Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Model-based Abnormal Traffic Detection Method

Information security has become a concern of all walks of life, and anomaly detection can protect information security, so anomaly detection has become a research hotspot. In this paper, the principles of four commonly used model-based anomaly detection methods, namely, depth-based, distance-based, density-based and deep learning-based detection methods are introduced and their research status is reviewed. Analyzed the characteristics of the four methods, and finally pointed out the future development trend of anomaly detection methods and gave a conclusion.
Network Anomaly Traffic Detection Method Based on Multi-SAE and LSTM

For the purpose of faster and more accurate anomalous traffic detection with increasing classes of data traffic in the network, this paper proposes a new anomalous traffic detection method based on stacked auto-encoders and a long short-term memory network model. The method uses Multi-SAE to extract the effective features of sequential traffic, which is obtained by concatenating multiple stacked auto-encoders, and a long short-term memory network to extract the temporal structure of the effective features, with the Multi-SAE and the long short-term memory network in a back-and-forth tandem structure. To further improve the efficiency of the detection, redundant MAC addresses are also removed in the pre-processing. From the experimental results, this paper achieves effective detection of twenty types of data traffic with an accuracy rate of 98.25%, which is higher than that of the same category of research by nearly 2 percentage points, and the parameters of precision, recall and f1-score also reach over 96%, improving the detection results.
Ballot character recognition based on image processing

According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting regulations for "differential elections", "equal elections", and "alternative elections". This paper proposes an automatic ballot recognition system based on image processing, which is suitable for all levels of people’s congresses, party and government meetings or other meetings. It has complete functions and stable recognition. It can automatically count and summarize the voting results and display the voting results on the big screen. Show on the display system. Firstly, the paper ballot is scanned by a scanner to obtain the image of the ballot. Secondly, the system will preprocess the image of the ballot and segment the image into regions.Thirdly, according to the handwritten character recognition algorithm based on image processing and Shen filter proposed in this paper, the handwritten characters "√" and "×" in the subregion are recognized.The final experimental results show that the recognition accuracy of the system is as high as 99.82%, which has an absolute advantage over the traditional manual voting, and can meet the performance requirements of high efficiency, accuracy, objectivity and security of large-scale conference election.
Face feature matching based on semantic Information

Face feature matching problems have always been a very important research topic in the field of image processing. Face feature matching is always the most important part of common face recognition algorithms, but how to match features quickly and accurately is a quite essential and urgent problem to be solved. Therefore, we propose a face feature matching method which can effectively improve the matching efficiency and accuracy. For each feature point in the input image, face feature matching based on nearest neighbor ratio method requires global search for the best matching in the reference image. However, we find that such matching method can achieve better matching results, but it costs a lot of computing resources and time. Therefore, we use the feature vectors constructed by CNN convolution feature map to provide the local matching region based on semantic information for handcrafted descriptors after feature matching. Although two feature matching processes are used in this paper, the matching efficiency is improved several times compared with the traditional global search scheme. Experiments show that our proposed can be used as an important module of face recognition system to improve the accuracy of face recognition.
Light-SAR-ShipNet (LSSNet): A Lightweight SAR Image Ship Detection System with Enhanced Deep Supervision

Applying deep learning methods to achieve synthetic aperture radar (SAR) image ship target detection has become a current hot research topic. However, most of the existing detection networks are designed based on optical images, so when they are directly applied to SAR image target detection, there will be the following two problems: 1) There are big differences between SAR image and optical image in imaging mechanism, geometric characteristics and radiation characteristics, etc, which makes the detection network have more redundant imformation; 2) Real-time performance and accuracy are both very important in SAR image target detection task, and applying a network designed based on optical image cannot balance the performance of the two well. In response to the above problems, this paper proposes a lightweight network LSSNet specially designed for SAR image ship detection task. In earlier layers, we use depthwise separable convolution instead of conventional convolution to design dense block; in deeper layers, stacked dense blocks with shortcuts are used to enhance deep supervision, and finally achieve high-speed and high-accuracy detection. This paper uses SSDD dataset as baseline for expeiment and results show that LSSNet have higher accurracy and detecition speed. Its lightweight structure will also help transplant to hardware devices in the future.
ForestResNet: A Deep Learning Algorithm for Forest Image Classification

