model reference controller (PNMRC) uses three RPNNs: two of them to model the arm and pendulum angles and the third one to control the system while tracking a reference trajectory. The hidden neurons of the RPNN use the paraconsistent annotated logic by 2-value annotations (PAL2v) rules as an activation function. PAL2v, as a member of the paraconsistent logics family, deals with uncertain and contradictory data, representing a potentially robust alternative to applications of artificial neural networks in control. The PAL2v neuron is detailed and compared with other activation functions in recurrent neural networks (RNN). With real-time experiments, the PNMRC strategy is compared with classical control methodology, presenting excellent performance.
Multiple optical spin-orbit Hall effect at the tight focus

We reveal that a multiple optical spin-orbit Hall effect occurs at the tight focus of light fields with hybrid m-th order polarization thanks to spin-orbit coupling and angular momentum conservation, or symmetry violation (e.g. at non-integer m). The effect manifests itself through the formation of multiple, spatially separated subwavelength regions such that circular and elliptical polarization vectors in adjacent regions are oppositely directed, with the corresponding on-axis components of the spin angular momenta being opposite in sign. Besides, subwavelength regions are formed where transverse energy flows rotate along closed paths, being oppositely directed in the adjacent regions. The optical spin-orbit Hall effect occurs at the tight focus of a cylindrical m-th order vector field if cylindrical symmetry is violated by one way or another.
Assessment of Potential Heavy Metal Contamination Hazards Based on GIS and Multivariate Analysis in Some Mediterranean Zones

One of the most significant challenges that global decision-makers are concerned about is soil contamination. It is also related to food security and soil fertility. The quality of the soil and crops in Egypt are being severely impacted by the increased heavy metal content of the soils in the middle Nile Delta. In Egypt’s middle Nile Delta, fifty random soil samples were chosen. Inverse distance weighting (IDW) was used to create the spatial pattern maps for four heavy metals: Cd, Mn, Pb, and Zn. The soil contamination levels in the research area were assessed using principal component analysis (PCA), contamination factors (CF), the geoaccumulation index (I-Geo), and the improved Nemerow pollution index (In). The findings demonstrated that using PCA, the soil heavy metal concentrations were divided into two clusters. Moreover, the majority of the study region (44.47%) was assessed to be heavily to extremely polluted by heavy metals. In conclusion, integrating the contamination indices CF, I-Geo, and In with the GIS technique and multivariate model, analysis establishes a practical and helpful strategy for assessing the hazard of heavy metal contamination. The findings could serve as a basis for decision-makers to create effective heavy metal mitigation efforts.
Dichotomy of the Personal and the Public in Virtual Communities

The paper is dedicated to the characteristics of online communities in the virtual space. The study is based on the review of manuscripts on social interactions within online and offline communities in social reading of this term, and represents the key characteristics, similarities and differences of interaction in such communities. The analysis of sources suggests that online and real (offline) communities are largely similar in their features, and that virtual communities as self-organizing networks of interactive interaction, like real social communities, are formed on the basis of the common interests and goals of their participants. Real and virtual communities are located in the same social space, virtual communities take on some characteristics of traditional ones, and vice versa. The possibility of supplementing or replacing spatial connections in real communities with virtual interaction forms the socalled convergent communities. Like virtual ones, they are characterized by a high emotional involvement of community members in the communication process, but at the same time, they lose some of the properties traditionally inherent in communities due to a single territory, history, culture, and have a high potential to form a modified, virtual environment of interaction.
Theory for the Beam Splitter in Quantum Optics: Quantum Entanglement of Photons and Their Statistics, HOM Effect

The theory of the beam splitter (BS) in quantum optics is well developed and based on fairly simple mathematical and physical foundations. This theory has been developed for any type of BS and is based on the constancy of the reflection coefficients R (or the transmission coefficient T, where R+T=1) and the phase shift ϕ. It has recently been shown that the constancy of these coefficients cannot always be satisfied for a waveguide BS, where R and ϕ depend in a special way on photon frequencies. Based on this, this review systematizes the concept of BS in quantum optics into “Conventional” and frequency-dependent BS, and also presents the theory of such BS. It is shown that the quantum entanglement, photon statistics at the output ports, and the Hong–Ou–Mandel (HOM) effect for such BS can be very different. Taking into account the fact that the waveguide BS is currently acquiring an important role in quantum technologies due to the possibility of its miniaturization, this review will be useful not only for theoreticians, but also for experimenters.