Reviewerly/exHarmony huggingface dataset:
Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks

Phase-shifting profilometry (PSP) has been widely used in the measurement of dynamic scenes. However, the object motion will cause a periodical motion-induced error in the phase map, and there is still a challenge to eliminate it. In this paper, we propose a method based on three-stream neural networks to reduce the motion-induced error, while a general dataset establishment method for dynamic scenes is presented to complete three-dimensional (3D) shape measurement in a virtual fringe projection system. The numerous automatically generated data with various motion types is employed to optimize models. Three-step phase-shift fringe patterns captured along a time axis are divided into three groups and processed by trained three-stream neural networks to produce an accurate phase map. The actual experiment’s results demonstrate that the proposed method can significantly perform motion-induced error compensation and achieve about 90% improvement compared with the traditional three-step phase-shifting algorithm. Benefiting from the robust learning-based technique and convenient digital simulation, our method does not require empirical parameters or complex data collection, which are promising for high-speed 3D measurement.
Comparison of Machine Learning Approach for Waste Bottle Classification

The use of machine learning for the image classification process is growing all the time. Many methods can be used to classify an image with good accuracy. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are popular methods for this case. The two approaches have differences in the data training process to achieve classification objectives. Although there are some differences between these approaches, there are some advantages to both of them. This research explores the comparison of the two CNN and SVM methods by comparing the training process carried out and the accuracy results of the classification. The process stages are divided into pre-processing, training, and testing. The objects used are ten waste plastic bottles with different brands of medium size with a total data of 1100 images. Based on the observations, both methods have advantages and disadvantages in the data training and classification process. However, from the results, CNN's accuracy is better than SVM. The accuracy of both networks is 99% for CNN and 74% for SVM, respectively. So, from the results of experiments that have been carried out in the study, it was found that CNN was still better than SVM. Doi: 10.28991/ESJ-2022-06-05-011 Full Text: PDF
Quantum-behaved particle swarm optimization based on solitons

This paper introduces a novel variant of the quantum particle swarm optimization algorithm based on the quantum concept of particle-like solitons as the most common solutions of the quantum nonlinear Schrödinger equation. Soliton adaptation in external potentials is one of their most remarkable features which allows them to be stabilized even without a trapping potential, while the potential must be bounded for quantum particles to be localized. So we consider the motion scenario of the present algorithm based on the corresponding probability density function of quantum solitons. To evaluate the efficiency, we examine the proposed algorithm over a set of known benchmark functions, including a selection of test functions with different modalities and dimensions. Moreover, to achieve a more comprehensive conclusion about the performance, we compare it with the results obtained by particle swarm optimization (PSO), standard quantum-behaved particle swarm optimization QPSO, improved sine cosine Algorithm (ISCA), and JAYA optimization algorithm. The numerical experiments reveal that the proposed algorithm is an effective approach to solving optimization problems that provides promising results in terms of better global search capability, high accuracy, and faster convergence rate.
Characterization and Verification of Trotterized Digital Quantum Simulation Via Hamiltonian and Liouvillian Learning

The goal of digital quantum simulation is to approximate the dynamics of a given target Hamiltonian via a sequence of quantum gates, a procedure known as Trotterization. The quality of this approximation can be controlled by the so called Trotter step, that governs the number of required quantum gates per unit simulation time. The stroboscopic dynamics generated by Trotterization is effectively described by a time-independent Hamiltonian, referred to as the Floquet Hamiltonian. In this work, we propose Floquet Hamiltonian learning to reconstruct the experimentally realized Floquet Hamiltonian order-by-order in the Trotter step. This procedure is efficient, i.e., it requires a number of measurements that scales polynomially in the system size, and can be readily implemented in state-of-the-art experiments. With numerical examples, we propose several applications of our method in the context of verification of quantum devices: from the characterization of the distinct sources of errors in digital quantum simulators to determining the optimal operating regime of the device. We show that our protocol provides the basis for feedback-loop design and calibration of new types of quantum gates. Furthermore it can be extended to the case of non-unitary dynamics and used to learn Floquet Liouvillians, thereby offering a way of characterizing the dissipative processes present in NISQ quantum devices.
A privacy-preserving blockchain-based tracing model for virus-infected people in cloud

