Reviewerly/exHarmony huggingface dataset:
Path Planning of Mobile Robot Based on Improved Differential Evolution Algorithm

With the rapid development of intelligent algorithms, it has become an inevitable trend to use intelligent algorithm with high intelligence and strong adaptability for robot path planning. Differential evolution algorithm (DE) is a typical intelligent algorithm applied in the robot path planning, yet there still exist some disadvantages, such as local optimum and computational redundancy. Aiming at these defects, this paper proposed an improved differential evolution algorithm (IDE) for robot path planning problem. In the proposed algorithm, chaos initialization population method is used to improve the ergodicity of search, and a judgment mechanism is introduced to ensure that the paths are available. Furthermore, the control parameters are updated according to a modified parameter adaptation strategy in each generation. Finally, the simulation results show that the IDE has better performance in convergence speed, optimization precision and stability compared with DE. Moreover, the experimental results of global path planning based on the robot operating system (ROS) further verify the feasibility and validity of the proposed algorithm.
A general model compression method for image restoration network

Convolutional neural networks have achieved prominent performance in the image restoration field at the cost of massive network parameters and computations. Several model compression methods have been proposed, however, most of them are designed for high-level vision tasks, which originally have some tolerance for information loss. To make image restoration networks targeting for low-level vision tasks more compact and efficient while preserving comparable good performance, we propose a general model compression method. More specifically, a deformable convolution kernel and standard convolution factorization are proposed to compress the network. Then, symmetric dilated convolutions and attention mechanism are employed to compensate for the performance loss induced by former compression. The process can be regarded as a micro-to-macro network rebuilding. Extensive experiments conducted in three typical image restoration tasks demonstrate that the proposed method attains up to 8× network compression ratio while achieving comparable or even better performance compared to the original network.
Research on Path Planning of Orchard Spraying Robot Based on Improved RRT Algorithm

Aiming at the problems of low efficiency, high labor cost, and high drug harm in the traditional manual spraying method in orchard planting, an autonomous spraying robot for orchard was designed to replace manual pesticide spraying. In order to solve the problem of path planning for orchard spraying robots, the traditional RRT algorithm has the disadvantages of poor target, strong randomness, and the planned path is not necessarily the optimal path in the search path process. With the help of the optimization ability of Dijkstra algorithm, a method is proposed. An orchard spraying robot path optimization method based on improved RRT algorithm. In order to verify the feasibility and effectiveness of the improved RRT algorithm in the path planning application of the orchard spraying robot, the MATLAB platform is used for simulation analysis, and the traditional RRT algorithm is compared with the improved RRT algorithm. The simulation experiment results show that: compared with the traditional RRT algorithm, the improved RRT path planning algorithm's optimization ability and convergence performance have been significantly improved, the planned path length is shortened by 10%, the number of nodes is reduced by nearly 86%, and the path is improved. The ability to find the best, reduce the degree of tortuous path, help reduce the energy loss of agricultural spraying robots in the working process, and effectively solve the path planning problem of orchard spraying robots, making it safer and more efficient to avoid obstacles to achieve autonomy Spraying task.
Perfect Secure Computation in Two Rounds

We show that any multiparty functionality can be evaluated using a 2-round protocol with perfect correctness and perfect semihonest security, provided that the majority of parties are honest. This settles the round complexity of information-theoretic semihonest multiparty computation, resolving a longstanding open question [Y. Ishai and E. Kushilevitz, Randomizing polynomials: A new representation with applications to round-efficient secure computation, in Proceedings of the 41st Annual Symposium on Foundations of Computer Science FOCS 2000, IEEE Computer Society, 2000, pp. 294--304]. The protocol is efficient for ${NC}^1$ functionalities. Furthermore, given black-box access to a one-way function, the protocol can be made efficient for any polynomial functionality, at the cost of only guaranteeing computational security. Our results are based on a new notion of multiparty randomized encoding which extends and relaxes the standard notion of randomized encoding of functions [Y. Ishai and E. Kushilevitz, Randomizing polynomials: A new representation with applications to round-efficient secure computation, in Proceedings of the 41st Annual Symposium on Foundations of Computer Science FOCS 2000, IEEE Computer Society, 2000, pp. 294--304]. The property of a multiparty randomized encoding (MPRE) is that if the functionality $g$ is an encoding of the functionality $f$, then for any (permitted) coalition of players, their respective outputs and inputs in $g$ allow them to simulate their respective inputs and outputs in $f$, without learning anything else, including the other outputs of $f$. We further introduce a new notion of effective degree, and show that the round complexity of a functionality $f$ is characterized by the degree of its MPRE. We construct degree-2 MPREs for general functionalities in several settings under different assumptions, and use these constructions to obtain 2-round protocols. Our constructions also give rise to new protocols in the client-server model with optimal round complexity.
Fuzzy Lexicon-Based Approach for Sentiment Analysis of Blog and Microblog Text

