Reviewerly/exHarmony huggingface dataset:
Equivalence between finite state stochastic machine, non-dissipative and dissipative tight-binding and Schrödinger model

The mathematical equivalence between finite state stochastic machine and non-dissipative and dissipative quantum tight-binding and Schrödinger model is derived. Stochastic Finite state machine is also expressed by classical epidemic model and can reproduce the quantum entanglement emerging in the case of electrostatically coupled qubits described by von-Neumann entropy both in non-dissipative and dissipative case. The obtained results show that quantum mechanical phenomena might be simulated by classical statistical model as represented by finite state stochastic machine. It includes the quantum like entanglement and superposition of states. Therefore coupled epidemic models expressed by classical systems in terms of classical physics can be the base for possible incorporation of quantum technologies and in particular for quantum like computation and quantum like communication. The classical density matrix is derived and described by the equation of motion in terms of anticommutator. Existence of Rabi like oscillations is pointed in classical epidemic model. Furthermore, the existence of Aharonov–Bohm effect in quantum systems (Becker et al., 2019) can also be reproduced by the classical epidemic model or in broader sense by finite state stochastic machine. Every quantum system made from quantum dots and described by simplistic tight-binding model by use of position-based qubits can be effectively described by classical statistical model encoded in finite stochastic state machine with very specific structure of S matrix that has twice bigger size as it is the case of quantum matrix Hamiltonian. Furthermore, the description of linear and non-linear stochastic finite state machine is mapped to tight-binding and Schrödinger model. The concept of N dimensional complex time is incorporated into tight-binding model, so the description of dissipation in most general case is possible. Obtained results help in approximation of non-dissipative or dissipative quantum mechanical phenomena by classical statistical physics expressed by finite state stochastic machine. Furthermore, the equivalence of Wannier tight-binding dissipative or non-dissipative formalism to dissipative or non-dissipative Schrödinger formalism and to finite state stochastic machine was shown.
XyGen: Synthetic data generator for feature selection

Given the large number of feature selection algorithms, it has become imperative to have a uniform procedure for evaluating the performance of the algorithms. We propose a library of synthetic datasets designed specifically to test the effectiveness of feature selection algorithms. The datasets are inspired by applications in the field of electronics and have a range of characteristics to provide a variety of test scenarios. The software comes in the form of a Python library with standard interface for loading and generating datasets. Each dataset is implemented as a function that allows control of various parameters of the data.
Privacy-Preserving Decision-Tree Evaluation with Low Complexity for Communication

Due to the rapid development of machine-learning technology, companies can build complex models to provide prediction or classification services for customers without resources. A large number of related solutions exist to protect the privacy of models and user data. However, these efforts require costly communication and are not resistant to quantum attacks. To solve this problem, we designed a new secure integer-comparison protocol based on fully homomorphic encryption and proposed a client-server classification protocol for decision-tree evaluation based on the secure integer-comparison protocol. Compared to existing work, our classification protocol has a relatively low communication cost and requires only one round of communication with the user to complete the classification task. Moreover, the protocol was built on a fully homomorphic-scheme-based lattice that is resistant to quantum attacks, as opposed to conventional schemes. Finally, we conducted an experimental analysis comparing our protocol with the traditional approach on three datasets. The experimental results showed that the communication cost of our scheme was 20% of the cost of the traditional scheme.
3D Autonomous Surgeon’s Hand Movement Assessment Using a Cascaded Fuzzy Supervisor in Multi-Thread Video Processing

