Reviewerly/exHarmony huggingface dataset:
Mobile Perimeter Guard System based on Deep Convolutional Neural Networks

In recent years, with the remarkable performance of CNN in the field of computer vision, various intelligent analysis tasks have been widely used in real world. At present, intelligent analysis tasks can be deployed both on GPU servers and on mobile terminals (e.g., smart phones, embedded boards). Compared with GPU server mode, mobile-terminal-based mode can not only greatly reduce the bandwidth, but also effectively reduce the calculation load of the server. More importantly, it can build a more stable intelligent analysis system. Based on the above considerations, a MobileNetSSD-based perimeter guard system is proposed and a series of performance evaluations are conducted in this paper. Through experimental evaluations, it is shown that the proposed perimeter guard system can be applied to the service of perimeter guard in a factory or a residential area.
Comparative Study on the Review Text of COVID-19 Epidemic Prevention and Control Policy Between China and Japan

COVID-19 continues to spread and spread, and the epidemic situation in China and Japan is not optimistic, which brings huge hidden dangers and serious impact to the economic cooperation and development of China and Japan. Aiming at the comments and feedback of Chinese and Japanese people on government policies, this paper proposes a text emotional tendency analysis method based on maximum entropy model, which extracts word relation features and semantic features from the comment context. The maximum entropy model is used to identify the emotional tendency of words, and the smoothing technique is used to solve the problem of sparse features. At the same time, through the weighting and normalization of specific emotional words and punctuation marks, the uncertainty of the emotional tendency of words in the context is further reduced, and the experimental results verify the effectiveness of the model.
Pedestrian detection method based on feature fusion Centernet

Mainstream pedestrian detection methods require manually predefined anchor frames, and the anchor-free frame algorithm avoids the influence of manually designed anchor frames on pedestrian detection accuracy. To improve the detection performance of the pedestrian detection field, we propose a CenterNet-based feature fusion and enhanced pedestrian detection algorithm with spatial attention. Firstly, the fusion method of the backbone network feature layer and upsampled feature layer is designed to improve the utilization efficiency of feature information, redesign multiple receptive field convolution combinations to improve the feature expression ability, and finally use the spatial attention method to highlight the contribution of key information to detection. After many experiments, the present algorithm effectively improves the detection performance of the benchmark method.
Pedestrian fall detection based on AC-YOLOv5s

In view of the serious occlusion phenomenon of pedestrian fall detection, the difficulty of extracting small target details, and the slow detection speed, this paper proposes a high-precision lightweight detection network AC-YOLOv5s. First, the convolution module in the backbone is replaced by ACBConv, and the C3 module is replaced by ACBC3 to improve the detailed feature extraction capability. Secondly, a small target detection layer is added to the feature fusion network (FPN) to improve the detection accuracy of small targets. Finally, use Alpha IoU loss replaces CloU loss to improve the loss and regression accuracy of the High IoU target. Finally, compared with the original YOLOv5s, the network in this paper improves the mAP by 2.33%, and the FPS reaches 21 during detection. The experimental results show that our network achieves better results than other networks.
Histogram Equalization Based Cable Tunnel Image Defogging Method

The image atomization of urban cable tunnel is serious., which affects the image quality. Aiming at the atomization problem of cable tunnel image., this paper proposes a local distribution equalization algorithm., collects the typical images in cable tunnel., and analyzes the images. The defogging method of evenly distributed image is improved, and an improved method is proposed. After the image is divided into small areas., the local parameters are optimized, and the improved method is tested. The experimental results show that this algorithm can better defog the cable tunnel image.
Unified coprocessor for high-speed AES-128 and SM4 encryption

In modern systems-on-chip, cryptographic coprocessors bring more flexibility to design, not only accelerating the encryption process but also compressing design resources by sharing algorithm units. A high- performance unified coprocessor for AES-128 and SM4 encryption is proposed in this paper. On the one hand, based on the similarities between the two types of algorithms, the multiplicative inverse (MI) unit of the Sbox is realized in the composite field, and by sharing the reconfigurable Sbox logic (RCSL), the hardware resource consumption of circuits compatible with both algorithms is reduced. On the other hand, global pipeline technology based on shared-RCSL is used to improve the throughput of the coprocessor. In TSMC 65nm 1.08V CMOS technology, compared to the synthesized results of independently AES-128 and SM4, the area and power at 500MHz of the unified coprocessor are reduced by 42% and 43%, respectively.
The Low-light Image Enhancement Method Based on Improved LSID Algorithm

