Reviewerly/exHarmony huggingface dataset:
A Method for Image Recognition and Processing of Windmill Contour

In this paper, an image recognition and processing method for windmill contour features is proposed. In this paper, the luminous color features of the windmill are screened by segmenting the color channel, the contour features of the windmill are extracted by the wheel galley screening algorithm, and the discrimination between the blade to be hit and the blade to be hit is realized by the template matching algorithm. Atter debugging, the image recognition and processing method designed in this paper can effectively and accurately identify the wind wheel corridor, and has certain anti-interference ability.
An Automatic Generation Method of Patent Specification Abstract Based on "Extraction- Abstraction "Model

China has a large number of patent applications, which are more than millions each year. How to extract useful information from massive patent specifications to improve one's own work and avoid meaningless research and waste of funds has become an urgent problem to be solved. To solve these problems, this paper proposes an automatic generation model of patent specification abstract based on "extraction-abstraction". This model uses the output of the DGCNN-based extraction model as the input of the NEZHA-UniLM-based abstraction model, and the abstraction model optimizes the results of the extraction model to obtain a patent specification abstract with strong readability and generality.
Construction of CNN model based on hard-assigned coding of image features

This paper proposes a CNN model based on hard-assigned coding processing of image features. By clearly describing the relationship between the input image sample features participating in the learning dictionary training and the atomic description features in the learning dictionary in the HC hard-assigned image feature encoding transformation, two sets of parameters are designed to calculate the input image sample features and the atomic description features in the learning dictionary. The minimum distance between them, and the HC hard-assigned image feature coding transformation is improved; the HC hard-assigned coding transformation algorithm is introduced into the last convolutional layer and pooling layer in the CNN model, and a complete CNN deep neural network is designed. Improve the model. The advantages of the model constructed in this paper are verified by experimental comparison with a variety of image feature encoding transformation algorithms.
Research on turn-based war chess game based on reinforcement learning

The search algorithm for turn-based war chess game is still using the traditional algorithm, and the whole war chess game needs a huge computational power, resulting in the research of turn-based war chess game in artificial intelligence is relatively backward. In this paper, we design a turn-based war chess game algorithm based on reinforcement learning, which is composed of Monte Carlo tree search (MCTS) and deep neural network. Monte Carlo tree search relies on strategy network to simulate chess several times, to choose better chess quality; The deep neural network reversely trains the strategy network through the final chess outcome. The experimental results show that there is no defeat in 10Kvs5K and 10Kvs3K game. This algorithm can improve the chess skill through self-game when there is no human knowledge and learning from scratch in turn-based war games.
Research Control a Smart Car Based on Chinese Voice Recognition Technology through the Raspberry Pi 4B Platform

A substantial proportion of ways controlling small smart cars such as wireless remote control and mobile phone remote control are achieved through manual controlling the cars. This paper proposes a smart car control system based on speech recognition technology, which controls smart car movement via voice command and improves the level of intelligence and individuality efficiently. The key of the project is to combine voice recognition with intelligent car control through the Raspberry Pi 4B platform. This paper combines the voice recognition module and coding in Python to realize the effective voice control of the intelligent car.
Research on Blockchain-Based Food Safety Traceability Technology

Food traceability can be used to quickly pinpoint problematic links and minimise the risks associated with food safety incidents by viewing the trajectory of food circulation after a food safety incident has occurred. Blockchain, as an emerging technology with characteristics such as decentralisation, asymmetric encryption, tamper-proof and traceability, can be used to generate link traceability technology, which provides great convenience for us to effectively regulate food safety. In response to the need for multiple parties to share information in the traditional food safety traceability system there are many problems such as non-uniformity of the traceability chain, user cultivation and high traceability costs. This paper introduces Blockchain technology to improve the food safety traceability system, using the decentralised and fully distributed DNS service provided by Blockchain to achieve domain name query and resolution through peer-to-peer data transfer services between various nodes in the network, which can be used to ensure that the operating system and firmware of the infrastructure of the food production process have not been tampered with, introducing QR code technology, RFID, ZigBee.Web Web server and other key IoT technologies to achieve information identification and coding for identifying production objects, and identify the bottlenecks that limit the performance of the system by analysing and studying the consensus mechanism of the super ledger Fabric, and use smart contracts and consensus mechanisms as support to build food safety data assurance and food supervision methods to improve overall traceability efficiency.
A Dynamic Taint Analysis-Based Smart Contract Testing Approach