Due to its large area and rugged terrain, the forest often fails to be detected in time and eventually causes severe losses[1]. Therefore, early detection of forest fires is significant for forest fire protection. The application of deep learning to the classification of smoke and fire in forest images can detect forest conditions more accurately. In this paper, a classification network, named ForestResNet, is proposed to efficiently detect forest conditions, which uses ResNet50[2] as a feature extraction network to achieve rapid and accurate extraction of image feature information. Experimental results show that the proposed network achieves excellent segmentation performance in terms of efficiency and accuracy.
Experimental investigations on mechanical properties of cotton/hemp fiber reinforced epoxy resin hybrid composites

In this experiment, epoxy resin, fine hemp fibre, and cotton fibres were combined to create bio-composites. The weight percentages of hemp fibre (0, 10, 20, 30 wt. percent), cotton fibres (35, 25, 15, 5 wt. pewrcent), and epoxy resin have been varied to produce four composite mates. As per ASTM standards, composite specimens were prepared using the water jet machining process. Tensile, flexural, and impact tests were performed on the composite specimens in order to determine their mechanical properties and water absorption behaviour. Composites containing 20 wt. % fine hemp fibre in weight percentage showed significant improvements in tensile properties. Composites containing 30 wt% hemp fibre improve both flexural and impact properties, according to the test results.
Image Super Resolution Reconstruction Algorithm Based on Convolution Neural Network

Aiming at the characteristics of large amount of remote sensing image data, large terrain fluctuations and wide coverage, this paper proposes a method for super-resolution reconstruction of remote sensing image. This method combines dense network and deep back projection network to form dense projection. The unit forms a deep dense projection network, which solves the problems of insufficient texture, loss of image details and training difficulty in the super-resolution reconstruction of remote sensing image. The experimental results show that on multiple remote sensing image datasets, compared other traditional methods, the PSNR and SSIM are significantly improved, and reconstructed remote sensing image texture signs and details are more abundant.
Research on Building Chinese Blog Semantic Lexicon Based on Semantic Comprehension

Nowadays, investigators of phrases and text sentimental orientation identification mainly divided into two ways: semantic comprehension and machine learning. Machine learning needs a lot of training corpus and also cannot handle general field words effectively, meanwhile, semantic comprehension cannot get high scores at precision and recall. Therefore, in this paper, we propose a new fusion method based on our semantic comprehension for judging phrases’ polarity. In this paper, firstly, we modify the knowledge of Hownet, on the basis of its four primitives, the fifth primitive—sentimental primitive was proposed by our research, and annotated it into Hownet manually; secondly, we propose a new phrase sentimental similarity calculation method to compute word’s sentimental value; at last, we integrate transductive learning into this method to identify phrases’ sentimental orientation. The performance of experiment show that, compared with SVM or traditional semantic comprehension, it can get better results.
Features of Reproduction of Dairy Cattle of the “Levochsky” Breeding Enterprise of the Novgorod Region

In modern conditions of intensification of dairy cattle breeding, it is necessary to increase or maintain at a certain level the livestock of cattle. With a general decrease in the indicators of the dairy cattle breeding industry in the region under study, it is necessary to search for ways and methods of organizing production that would improve the efficiency of the manufactured products. As an example, the results of the work of a breeding farm are offered, which successfully solves the problems facing it in the reproduction of the breeding stock and receives seven thousandth milk yield with a high content of mass fraction of fat in milk from farmed cows. In the course of the research, the features of herd reproduction in the breeding reproducer of Ayrshir cattle of the agricultural production cooperative “Levochsky” in the Novgorod region were studied. The article presents tabular data with the main indicators of reproduction of the studied breeding reproducer and an analysis of these data. The conducted research shows that the breeding reproducer of the agricultural production cooperative “Levochsky” successfully solves the issues of reproduction of its dairy herd applying modern methods and technologies of livestock breeding. But for a stable position of the enterprise as a breeding farm, it is necessary to increase the output of calves per hundred cows to at least 85%. In the course of research, the authors found that in order to achieve real success in dairy production, it is necessary to engage not only in reproduction, but also in selection and breeding work in general. Based on the studies carried out, well-grounded conclusions were made about the need to further increase the efficiency of work on the reproduction of the breeding stock of the studied breeding reproducer.
TCN-HBP: A Deep Learning Method for Identifying Hormone-Binding Proteins from Amino Acid Sequences Based on a Temporal Convolution Neural Network