The purpose of the Fundamentals of Laparoscopic Surgery (FLS) training is to develop laparoscopic surgery skills by using simulation experiences. Several advanced training methods based on simulation have been created to enable training in a non-patient environment. Laparoscopic box trainers-cheap, portable devices-have been deployed for a while to offer training opportunities, competence evaluations, and performance reviews. However, the trainees must be under the supervision of medical experts who can evaluate their abilities, which is an expensive and time-consuming operation. Thus, a high level of surgical skill, determined by assessment, is necessary to prevent any intraoperative issues and malfunctions during a real laparoscopic procedure and during human intervention. To guarantee that the use of laparoscopic surgical training methods results in surgical skill improvement, it is necessary to measure and assess surgeons' skills during tests. We used our intelligent box-trainer system (IBTS) as a platform for skill training. The main aim of this study was to monitor the surgeon's hands' movement within a predefined field of interest. To evaluate the surgeons' hands' movement in 3D space, an autonomous evaluation system using two cameras and multi-thread video processing is proposed. This method works by detecting laparoscopic instruments and using a cascaded fuzzy logic assessment system. It is composed of two fuzzy logic systems executing in parallel. The first level assesses the left and right-hand movements simultaneously. Its outputs are cascaded by the final fuzzy logic assessment at the second level. This algorithm is completely autonomous and removes the need for any human monitoring or intervention. The experimental work included nine physicians (surgeons and residents) from the surgery and obstetrics/gynecology (OB/GYN) residency programs at WMU Homer Stryker MD School of Medicine (WMed) with different levels of laparoscopic skills and experience. They were recruited to participate in the peg-transfer task. The participants' performances were assessed, and the videos were recorded throughout the exercises. The results were delivered autonomously about 10 s after the experiments were concluded. In the future, we plan to increase the computing power of the IBTS to achieve real-time performance assessment.
Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.
Shuffled-Xception-DarkNet-53: A content-based image retrieval model based on deep learning algorithm

This paper proposes Shuffled-Xception-DarkNet-53, an advanced version of DarkNet-53 for Content-Based Image Retrieval (CBIR). The proposed model introduced the notion of the Shuffled -Xception module, which uses three sets of 1 × 1, 3 × 3, and 5 × 5 size filters using a serial connection in place of a single 3 × 3 size filter to extract more refined features from the input images. Instead of the standard 2D Convolution operation, 'Group Convolution' is employed in the proposed Shuffled -Xception module to make the training process of the proposed CNN more efficient. Furthermore, 'Group Convolution' improves the co-relations among the filters of the corresponding Shuffled -Xception module, resulting in more informative features. Between every two serial Group Convolution layers of the same size, one Channel Shuffle layer is used to prevent the loss of information flow among the channels of different groups. The proposed method outperformed the twelve compared methods, including conventional and CNN-based CBIR methods, in ten standard image datasets.
Remote Sensing Image Dehazing Using Heterogeneous Atmospheric Light Prior

Remote sensing images (RSIs) captured in haze weather will suffer from serious quality degradation with color distortion and contrast reduction, which creates numerous challenges for the utilization of RSIs. To address these issues, this paper proposes a novel haze removal algorithm, named HALP, for visible RSIs based on a heterogeneous atmospheric light prior and side window filter. HALP is comprised of two key components. Firstly, given the large imaging space of RSIs, the atmospheric light is treated as a globally non-uniform distribution instead of a global constant. Therefore, a simple and effective method for non-uniform atmospheric light estimation is presented, which utilizes the brightest pixel color in each local image patch as the atmospheric light of the local region. Secondly, a side window filter-based transmission estimation algorithm is proposed, which can effectively suppress the block effect in the transmission map caused by the large window of the minimum filter used in the dark channel algorithm. Experiments on both real-world and synthetic remote sensing haze images demonstrate the effectiveness of HALP. In terms of no-reference and full-reference image quality assessments, HALP yields excellent results, outperforming existing state-of-the-art algorithms, including physics-based and neural network-based methods. The visual comparison of dehazed results also shows that HALP can restore degraded RSIs with uneven haze, producing clear images with rich details and natural colors.
Conceptual Architecture of a Blockchain Solution for E-Voting in Elections at the University Level

