Reviewerly/exHarmony huggingface dataset:
Advanced signature functionalities from the code equivalence problem

The LESS signature scheme, introduced in 2020, represented a fresh start for codebased signatures.In this paper we explore advanced functionalities for signature schemes, stemming from the work of LESS.First, we adapt a recent protocol of Beullens et al. to obtain a construction for (linkable) ring signatures.Then, we realize an identity-based signature scheme following the traditional approach by Joye and Neven.Our performance numbers confirm that signature schemes based on the code equivalence problem have considerable potential for practical applications.
Objective Quality Assessment Metrics for Light Field Image Based on Textural Features

Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account the image content and human visual system such as SSIM and IW-SSIM. However, most of these metrics are designed for images and video with natural content. Additionally, other models based on the LF characteristics (e.g., depth information, angle information) trade high performance for high computational complexity, along with them possessing difficulties of implementation for LF applications due to the immense data requirements of LF images. Hence, this paper presents a novel content-adaptive LF-IQA metric to improve the conventional LF-IQA performance that is also low in computational complexity. The experimental results clearly show improved performance compared to conventional objective IQA metrics, and we also identify metrics that are well-suited for LF image assessment. In addition, we present a comprehensive content-based feature analysis to determine the most appropriate feature that influences human visual perception among the widely used conventional objective IQA metrics. Finally, a rich LF dataset is selected from the EPFL dataset, allowing for the study of light field quality by qualitative factors such as depth (wide and narrow), focus (background or foreground) and complexity (simple and complex).
3D Search Based Hybrid Optimal Trajectory Planning for Autonomous LHD in Turning Maneuvers

Load-haul-dump (LHD) is the main equipment for mining and transportation in underground metal mines. Autonomous driving represents an essential role in the daily operation of LHD. Nowadays, LHD autonomous driving is based on the human driver’s artificial teaching. However, it is not easy to obtain high quality teaching information for all the underground roadways in a mine, which not only affects the commissioning and deployment time of autonomous driving systems for LHD vehicles, but also not conducive to the full play of automation equipment performance, and finally impacts the production efficiency. Planning a high-quality target reference trajectory for LHD in the underground roadway is an effective way to overcome the above issues. This paper proposes an optimal trajectory planning method for LHD in turning maneuvers. This planning method is based on longitudinal and lateral decomposing, integrated sampling method, and optimization method. The contribution of this work lies in the 3D key factors search strategy design, the key parameters value range exploration and the reasonable key factor initial values design, which can improve the online execution ability of the algorithm and reduce the driving time of the LHD. The 3D key factors refer to the terminal state of the trajectory, which are longitudinal driving distance, the lateral driving distance, and travel time. The proposed method is simple and easy to implement. The initial values of the key factors are given, and the effectiveness of the proposed method is proved. The feasibility and effectiveness of the proposed method are verified by the comparative analysis of a series of examples.
Quantum supremacy and hardness of estimating output probabilities of quantum circuits

Motivated by the recent experimental demonstrations of quantum supremacy, proving the hardness of the output of random quantum circuits is an imperative near term goal. We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that approximating the output probabilities of random quantum circuits to within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\exp(-\Omega(m\log m))$</tex> additive error is hard for any classical computer, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> is the number of gates in the quantum computation. More precisely, we show that the above problem is #P-hard under BPP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NP</sup> reduction. In the recent experiments, the quantum circuit has n-qubits and the architecture is a two-dimensional grid of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sqrt{n}\times\sqrt{n}$</tex> [1]. Indeed for constant depth circuits approximating the output probabilities to within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{-\Omega(n\log n)}$</tex> is hard. For circuits of depth <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\log n$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sqrt{n}$</tex> for which the anti-concentration property holds, approximating the output probabilities to within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{-\Omega(n\log^{2}n)}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{-\Omega(n^{3/2}\log n)}$</tex> is hard respectively. We then show that the hardness results extend to any open neighborhood of an arbitrary (fixed) circuit including the trivial circuit with identity gates. We made an effort to find the best proofs and proved these results from first principles, which do not use the standard techniques such as the Berlekamp–Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.
Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition

Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dance genre). We propose a Hierarchical Dance Video Recognition framework (HDVR). HDVR estimates 2D pose sequences, tracks dancers, and then simultaneously estimates corresponding 3D poses and 3D-to-2D imaging parameters, without requiring ground truth for 3D poses. Unlike most methods that work on a single person, our tracking works on multiple dancers, under occlusions. From the estimated 3D pose sequence, HDVR extracts body part movements, and therefrom dance genre. The resulting hierarchical dance representation is explainable to experts. To overcome noise and interframe correspondence ambiguities, we enforce spatial and temporal motion smoothness and photometric continuity over time. We use an LSTM network to extract 3D movement subsequences from which we recognize dance genre. For experiments, we have identified 154 movement types, of 16 body parts, and assembled a new University of Illinois Dance (UID) Dataset, containing 1143 video clips of 9 genres covering 30 hours, annotated with movement and genre labels. Our experimental results demonstrate that our algorithms outperform the state-of-the-art 3D pose estimation methods, which also enhances our dance recognition performance.
General lower and upper bounds under minimum-error quantum state discrimination

For the optimal success probability under minimum-error discrimination between $r\ensuremath{\ge}2$ arbitrary quantum states prepared with any a priori probabilities, we find new general analytical lower and upper bounds and specify the relations between these new general bounds and the known general bounds, lower and upper. We also present the example where the values of the new general lower and upper bounds on the optimal success probability are tighter than the values of most of the general analytical bounds known in the literature. The new upper bound on the optimal success probability explicitly generalizes to $r&gt;2$ the form of the Helstrom bound. For $r=2$, each of our new bounds, lower and upper, reduces to the Helstrom bound.
Hybrid Regression Model for Medical Insurance Cost Prediction and Recommendation

As the global value of gross insurance premiums continues to rise beyond $5 trillion, we know that the majority of these costs are avoidable. The cost prediction of people's medical insurance is a useful method for improving the transparency of health care. We use different regression models to analyze personal health data in order to predict insurance amounts for individuals in this study. The cost of insurance premiums is influenced by a number of factors. Health insurers would benefit from using a Stacking Regression model to predict insurance costs for individuals. According to this perception using recommendation graphs, a person can lower his insurance costs by lowering his BMI, moving to a different area, or switching to a non-smoker.
Your Model Trains on My Data? Protecting Intellectual Property of Training Data via Membership Fingerprint Authentication

In recent years, data has become the new oil that fuels various machine learning (ML) applications. Just as the oil refining, providing data to an ML model is a product of massive costs and expertise efforts. However, how to protect the intellectual property (IP) of the training data in ML remains largely open. In this paper, we present MeFA, a novel framework for detecting training data IP embezzlement via Membership Fingerprint Authentication, which is able to determine whether a suspect ML model is trained on the to be protected target data or not. The key observation is that a part of data has a similar influence on the prediction behavior of different ML models. On this basis, MeFA leverages membership inference techniques to extract these data as the fingerprints of the target data and constructs an authentication model to verify the data’s ownership by identifying the obtained membership fingerprints. MeFA has several salient features. It does not assume any knowledge of the suspect model except for its black-box prediction API, through which we can merely get the prediction output of a given input, and also does not require any modification to the dataset or the training process, since it takes advantage of the inherent membership property of the data. As a by-product, MeFA can also serve as a post-protection to verify the ownership of ML models, without modifying the training process of the model. Extensive experiments on three realistic datasets and seven types of ML models validate the effectiveness of MeFA, and demonstrate that it is also robust to scenarios when the training data is partially used or preprocessed with representative membership inference defenses.
Blockchain Integrated Machine Learning for Training Autonomous Cars

