Reviewerly/exHarmony huggingface dataset:
A Study of Instrument Playing Action Recommendation Scheme with AI Vision Technology

To address the problem of low accuracy rate of musical instrument playing movements with AI vision technology at this stage, we propose to build a logical statistical model of musical instrument playing movements using improved particle swarm algorithm to collect data and pinpoint simple human movements. The model is implemented based on a certain sports bracelet to recognize the basic playing movements. Firstly, the support vector machine (SVM) model is optimized by improved particle swarm optimization (IPSO) algorithm and structured local dictionary learning, and then the optimized SVM is used to classify the human activity detection data. And the error information is corrected by using scenario analysis method. The results demonstrate that the accuracy rate of performance action information detection can reach 94.3% by applying the model on a sports bracelet, which shows that AI vision technology can achieve accurate pushing of target human performance action solutions by combining with this user portrait model. The creation of complex musical compositions requires the display of true creativity, which depends on various factors related to the hierarchy of musical language. Music generation faces algorithmic problems, and in this paper we want to apply deep learning models based on AI vision techniques to the human music composition and creative process.
wAdvMTD: A Mitigation to White-box Adversarial Examples Using Heterogeneous Models and Moving Target Defense

In recent years, the adversarial example has become a non-trivial threat to the deep learning model. It harms the availability and integrity of deep learning as the inconspicuous perturbation is hard to be distinguished by a human observer. Among the mitigation strategies, those schemes using moving target defense as the methodology to defend adversarial examples proclaims a satisfactory performance in the black-box context. As for protecting deep learning models from white-box adversaries, however, only scheduling a model from the candidate set randomly to handle these stronger adversaries seems insufficient. Furthermore, the mitigation is vulnerable if the scheduling process is exposed to the adversary. In this paper, we proposed a white-box adversarial examples mitigation mechanism called wAdvMTD. It can automatically diversify the model structure of deep neural networks to make the attack vector to a certain model fails on the rest of its heterogeneous models. We make a proof-of-concept by deploying this mechanism to ResNet. Compared with the methodology that only schedules one of the pre-built models, using a set of structure-diversified models can reach a higher accuracy even with the constraint that this mechanism is completely exposed to the adversary.
EBSNet: Efficient Binary Semantic Segmentation Network

Aiming at the problem of large amount of computation and complex decoding structure of encoder-decoder semantic segmentation network, an efficient binary semantic segmentation model EBSNet was proposed. Firstly, the model removed the complex decoding structure and skip connections in the mainstream encoder-decoder segmentation network, and used convolutional remodeling upsampling to directly recombine the final encoded features to generate segmentation masks, which simplified the parameter structure of the model. Secondly, the ECASA attention mechanism and Poly1CE loss are combined to further enhance the model coding ability and improve the final accuracy of the model. Segmentation experiments on DeepGlobe and CrackFores datasets show that the segmentation accuracy of the proposed model has reached the effect of surpassing the existing mainstream segmentation networks, and the segmentation speed has also been improved by multiple times.
Lung Nodule Segmentation Method Based on 3D Rem-Unet

For the problems of inaccurate positioning and imprecise edge segmentation of lung nodules in 3D medical computed tomography (CT) images, a 3D Rem-UNet method for lung nodule segmentation is proposed. Using 3D UNet as the reference model, a multi-branch hybrid attention (MHA) module is added to the encoder structure to improve the extraction of detailed features in the target region; The residual connection is used to realize feature reuse; And the Group Normalization (GN) algorithm is introduced to achieve the feature normalization. Finally, ablation experiments are conducted on the LUNA16 dataset to verify the effectiveness of the model. The experimental results show that 3D Rem-UNet has the fastest loss convergence and the most stable loss change during the training process. Meanwhile, compared with 3D UNet model, the dice coefficient, accuracy, recall and F1 score are increased by 4.73%, 14.6%, 1.76% and 4.71%, respectively. The model can effectively improve the segmentation effect of lung nodules.
Sports Action Pattern Recognition Method Based on Fuzzy Neural Network Theory

In recent years, with the continuous progress of computer deep learning, neural network and other technologies, a large number of analysis and action recognition have been carried out for various types of motion behaviors through the establishment of relevant models, and the recognition methods of human behavior characteristics characterized by pictures have also been rapidly developed. In this paper, a three-dimensional convolutional neural network with dual resolution is established, and then the motion technology action data set is established through the model, Using the SSD target detection algorithm, the feature vectors extracted from the dual-resolution network are fused and the SVM classification experiment is carried out. The final experimental results show that the algorithm flow designed in this paper is based on the motion recognition of the motion technology action data set established in this paper. On this basis, the recognition results of two resolution neural networks and support vector machines are compared. The effectiveness of this method is verified by the simulation of the constructed sports skill behavior samples.
Study on Vehicle Recognition Algorithm Based on Convolutional Neural Network

