Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Layer and Spatial Aggregation neural network for human pose estimation

The simplebaseline model achieves high performance of human pose estimation with simple network structure. But the model lacks the layer and spatial information fusion. In this paper, we propose DLSAnet, which fuse layers and spatial information efficetively. DLSAnet uses DLA as backbone which has excellent feature extraction capabilities in the field of object detection. In addition, a modified spatial pyramid pooling is introduced to pool and connect multi-scale local area features, allowing the network to learn object features more comprehensively. Using a four-branch SPP module instead of a single-branch SPP module connected by a single hopping layer. This method is effective in alleviating the problem of slow loss drop late in training. Experiments show that DLSAnet can achieve better accuracy.
A review on impact of distributed generation penetration in deregulated electricity market having renewable energy sources in India

Nowadays it is seen that, distributed generation (DG) system based on conventional energy sources and renewable energy sources (RESs), specially using solar energy, is playing an important role to match load demand across India. Energy policies are promoting renewable energy resources and in the coming time to increase the energy efficiency, DGs installation and RESs scheme will play a vital role. It can be observed that India’s load rate is increasing rapidly day by day. Unplanned management made without planning can adversely affect the grid and put a serious load demand on the power grid. Distributed generation systems can be very useful to avoid such problems, especially if we use renewable energy. In this paper, we have mentioned about how we can manage the load demand and how to meet the power supply of the people using the distributed generation. In this review, strength, stability and reliability of distributed generation (DG) system is discussed with different aspects including different scenarios. This analysis shows a major action on the power system planning and working of the grid system. Recent works are included in this review. Different steps have also been taken for the integration of DGs with various scenarios in electric power systems. The main objective of this review is, how to achieve better integration of flexible demand and demand response, so that demand side Management (DSM) with the help of distributed generation can be fulfilled and create grid proficiency. Different challenge in DG system has also been mention in this research.
The Research Progress of the Deep Hybrid Model in the Field of Text Classification

The traditional deep convolution neural network model cannot extract the context information effectively in dealing with complex long text data sets, and it is difficult to obtain the deep semantic information of the text. The deep hybrid neural network can optimize and improve the local feature extraction ability of CNN model while preserving the ability of local feature extraction. It has achieved good performance on complex data sets, so it has been paid more and more attention by researchers. Firstly, this article sorts out the current mainstream text classification data sets. Secondly, the model of the hybrid neural network based on the convolutional neural network construction is as follows: the improvement of the CNN model; technology fusion based on CNN model and the CNN-based model mixing three categories carry out analysis and sorting. Finally summarize the current problems in the text classification field, and look forward to future development and research.
Topic evolution analysis of radar research using a dynamic topic model based on latent Dirichlet allocation

This work applies a dynamic topic model based on latent Dirichlet allocation to investigate the evolution of radar research and applications. To address the problem that processing a large number of papers exceeds the capacity of computers, we propose a method to sample documents according to their citation count. Of 104,428 sampled articles, 108 research topics are extracted. The evolution of topics is analysed from two dimensions: content and strength. The results show that radar technology, which arose mainly from physics and engineering science, has been widely applied in studies in the civil engineering, geographical, environmental, meteorological, geological, agricultural, ecological, among others. In the long-term development of radar, new technologies have continuously been produced. At the individual topic level, the research content has changed over time. The development objectives of radar systems are to enhance functionality, extract more information and improve clarity.
A Nuclear detection method Based on improved You Only Look Once v5s Framework

Extracting human cell smear samples through automated scanners, using target detection algorithm to detect its nucleus is an important content in cell detection. Due to the different shapes, different sizes and complex backgrounds of nuclei in the samples, automatic detection of nuclei is a challenging task. In order to solve the problem of complex small target recognition in nuclear detection and improve detection accuracy, based on the YOLOV5S framework of one-stage target detection, make certain improvement, add new residual module to the original network Neck layer, at the same time carry on network widening operation to the subsequent framework. The algorithm visualizes the results of training and testing through Tensorboard. Experimental results show that the accuracy of the improved YOLOV5S network is 1.6% and 4.4% higher than that of the original YOLOV5S network and YOLOV3 network. the improved network has strong real-time performance and high accuracy, which can meet the actual use requirements.
Optimization Research on Initial Seeds of the Database Management Systems Fuzzing Framework Squirrel

Squirrel is one of the most effective fuzzing frameworks for finding memory errors in database management systems. But its efficiency is affected by initial seeds significantly. In order to improve the fuzzing efficiency of Squirrel, we optimized its initial seeds. The main factors affecting its efficiency are the average grammatical distance and the number of texts of initial seeds. Increasing these two factors at the same time can greatly improve the fuzzing efficiency of Squirrel.
Effectiveness of grassland crop rotation in the submontane area of the Republic of North Ossetia Alania