Aiming at the problem of insufficient exposure of images obtained by photography and photography in realistic low-light, backlight and other scenarios, this paper proposes a low-light image enhancement deep learning network model that improved the Learning-to-See-In-the-Dark (LSID) algorithm. This method implements the amplification factor estimation weight network by design. The defects of the previous artificially designed parameter amplification factors were compared, and through the training and testing of experimental data, the experimental results of different methods were compared in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) parameter indicators, and image visualization was compared at the same time. The experimental results show that the proposed method in this paper can improve the PSNR and SSIM indicators by 0.81 and 0.025, respectively, and it can obtain better image dark light enhancement effect.
The Vulnerability Testing Method and Management for Software Source Code

Source code security is the foundation of software security, so it is of great significance to test source code defects before the software system goes online. This paper first elaborated the causes of source code security defects, and introduced the identification methods and repair measures of source code vulnerabilities in detail. Finally, it described the testing process management of source code. This paper has a certain practical guiding significance for source code vulnerability testing.
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News categorization based on titles with SVM, Naïve Bayesian, random forest, and RNN algorithms

News categorization, a text classification task, is now commonly used in many news websites. However, many of these news classifiers require full content of the news, which would cost great amounts of time for computation. In this paper, we focus on the possibility of categorizing news by its title with Support Vector Machines, Random Forest Classifiers, Naive Bayes, and Recurrent Neural Network. First, we explore some widely used pre-processing methods, including Bag of Words and Word2Vec. Then we combine these different pre-processing methods with the machine learning algorithms mentioned above to create different models. We measure their performances on the News Aggregator Data Set from UCI Machine Learning Repository, which contains over 400,000 pieces of news over 4 main categories. To evaluate the related performances, we use 85% data as a training set and 5% data as a validation set, and finally, use 10% data as a testing set. Comprehensive experimental results demonstrate that even with only the news titles, some models can still perform well in this challenging task. Therefore, it is possible to categorize news through its title in high accuracy yet with a much lower computing cost compared to full-text classification.
Research on CTR prediction model based on deep neural network

With the advent of the era of big data, it takes a lot of time and manpower to build a model that can automatically mine the effective features of data and get the user click-through rate prediction model through training, because the single model classifier needs to extract effective features and input them into the model training by means of feature processing and data mining. In view of the shortcomings of website click-through rate prediction technology, this paper proposes a model based on compressed incentive network to extract the influence of a single factor in the overall project characteristics. The paper also applies the neuron mechanism to extract the effective features of the original features, fuses the features proposed by the two models into new features and adds them to the deep neural network for training. Experiments are designed to prove the rationality of the method. The experimental results show that compared with the current commonly used model, it can improve the AUC index and keep the efficiency within a reasonable range. The research results of this paper are not only of great significance to the development of science and technology, but also affect our daily life and economic consumption all the time.
A survey: researches advanced in weakly supervised fine-grained visual classification

With the development of multimedia technology, Fine-Grained Visual Classification (FGVC) has gradually become one of the new hot tasks in computer vision community, whose goal is to identify images that belong to the same species. Though the accuracy of FGVC has made a great breakthrough, the performance is still limited by the issue of locating objects’ discriminative regions, as common state-of-the-art convolutional neural networks that perform excellently in image classification task such as ImageNet-1k cannot be directly applied to FGVC tasks. In this case, we provide a comprehensive and systematic survey of recent advances in FGVC field and divide the existing methods into: creative application of attentive structures, aids of diverse pretraining methods, various designs of loss functions and other innovative methods. We further analyze the performance of representative methods on common data sets, and finally summarize the existing problems in the FGVC research field and predict the solutions to these problems in the future.
Enhancing stock price prediction models by using concept drift detectors

