Reviewerly/exHarmony huggingface dataset:
Noise-tolerant clustering via joint doubly stochastic matrix regularization and dual sparse coding

Clustering has received a lot of attention and research in many important fields, such as machine learning and data mining. Especially, the clustering method based on non-negative matrix factorization (NMF) has been widely used. However, the following problems still exist. First, the clustering method based on the traditional NMF cannot handle noise and outliers well, although the reconstruction error can be measured by the l2,1-norm instead Frobenius norm to improve the robustness, the effect is not obvious. Second, NMF based on graph regularization mostly relies on initial similarity graph, the method of constructing the graph is fixed and cannot update the graph adaptively. Third, there is no sparse constraints both on the basis matrix and the coefficient matrix, which may cause important information to be ignored during clustering. Therefore, in order to solve the above problems, we propose a joint doubly stochastic matrix regularization and dual sparse coding framework (DSNMF). Specifically, we use correntropy instead of the Euclidean distance to overcome the influence of non-Gaussian noise and outliers, making NMF more robust. In addition, the adaptive graph learning can learn a high-quality graph through doubly stochastic matrix to fully maintain local smoothness. Furthermore, we perform sparse coding both on the basis matrix and the coefficient matrix to make full use of the sparsity of the matrix. Finally, the experimental results on eleven datasets show that our method is better than other methods in most cases, and our DSNMF is robust to noise and outliers.
Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking

Multi-Optical Theodolite Tracking systems (MOTTs) can stealthily extract the target's status information from bearings only through non-contact measurement. The constrained MOTTs are partially compatible, yet many existing research works and results are based on the known model, ignoring its discrimination with the target maneuvering behavior pattern. To compensate for these mismatches, this paper develops a Measurement-driven Gauss-Hermite Particle Filter (MGHPF), which elegantly fuses the spatiotemporal constraints and its soft form to perform MOTT missions. Specifically, the target dynamic model and tracking algorithm are based on the target behavior pattern with the adaptive turn rate, fully exploiting the spatial epipolar geometry characteristics for each intersection measurement by a minimax strategy. Then, the center of the feasible area is approximated via the analytic coordinate transformation, and the latent samples are updated via the deterministic Gauss-Hermite integral method with the target's predictive turn rate. Simultaneously, the effects of truncation correction and compensation feedback from the current measurement and historical estimation data are adaptively incorporated into the PF's importance distribution to cover the mixture likelihood. Besides, an effective causality-invariant updating rule is provided to estimate the parameters of these soft spatiotemporal constrained MOTTs with convergence guarantees. Simulated and measured results show good agreement; compared with the state-of-the-art Multi-Model Rao-Blackwell Particle Filter (MMRBPF), the proposed MGHPF improves the filtering accuracy by 7.4%-34.7% and significantly reduces the computational load.
Cross-sensor vision system for maritime object detection

Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.
Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer

Airborne quantum key distribution (QKD) that can synergize with terrestrial networks and quantum satellite nodes is expected to provide flexible and relay links for the large-scale integrated communication network. However, the photon transmission rate would be randomly reduced, owing to the random distributed boundary layer that surrounding to the surface of the aircraft when the flight speed larger than Mach 0.3. Here, we investigate the airborne QKD performance with the BL effects. Furthermore, we take experimental data of supersonic BL into the model and compare the airborne QKD performance under different conditions. Simulation results show that, owing to the complex small-scale turbulence structures in the supersonic boundary layer, the deflection angle and correspondingly drifted offset of the beam varied obviously and randomly, and the distribution probability of photons are redistributed. And the subsonic and supersonic boundary layer would decrease ~35.8% and ~62.5% of the secure key rate respectively. Our work provides a theoretical guidance towards a possible realization of high-speed airborne QKD.
Look at me now! Enfacement illusion over computer-generated faces