The outbreak of COVID-19 has exposed the privacy of positive patients to the public, which will lead to violations of users’ rights and even threaten their lives. A privacy-preserving scheme involving virus-infected positive patients is proposed by us. The traditional ciphertext policy attribute-based encryption (CP-ABE) has the features of enhanced plaintext security and fine-grained access control. However, the encryption process requires the high computational performance of the device, which puts a high strain on resource-limited devices. After semi-honest users successfully decrypt the data, they will get the real private data, which will cause serious privacy leakage problems. Traditional cloud-based data management architectures are extremely vulnerable in the face of various cyberattacks. To address the above challenges, a verifiable ABE scheme based on blockchain and local differential privacy is proposed, using LDP to perturb the original data locally to a certain extent to resist collusion attacks, outsourcing encryption and decryption to corresponding service providers to reduce the pressure on mobile terminals, and deploying smart contracts in combination with blockchain for fair execution by all parties to solve the problem of returning wrong search results in a semi-honest cloud server. Detailed security proofs are performed through the defined security goals, which shows that the proposed scheme is indeed privacy-protective. The experimental results show that the scheme is optimized in terms of data accuracy, computational overhead, storage performance, and fairness. In terms of efficiency, it greatly reduces the local load, enhances personal privacy protection, and has high practicality as well as reliability. As far as we know, it is the first case of applying the combination of LDP technology and blockchain to a tracing system, which not only mitigates poisoning attacks on user data, but also improves the accuracy of the data, thus making it easier to identify infected contacts and making a useful contribution to health prevention and control efforts.
STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation

Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be learned in the optimisation process. Here, we develop the STAMP algorithm to allow the simultaneous training and pruning of a UNet architecture for medical image segmentation with targeted channelwise dropout to make the network robust to the pruning. We demonstrate the technique across segmentation tasks and imaging modalities. It is then shown that, through online pruning, we are able to train networks to have much higher performance than the equivalent standard UNet models while reducing their size by more than 85% in terms of parameters. This has the potential to allow networks to be directly trained on datasets where very low numbers of labels are available.
A Low-Cost Fully Integer-Based CNN Accelerator on FPGA for Real-Time Traffic Sign Recognition

Traffic sign recognition (TSR) technology allows the vehicle to recognize road signs through a camera and use it for driving. For traffic safety, TSR is one of the core technologies constituting advanced driver assistance systems (ADAS), and several researches have been studied. The advent of convolutional neural networks (CNNs) has opened up new possibilities in automotive environments, especially for ADAS. However, deploying a real-time TSR application in resource-constrained ADAS is challenging because most CNNs require high computing resources and memory usage. To address this problem, some works have been studied to consider optimization in embedded platforms, but existing works used many hardware resources or showed low computation performance. In this paper, we propose a low-cost CNN-based real-time TSR hardware accelerator. Firstly, we extend a novel hardware-friendly quantization method to reduce computational complexity. The quantization method can reconstruct the CNN so that all operations, including the skip connection path of residual blocks, use only integer arithmetic and reduce the computational overhead by replacing the quantization affine mapping process with a shift operation. Secondly, the proposed hardware accelerator applied two parallelization strategies to balance real-time inference and resource consumption. In addition, we present a simple and effective hardware design scheme that handles the skip connection path of residual blocks. This design scheme can optimize the dataflow of the skip connection path and reduce additional internal memory usage. Experimental results show that the reconstructed fully integer-based CNN only requires 24M integer operations (IOPs) and possesses a model size of 0.17MB. Compared with the previous work, the proposed CNN model size was reduced by &#x00D7;105, and the number of operations was reduced by &#x00D7;58. In addition, the proposed CNN can achieve a TSR accuracy of 99.07%, which is the highest accuracy among CNN-based TSR works implemented on embedded platforms. The proposed hardware accelerator achieves a computation performance of 960 MOPS and a frame rate of 40 FPS when implemented on a Xilinx ZC706 SoC. Consequently, this work improves by &#x00D7;11.87 and &#x00D7;36.7 on computation performance and frame rate compared to the previous work.
A Coupling Mechanism and the Measurement of Science and Technology Innovation and Rural Revitalization Systems