Autonomous cars have always been fascinating towards the coming generation of we techies and since then training them has also been an important concern. That’s when we can consider Machine Learning integrated with Blockchain to provide high security to build this model. Machine Learning and Blockchain are two very innovative domains of computing. There has been a constant improvement in neural networks in past years. Since Artificial Intelligence-based learning algorithms are taken into account and a drive towards the training of autonomous cars is seen. Here, we are going to train a single car with great precision and accuracy, and then this alone trained car will share the data with all the other cars in its network. Hence, all of them will be sharing a particular network and the data will be exchanged. Now, when it comes to the learning of cars, we will be creating a blockchain network that will connect every car for that particular company. In this way, while in a dynamic condition also, the cars will stay connected with each and every one and the data will be exchanged. So, the training will be done using Deep Neural Networks and since the data transfer and weights update requires high security, we will be using Blockchain. For example, if any car gets hit by an accident or due to any possible fatal breakdown or due to any changes in the route or signals (government laws), this data will be transmitted to each other car in this network. Hence every car will get its weight updated to avoid or tackle the situation. This in the end will decrease the computational time and increase the measure of safety and well-being.
Generative Metric Learning for Adversarially Robust Open-world Person Re-Identification

The vulnerability of re-identification (re-ID) models under adversarial attacks is of significant concern as criminals may use adversarial perturbations to evade surveillance systems. Unlike a closed-world re-ID setting (i.e., a fixed number of training categories), a reliable re-ID system in the open world raises the concern of training a robust yet discriminative classifier, which still shows robustness in the context of unknown examples of an identity. In this work, we improve the robustness of open-world re-ID models by proposing a generative metric learning approach to generate adversarial examples that are regularized to produce robust distance metric. The proposed approach leverages the expressive capability of generative adversarial networks to defend the re-ID models against feature disturbance attacks. By generating the target people variants and sampling the triplet units for metric learning, our learned distance metrics are regulated to produce accurate predictions in the feature metric space. Experimental results on the three re-ID datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17 demonstrate the robustness of our method.
A novel quantum model of forward kinematics based on quaternion/Pauli gate equivalence: Application to a six-jointed industrial robotic arm

A novel quantum-based kinematic model for computing the position and the orientation of the six-jointed IRB 140 ABB robotic arm is presented. Position is determined through successive elementary translations and rotations from the end effector down to the base frame, whereas orientation is derived from quaternion representation. The model is based on the equivalence between quaternions and Pauli matrices or gates. Based on comparison to experimental results, the quantum kinematic model is valid. Its main advantage is that it uses only one qubit to model both position and orientation, which represents a major saving in terms of computing resources and time. This model could guide development of other quantum models and simulation software to be used in future quantum computers and could also inform classical computing theory for robotic arm kinematics modeling.
Real-time uncertainty estimation of stripe center extraction results using adaptive BP neural network

Center extraction of stripe images is a key technique of line structured light sensors (LSLSs). As the original data for profile computation, the extraction results, which are highly affected by image quality, should be accurate and reliable. While, few works can be found for real-time uncertainty evaluation of the extraction results due to the diversity of center extraction methods and the complexity of uncertainty models. Here, a universal method is proposed to evaluate the uncertainty of center extraction results obtained from classical methods including, but not limited to, the gray gravity method (GGM), Gaussian fitting method (GFM) and Steger method. The proposed method is based on an adaptive BP neural network (ABPNN) where its weights and thresholds are adjusted automatically according to the width of each cross section profile. Experimental results show that the ABPNN can predict the uncertainty value of center extraction results accurately and efficiently.
A Lightweight Network for Violence Detection

Video violence detection is an application area under the field of action recognition, which refers to the detection of violent behavior in video sequences. Existing methods or deep learning models, while capable of effective detection, are not as efficient when applied to real-time violence detection scenarios. Based on the purpose of reducing model computation and coping with the specificity of violence detection scenarios, a new violence detection network is proposed in this paper. This network uses depthwise separable convolution to reduce the number of model parameters and the number of computations; in addition, the network is based on the idea of mixing 2D and 3D convolution to further reduce the number of parameters. The model proposed in this paper is compared with other mainstream models on several datasets (Movies, Hockey, Crowd, RWF-2000). The experiments show that this model significantly reduces the number of parameters as well as improves the inference speed within an acceptable range of accuracy degradation.
Testing the Plasticity of Reinforcement Learning-based Systems