According to embodied cognition research, one's bodily self-perception can be illusory and temporarily shifted toward an external body. Similarly, the so-called "enfacement illusion" induced with a synchronous multisensory stimulation over the self-face and an external face can result in implicit and explicit changes in the bodily self. The present study aimed to verify (i) the possibility of eliciting an enfacement illusion over computer-generated faces and (ii) which multisensory stimulation condition was more effective. A total of 23 participants were asked to look at a gender-matched avatar in three synchronous experimental conditions and three asynchronous control conditions (one for each stimulation: visuotactile, visuomotor, and simple exposure). After each condition, participants were asked to complete a questionnaire assessing both the embodiment and the enfacement sensations to address different facets of the illusion. Results suggest a stronger effect of synchronous vs. asynchronous stimulation, and the difference was more pronounced for the embodiment items of the questionnaire. We also found a greater effect of visuotactile and visuomotor stimulations as compared to the simple exposure condition. These findings support the enfacement illusion as a new paradigm to investigate the ownership of different face identities and the specific role of visuotactile and visuomotor stimulations with virtual reality stimuli.
Privacy-preserving data dissemination scheme based on Searchable Encryption, publish–subscribe model, and edge computing

Attribute-based Searchable Encryption is emerging as a promising cryptographic technique supporting data protection, flexible access control, and keyword search over encrypted data. The current scientific literature already investigated its adoption in cloud-based services and additionally explored the usage of edge computing to implement some of the cryptographic tasks in scenarios with limited computational capabilities (such as Internet of Things). In the majority of the available solutions, however, the remote cloud is still responsible for data storage, keyword search over encrypted data, and delivery tasks. Indeed, the heavy computational load generated in scenarios with multiple data producers and data consumers (never studied yet) and large communication latencies can inevitably compromise the overall system performance. To bridge this gap, this work proposes a decentralized service architecture offering privacy-preserving data dissemination, by jointly leveraging attribute-based Searchable Encryption techniques, publish–subscribe communication model, and edge computing capabilities. Here, customized Edge Servers are deployed at the network edge to (i) collect subscription requests encoded via Searchable Encryption Trapdoors, (ii) receive data publications, encrypted via Attribute-based Searchable Encryption scheme, (iii) implement keyword search over encrypted data, and (iv) deliver encrypted data only to authorized requesters. Experimental tests explored the impact of network configuration and traffic load on both communication latency and energy consumption. Obtained results demonstrated the unique ability of the proposed solution to achieve shorter data delivery delays as well as less energy consumption with respect to cloud-based alternatives.
FLIP: A New Approach for Easing the Use of Federated Learning

Over the last few years, there have been several attempts to provide software tools for the development of federated learning (FL) models. However, both the complexity of the concept itself and the high entry barrier of these tools have meant that their adoption has been limited. Considering the related benefits, especially in terms of preserving data privacy, and the need for this type of solution in specific areas where data sharing is impossible, not only from a practical point of view but also from a legal and even ethical perspective, it is necessary to advance in solutions that allow its use to be democratised and its deployment to be extended. With this objective in mind, FLIP (Federated Learning Interactive Platform) has been developed as a comprehensive, easy-to-use fully functional web-based FL network management platform that eases and accelerates the usage of federated datasets by researchers in real scenarios. In this sense, FLIP has achieved a SUS score of 84.64, confirming a high level of perceived usability as expected. Taking this into account, FLIP can help increase the productivity and adoption of FL by a wider audience.
Copyright Protection of Music Multimedia Works Fused With Digital Audio Watermarking Algorithm

Copyright law is important in the media sector since an original creative work owner has the exclusive right to consent, publish, broadcast, and even translate or modify their work. A growing number of digital copyright issues can be found behind the widespread use of multimedia technologies. Improvements must be made right away to the copyright infringement prevention approach using digital watermarking. Zero-watermarking has lately gained popularity as one of the alternatives being considered. A novel sparse representation persistent-based digital audio watermarking algorithm (SRP-DAWA) has been presented to increase zero resilience. Using the improved singular value decomposition (iSVD) technique, an optimum over-complete dictionary can be generated from the background audio signal in the suggested method. Using the orthogonal matching pursuit (OMP) method, the sparse coefficient of a fragmented sample data is calculated, and the corresponding sparse matrix is generated.
Phenomenological model of superconducting optoelectronic loop neurons