Sentiment analysis refers to determining the polarity of a document through natural language processing, text analytics and computational linguistics. With the continuous explosion of opinionated online text from blog Web sites, microblogs and product review Web sites, investigations in sentiment analysis are growing manifold. However, most of the sentiment analysis approaches are either lexicon-based or machine learning-based or a hybrid of these two. Very few researchers have explored the use of fuzzy logic in sentiment analysis. In this work, we propose a fuzzy lexicon-based approach for unsupervised sentiment analysis involving multiple lexicons and datasets. The crux of this work is the set of fuzzy rules proposed to assess the sentiment of blogs and microblogs. Experimental results verify the suitability of this approach for SA. We also contrast our work with the existing fuzzy sentiment analysis approaches to establish the superiority of the proposed system.
Making models, making predictions

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Verifiable Blockchain Redacting Method for a Trusted Consortium with Distributed Chameleon Hash Authority

Blockchain is highly evaluated for its vantages of decentralization, anonymity, and information immutability, where the last one means historical data recorded on a blockchain ledger cannot be manipulated once committed under consensus. This guarantees the integrity and reliability of data on blockchain. However, when fake, illegal, expired or redundant transactions on a blockchain ledger need be corrected or removed, immutability disables blockchain from redacting or revoking data without violating consensus. As a complement, this paper proposes a chameleon-hash based design of a decentralized ledger redacting method for consortium blockchain. Considering the necessity of decentralized transaction redaction, that is, the trap door of the chameleon hash should not be generated and stored at a single node, the traditional chameleon hash is optimized for decentralization. The improved chameleon hash algorithm allows authority nodes in consortium blockchain cooperatively generating chameleon hash key pairs. Meanwhile, the usability and accountability of the proposed blockchain system are measured. Finally, the optimized chameleon hash algorithm is applied to construct a prototype chain enabling maintenance and compression of historical transactions. Experiments show that according to the ledger redacting method proposed, block data can be redacted and compressed for at least 30% with consistency, and the crucial phases of ledger redaction are less time-consuming compared with existing works, with no harm on security strength as before.
Multi-path Neural Networks for On-device Multi-domain Visual Classification

Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constrained mobile devices. However, hand-crafting a multi-domain/task model can be both tedious and challenging. This paper proposes a novel approach to automatically learn a multi-path network for multi-domain visual classification on mobile devices. The proposed multi-path network is learned from neural architecture search by applying one reinforcement learning controller for each domain to select the best path in the super-network created from a MobileNetV3-like search space. An adaptive balanced domain prioritization algorithm is proposed to balance optimizing the joint model on multiple domains simultaneously. The determined multi-path model selectively shares parameters across domains in shared nodes while keeping domain-specific parameters within non-shared nodes in individual domain paths. This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains. Extensive evaluations on the Visual Decathlon dataset demonstrate that the proposed multi-path model achieves state-of-the-art performance in terms of accuracy, model size, and FLOPS against other approaches using MobileNetV3-like architectures. Furthermore, the proposed method improves average accuracy over learning single-domain models individually, and reduces the total number of parameters and FLOPS by 78% and 32% respectively, compared to the approach that simply bundles single-domain models for multi-domain learning.
Toward Access Equality: Bridging the Digital Divide

Before the Internet era, telephone penetration was the measure of U.S. progress in securing universal access to all its citizens (access to affordable telephone service). The telephone referred to herein was obviously the “landline”, which evolved from analog to digital throughout the second half of the 20th Century. By the mid-1990s, the notion of universal access had included access to computers and modems. Today, it encompasses a spectrum of Information and Communication Technology (ICT) devices, including desktop, laptop, handheld, wearable, and airborne devices, all of which connect to the Internet. And the term we use now to refer to the level of access pertinent to today's needs is Broadband Access.
Comparison of Swarm and Graph Algorithms for Solving Travelling Salesman Problems