Hormone-binding proteins (HBPs) are carrier proteins that specifically bind to targeted hormones. Some evidence suggests that the abnormal expression of HBPs causes various diseases. Therefore, it is significant to accurately identify HBPs to study these diseases. Recently, many researchers have proposed traditional machine learning methods to complete this work, but these methods are neither suitable for training on large-scale datasets nor take into account the contextual features of HBPs. In this paper, I propose a new deep learning method, TCN-HBP, to distinguish HBPs. TCN-HBP consists of a coding layer, embedding layer, convolutional neural network (CNN) layer and temporal convolutional network (TCN) layer. The coding and embedding layers extend the protein sequences into two-dimensional matrix data. The CNN layer convolves the matrix data to form feature maps. The TCN layer captures the contextual features present in the feature maps. Experiments show that the data generalization capabilities and recognition accuracy (99.15%) of TCN-HBP on large datasets perform better than previous methods.
MDCount: A Lightweight Encoder-Decoder Architecture for Resource-Saving Crowd Counting

This work put forwards a lightweight encoder-decoder architecture MDCount designed unambiguously for real-time high-precision crowd counting with constricted calculation resources. The lightweight backbone network MobileNetV2 is tailored to decrease parameter numbers in an acceptable accuracy. The encoder-decoder architecture with atrous spatial pyramid pooling modules is proposed to recover the spatial and contextual information at multiple scales. Experiments on realistic and challenging datasets and comparing contemporary approaches demonstrate that our method MDCount accomplishes equivalent performance with smaller computation costs.
BERT Pre-training Acceleration Algorithm Based on MASK Mechanism

With the development of natural language processing (NLP) science, machines can understand human language better and better. Especially with the emergence of pre-training models in recent years, such as ELMO, GPT, BERT, etc. Researchers can easily transfer model knowledge and apply them to related fields, but at the same time they often ignore the pretraining process. Due to the huge number of parameters and calculations of the pre-training model, it often takes a lot of resources and much time to get a good pre-training model. So many people are daunted by the pre-training process. From a different perspective, this article mainly focuses on the pre-training process of BERT, and conducts in-depth research and related improvements on its own mask mechanism, so as to accelerate the training process. The experimental results show that our algorithm can get better result than the original scheme which the BERT use. Also it is better than the original BERT in terms of training speed and can better understand the training text input. What is more, we have also conducted a detailed analysis of the experimental result.
An image reconstruction algorithm based on classified block

This paper constructs a compression image reconstruction algorithm(ABCS-MMSE). ABCS-MMSE overcomes the defects of compression sensing sampling. It not only constructs the block measurement matrix at encoding end but also generates the block recovery matrix at decoding end. Experimental results show that the average reconstruction time of ABCS-MMSE is much lower than that of BCS-SPL-DDWT and that of BCS-SPL-DCT. At the same time, for the images with smooth information, the reconstruction quality of ABCS-MMSE is not much worse than that of BCS-SPL-DDWT and that of BCS-SPL-DCT.
Network Traffic Anomaly Detection Method Based on CAE and LSTM

This paper constructs a deep learning method for detecting network traffic anomalies to enhance the secure transmission of data in networks due to the complex, diverse and numerous types of anomalous traffic in current networks. The method combines multiple convolutional auto-encoders (Multi-CAE) with a long short-term memory network. The convolutional auto-encoders are obtained by combining stacked auto-encoders with convolutional layers, which can not only reduce feature loss but also effectively extract the spatial structure of samples. The use of Multi-CAE greatly improves the feature extraction capability, and combined with the long short-term memory network to extract temporal features, the effective features extracted in this paper are more comprehensive and less losses compared to the models used in other researches. A comparison of the loss values in the training of CAE (Convolutional Auto-Encoders) and SAE (Stacked Auto-Encoders) in the experiments shows that the loss values of CAE are about one-tenth lower than those of SAE, and the method consisting of Multi-CAE and LSTM for the USTC- TFC2016 dataset was trained with accuracy values up to 99.98%, and the precision, recall and f1-score parameters were also above 99%, outperforming other studies.
Research on Role Access Control Model Based on Identity Authentication