Under the concept of scientific and technological progress and new rural development, the relationship between scientific and technological innovation and rural revitalization is becoming closer and closer. The purpose of this study is to reveal the coupling coordination mechanism between the two and promote agricultural and rural construction and regional high-quality development through quantitative analysis and scientific decision making. This paper analyzes the systematic coupling mechanism of scientific and technological innovation and rural revitalization. An evaluation index system coupled with a coordination measure model and grey prediction GM (1,1) model are constructed. We demonstrate the implementation process of these models using data from Hebei province from 2010 to 2019. According to the application results, some suggestions for policy and measures are put forward. The results verify the coupling and coordination relationship between the two and the feasibility of the method. The results show that the state of coupling coordination of scientific and technological innovation and rural revitalization systems in Hebei province has transitioned from a mild imbalance to the primary coordination stage, and it is predicted that it will reach good coordination in 2024. This study provides theoretical and methodological support for the coupling coordination between regional scientific and technological innovation and rural revitalization and can serve as a useful reference for similar regional rural construction.
Research on Image Matching of Improved SIFT Algorithm Based on Stability Factor and Feature Descriptor Simplification

In view of the problems of long matching time and the high-dimension and high-matching rate errors of traditional scale-invariant feature transformation (SIFT) feature descriptors, this paper proposes an improved SIFT algorithm with an added stability factor for image feature matching. First of all, the stability factor was increased during construction of the scale space to eliminate matching points of unstable points, speed up image processing and reduce the dimension and the amount of calculation. Finally, the algorithm was experimentally verified and showed excellent results in experiments on two data sets. Compared to other algorithms, the results showed that the algorithm proposed in this paper improved SIFT algorithm efficiency, shortened image-processing time, and reduced algorithm error.
Ransomware: a comprehensive study of the exponentially increasing cybersecurity threat

Ransomware threats and incidents have exponentially increased causing both financial and reputational losses to organizations of all sizes and sectors.Ransomware attacks became the talk of the news when the world was hit by COVID 19 pandemic and people shifted to remote work in large numbers (Brynjolfsson et al., 2020, p. 13-14).Cybercriminals and threat groups are using various types of social engineering techniques such as email phishing, smishing, spear phishing attacks to spread ransomware infections in systems and networks.To protect organizations, users, and IT infrastructures it is important to understand how ransomware works, and how various threat actors use it to exfiltrate confidential data and information.Hence a critical approach toward ransomware infection and its mitigation by using different techniques is discussed and analyzed in this research paper concerning other scholarly articles and papers.
What neural networks know about linguistic complexity

Linguistic complexity is a complex phenomenon, as it manifests itself on different levels (complexity of texts to sentences to words to subword units), through different features (genres to syntax to semantics), and also via different tasks (language learning, translation training, specific needs of other kinds of audiences). Finally, the results of complexity analysis will differ for different languages, because of their typological properties, the cultural traditions associated with specific genres in these languages or just because of the properties of individual datasets used for analysis. This paper investigates these aspects of linguistic complexity through using artificial neural networks for predicting complexity and explaining the predictions. Neural networks optimise millions of parameters to produce empirically efficient prediction models while operating as a black box without determining which linguistic factors lead to a specific prediction. This paper shows how to link neural predictions of text difficulty to detectable properties of linguistic data, for example, to the frequency of conjunctions, discourse particles or subordinate clauses. The specific study concerns neural difficulty prediction models which have been trained to differentiate easier and more complex texts in different genres in English and Russian and have been probed for the linguistic properties which correlate with predictions. The study shows how the rate of nouns and the related complexity of noun phrases affect difficulty via statistical estimates of what the neural model predicts as easy and difficult texts. The study also analysed the interplay between difficulty and genres, as linguistic features often specialise for genres rather than for inherent difficulty, so that some associations between the features and difficulty are caused by differences in the relevant genres.
Synthesis and Properties of Optimally Value-Aligned Normative Systems

