Reviewerly/exHarmony huggingface dataset:
FRN-YOLO: A Feature Re-fusion Network for Remote Sensing Target Detection

With the development of artificial intelligence technology, remote sensing target detection has gradually become a hot issue in the field of computer vision, which can be widely used in navigation, exploration, disaster warning, etc., and it has important research significance and application value for remote sensing target detection. However, the scale difference of remote sensing targets makes detection very difficult. Therefore, we propose a feature re-fusion network based on YOLO-FRN-YOLO. Based on the original three detection layers of YOLO, by re-fusing the features of the three output layers of the backbone, each feature layer can be deeply combined with The semantic information before sampling or after sampling, and the depth of the detection layer after feature re-fusion retains the semantic information of targets of different scales, and improves the detection ability of targets of different scales. The results show that on the RSOD datasets, the average precision of our method exceeds YOLOv3, and it is also better than other advanced networks.
Skeleton Based Action Quality Assessment of Figure Skating Videos

Action quality assessment(AQA) aims at achieving automatic evaluation the performance of human actions in video. Compared with action recognition problem, AQA focuses more on subtle differences both in spatial and temporal dimensions during the whole executing process of actions. However, most existing AQA methods tried to extract features directly from RGB videos through a 3D ConvNets, which makes the features mixed with useless scene information. To overcome this problem, We propose a deep pose feature learning AQA method that captured detailed and meaningful representations for skeleton information to discover the subtle motion difference of AQA problem. We first apply pose estimation method to obtain human skeleton data from RGB videos. Then a spatio-temporal graph convolutional network (ST-GCN) is employed to extract the dynamic changes of skeleton data and obtain the representative pose features. Finally, a regressor composed of three fully connected layers is developed to reduce the dimension of the obtained pose features and predict the final score. Experiments on MIT figure skating dataset have been extensively conducted, and the results demonstrate that the proposed method has achieved improvements that outperformed current state-of-the-art methods.
Classification of UML Diagrams to Support Software Engineering Education

There is a huge necessity for tools that implement accessibility in Software Engineering (SE) education. The use of diagrams to teach software development is a very common practice, and there are a lot of UML diagrams represented as images in didactic materials that need an accessible version for visually impaired or blind students. Machine learning techniques, such as deep learning, can be used to automate this task. The practical application of deep learning in many classification problems in the context of SE is problematic due to the large volumes of labeled data required for training. Transfer learning techniques can help in this type of task by taking advantage of pre-trained models based on Convolutional Neural Networks (CNN), so that better results may be achieved even with few images. In this work, we applied transfer learning and data augmentation for UML diagrams classification on a dataset specially created for the development of this work, containing six types of UML diagrams. The dataset was also made available as a contribution of this work. We experimented three widely-known CNN architectures: VGG16, RestNet50, and InceptionV3. The results demonstrated that the use of transfer learning contributes for achieving good results even using scarce data. However, there is still a room for improvement regarding the successful classification of the UML diagrams addressed in this work.
Classification and Identification of Domestic Catsbased on Deep Learning

In recent years, computer technology has developed very rapidly, and the hardware conditions are getting better and better. The time used to train deep neural networks has been greatly reduced. Deep learning is rapidly becoming an important hotspot of scientific research. Deep learning technology is widely used in digital recognition, speech recognition, unmanned driving, image recognition, and other fields [5]. The new generation of artificial intelligence technology represented by deep learning is gradually penetrating people&#x0027;s lives and promoting the development of society. As a representative of deep learning technology, the convolutional neural network has also developed rapidly in recent years. To improve the accuracy of cat breed classification and enable more people to clearly understand cat species, this paper cites and compares different deep learning models, and compares the performance of VGGNet, Inception-v3 and the optimized deep learning model in cat breed recognition. From the experimental results, the accuracy of the improved model is about 84&#x0025;, which is higher than other models.
Decentralized Dynamic Identity Authentication System Based on Blockchain

Identity authentication is a very important aspect of information security theory. Usually, the problem that identity authentication is prone to is that the user name and password are transmitted in the network in plaintext. The security is not enough, and it is very vulnerable to replay attacks and dictionary attacks. Dynamic passwords Although authentication is more secure, if there is a problem with the time synchronization between the server and the client, the user will not be able to login for a long time. Moreover, the password of the dynamic password is the same within a period of time, and it is also vulnerable to replay attacks. Therefore, a decentralized dynamic identity authentication system based on blockchain isn’t proposed. By using a public key + nonce (integer number that increases from 0) to sign in, It only solves the problem of over-centralized identity authentication, but also solves the replay attack. Improved the security of the identity authentication process.
Basic Operations And Structure Of An FPGA Accelerator For Parallel Bit Pattern Computation