In this paper, we compare different nature-inspired, swarm algorithms (4 variants of ACO and PSO) and five graph algorithms for solving the travelling salesman problem based on defined key metrics. We focus on describing the experimental setup including input data and the outline of the metrics applied in the analysis part. The aim is to compare the performance of metaheuristics (nature-based) algorithms with traditional algorithms used for solving travelling salesman problems (TSP).
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We show that solvers using feature combinations can recover more accurate calibrations than solvers using only one feature type on scenes that have a balance of lines and texture. We also show that the proposed solvers are complementary and can be used together in a RANSAC-based estimator to improve auto-calibration accuracy. State-of-the-art performance is demonstrated on a standard dataset of lens-distorted urban images. The code is available at https://github.com/ylochman/single-view-autocalib.
Approximate Ternary Quantum Error Correcting Code with Low Circuit Cost

Quantum error correcting code (QECC) with encoding and decoding circuits having low gate count, is an important criterion for realizing quantum computing systems. CSS QECCs are known to have simple circuits. Shaw et al. showed that it is not possible to have a 6-qubit CSS type QECC without sharing entanglement between the encoder and the decoder. In this paper, we propose a 6-qutrit approximate QECC (AQECC) of CSS structure which can simultaneously correct phase errors in upto six qutrits, and one bit error in only four of the six qutrits, without sharing prior entanglement. Our AQECC corrects a single error with probability 0.75 for symmetric error model, and probability 0.9988 for asymmetric error model. It also maintains CSS structure without sharing prior entanglement. Furthermore, the quantum cost of a circuit for this AQECC is 55.72% less than that for the 9-qutrit exact QECC. Overall, low qutrit count, low cost circuit realization with low depth and the ability to correct multiple phase errors make our proposed AQECC a suitable candidate for real life quantum channels.
Parallel and fault-tolerant k-means clustering based on the actor model

K-means algorithm is a well-known unsupervised machine learning tool that aims at splitting a given dataset into a fixed number of clusters via iterative refinement approach. Running such an algorithm on today’s datasets that are characterized by its high multidimensionality and huge size requires using fault-tolerance mechanisms to mitigate the impact of possible failures. In this paper, we propose an actor-based implementation of k-means algorithm. The algorithm was made fault-tolerant by periodically saving the centroids into a stable storage during the failure-free execution, and restarting from the last saved centroids upon a failure. This was implemented in two different ways: optimistic checkpointing (blocking) and pessimistic checkpointing (non-blocking). The actor-based k-means algorithm was evaluated on a machine with eight cores. The experiments showed that the proposed algorithm scales very well as the number of workers increases, and can be up to ∼ 2x faster than a Java-thread-based implementation of k-means algorithm. The results also showed that the optimistic algorithm outperformed the pessimistic one, specifically, in the presence of competing I/O operations. Several failures were forced to occur during the execution to evaluate the performance of the fault-tolerant implementations. The experiments showed that the average amount of lost work ranged from 3–6%.
WITHDRAWN: Enhanced key frame identification and background subtraction methods for automatic license plate recognition of motorcyclists without helmets

With the increasing number of cameras available in the cities, there is an increasing interest in recognizing license plate of two wheelers riders who do not wear helmet. This can be performed in four stages, background modeling, vehicle detection, helmet detection and automatic license plate recognition. This work is focused on the background modeling to extract foreground objects from its background. In this work, a 2-step method is proposed. The first step proposes and uses a key frame identification algorithm to extract a set of optimal frames. The background subtraction is then performed on these frames in the second step. A discrete wavelet packet transformation based method is used to separate background and foreground objects. Experimental results prove that the proposed algorithm is more effective when compared with the existing approaches.
RETRACTED: Simulation of oil painting art design based on 5 G mobile network and dynamic image sampling

Color quantization is the first critical step in automatic generation of oil as the main research goals. Design and Application of oil which line of the display system, and constructing a set of oil-based platform must be adapted to the layout of the system rise before the mobile terminal device in response to the movement. Use MVC development framework. Front-end uses the latest technology HTMLS and Bootstrap framework, the system has an effective cross platform compatibility and provide useless consistent device experience. The system established control systems and content colleague role-based authority, assign the appropriate role of detailed character content, so the development of the business logic of the system more standardized. Finally, the feasibility of the system to test and demonstrate the development of the various functions and performance 5 G mobile network and a moving image is displayed in the sample line painting.
An Overview on Big Data Mining Using Evolutionary Techniques

Big Data processing suffers from several limitations due to its magnitude making it time consuming to implement any kind of analysis such as data mining. Evolutionary Algorithms (EAs) are metaheuristic optimization algorithms inspired by the natural behavior of the population evolution such as Genetic Algorithm, Artificial Bee Colony, Artificial Ant Colony and other swarm intelligence algorithms. EAs have recently been used to overcome Big Data limitations, especially memory consumption and the long execution time. This paper provides an overview of the recent research papers that utilize evolutionary algorithms to deal with the optimization problems related to Big Data mining such as clustering, classification and features' selection.
Digital Image Forgery Detection Using Ternary Pattern and ELM