Due to the unique global state and transaction sequence characteristics of smart contracts, the detection method based on a single test case cannot improve the vulnerability detection rate during contract detection. The current contract testing methods based on genetic algorithms have not yet solved the problems caused by these characteristics. Therefore, we propose an adaptive fuzzing method based on dynamic taint analysis and genetic algorithm, SDTGfuzzer. SDTGfuzzer focuses on dynamic taint analysis to collect runtime information as feedback, and focuses on solving the challenges brought by global variables and transaction sequences for contract testing. Genetic Algorithms work well in test case generation for fuzzing. Therefore, SDTGfuzzer optimizes the genetic algorithm based on an efficient and lightweight multi-objective adaptive strategy, focusing on solving the problem that the contract constraints cannot be covered due to the global state. Experimental results show that our method has a higher vulnerability detection rate than other tools for detecting contract vulnerabilities.
The Role of Information Technologies in Developing Future Specialists’ Communicative Competence

The use of information technologies in developing communicative competence is a research issue, which consists of analyzing the integration of innovative technologies into the educational process. The issue of the quality of organizing the educational process and the possibility of improving future specialists’ practical abilities is an important task for modern educational institutions. The primary purpose of the academic paper is to analyze the role of information technologies in education and their potential application for forming communicative competence, taking into account the format of remote communication, the organization of educational conferences, etc. Analysis of contemporary educational outcomes following the rise in popularity of distance learning, which served as a catalyst for advancing information technologies in education, is a key component of the research. The theoretical and practical principles of developing communicative competence were outlined and its components were characterized using the scientific research methods. The academic paper examines the features of the modern educational process and information technologies used to improve the communicative component, which is one of the tasks of an educational institution. In addition, the research analyzes the current state of educational services and characterizes the key tendencies in using information technologies, taking into account the opportunities for developing communication competence. The implementation of information technologies in the educational process, whether in a traditional or distance learning format, has the potential to improve it in terms of practicality and efficiency. Using information technologies in education remains a priority, but a debatable issue regarding the possibilities of their implementation, the feasibility of application and prospects for development. The obtained research results can become socially useful to improve the educational process and enhance future specialists’ communicative competence.
Research on Sharding Strategy of Blockchain Based on TOPSIS

Cryptocurrency applications with blockchain technology as the underlying architecture have gradually developed into a new means of payment, and are expanding to all walks of life with the support of cryptography and consensus algorithms. Due to the disadvantages of low throughput and high latency, the blockchain has seriously hindered the widespread use of upper-layer applications and cannot meet the growing demand for users and transaction volumes. Drawing on the sharding idea of traditional databases, blockchain sharding, as a representative of on-chain scaling solutions, greatly improves the throughput of the blockchain system. At present, most of the network sharding schemes in the sharded blockchain adopt a strategy based on random sharding. This strategy does not take into account the performance of the node itself, resulting in large performance differences between different shards, further reducing the throughput of the entire system. In addition, the aggregation behavior of malicious nodes may also occur, reducing the security of the system. Aiming at the performance of each node, this paper proposes a sharding strategy based on the approximate ideal solution model (TOPSIS). Through the TOPSIS model, the nodes are scored according to the hardware performance of the node, the response time to the transaction and the results, etc., and the nodes are allocated to the corresponding shards according to the scoring results. The sharding strategy based on this model balances the performance differences among shards and improves the throughput of the entire system.
AutoRF: Auto Learning Receptive Fields with Spatial Pooling

The search space is crucial in neural architecture search (NAS), and can determine the upper limit of the performance. Most methods focus on the design of depth and width when designing the search space, ignoring the receptive field. With a larger receptive field, the model is able to aggregate hierarchical information and strengthen its representational power. However, expanding the receptive fields directly with large convolution kernels suffers from high computational complexity. We instead enlarge the receptive field by introducing pooling operations with little overhead. In this paper, we propose a method named Auto Learning Receptive Fields (AutoRF), which is the first attempt at the auto attention module design with regard to the adaptive receptive field. In this paper, we present a pooling-based auto-learning approach for receptive field search. Our proposed search space encompasses typical multi-scale receptive field integration modules theoretically. Detailed experiments demonstrate the generalization ability of AutoRF and outperform various hand-crafted methods as well as NAS-based ones.
RLSCNet: A Residual Line-Shaped Convolutional Network for Vanishing Point Detection