Parallel Bit Pattern computing (PBP) has been proposed as a way to dramatically reduce power consumption per computation by minimizing the total number of gate operations. In part, this reduction is accomplished by employing aggressive compiler optimization technology to gate-level representations of computations at runtime. Massive SIMD parallelism is used to obtain speedups while executing the optimized bit-serial code. However, the PBP model also can potentially exponentially reduce the number of active gates for each such operation by recognizing and operating on symbolically-compressed patterns of bits, rather than on each individual bit within a vector. This not only provides for efficient execution of traditional parallel code, but by using bit vectors to represent entangled superposition, enables quantum-like computation to be efficiently implemented using conventional circuitry. Building on lessons learned from various software and Verilog prototypes, this paper proposes a new set of basic operations and interface structure suitable for using inexpensive Xilinx Zynq-7000 boards to implement FGPA-hardware-accelerated PBP computation. Emphasis is on how these operations will implement quantum-like computation, as the first prototype system is currently still under development.
An Improved Ant Colony Optimization Algorithm for Multi-Agent Path Planning

In recent years, Ant Colony Optimization algorithm has become one of the most widely used heuristic algorithms and has been apply to solve different types of path planning problems. However, there still are some problems in Multi-Agent Path Finding, such as low convergence efficiency, easy to fall into local optimum and vertex conflict. In this paper, we proposed an Improved Ant Colony Optimization algorithm based on parameter optimization and vertex conflict resolution. First of all, we initialize the distribution of pheromones to reduce the blindness of the algorithm in the early stage. Secondly, we introduce an adaptive pheromone intensity and pheromone reduction factor to avoid the algorithm falling into local optimum. On this basis, the algorithmÿs global search ability and convergence speed are improved by dynamic modification of the evaporation factor and heuristic function. In addition, the strategy of dynamically modifying the influence factor and heuristic function improves the global search ability and convergence speed of the algorithm. To solve vertex conflict in MAPF, we use the design conflict prediction and resolution strategy to effectively avoid vertex conflict and improve the reliability of the multi-agent system. Simulation experiments verify the effectiveness and adaptability of IACO under different complexity environments, and prove that IACO has good convergence speed and path global optimization ability.
Neural Network for Image Classification of Laryngeal Cancer

At present, convolution neural network has been applied to the classification of laryngeal cancer. An improved network model, RICN model, is constructed by combining Resnet model with Inception model. By using Resnet model, Inception model and RICN model to classify laryngeal cancer images, the performance of these three network models is evaluated and compared. The experimental data show that the improved RICN network model is superior to the traditional two network models in terms of accuracy, specificity and sensitivity. And the validity and robustness of the RICN network model are also verified.
Identifying Non-Intentional Ad Traffic on the Demand-Side in Display Advertising

The ad traffic from fraudulent or invalid activities costs advertisers a significant proportion of their ad spending. For advertisers, the ad traffic from fraudulent or invalid activities is non-intentional, and this non-intentional ad traffic should not be considered for ad delivery. In this paper, we would like to safeguard the interests of advertisers by identifying the non-intentional ad traffic from the perspective of the Demand-Side Platform (DSP), which serves the advertisers by managing their advertising budget and delivering ads to the right audience in display advertising. Then, DSPs could filter out the identified non-intentional ad traffic to avoid ad spending on and ad delivery of this traffic. To identify the non-intentional ad traffic, our approach is based on Positive-Unlabeled (PU) learning. In particular, we first extract the features which represent the corresponding access behavior, and label the partial non-intentional ad traffic instances we confirmed. Then, given the labeled non-intentional ad traffic instances and the unlabeled ad traffic instances, we build a model to infer the degree of non-intention for each incoming ad request based on our feature space. Our experimental results show that our approach outperforms the baselines on various metrics on one real dataset.
A Clustering-Based Gauss Chaotic Mapping Particle Swarm Optimization for Auto Labeling in Human Activity Recognition