Discovering image forgery is one of the conspicuous areas of investigation and progress. This article points to propose a splicing tamper discovery technique of passive approach by extracting the local texture descriptors as well as employing the machine learning algorithms. In this anticipated process, first, an input of the RGB image is renewed in YCbCr color space as a pre-processing step. Then, the local ternary pattern (LTP) algorithm is employed to acquire the histogram features and is fed into the extreme learning machine (ELM) to discern forged images. The experimental tests are accomplished with data sets and their performances are analyzed quantitatively using various measures. It demonstrates that the proposed forgery detection method is attained superior results.
A Data-Driven Approach to Understanding the State of Internet Routing Security

This paper is concerned with the global routing system of the Internet, its serious and persistent vulnerabilities, the multi-disciplinary considerations shaping this space, and recent trends that finally improve its security. We report real-world evidence of the state of routing security with the goal of discussing possible options for better security. We offer two high-level conclusions. First, in designing an approach it is all-important to understand complexities and incentives for deployment. Second, any scheme that is picked for deployment will take time to have an effect, so having a realistic view of how quickly change can be accomplished is essential.
Fog Computing enabled Smart Grid Blockchain Architecture and Performance Optimization with DRL Approach

Smart grid is willing to make full advantage of distributed clean energy to alleviate energy crisis and environmental problems. However, distributed renewable energy is usually invisible and uncontrollable for the current power system, and there are intermittent problems in its generation. Therefore, how to achieve the power balance, maintain safe operation, and ensure the reliability and quality of power supply when the distributed energy reaches a high penetration in the grid is a huge challenge. Blockchain as one of the research hotspots brings about new solution approach to the dilemma. The two fields have many commons on decentralization, autonomy, marketization and intelligence. In this paper, we discuss the feasible scheme of the integrated system and add fog computing to reduce costs. Considering the realization of system, we choose Hyper Fabric as the basic structure and add verifiable random function to the consensus aimed to improve randomness and security in the encrypted election. Meanwhile, in order to satisfy the business requirements, a flexible adjustment method based on Deep Q Learning algorithm is designed to realize the joint optimization of throughput, latency and storage cost. The proposed scheme provides the advantages including privacy, flexibility, extensibility and implantation simplicity.
Association Rule Mining Based Algorithm for Recovery of Silent Data Corruption in Convolutional Neural Network Data Storage

Embedded systems are finding their way into almost every aspects of our daily life from mp3 players and console games to the mobile phones. Different Artificial Intelligence (AI) based applications are commonly utilized in embedded systems from which computer vision based approaches are included. The demand for higher accuracy in computer vision applications is associated with the increased complexity of convolutional neural networks and the storage requirement for saving pre-trained networks. Different factors can lead to the data corruption in the storage units of the embedded systems, which can result in drastic failures due to the propagation of the errors. Hence, the development of software-based algorithms for the detection and recovery of data corruption is crucial for improvement and failure-prevention of embedded systems. This paper proposes a new algorithm for the recovery of the data in the case of single event upset (SEU) error. The association rule mining based algorithm will be used to find the probability of the corruption in each of the bits. The recovery algorithm was tested on four different pre-trained ResNet (ResNet32 and ResNet110 at two different accuracy levels each) and the best recovery rate of 66% was found in the most complex scenario, i.e., random bit corruption. However, for the special cases of SEU errors, e.g. error in the frequently repeated bits, the recovery rate was found to be perfect with a value of 100%.
Comparing the Results of Applying DE, PSO and Proposed Pro DE, Pro PSO Algorithms for Inverse Kinematics Problem of a 5-DOF Scara Robot

In this research, the Differential Evolution (DE) and Particle Swarm optimization (PSO) algorithms as well as the Proposed DE and PSO (Pro DE and Pro PSO) Algorithms with searching space improvements were applied to solve the Inverse Kinematic (IK) problem of 5 Degree of Freedoms (DOF) Scara Robot These algorithms were tested with two different scenarios for the motion of the manipulator's end-effector. The 1st scenario uses one hundred randomly selected in the workspace of the robot, while the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> scenario also use 100 points belong to a spline trajectory in working space. These algorithms were compared by the execution time, the end-effector position error, the required number of generations and joints variable values. The results showed that the DE and Pro DE could be effectively used for the IK solution of the manipulator.