In conditions of the submontane zone of The Republic of North Ossetia Alania the investigated cultures of grassland crop rotation made positive effect on structurally-aggregate properties of soil, water stability of soil aggregates, and also on fruitfulness and energetic potential of cultivated crops. The most favorable impact on soil structure was made by clover and winter wheat, which are having more lengthy periods of vegetation, well developed root system, ensuring the better protection of soil in autumn and spring from the destructive effect of atmospheric precipitation. Intertilled cultures had low evaluation in this context. The capacity for structure formation is more weakly expressed in potatoes. In result of research it was revealed that the soil under study has good structural condition because it contains from 52.1 to 55.8% of water-stable aggregates. It was established that the weight by volume kept changing in all cultures of crop rotation. By the end of vegetation, the tendency of increasing weight by volume has been observed practically in all variants. In general, the average soil density under all cultures has been optimal during the whole period of research. The maximum output of feed units was marked among the following options: oat + clover – 9.83 t/ha, clover – 9.49 t/ha, corn – 9.49 t/ha. The more quantity of digested protein was contained in the crop of clover, for the rest of the cultures the index was approximately at the same level. The grass rotation where the pickup of feed units and digested protein was credibly exceeding the indices of intertilled rotation, was the more fruitful (7.96 t/ha – rotation grass and 6.33 t/ha – intertilled rotation).
Enrichment of Biogas by Microalgal Scrubbing System and Value added Products Synthesis

Biogas is composed of CH4 (50-60%) and CO2 (40-50%), therefore scrubbing (removal) of CO2 from biogas is imperative as high concentration of it reduces the calorific value and limits its end application. Problems associated with physiochemical process can be alleviated through microalgae as they are efficient in utilizing the CO2 as its carbon source that can be technically termed as CO2 sequestration. This study aims to apply the use of a microalgae CO2 capturing system using Chlorella sorokiniana and Scenedesmus obliquus that have higher photosynthetic capacity and sequestration of carbon dioxide subsequently and transforming into biomass. Chlorella sorokiniana had a total biomass yield of 1.59 g/L and 1.98 g/L for unpurged and purged respectively. Scenedesmus obliquus on the other had a better yield over Chlorella sorokiniana with an overall biomass of 1.7 g/L for unpurged and 2.4 g/l under mixotrophic condition. Upon analysis of lipid content in the biomass, the cultures that were grown under mixotrophic condition were found to yield 3.45 and 3.28 fold higher as compared to unpurged sample in Chlorella and Scenedesmus respectively. Under purged conditions, Chlorella sorokiniana had the maximum yield of 5.5 mg/g of vitamin E compared to Scenedesmus obliquus with a comparatively lower yield of 2.32 mg/g. Overall, the increase in the specific growth rate and doubling time indicated that a better adaptability and utilization of carbon source in the purged sample which was provided in the form of 50% citric acid and carbon dioxide. This provides evidence in support of Chlorella sorokiniana for having a higher potential in the area of nutraceuticals. Along with purifying the biogas produced from an anaerobic digester fed with food waste.
MOOC Dropout Prediction Based on Multi-view Learning

Massive Open Online Courses (MOOCs) provides a promising way to support education for all. Nonetheless, one central challenge is the remarkably high dropout rate, with completion rates for MOOC recently reported to be below 5%, and high dropout rates limiting their effectiveness. Building on the analysis of dropout as closely related to user and course interaction patterns, this paper presents an end-to-end multi-view interactive framework (EMIF) to predict user dropout in MOOC. We extract learning activity information, user enrolment information, and course knowledge information through a set of lD-CNNs, obtain mutual information of these and fuse the above information-view loss through a self-weighing method. Experimental evaluation of our solution on the KDDCUP 2015 and MOOCCube datasets showed better results (AUC & F1 scores). We have a better performance compared to several existing methods, without using different architectures for different datasets.
AdaDiff: Adaptive Gradient Descent with the Differential of Gradient

Optimization methods are crucial to train deep neural networks. Adaptive optimization methods, especially Adam, are wildly used because they aren’t sensitive to the selection of learning rate and converge fast. Recent work point out Adam has a performance gap with SGD and even not converge because of the unstable and extreme learning rates. Many variants of Adam are proposed to solve the problem, such as AMSGrad, AdaBound and AdaBelief. In this paper, we propose a new variant of Adam, called AdaDiff. AdaDiff computing gradient descent step size by the exponential moving average(EMA) of gradient and differential of gradients aiming to make the training of networks more stable. We compare our method with other optimizers on various tasks. The results show AdaDiff outperforms Adam and minimizes the performance gap with SGD.
The Quality Evaluation of Online Book Reviews Based on Graph Neural Network