Stock price movement prediction is faced with the problem that the distribution of certain underlying variables change over time. This phenomenon is defined as concept drift. Due to this phenomenon, stock price prediction models tend to give less accurate results, since the data distribution that the model has been trained on is no longer in-line with the current data distribution. In this paper an Adversarial Attentive Long Short-Term Memory (Adv-ALSTM) model is used together with a Hoeffding’s inequality based Drift Detection Method with moving Average-test (HDDMA) concept drift detector in order to make price movement predictions on 50 different stocks. Every time the HDDMA concept drift detector detects a concept drift, the model undergoes one of four possible retraining methods. The conducted experiments highlight the effectiveness of each of the proposed retraining methods, as well as how each of the methods mitigate the negative effects of concept drift in different ways. The best observed results were a 2.5% increase in accuracy and a 135.38% increase in Matthews Correlation Coefficient (MCC) when compared to the vanilla Adv-ALSTM model. These results validate the effectiveness of the proposed retraining methods, when applied to a model that has been trained on a financial dataset.
Researches advanced in multi-agent credit assignment in reinforcement learning

The multi-agent system (MAS) has always been one of the hot tasks in the distributed computing community. While with the development of reinforcement learning, the novel Multi-agent Reinforcement Learning (MARL) has gradually attracted more researchers’ attention, which aims to solve complex real-time tasks in dynamic multi-agent environment by their interaction and has been widely used in robotics, human-computer match, automatic driving and so on. Different from simple single-agent reinforcement learning, MARL faces some challenges due to the complex relationships among agents and the most influential one is the issue of credit assignment. The credit assignment often causes a substantial impediment to reward distribution, which is because the model only generates the global rewards while the own credit of each individual agent is needed during the model training phase. How to estimate and deduce the reward for each agent becomes a key issue in MARL. According to the difference of strategies, in this paper, we present an overview of the main approaches for credit assignment in MARL from three aspects, including the value-based algorithm, policy-based algorithm and mixing network-based algorithm. Also, this paper makes performance comparisons among these algorithms in different multi-agent experimental environments and finishes basic evaluation of approaches by analyzing the results of experiments. Finally, this paper summarizes the main challenges in multi-agent credit assignment (MACA) with their related solutions, current defects of algorithms about these challenges, and prospects the possible future development direction of the MACA.
Research on energy blockchain architecture design and operating cost based on blockchain and edge computing technology

With the development of power grid technology, the coordinated development of multiple types of source, network, load and storage is the future development trend. Advanced technologies based on blockchain and edge computing can efficiently utilize the advantages of energy blockchain and solve problems such as data security and flexible user transactions. To this end, this article formulates an important business application scenario of blockchain in the source network load storage, and proposes its design plan. At the same time, this article combines typical actual cases to calculate and analyze the relationship between the degree of decentralization and the operating efficiency of the microgrid. On this basis, this paper calculates the operating costs under different conditions, and finally puts forward the best operating strategy. The design scheme proposed in this paper can provide a new calculation reference for realizing the economic, high-efficiency and safe processing of a large amount of power transaction information.
Researches advanced in object tracking based on deep learning

Object tracking has always been one of the hot tasks in the computer vision community. Given the object’s size and position in the initial frame of a certain video sequence, the task of object tracking aims to predict the object’s size and position in each subsequent frame. Thanks to the rapid development of convolutional neural networks, the object tracking has achieved breakthroughs in accuracy and speed, while still faces many challenges in the large-scale applications such as appearance distortion, lighting changes, motion blur and similar background interference. In this paper, with in-depth literatures survey, we first introduce the typical tracking methods based on machine learning. We also detail the representative tracking methods based deep learning from the perspective of convolutional neural networks, recurrent neural networks, generative adversarial networks, autoencoders and other models. In addition, we analyze the performance of these representative algorithms on the common tracking datasets. Finally, we point out the unsolved problems and prospect the possible future development direction in research field of object tracking.
Research advanced in object detection of autonomous driving based on deep learning

Object detection has always been one of the hot tasks in the computer vision community, whose goal is to accurately and efficiently identify and locate a large number of predefined categories of object instances from the image. With the widespread application of deep learning, the accuracy and efficiency of object detection have been greatly improved, and thousands of methods have be proposed to improve the detection accuracy and speed. In this paper, we first introduce and analyze the representative methods from the 2D object detection to 3D object detection, and then give an exhaustive introduction to common datasets and conduct a comparative performance analysis of the core ideas of different algorithms. Also, we summarize the existing problems in the detection research field and predict the solutions to these problems in the future.
Wasserstein gradient flows policy optimization via input convex neural networks