Cryptography and access control are the core technologies for a secure operating system. Cryptography can guarantee the confidentiality, integrity, availability and unforgeability of information in the access control system. In the identity-based cryptosystem, the user’s private key is centrally generated by the key distribution center, and key escrow is a major security risk. In view of this problem, it is proposed that multiple trusted entities participate in the key distribution scheme in a serial manner. At the same time, this paper adds a separate blind factor to each user to increase security of the scheme; it also introduces the concept of virtual key, which uses additivity of the key space in the pair-based encryption scheme. In key generation, a timestamp T is added, which enables one-time pad of the system, enhances security of the system, and eliminates collusion attacks by KGC and multiple KPA. Finally, the improved signcryption scheme is combined with the RBAC model, and the I_RBAC model is proposed, which enables identity authentication and information confidentiality of users in simple communication in the RBAC model. Finally, the efficiency and safety of this scheme are analyzed.
Reverse Logistics Enterprise Performance Research Based on Super-Efficiency DEA and LMBP Neural Network

On the basis of considering the characteristics of reverse logistics enterprises, this paper uses stakeholder theory to establish an evaluation system containing 36 indicators in five dimensions, based on the balanced scorecard, and adopts PCA optimization index. In view of the performance evaluation problem, first, the performance data is obtained by using the super-efficiency DEA, then the enterprise performance of A company is fitted by LMBP neural network to obtain the quantitative model of performance evaluation, and finally, according to the resulting weight reference performance benchmark, the direction of reverse logistics enterprise performance improvement is given. The simulation results show that the index system can react to the reverse logistics characteristics very well, and the BP neural network based on LM algorithm can accurately and efficiently evaluate the performance of reverse logistics enterprises which has better generalization, and can find out the key performance indicators and give reasonable optimization direction for enterprises by reverse tracking the weights of the neural network.
Does belonging to an appellation make a difference?

Assuming that wine markets are efficient, ultimately a bottle of wine’s cost and therefore its price should reflect its vintage, grape variety as well as how it is vinified. Yet, being an experiential good, a wine’s price is also closely related to its place of origin. If the designated viticultural area of wine is coming from is not considered, even in a relatively new wine country, wine makers may end up over-estimating the premium attached to vintage, variety as well as how it is vinified. Regression results indicate that, for Ontario wines, the over-estimations vary between 1% points and 18% points.
Air Quality Tracking and Traffic Flow Prediction in Cities

: Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. After collecting, it presents an ambient air quality monitoring and prediction system. The system consists of several distributed monitoring stations that communicate wirelessly to a backend server using machine-to-machine communication protocol. Each station is equipped with gaseous and meteorological sensors as well as data logging and wireless communication capabilities. The backend server collects real time data from the stations and converts it into information delivered to users through web portals and mobile applications. We also collect real time Air Quality Index is using real time API in different area of smart city and show the pollution of that city.Suggesting path to travel from current place to that particular place on the map. The system designs common process of traffic organization and management for different large special events The experimental results show that the model proposed in this paper can achieve better performance than the performances of other benchmarks.
Mechanism of Centella asiatica extract in Increasing Alveolar Macrophages Apoptosis in Rat Tuberculosis Models

Mycobacterium tuberculosis has the capacity to manipulate host defense pathway, particularly the ability to inhibit apoptosis of infected host cells. Centella asiatica is a medicinal plant which is able to reduce growth of Mycobacterium tuberculosis and enhance the cellular immune response and apoptosis in cancer cells. The research is to determine effect and mechanism of the extract of Centella asiatica in enhancing macrophages apoptosis from rat tuberculosis models. Rat tuberculosis models were divided randomly into 4 groups. Group 1, 2, and 3 were given extracts of Centella asiatica at 375mg/kgbw, 750mg/kgbw, and 1500mg/kgbw daily for 14 days. The 4 group is a control group. Apoptosis, expression of Bcl 2, Bax, Caspase 8 were performed by immunohistochemistry. The study shows reduce expression of Bcl-2, increase expression of Bax and Caspase 8 also enhance the macrophages apoptosis. Ethanol extract of Centella asiatica have active ingredients to enhance of apoptosis alveolar macrophages from rat lung tissue that were infected by Mycobacterium tuberculosis through the increase of Caspase 8’s expression, Bax’expression and decreasing of Bcl-2’s expression. Experimental article J Int Dent Med Res 2021; 14(2): 812-819)