Superconducting optoelectronic loop neurons are a class of circuits potentially conducive to networks for large-scale artificial cognition. These circuits employ superconducting components including single-photon detectors, Josephson junctions, and transformers to achieve neuromorphic functions. To date, all simulations of loop neurons have used first-principles circuit analysis to model the behavior of synapses, dendrites, and neurons. These circuit models are computationally inefficient and leave opaque the relationship between loop neurons and other complex systems. Here we introduce a modeling framework that captures the behavior of the relevant synaptic, dendritic, and neuronal circuits at a phenomenological level without resorting to full circuit equations. Within this compact model, each dendrite is discovered to obey a single nonlinear leaky-integrator ordinary differential equation, while a neuron is modeled as a dendrite with a thresholding element and an additional feedback mechanism for establishing a refractory period. A synapse is modeled as a single-photon detector coupled to a dendrite, where the response of the single-photon detector follows a closed-form expression. We quantify the accuracy of the phenomenological model relative to circuit simulations and find that the approach reduces computational time by a factor of ten thousand while maintaining an accuracy of one part in ten thousand. We demonstrate the use of the model with several basic examples. The net increase in computational efficiency enables future simulation of large networks, while the formulation provides a connection to a large body of work in applied mathematics, computational neuroscience, and physical systems such as spin glasses.
Variational Quantum Algorithms for Computational Fluid Dynamics

Quantum computing uses the physical principles of very small systems to develop computing platforms which can solve problems that are intractable on conventional supercomputers. There are challenges not only in building the required hardware but also in identifying the most promising application areas and developing the corresponding quantum algorithms. The availability of intermediate-scale noisy quantum computers is now propelling the developments of novel algorithms, with applications across a variety of domains, including in aeroscience. Variational quantum algorithms are particularly promising because they are comparatively noise tolerant and aim to achieve a quantum advantage with only a few hundred qubits. Furthermore, they are applicable to a wide range of optimization problems arising throughout the natural sciences and industry. To demonstrate the possibilities for the aeroscience community, we give a perspective on how variational quantum algorithms can be used in computational fluid dynamics. We discuss how classical problems are translated into quantum algorithms and their logarithmic scaling with problem size. For an explicit example, we apply this method to Burgers’s equation in one spatial dimension. We argue that a quantum advantage over classical computing methods could be achieved by the end of this decade if quantum hardware progresses as currently envisaged and emphasize the importance of joining up development of quantum algorithms with application-specific expertise to achieve a real-world impact.
Multicycle disassembly-based decomposition algorithm to train multiclass support vector machines

Employing the classic optimization solver to train a multiclass support vector machine (SVM) requires prohibitive training time as the sample size and number of categories increase. It has been proposed to develop the corresponding decomposition algorithm (DA) as it is efficient for training SVMs. However, the dual problem of multiclass SVM comprises complex constraints that complicate DA design, so no corresponding DA has yet been developed. We propose a multicycle disassembly-based DA (MCD-DA) to efficiently solve the training problem of multiclass SVM. First, a graph model is constructed to re-express the constraints in multiclass SVM. Then, the original complex feasible region is partitioned into several simple sub-feasible regions, and multiple cycle-based disassembly strategies are designed to update the working variables analytically within each specific sub-feasible region. We mathematically verify that MCD-DA can stop within a finite number of cycle disassemblies and reach the τ-optimal solution satisfying relaxed Karush–Kuhn–Tucker conditions. Remarkably, MCD-DA as a universal decomposition algorithm can be used to solve many other SVM variants, including C-SVM, v-SVM, and one-class SVM. Experimental results using six UCI datasets demonstrate that MCD-DA outperforms typical optimization algorithms for more sample cases.
Towards an Accessible Mathematics Working Environment Based on Isabelle/VSCode

The paper collects preparatory work for interdisciplinary collaboration between three partners, between (1) expertise in improving accessibility of studies for impaired individuals, (2) expertise in developing educational mathematics software and (3) expertise in designing and implementing interactive proof assistants. The cooperation was started with the goal to develop an accessible mathematics working environment for education with reasonable efforts. The start was triggered by the lucky discovery that the upcoming Isabelle/VSCode is greatly accessible for blind users without further impairments; this is envisaged as the project's target group. Technical details are described to an extent necessary to understand essential details of efforts required for development. A survey of demand from practice of education with respect to (1) and (2) leads to a vision for educational math software, which necessarily is sketchy but suffices to guide development and which shall invite experts in didactics of mathematics to collaborate.
On Exams with the Isabelle Proof Assistant