Digitalization advances in many fields due to its clear advantages and facilities especially in pandemic times when crowds are to be avoided. E-voting is gaining importance as voting using laptops, mobile phones or tablets is more practical. Hence, human counting which leads to unintentional errors or fraud could be eliminated. Furthermore, software applications can reduce the costs of the classic election process and physical infrastructure. In this paper, we propose to identify the most critical specifications of an e-voting application, find a solution for elections in universities and compare our solution with others. The goal is to propose a conceptual architecture using encrypted functions and two stages: voting and validation, separating layers and roles, that is based on blockchain tables and innovative interactions between actors (voters and voting committee) and the two software components (web application and database). For replicability, the conceptual architecture is depicted and formalized using several Unified Modeling Language (UML) diagrams. Furthermore, in order to provide proof of concept, the initial steps in implementing the proposed solution are showcased.
High-Precision External Parameter Calibration Method for Camera and Lidar Based on a Calibration Device

The fusion of camera and lidar plays an important role in the field of robotic perception. The accurate external parameter calibration is a necessary prerequisite for sensor fusion. Herein, an auxiliary calibration device with distinctive geometric features was designed to address the problems of low accuracy and poor robustness associated with external parameter calibrations of camera and lidar. Moreover, a coarse-to-fine two-stage calibration method was proposed for the external parameters of the camera and lidar. The first stage of the method is the extraction of multiple groups of two-dimensional (2D) and three-dimensional (3D) lines corresponding to the edge of the calibration device from the image and lidar point cloud that yields a unique initial estimation of the external parameters. In the second stage, the 2D–3D center point of the sphere of the calibration device was detected, and the initial external parameters were further optimized using a nonlinear optimization method. The proposed method provides two different features that add stability against noise to the calibration system. Both simulated and actual experiments show that the method can yield high-precision external parameters without an initial value. Compared to state-of-the-art methods, our method has advantages in terms of accuracy. the calibration system has a certain degree of noise resistance and stability under different laser noise and vertical resolution.
Large-Scale Dense Mapping System Based on Visual-Inertial Odometry and Densely Connected U-Net

SLAM-based 3D reconstruction system mainly relies on visual odometry, which uses detailed scanning to obtain better reconstruction results. Among the feature-based SLAM methods, it is difficult to have good reconstruction results where features are missing even when the camera moves slowly. For the direct methods, when exposure changes and blurring occurs, tracking loss will cause unsatisfactory reconstruction effects. This paper proposes a large-scale mapping system based on visual-inertial odometry to solve these problems. The combination of vision and IMU is used to constrain the trajectory estimation in areas where the corners are missing. The system supports depth both obtained by the depth camera and estimated by the neural network. According to the voxel hash mechanism, we only focus on the voxels within the cutoff distance and use the hash table to represent the voxels sparsely to reduce the memory usage. Experiments show that the proposed system can obtain an ideal 3D reconstruction model.
Visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.
Semi-supervised Multi-task Learning with Auxiliary data

Compared with single-task learning, multi-tasks can obtain better classifiers by the information provided by each task. In the process of multi-task data collection, we always focus on the target task data in the training process, and ignore the non-target task data and unlabeled data that may be contained in the target task. In response to this issue, this paper introduces auxiliary or Universum into semi-supervised multi-task problem, and proposes a multi-task support vector machine (SU-MTLSVM) method based on semi-supervised learning to handle the case where each task contains the labeled, unlabeled, and Universum samples in the training set. This method introduces Universum as prior knowledge and provides high-dimensional information for semi-supervised learning, and builds a unique classifier from a large amount of unlabeled data. We then use KKT conditions and Lagrangian method to optimize the formulation of the model, and get the model parameters. Finally, we collect different data sets in the experiment part, and compare the performance of multiple baselines with the proposed method. Experiments prove that the method proposed in this paper is more effective for multi-task applications.
Byzantine Resilient Joint Localization and Target Tracking of Multi-Vehicle Systems