The convolutional neural network (CNN) is an effective model for vanishing point (VP) detection, but its success heavily relies on a massive amount of training data to ensure high accuracy. Without sufficient and balanced training data, the obtained CNN-based VP detection models can be easily overfitted with less generalization. By acknowledging that a VP in the image is the intersection of projections of multiple parallel lines in the scene and treating this knowledge as a geometric prior, we propose a prior-guided residual line-shaped convolutional network for VP detection to reduce the dependence of CNN on training data. In the proposed end-to-end approach, the probabilities of VP in the image are computed through an edge extraction subnetwork and a VP prediction subnetwork, which explicitly establishes the geometric relationships among edges, lines, and vanishing points by stacking the differentiable residual line-shaped convolutional modules. Our extensive experiments on various datasets show that the proposed VP detection network improves accuracy and outperforms previous methods in terms of both inference speed and generalization performance.
Lightweight Image Hashing Based on Knowledge Distillation and Optimal Transport for Face Retrieval

This paper proposes a lightweight image hashing based on knowledge distillation and optimal transport for face retrieval. A key contribution is the attention-based triplet knowledge distillation, whose loss function includes attention loss, Kullback-Leibler (KL) loss and identity loss. It can significantly reduce network size with almost no decrease of retrieval performance. Another contribution is the hash quantization based on optimal transport. It partitions the face feature space by calculating class-centers and conducts binary quantization based on the optimal transport. It can make performance improvement in face retrieval with short bit length. In addition, an alternating training strategy is designed for tuning network parameters of our lightweight hashing. Many experiments on two face datasets are carried out to test performances of the proposed lightweight hashing. Retrieval comparisons illustrate that the proposed lightweight hashing outperforms some well-known hashing methods.
Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators

Spectroscopy devices suffer from the pulse pileup phenomenon, caused by overlapping of the signals. The energy-domain based pileup correction algorithm estimates the pulse energy distribution by measuring the duration and energies of pileups directly and does not need to identify each individual pulses. The correction algorithm can efficiently recovers the energy spectrum even under a very high photon arrival rate. However, the correction algorithm is sequential in nature and is slow when the energy resolution is high. A fast parallel implementation of the original correction algorithm is proposed in this paper. The parallel counterpart leverages state-of-the-art many-core system technology and achieves a nearly linear acceleration when the problem size scales. The speedup ratio exceeds 1,000 when the energy spectrum is split into 2,048 bins.
Research on Power Border Firewall Policy Import and Optimization Tool

At present, the security strategy of the lower boundary protection equipment of the power system can no longer meet the needs of the current business growth. A large number of redundant strategies cause the protection performance of the boundary firewall to decline. At the same time, the large number of business growth causes the network boundary order of the power grid system to be blurred. In order to prevent the paralysis and partial collapse of the network and ensure the reliability and integrity of the power business data and enterprise information, this paper develops a smart border firewall optimization tool. This tool can not only integrate the security device policies of different manufacturers through Simple Policy Specification Description Language (SPSDL), but also prioritize security rules according to the frequency of use through keyword filtering algorithms and rule optimization decision trees, then realize the classification, streamlining, optimization and upgrading of firewall security rules. The research results show that the power system firewall can achieve an accuracy rate of more than 90% when the strategy is imported. The rule optimization part can reduce the unique correlation addition index of this paper to about 0.2, which solves the problem of firewall security strategy import language diversification. It further eases the pressure of firewall policy redundancy under the power system.
Evolutionary Discrete Optimization Inspired by Zero-Sum Game Theory