Human activity recognition (HAR) has become a vital research field owing to its significant contribution to pervasive computing. In order to train practical models based on machine learning in HAR, a large number of samples need to collect sensor information and mark each sample as belonging to a specific activity. Therefore, designing an efficient labeling method is one of the important research topics in HAR. In this study, we applied an effective clustering method based on Gauss chaotic mapping particle swarm optimization (GCMPSO) to improve the accuracy of automatic labeling results. Six human activities with 561 features were used to evaluate the GCMPSO clustering method, including walking, sitting, standing, walking upstairs, walking downstairs, and lying in repose. The results of these six activities were automatically labeled by K-means and GCMPSO clustering methods. Our results show that the automatic labeling performance of the GCMPSO clustering method in HAR is superior to the K-means algorithm.
A blockchain-based smart agriculture privacy protection data aggregation scheme

Smart agriculture is a new agricultural production mode. The Internet of things has been widely used in smart agriculture. The goal is to monitor, collect and effectively employ the relevant data of agricultural process, so as to realize the sustainable development of modern agriculture. The accuracy and reliability of the collected data affect the intelligent decision, so the security and privacy protection of the data are very necessary. Data aggregation solution is one of the most commonly used methods to solve the privacy protection and security problems of data collection. Existing solutions generally require the collaboration of a trusted authority, which may not be practical. Blockchain is a new distributed protocol that can be decentralized. Hence, this paper proposes a privacy protection data aggregation solution with a fault-tolerant property based on blockchain in smart agriculture. It is proved to be data security, privacy protection.
Research on Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision

Deep correlation filter tracking method based on the fusion of correlation filter and deep convolutional neural network is one of the research hot topics in the field of visual object tracking. But how to choose an effective decision-making mechanism for implementing the online updating of feature network to fully adapt to the changes of target and environment in the tracking process is one of the key problems in the research of deep correlation filter tracking. It is obvious that the decision-making mechanism that only considers single factor can hardly meet the complex situation of the changes of target and environment. To address such an issue, this paper proposes a &#x201C;Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision&#x201D;. In the process of tracking, the cosine similarity based on Siamese network and the SSIM similarity both for the predicting tracking targets in two consecutive frames are calculated in real time. And then these two kinds of the similarity are fused together into the final similarity of the predicting tracking targets by full use of the fuzzy decision, which is taken as the criterion to determine whether the feature network needs updating and whether the tracking fails. When the feature network needs to be updated, the model is updated online while the tracking continues. In the case of tracking failure, the target is searched again, and the tracking is resumed. We tested the model on the OTB data set, and the experiments show that the tracking model designed in this paper can improve the tracking accuracy under the conditions of real-time tracking.
Interpolation based reversible hiding scheme by using center folding strategy and adjusting hiding operator

Reversible data hiding (RDH) scheme has been widely used in many applications. One of the techniques which used in RDH is interpolation hiding data technology (IHDT). The paper proposed an IHDT based RDH scheme by using the concept of Center Folding Strategy (CFS). To improve the image quality of the stego-image, the proposed scheme adopts the enhanced neighbor means interpolation (ENMI) technique to generate the interpolated image. Furthermore, the proposed scheme refers the original image to flexibly adjust the embedding equation such that the stego-pixel can closer to the original value. The experimental results show that the proposed scheme can get better experimental results.
An Improved Mutation Operator Which Can Improve the Performance of Genetic Algorithm

An improved combined mutation operator (CM) is proposed for the problems of premature convergence and local optimization which often occur in genetic algorithm (GA). The CM operator combines the Gaussian mutation and the initial mutation to perform local initialization operations on individuals in the population, and maintain the population diversity while improving the local search ability of the operator. The results of 15 benchmark optimization problems show that the proposed CM operator can effectively improve the performance of the algorithm, and compared with other advanced algorithms, the improved algorithm (IRCGA) has stronger search capabilities and faster convergence speed.
Family Eggsperts by Eggy

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Collaborative Security Annotation and Online Testing for Web APIs

As API security gains more and more attention, we devised a system, called API Prober 2.0 (AP2 for short), to better support the discovery and management of security-enabled Web APIs. AP2 supports two main features for Web APIs: security schemes annotation and an online testing mechanism. Based on the concept of collaborative development, the users in AP2 can augment the OAS (OpenAPI Specification) documents of Web APIs with appropriate security schemes, semi-automatically extract access tokens, and perform online testing on service endpoints of Web APIs. Besides, we use the AP2 system to actually annotate security schemes and execute tests for nine actual APIs. The evaluation results show that all core features of AP2 were successfully realized. It is verified that the AP2 system is feasible and can effectively help developers use security-enabled Web APIs more efficiently.
A Logistics Proof Chain for the Decentralized Trading Model Based on Two-Tier Identities