As the application of vehicles expands, the types of vehicles have also become increasingly diverse. While vehicles bring convenience to daily life, they also pose certain traffic hazards due to their different types, such as large transport vehicles entering main roads and violations in prohibited areas and dedicated lanes. Therefore, it has become important to classify and manage actual vehicles. In the field of image processing, the key to vehicle classification is to design classifiers and classify images accordingly. Against this backdrop, relevant classifier algorithms are studied, and classifier design and optimization are completed. Currently, convolutional neural networks (CNN) have been widely applied in image processing and achieved good classification results. To address the issue of actual vehicle classification, this paper selects classic convolutional models, including LeNet5, VGG16, and ResNet50, for transfer learning. The image enhancement processing and optimizer selection are conducted to achieve the final classification. After multiple parameter iterations and comparative experiments, the optimal algorithm combination of VGG16 model, cross-entropy function, and Adam optimizer is selected. The validation accuracy rate of the last iteration under this algorithm combination is 93%, and the optimal accuracy rate throughout the entire training process is 94%.
Complementary Mask Self-Supervised Pre-training Based on Teacher-Student Network

In this paper, we propose a complementary self-supervised mask model based on teacher-student networks. This model contains a student network, a teacher network, and a mask prediction module. The student's network is an encoder structure, and the teacher's network consists of encoders and decoders. The teacher and student network encoders are used for learning image representations and have the same network structure and model parameters. The pre-training has two pre-tasks: First, the mask image block representation predicted by the decoder in the teacher network predicts the actual image pixels through the mask prediction module. Then, we introduce a comparative learning loss function to compare the outputs of the teacher and student modules in representation space. This paper proposes a complementary masking mechanism to reduce the gap between upstream and downstream mismatches in the pre-training model based on mask image modeling (MIM). For example, a complete picture is an input into the teacher and the student network. For the teacher network, the input picture is randomly masked off, for example, 75 %; the student network masks the remaining part of the input picture, 25 %. The student network masks the rest (25%) of the input image. The pre-trained model proposed in this paper has been pre-trained on COCO and other data sets, and downstream tasks are performed on four conventional data sets. By comparing with some of the latest self-supervised pre-trained models, it is proved that the pre-trained model proposed in this paper can learn better representational information.
Design of tobacco industry hierarchical early warning system based on big data

In view of the flaw found in the existing industrial control network system which cannot efficiently and stably receive and process the massive sensing data from the cigarette factory during the production process, a tobacco industrial data processing and storage system for sensor data of cigarette factory, based on the Hadoop framework, has thus been designed and implemented. This system is based on the existing Cyber Physical System (CPS) and uses Message Queuing Telemetry Transmission (MQTT) as the data access protocol, with Flink on Yarn-Cluster deployment mode as the flow computing platform, and with Kafka as the message queue buffer diversion. Flink is used to send different types of data to Kafka for temporary storage and processing. A grading pre-warning system for cigarette manufacturing department is designed to realize dynamic monitoring and real-time pre-warning of production equipment status. Finally, by combining production business with big data technology, a hierarchical early warning system for cigarette manufacturing department was designed to realize dynamic monitoring and real-time early warning of production equipment status.
APTTOOLNER: A Chinese Dataset of Cyber Security Tool for NER Task

The existing named entity recognition work in cyber security focuses on the threat intelligence field, and two problems appeared: 1) The language for NER datasets in threat intelligence mainly is in English. 2) The definition of named entities in the field of threat intelligence is confusing. For these two issues, we collected 244 APT analysis reports and analyzed the flow of threat intelligence, then predefined 7 APT tool related (which is the base of threat intelligence) entities. Two experts labeled the reports twice for consistent understanding. Finally, we got a dataset for APT tool entity in Chinese with 31423 predefined entity tags and 424930 O tags, and the dataset was validated on a regular NER model.
Design and Implementation of Satellite Knowledge Graph and Its Application