Reinforcement learning (RL) is a widely used learning paradigm today. As a common RL method, policy optimization usually updates parameters by maximizing the expected cumulative rewards and other information obtained in the process of environment interaction. To get a better understanding of the RL and its learning theory, there is also proposed that RL can be regarded as the optimal transport problem in a probability measure space. On this basis, we get a large-scale Wasserstein gradient flow RL method by introducing input convex neural networks (ICNNs) to improve the Jordan-Kinderlehrer-otto (JKO) scheme.
Visual tracking based on multi-feature fusion

Object feature representation is one of the most important links in visual tracking. Traditional single feature are difficult to accurately describe the appearance characteristics of the target, and it is difficult to accurately track the target for large appearance changes. In view of the above problems, we use the convolution feature and color histogram feature to train the deep and shallow correlation filters respectively to jointly represent the object features, and propose a dynamic weight strategy to adaptive combine the two features. The background information around the target is introduced by introducing the context aware framework as the training samples to jointly train the filter. The combination weights of deep and shallow layers are calculated through the dynamic weight strategy, so as to realize the adaptive combination of the two features. The proposed algorithm is tested on OTB2015 data set. The results show that the algorithm can improve the accuracy and success rate of tracking
Sensitivity analysis of reinforcement learning for massive-multi-agent systems

Over the past few years, reinforcement learning has become one of the most popular topics in the field of Machine Leaning. Its nature of unsupervised learning has made it rather powerful and convenient for solving specific tasks. Unlike decision trees, reinforcement learning models do not need a pre-configured policy tree to conduct actions, which makes it possible for machinery to solve extreme complex tasks such as playing GO or other games. One of the most powerful networks is the Deep-Q network. DQN picks the optimal action under current situation to achieve the largest reward. However, even though DQN has been tested to work well on single agent environments, there lack experiments of its performance on large massive multi-agent environment trainings. This paper conducts several experiments in Petting Zoo to test the performance and other factors that affect the performance of the DQN in a massive-multi-agent system. Results demonstrates that lager number of agents and experience replay size improve the performance of DQN in a "Battle" environment.
An efficient training strategy for multi-agent reinforcement learning in card games

Most of the previous researches on reinforcement learning focused on modifying the learning mechanism of the Markov Decision Process. Researches on training strategy to improve the performance of the MARL algorithm has not received enough attention. Therefore, this paper focuses on the improvement during training process and proposes a gradual promotion training strategy. It is mainly divided into two stages, single combat stage and multi combat stage. Scenario-transfer training, rule-based training, self-playing training and mixed training are used at single combat stage to obtain a strong single agent, namely the pretrain process. At multi combat stage, multi-agent training is introduced, which increases the complexity of the game, so that the strong single agent gradually adapts to the multi-agent task, and the strong multi agent is obtained. This paper combines these two stages of learning tasks with two popular multi-agent reinforcement learning methods, namely Deep Q-learning and Neural Fictitious Self-Play. The experiment found that the gradual promotion training strategy can effectively improve the winning rate and average reward of the agent. Compared with the un-pretrained agent, the average reward is improved by 25% and winning rate is improved by 44%; at the same time, it is an extremely convenient and easy training strategy to implement.
Deep learning agent for card game ZhaJinHua

Neural network has a good performance on playing cooperative multi-player games. In this paper, we aim to explore the use of neural network in the Chinese hidden-information, competitive card game ZhaJinHua. We observe the mathematical characteristics of this game, and set up criteria to evaluate the players’ performance. Some rule-based agents and random agents are implemented in our system to contrast with the one using neural network. Result of our experiments show that our neural network can make full use of the observation to make a better decision than the other players, and get more rewards. This result is significant to confirm that neural network could be considered as a tool to resolve realistic multiplayer problems.
Face progression and regression: a taxonomy and outlook

Age progression and regression is a task that aims at rendering face images with or without the "aging" effects. The problem is originally generated from the psychophysics and human perception community but now has found tremendous interests in the computer vision community in recent years. In this paper, we give a detailed analysis of the facial aging problem and conduct a comprehensive survey on the existing methods. There are many different methods available for face aging rendering, and each has its own advantages and purpose. We categorize the existing methods into three classes: physical-based models, example-based methods, and Deep learning-based methods. The first two classes are more traditional methods that have been developed in the last few decades, while the deep learning-based methods are leveraged on the huge success of the deep learning models that emerged in recent years. We review the representative works in each category and offer insights into future research on this topic.