We present an approach for testing student learning outcomes in a course on automated reasoning using the Isabelle proof assistant. The approach allows us to test both general understanding of formal proofs in various logical proof systems and understanding of proofs in the higher-order logic of Isabelle/HOL in particular. The use of Isabelle enables almost automatic grading of large parts of the exam. We explain our approach through a number of example problems, and explain why we believe that each of the kinds of problems we have selected are adequate measures of our intended learning outcomes. Finally, we discuss our experiences using the approach for the exam of a course on automated reasoning and suggest potential future work.
A differential privacy based multi-stage network fingerprinting deception game method

As an active deception defense mechanism, network fingerprint obfuscation plays an important role in deceiving the attacker's fingerprinting actions. However, the existing methods show increasing limitations in coping with strategic multi-stage fingerprinting behavior in resource constrained situations. In order to solve this problem, we propose a Differential Privacy based Multi-stage fingerprinting deception Game method (DPMG). DPMG uses the multi-stage game model and the belief update mechanism to give a better description on the continuous fingerprinting interaction process. Besides, the equilibrium existence of such a game model is analyzed. Then a defender strategy optimization mechanism under the resource constraints is designed and a Recursive Algorithm Solving Equilibrium algorithm (RASE) to solve the equilibrium strategy is implemented. Experiment results show that DPMG can deceive the network fingerprinting behavior effectively under the defense resources constraints, and scale well to different network size.
Flexible High-Resolution Continuous 3-D Scanning for Large-Scale Industrial Components

Generating a large-scale high-density point cloud is crucial for three-dimensional shape measurements. Both area-scan and line-scan camera systems have advantages and limitations for large-scale structured light measurement. Consequently, we propose a method that achieves complementary advantages of both the camera systems through flexible continuous scanning with high resolution and frequency. For point cloud generation, efficient scanning in continuous motion with high resolution, large field of view, and high frequency is realized using line-scan cameras. A flexible registration strategy without mechanical positioning is proposed for point cloud registration after referring to the registration methods in area-scan camera systems. To realize the above functions, system structure and calibration are designed. Techniques for point cloud generation with high dynamic performance and interference resistance are introduced. The point cloud registration strategy includes methods for high-frequency pose acquisition and processing. Moreover, an optimization method is designed for the point cloud after registration, and the texture is attached. The proposed system has excellent prospects for large-scale industrial component measurements.
Toward Full-Immersive Multiuser Virtual Reality With Redirected Walking

We are witnessing a surge in the popularity and mass-availability of Virtual Reality (VR) technology. Toward enhancing its maturity, current research is focusing on achieving continuous full immersiveness of the VR users during their experiences in Virtual Environments (VEs), as well as on enabling multiuser experiences. In many cases, achieving the full immersiveness requires enabling the users’ perception of walking naturally in unbound VEs. Redirected Walking (RDW) algorithms provide a solution to this problem by exploiting the limitations of the human spatial perception. In other words, the RDW algorithms are envisioned to unnoticeably steer the VR users for constraining them within a certain tracking space. Regardless of notable research efforts targeting RDW, there are still not many experimental evaluations of the performance of RDW algorithms, especially in multiuser settings and utilizing contemporary Head-Mounted Devices (HMDs). Toward addressing this issue, we have developed a modular framework that enables straightforward experimentation with single and multiuser RDW in different tracking spaces and for varying VEs. The capabilities of the developed framework were demonstrated by carrying out an extensive experimentation study to capture the performance and noticeability of a contemporary RDW algorithm for a varying number of users in different tracking spaces and VEs. The lessons learned during the study have been conceptualized in a form of a set of RDW design enhancements that can be generically applied to existing RDW algorithms. We show that the proposed enhancements significantly increase the overall performance of RDW and decrease the noticeability of RDW-supported experiences in VEs.
Graph-Based Profiling of Blockchain Oracles

The usage of blockchain technology has been significantly expanded with smart contracts and blockchain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oracles</i> . While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly. this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.