This work investigates the problem of joint localization and target tracking (JLATT) of multi-vehicle systems (MVSs) in the presence of Byzantine attacks (BAs). For MVSs, there may exist malicious and misbehaving vehicles, called Byzantine vehicles, which act as deceivers and pass false information to their neighbors. For the first time, we formulate a resilient JLATT framework to synthesize the hybrid problems of distributed cooperative localization and flock tracking in the presence of BAs. More specifically, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covariance weighted mean-subsequence-filter</i> (CW-MSF) algorithm to guarantee that the MVS achieves flock tracking w.r.t a target even in the presence of a certain fraction of Byzantine vehicles. Note that this resilient JLATT scheme does not require global information and thus it is fully distributed. The practicality and validity of this novel JLATT framework are verified via an illustrative numerical simulation example.
Feedback Pyramid Attention Networks for Single Image Super-Resolution

Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement. However, most CNN-based methods mainly focus on feed-forward architecture design and neglect to explore the feedback mechanism, which usually exists in the human visual system. In this paper, we propose feedback pyramid attention networks (FPAN) to fully exploit the mutual dependencies of features. Specifically, a novel feedback connection structure is developed to enhance low-level feature expression with high-level information. In our method, the output of each layer in the first stage is also used as the input of the corresponding layer in the next state to re-update the previous low-level filters. Moreover, we introduce a pyramid non-local structure to model global contextual information in different scales and improve the discriminative representation of the network. Extensive experimental results on various datasets demonstrate the superiority of our FPAN in comparison with the state-of-the-art SR methods.
A Generalized Residue Number System Design Approach for Ultralow-Power Arithmetic Circuits Based on Deterministic Bit-Streams

The peak power consumption has become an important concern in the hardware design process of some of today’s applications, such as energy harvesting (EH) and bio-implantable (BI) electronic devices. The limited peak harvested power in EH devices and heating concerns in BI devices are the main reasons for power control’s importance in these devices. This article proposes a generalized design approach for ultralow-power arithmetic circuits. The proposed circuits are based on residue number system (RNS) combined with deterministic bit-streams. The resulting circuits can be used in systems with a restricted power budget. We suggest several approaches to design generic hardware-efficient adders, multipliers, multiply-accumulate (MAC) unit, forward converters (FCs), and reverse converters (RCs). Using the proposed approach, designing these components for any moduli of the RNS can be performed through simple bit-width adjustments in the circuits. The synthesis results show that the proposed adder achieves, on average, 69% and 2% lower area compared to the bit-serial and a state-of-the-art RNS adder, respectively. Furthermore, the proposed multiplier outperforms the bit-serial, interleaved, and a state-of-the-art design for multiplying RNS numbers by, on average, 57%, 60%, and 77% in terms of power consumption, respectively. The efficiency of our approach is shown via two essential applications, digital signal processing, and machine learning. We implement an FFT engine using the proposed method. Compared to prior RNS implementations, our design achieves 47% lower power consumption. We also implement a CNN accelerator’s processing element (PE) with the proposed computation elements. Our design provides considerable speedup and lower power consumption compared to a state-of-the-art ultralower-power design.
Two-Stage Reinforcement Learning-Based Differential Evolution for Solving Nonlinear Equations

Solving nonlinear equations (NEs) requires the algorithm to locate multiple roots of NEs in one run. In this article, a generic framework based on two-stage reinforcement learning (RL) and differential evolution (DE) is proposed to effectively deal with NEs problems. The major advantage of our approach are: 1) different niching methods and mutation strategies are integrated into the DE algorithm to assist evolution; 2) the diversity is maintained by utilizing the search characteristics of different niching methods at population level; 3) additionally, each individual is regarded as an agent, and three classical mutation strategies are used as the agent’s alternative actions; and 4) different state settings and reward function can be easily integrated into this framework. To verify the performance of our approach, 30 problems and 18 new NEs are selected as the test suite. The experimental results demonstrate that RL can facilitate the algorithm and improve the problem-solving ability. Moreover, the proposed method also obtains competitive performance compared with other peer algorithms.