Traditional online trading platforms provide services with user data centrally stored on their servers, posing risks to user privacy and assets. On the contrary, cryptocurrencies based on blockchains avoid many problems of the centralized platforms. However, the intrinsic anonymity makes it difficult to trace the identities if someone defaults on a transaction. To address this issue, we proposed a blockchain trading prototype based on regulatable two-tier identities in our previous work. Users can make off-chain promises about transactions through an off-chain transaction confirmation protocol and appeal to the on-chain supervision contract for reimbursement if the proof of the default is provided to the contract. The model has both anonymity and regulatability but how the proof is constructed is not designed in the previous work. In this paper, we propose a logistics proof chain based on blockchains to log the delivery information of the goods. If the goods do not arrive as expected, customers can lodge appeals to the supervision contract with a proof chain created by this logistics proof blockchain. Blockchains, smart contracts and other technologies can ensure logistics information is completely logged, and the proof chain is correctly constructed.
Enhanced methods for Evolution in-Materio Processors

Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and physical experimentation hinder their development. Simulations based on a physical model were used to efficiently investigate three specific enhancements to EiM processors which operate as classifiers. Firstly, an adapted Differential Evolution algorithm that includes batching and a validation dataset. This allows more generational updates and a validation metric which could tune hyper-parameters. Secondly, the introduction of Binary Cross Entropy as an objective function for the EA, a continuous fitness metric with several advantages over the commonly used classification error objective function. Finally, the use of regression to quickly assess the material processor's output states and produce an optimal readout layer, a significant improvement over fixed or evolved interpretation schemes which can ‘hide’ the true performance of a material processor. Together these enhancements provide guidance on the production of more flexible, better performing, and robust EiM processors.
Real-time Feedback and Evaluation Algorithm for Children's Digital Writing Practice

At present, the application of handwritten numeral practice is common. However, these applications only focus on the imitation practice of drawing red, that is, they only focus on the similarity of shape, and they don't pay attention to the basic stroke order and stroke number. What's more, they can't judge the quality of digital writing and give real-time feedback. In this paper, the handwriting of children is preprocessed by mathematical morphology operation, and then the convolution neural network is used to recognize the number. After recognition, the handwriting is processed with fine lines, and the improved Hilditch algorithm is used for skeleton extraction. The next the features of handwritten numerals are extracted, such as corners, lines, arcs, the proportion and position of handwriting pixels. These features are used in the fuzzy comprehensive evaluation method to realize the real-time evaluation and feedback of handwritten numbers, and truly achieve the purpose of digital writing practice. Experiments show that the algorithm has good real-time and accuracy, and can improve the efficiency and enthusiasm of children's independent practice of digital writing.
Optimization of neural network structure using involution operator based on particle swarm optimization for image classification

Deep neural networks have made signifi-cant progress in image classification in recent years, however good deep neural networks take a lot of hu-man labor and computational resources, and they must be developed by person with professional expe-rience. Most good deep neural networks now employ convolution operators for feature extraction, however due to convolution spatially agnostic and channel-specific, they lose their capacity to deal with diverse spaces and visual modes. As a result, this article uses a new operator involution based on the inverse con-volution operator's design principle, which is com-bined with the particle swarm optimization algorithm's (PSO) high precision and quick convergence features, as well as the variable length encoding approach. Convolution operator problems can be solved, and the most effective deep neural network structure for the image classification problem can be generated automatically. Experiments demonstrate that the neu-ral network structure created by the method presented in this study outperforms several similar algorithms in terms of recognition accuracy and number of pa-rameters generated, as well as saving a lot of time and computer resources.
Smart Education Under 5G+

As the latest generation of cellular mobile communication technology, 5G is profoundly changing our production and life, and the education industry is also facing changes and challenges. This article combines outstanding information such as high-speed data transmission rate, ubiquitous network, low latency, low energy consumption, and Internet of Everything of the 5th generation mobile communication technology, combining artificial intelligence, virtual reality, Internet of Things, cloud computing, big data and other popular information technologies. The application of technology in education has analyzed the coming of the era of “5G +”, which will have a profound impact on the educational cause. It will make educational wisdom, digitization, teaching models and teaching methods will continue to be innovative.5G integration of IT will make a series of smart education ideas such as virtual laboratory, virtual assistant, online live teaching, remote control, and smart campus become a reality. Finally, this article reflects on the challenges faced by the roles involved in the education industry such as students, teachers, and schools in the era of smart education and how to deal with them. All parties work together to look forward to the bright future of smart education.