The value alignment problem is concerned with the design of systems that provably abide by our human values. One approach to this challenge is through the leverage of prescriptive norms that, if carefully designed, are able to steer a multiagent system away from harmful outcomes and towards more beneficial ones. In this work, we first present a general methodology for the automated synthesis of value aligned normative systems, based on a consequentialist view of values. In the second part, we provide analytical tools to examine such value aligned normative systems, namely the Shapley value of individual norms and the compatibility of several values under a fixed set of norms. We illustrate all of our contributions with a running example of a society of agents where taxes are collected and redistributed according to a set of parametrised norms.
An intelligent control system for robot massaging with uncertain skin characteristics

Purpose To ensure accurate position and force control of massage robot working on human body with unknown skin characteristics, this study aims to propose a novel intelligent impedance control system. Design/methodology/approach First, a skin dynamic model (SDM) is introduced to describe force-deformation on the human body as feed-forward for force control. Then a particle swarm optimization (PSO) method combined with graph-based knowledge transfer learning (GKT) is studied, which will effectively identify personalized skin parameters. Finally, a self-tuning impedance control strategy is designed to accommodate uncertainty of skin dynamics, system delay and signal noise exist in practical applications. Findings Compared with traditional least square method, genetic algorithm and other kinds of PSO methods, combination of PSO and GKT is validated using experimental data to improve the accuracy and convergence of identification results. The force control is effective, although there are contour errors, control delay and noise problems when the robot does massage on human body. Originality/value Integrating GKT into PSO identification algorithm, and designing an adaptive impedance control algorithm. As a result, the robot can understand textural and biological attributes of its surroundings and adapt its planning activities to carry out a stable and accurate force tracking control during dynamic contacts between a robot and a human.
Human preferences toward algorithmic advice in a word association task

Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios-will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13[Formula: see text] more frequently ([Formula: see text], [Formula: see text]) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13[Formula: see text] less likely to identify the correct solution ([Formula: see text], [Formula: see text]). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work.
A Hierarchical and Location-Aware Consensus Protocol for IoT-Blockchain Applications

Blockchain-based IoT systems can manage IoT devices and achieve a high level of data integrity, security, and provenance. However, incorporating existing consensus protocols in many IoT systems limits scalability and leads to high computational cost and consensus latency. In addition, location-centric characteristics of many IoT applications paired with limited storage and computing power of IoT devices bring about more limitations, primarily due to the location-agnostic designs in blockchains. We propose a hierarchical and location-aware consensus protocol (LH-Raft) for IoT-blockchain applications inspired by the original Raft protocol to address these limitations. The proposed LH-Raft protocol forms local consensus candidate groups based on nodes' reputation and distance to elect the leaders in each sub-layer blockchain. It utilizes a threshold signature scheme to reach global consensus and the local and global log replication to maintain consistency for blockchain transactions. To evaluate the performance of LH-Raft, we first conduct an extensive numerical analysis based on the proposed reputation mechanism and the candidate group formation model. We then compare the performance of LH-Raft against the classical Raft protocol from both theoretical and experimental perspectives. We evaluate the proposed threshold signature scheme using Hyperledger Ursa cryptography library to measure various consensus nodes' signing and verification time. Experimental results show that the proposed LH-Raft protocol is scalable for large IoT applications and significantly reduces the communication cost, consensus latency, and agreement time for consensus processing.
iYogacare: Real-Time Yoga Recognition and Self-Correction for Smart Healthcare

Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistances are implemented in yoga pose identification. In this work, a self-assistance-based yoga posture identification technique is developed, which helps users to perform yoga with the correction feature in real-time. The work also presents yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed, which includes ten yoga postures with around 400–900 images of each pose and also contains five mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and a hand for mudra poses. Two different algorithms have been used for creating a skeleton: one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as features for different machine learning and deep learning models. Among all the models, XGBoost with RandomSearch CV is the most accurate and gives 99.2% accuracy. The complete design framework is described in this article.