Knowledge graphs (KGs), representing multi-relational data as a semantic graph from structured information stored in triples, have attracted wide attention in industrial and academic communities. The previous study mainly focuses on the construction techniques of KG from semantically structured information; it is still challenging to understand how these KGs are being used for different applications and what methods they employ. Thus, we provide both design and implementation techniques of KG and its applications in relational query and question-answering systems. The applications implemented on satellite KG consists of over 5000 nodes and 38,000 relationships extracted from the union of concerned scientists (UCS) satellite database, gunter's space page, and other websites. For the satellite question-answering system, we used the NLTK tool with the NLP technology to segment and analyze the given questions that were trained by using the convolutional neural network model over the constructed satellite KG. Our proposed method presents a satellite KG and its application in a form that can be easily handled for further research and analysis.
Single target tracking method in low illumination video based on computer vision

Aiming at the problem that the single target tracking effect of low illumination video is poor, a low illumination video single target tracking method based on computer vision is proposed. The computer vision processing algorithm is used to enhance the video image, and the sharpening components in the images at all levels are controlled by parameters to suppress the high-frequency noise of low illumination video. In order to better expand the dynamic range of the overall gray level of the enhanced image, this paper adopts an improved computer vision method to adjust the contrast of the video image, so as to achieve the requirements of low illumination video but target tracking. Finally, from the simulation results, it can be seen that the low illumination video single target tracking method based on computer vision has better integrity and accuracy, and the short-term occlusion of moving targets can achieve a better tracking effect.
Serialized Recommendation Algorithm Based on Bidirectional Distillation

In the application process of computer recognition scene image text, the scene detection text recognition function of the computer natural language recognition algorithm is crucial. Therefore, most of the current research on natural language learning and analysis is focused on improving and applying this algorithm. Traditional natural language text recognition algorithms estimate the recognition position of the scene inaccurately. Furthermore, the complex text is difficult to recognize, adversely affecting algorithms' efficiency. Therefore, this paper proposes a two-way distillation serialization recommendation algorithm based on long short-term memory networks. In the knowledge distillation system, the traditional text is extracted using middle-level feature distillation, which greatly improves the accuracy of text recognition and the performance of long short-term memory networks.
Automatic CWE Assignment for Vulnerabilities with Graph Neural Networks

Vulnerability reports are essential for improving software security since they record many key information of vulnerabilities. In a report, CWE denotes the weakness of the vulnerability and thus helps understand the cause of the vulnerability quickly. Therefore, CWE assignment useful for categorizing newly discovered vulnerabilities. In this paper, we propose an automatic CWE assignment method with Graph Neural Networks. First, we prepare a dataset that contains 3,394 real world vulnerabilities from Linux, OpenSSL, Wireshark and many other software. Then, we extract statements with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax features. On top of slices, we represent these slices with graphs that characterize the data dependency and control dependency between statements. Finally, we employ the Graph Neural Network to learn the hidden information from these graphs, and leverage the Siamese Network to compute the similarity between vulnerability functions, thereby performing assignment of CWE ID for these vulnerabilities. The experimental results show that the proposed method is effective compared to existing methods.
Two-stage mean vector-based competitive representation for image classification

Collaborative-competitive representation based classification (CCRC), as a very novel collaborative representation classification method, has achieved good results in the field of image classification. In this paper, in order to enhance the discriminative ability of each class to achieve the improvement of classification performance, we propose a novel collaborative representation (CR) model called the two-stage mean vector-based competitive representation (TMCR) for image classification. The TMCR model introduces a class-specific mean vector constraint and uses a two-stage idea to further improve the classification performance of the model. TMCR divides representation into two stages: coarse representation and fine representation. In the first stage, we select classes in which training samples are similar to the query samples through the mean vector-based competitive representation, and then we use the classes to perform a more correct representation and classification of the query samples in the second stage. In addition, we make full use of the localities of data and introduce weight constraints by calculating the distances between the testing sample and each training sample. The discriminative ability of the model is further improved by the mean vector constraint, the idea of two-stage and weight constraint. Experimental results obtained on GT and ORL datasets indicate that the proposed model can surpass state-of-the-art CR-based methods.
Research on unsupervised MDS technology based on graph structure

At present, most text summary methods based on deep learning are supervised methods that require large dataset. However, large-scale multi-document summary(MDS) datasets are difficult to obtain in practical applications. This paper proposes an unsupervised MDS extraction method based on graph structure. First, we use a pre-trained language model based on BERT for sentence representation. Secondly, according to the semantic relation between sentences, the semantic relation graph of sentences is constructed. Then the centroid and maximum marginal correlation algorithm (MMR) were used to remove the redundancy of candidate summary. Finally, experiments are conducted on a Multi-news dataset to validate the effectiveness of the proposed method. The experimental results show that the method proposed in this paper achieves better results in the unsupervised domain and is comparable with some supervised models. The method proposed in this paper has wide application prospects in the field of multi-document sentiment summarization.
Design of Intelligent Test Paper Generation System for Online Psychological Testing Based on Ant Colony Algorithm