In a zero-sum game, the two players compete against each other, and the gain of one player means the loss of the other. Generative adversarial networks (GANs) are models of this kind of thinking. Evolutionary algorithms (EAs) are popular and high robust methods to solve combinatorial optimization problems. However, in the middle stages of evolution, EAs usually suffer from the problem of a serious lack of population diversity. This often results in that EAs fall into local optima. This paper presents a cooperative evolutionary algorithm driven by policy-based GANs (PGAN-CEA) for solving traveling salesman problems (TSPs). PGAN-CEA adopts a policy-gradient method in reinforcement learning to train GANs to generate discrete data. First, GANs are used to construct an initial population. Then, a cooperative evolution strategy driven by GANs is used in the middle of the evolution. Further, a dual-population mechanism is utilized to assist the co-evolution of the dominant solutions generated by GANs and the solutions from the population of EAs. Test cases from TSPLIB and the Mona Lisa Problems are used to evaluate the proposed algorithm. Compared with other GAN-based algorithms, the proposed algorithm can mitigate the problem of local convergence and achieves certain improvements in quite a few performance indicators.
Specific Emitter Identification Based on CNN via Variational Mode Decomposition and Bimodal Feature Fusion

Specific emitter identification (SEI) refers to the technology of identifying specific emitter individuals by the RF fingerprint carried by the emitter transmitting signals. RF fingerprint is a hardware difference feature caused by the nonlinearity and tolerance of the components inside the emitter, and can be used as the unique identification of the emitter. In this paper, we propose an emitter identification scheme based on convolutional neural network (CNN) via variational mode decomposition (VMD) and bimodal feature fusion. Firstly, four stray components of LoRa signals are extracted by VMD, which contain unintentional modulation features of the original signals. Then the time-frequency features (VMD-S) and temporal features (VMD-T) are extracted from the four stray components. Consequently, a CNN-based two-channel feature extraction network is built with an attention module and a bimodal feature fusion strategy which realizes the fusion of VMD-S and VMD-T. The experimental results show that the proposed SEI scheme achieves higher identification accuracy in comparison with the scheme without feature fusion at the low signal-to-noise ratio (SNR) region.
Self-supervised Multi-object Tracking with Cycle-Consistency

Multi-object tracking is a challenging video task that requires both locating the objects in the frames and associating the objects among the frames, which usually utilizes the tracking-by-detection paradigm. Supervised multi-object tracking methods have made stunning progress recently, however, the expensive annotation costs for bounding boxes and track ID labels limit the robustness and generalization ability of these models. In this paper, we learn a novel multi-object tracker using only unlabeled videos by designing a self-supervisory learning signal for an association model. Specifically, inspired by the cycle-consistency used in video correspondence learning, we propose to track the objects forwards and backwards, i.e., each detection in the first frame is supposed to be matched with itself after the forward-backward tracking. We utilize this cycle-consistency as the self-supervisory learning signal for our proposed multi-object tracker. Experiments conducted on the MOT17 dataset show that our model is effective in extracting discriminative association features, and our tracker achieves competitive performance compared to other trackers using the same pre-generated detections, including UNS20 [1], Tracktor++ [2], FAMNet [8], and CenterTrack [31].
An Occlusion Model for Spectral Analysis of Light Field Signal

Occlusion is a common phenomenon in actual scenes, this phenomenon will seriously influence application of light field rendering (LFR) technology. We propose an occlusion model of scene surface that approximating the scene surface as a set of concave and convex parabolas to solve the light field (LF) reconstruction problem. The first step in this model involves determining the occlusion function. After obtaining the occlusion function, we can then perform the plenoptic spectral analysis. Through the plenoptic spectral analysis, the plenoptic spectrum will reveal occlusion characteristics. Finally, the occlusion characteristics can be used to determine the minimal sampling rate and a new reconstruction filter can also be applied to calibrate the aliasing spectrum to achieve high quality view synthesis. This extends previous works of LF reconstruction that considering reconstruction filter. Experimental evaluation demonstrates that our occlusion model significantly address occlusion problem while improve the rendering quality of light field.
Constructing Safe Flight Corridors for Quadrotor Navigation in Cluttered 3-D Environments

As the use of quadcopters becomes more widespread, the need for safe and timely quadcopter flight increases. In particular, operating a quadrotor in a cluttered environment presents the challenge of non-convex obstacles and safe flight trajectory generation. This paper presents two methods for generating the safe flight corridor (SFC) step in the three steps of quadrotor 3D trajectory planning, octree-based flight corridor generation, and inflated ellipsoid flight corridor generation, and verifies their superiority by comparing them with existing algorithms (RRT* based planning and IRIS) through an experimental method of computer simulation. Finally, the limitations of both methods are presented and conjectures for solutions are made based on the establishment and experimental validation methods of both methods.