The dataset available for pre-release training of a machine-learning based system is often not representative of all possible execution contexts that the system will encounter in the field. Reinforcement Learning (RL) is a prominent approach among those that support continual learning, i.e., learning continually in the field, in the post-release phase. No study has so far investigated any method to test the plasticity of RL-based systems, i.e., their capability to adapt to an execution context that may deviate from the training one. We propose an approach to test the plasticity of RL-based systems. The output of our approach is a quantification of the adaptation and anti-regression capabilities of the system, obtained by computing the adaptation frontier of the system in a changed environment. We visualize such frontier as an adaptation/anti-regression heatmap in two dimensions, or as a clustered projection when more than two dimensions are involved. In this way, we provide developers with information on the amount of changes that can be accommodated by the continual learning component of the system, which is key to decide if online, in-the-field learning can be safely enabled or not.
RTNN

Neighbor search is of fundamental importance to many engineering and science fields such as physics simulation and computer graphics. This paper proposes to formulate neighbor search as a ray tracing problem and leverage the dedicated ray tracing hardware in recent GPUs for acceleration. We show that a naive mapping under-exploits the ray tracing hardware. We propose two performance optimizations, query scheduling and query partitioning, to tame the inefficiencies. Experimental results show 2.2X - 65.0X speedups over existing neighbor search libraries on GPUs. The code is available at https://github.com/horizon-research/rtnn.
SecureCPS: Cognitive inspired framework for detection of cyber attacks in cyber–physical systems

In the era of autonomous systems, the security is indispensable module for flexible computing environment. Due to increased computer power and network speed, a new computing paradigm, such as cognitive inspired computing, will emerge. Such a paradigm provides human-centered services that are convenient and enjoyable at any time, anywhere, and on any device. On the foundation of smart city environment, human computer interaction, intelligent services, and universal device connectivity, Cyber Physical Computing for Cyber Physical systems has recently been investigated. However, in this proposal, a cognitive inspired framework for securing CPS is scrutinized. The cognitive ability is conceded to the search engines by updating the PageRank ranking methodology. The proposed framework, named SecureCPS is trained with real time collective dataset for marking the relevancy of web page with the support the facial expressions. The eye regions are marked using Focal Point Detector algorithm. The framework is validated with machine learning models and resulted in achieving 98.51% accuracy and its outperforms the existing frameworks.
Parameter mismatch phase extraction method for spatial phase-shifting interferograms

Mismatch of interferogram parameters, such as the contrast and the fringe frequency inconsistency, can lead to phase error in simultaneous phase-shifting interferometry. In the dynamic Fizeau interferometer (DFI) based on the lateral displacements of light sources, an extended light source is used to suppress coherent noise. Aiming at the contrast mismatch generated by the extended light source and the inherent fringe frequency mismatch of the DFI, We propose a phase-extraction method to solve these problems. Based on this dynamic interferogram model, a linear relationship is constructed between the tilt angle and interferogram contrast, carrier frequency error, phase-shift error. Use the two steps of initial estimation and fine iterations to extract the phase. The RMS of the residuals was better than 1.5 × 10–5λ for the simulation and 0.004 λ for the experimental calculation, which demonstrates that the ripple error was suppressed during phase extraction.
ABFIA: A hybrid algorithm based on artificial bee colony and Fibonacci indicator algorithm

The artificial bee colony (ABC) is a metaheuristic optimization algorithm known for its simplicity, flexibility, and efficiency. The algorithm, however, suffers from slow convergence due to a lack of a powerful local search capability. The Fibonacci indicator algorithm (FIA), on the other hand, is a recently proposed derivative-free metaheuristic that incorporates a powerful local search mechanism based on the line search method. This paper proposes hybridizing the artificial bee colony with the Fibonacci indicator algorithm to achieve strong exploration and highly efficient exploitation capabilities. We show that the hybrid algorithm is better than ABC and FIA and delivers superior outcomes for various optimization functions widely used in the literature, including 20 scalable basic and ten complex CEC2019 test functions.