In the calculation process of ant colony algorithm, the ant colony transfer is guided by the pheromone intensity and distance of each path, and the path of ant colony movement always tends to the path with the strongest information. Due to the influence of pheromone concentration, the subsequent ants have a higher probability of choosing a shorter path, thus forming a positive feedback mechanism, and eventually the entire ant colony will find an optimal path to find food sources. Intelligent paper grouping problem is actually a process of combinatorial optimization of the target according to the given constraints, so as to obtain the best solution. The traditional psychological test has many problems such as single test question, low test frequency and disclosure of personal privacy, so the results of psychological test are often not authentic. The network psychological test database is constructed, and the paper is automatically assembled based on ant colony algorithm. The convergence speed is fast, the robustness is outstanding, and it is convenient to realize the global algorithm.
LHNet: Logic-assisted Hierarchical Neural Network Model for Image Classification

Neural networks cannot handle logic, but logic is an efficient way to perform classification when dealing with downstream tasks. In this study, domain knowledge is applied to set up logic for traffic sign classification. According to the similarities and functionalities of traffic signs, the dataset is divided into 4 subsets: prohibitory, danger, mandatory, and others. The heavy multi-class image classification task is split into the integral of several light multi-class image classifications, which is connected via logic justification. The complexity of model is significantly reduced and the inference time is much quicker.
White-Box Concealment Attacks Against Anomaly Detectors for Cyber-Physical Systems

Anomaly detection for cyber-physical systems is an effective method to detect ongoing process anomalies caused by an attacker. Recently, a number of anomaly detection techniques were proposed (e.g., ML based, invariant rule based, control theoretical). Little is known about the resilience of those anomaly detectors against attackers that conceal their attacks to evade detection. In particular, their resilience against white-box concealment attacks has so far only been investigated for the subset of neural network-based detectors. In this work, we demonstrate for the first time that white-box concealment attacks can also be applied to detectors that are not based on neural network solutions. In order to achieve this, we propose a generic white-box attack that evades anomaly detectors and can be adapted even if the target detection technique does not optimize a loss function. We design and implement a framework to perform our attacks, and test it on several detectors from related work. Our results show that it is possible to completely evade a wide range of detectors (based on diverse detection techniques) while reducing the number of samples that need to be manipulated (compared to prior black-box concealment attacks).
PwrLeak: Exploiting Power Reporting Interface for Side-Channel Attacks on AMD SEV

An increasing number of Trusted Execution Environment (TEE) is adopting to a variety of commercial products for protecting data security on the cloud. However, TEEs are still exposed to various side-channel vulnerabilities, such as execution order-based, timing-based, and power-based vulnerabilities. While recent hardware is applying various techniques to mitigate order-based and timing-based side-channel vulnerabilities, power-based side-channel attacks remain a concern of hardware security, especially for the confidential computing settings where the server machines are beyond the control of cloud users. In this paper, we present PwrLeak, an attack framework that exploits AMD’s power reporting interfaces to build power side-channel attacks against AMD Secure Encrypted Virtualization (SEV)-protected VM. We design and implement the attack framework with three general steps: (1) identify the instruction running inside AMD SEV, (2) apply a power interpolator to amplify power consumption, including an emulation-based interpolator for analyzing purposes and a more general interrupt-based interpolator, and (3) infer secrets with various analysis approaches. A case study of using the emulation-based interpolator to infer the whole JPEG images processed by libjpeg demonstrates its ability to help analyze power consumption inside SEV VM. Our end-to-end attacks against Intel’s Integrated Performance Primitives (Intel IPP) library indicates that PwrLeak can be exploited to infer RSA private keys with over 80% accuracy using the interrupt-based interpolator.
Web-Based Augmented Reality of Smart Healthcare Education for Machine Learning-Based Object Detection in the Night Sky

Visit Astrophile to a science centre to understand the night sky and astronomical philosophies. The learning environment is limited to virtually observing the constellations, and a significant difference can be observed between reality and the user’s imagination. In situations like this, where people can’t go out due to safety restrictions because of the COVID-19 pandemic, Space 360 will bring a virtual planetarium to their homes. Using Space 360, the user can see the celestial body present in the night sky at any particular time. Space 360 is about stargazing and observing the seasonal constellations and gives the user feature to find information about the celestial body. This feature will display details like visibility, declination, etc. This feature holds good for the 88 constellations visible from the earth and eight planets in our solar system.