With the development of the Internet, the way people read has changed, the readers are more and more willing to share their reading opinions via the reading platforms. However, as the continuous increase in the number of users and reviews on the book platforms, the review data is growing exponentially. It is difficult for people to find useful information quickly in the massive reviews. Therefore, using automated methods to identify high-quality reviews among a large number of book reviews has become a hot topic in current research. This paper studies a quality evaluation method of book reviews based on graph convolution neural network, which introduces the LDA document topic generation model to add topic nodes on the basis of review and word nodes, builds the relationship between the three nodes as edges to generate heterogeneous graph. The graph is sent to a two-layer GCN model for training. In the last, this article did experimental evaluation and analysis based on the book review data of non-commercial platforms. The results show that the proposed method is superior to the machine learning method and the traditional neural network model.
Multiple Granularity Network and Dynamic Label for Domain Adaptive Person Re-identification

The domain adaptive person re-identification (Re-ID) has be more popular among researchers. Because it can save a lot of resources as it only exploits the source domain knowledge and does not need the complex annotation efforts in target domain. It aims to extend a model trained on a labeled dataset to another dataset which is unlabeled. Many works reduce feature distribution gap between two different datasets to solve the problem. However, these works ignore the problem which is the variations within an unlabeled dataset. In the paper, we propose a domain adaptive person Re-ID framework based on multiple granularity network and dynamic label (MGDL). Specifically, we send the images of two different datasets into multiple granularity network at the same time for joint training to reduce feature distribution gap which is between the two different datasets. The network is trained by two different kinds of pseudo labels, namely, conservative label and radical label. The two kinds of pseudo labels are used to alternating pull and push the feature distribution in the target domain to reduce the variations within an unlabeled dataset. Experiments have shown that the MGDL achieves competitive performance in person Re-ID which is under the cross-domain setting.
An Efficient Spam SMS Analysis Model based on Multinomial Naïve Bayes model Using Passive Aggressive Algorithm

The social media can be a platform for information consumption nowadays. On the one hand, it’s free of cost, easy access, and different data dissemination lead people to hunt out and consume social media news. On the contrary, it allows for the broad spread of “spams,” i.e., inferiority news with deliberately false information. The widespread spread of spams has the potential for very negative impacts on people and society. Consequently, the detection of spam on social media has recently become an important research that draws tremendous attention. NLP, an artificial intelligence (AI) division, uses computers and human natural language to produce useful data. In text classification activities, such as spam detection and sentiment analysis, text generation, language translations and document classification, NLP is widely used.
Object classification based on RGB-D features fusion and LLC coding

An object classification method based on RGB-D features fusion is proposed in this paper. Firstly, image features in terms of texture, size, shape, from the RGB image and the depth image are extracted; secondly, the improved K-means clustering method is used to cluster the different extracted features to obtain seven different visual dictionaries respectively; then, the LLC coding model is used to perform feature coding on the extracted visual features to obtain seven different image feature expressions; meanwhile, above seven features expression are fused using the linear cascade mode to obtain the fusion feature; finally, linear SVM performs classifier is used to realize the object classification based on fusion feature. Experimental results show that compared with some existing algorithm based on RGB-D, the accuracy of our proposed method is increased to 87.3%. The accuracy of object classification is significantly improved.
Research on Facial Expression Recognition Based on Improved Deep Residual Network Model

Facial expressions are the main external manifestations of human emotions. Facial expression recognition technology can be used in medical, investigation, education and other application scenarios. Aiming at the disadvantages of slow convergence speed and low recognition accuracy of traditional neural networks, in order to optimize the network more efficiently and improve the recognition rate of facial expressions, a facial expression recognition method based on improved convolutional neural networks is proposed.First, the deep residual network ResNet18 is improved, and then the improved ResNet18 network is used to extract global expression features of face images, and then the self-attention weighting module is introduced to calculate the expression features of each face image and output one The corresponding weights are used to weight the loss function. Finally, the two public expression data sets of CK+ and RAF-DB are used to experimentally verify the method in the article. The accuracy of facial expression recognition reaches 98.89% and respectively. 87.13%, compared with the original deep residual network ResNet18 model and other types of network models, the facial expression recognition rate has been significantly improved, which proves that this method can effectively improve the accuracy of facial expression recognition and has certain application value.
Feature Purity: A Quantitative Evaluation Metric for Feature Extraction in Convolutional Neural Networks

The quantitative analysis of specific convolutional layer feature information in Convolutional Neural Networks (CNN) has received considerable critical attention in the field of deep learning. In this paper, we proposed an innovative measuring method based on the principle of information entropy to quantitatively analyze the performance of feature extraction in CNN. In the method, the feature purity was defined as the normalized entropy of activation histogram, which was calculated from all kernels in feature layer of CNN. Feature purities were evaluated respectively in different layers of CNN as the inspection for internal structure in specific classification models. At the same time, general degrees of purities among different models were compared with classification performances. The experiments were performed in models of AlexNet, VGG, ResNet, and SENet with different epochs trained by cifar10 and ImageNet1000 datasets. Additionally, a visualization evaluation was performed by grad-CAM method in the manners of the intra-model and inter-model purities. Experimental results showed a strong relationship between feature purity and the classification accuracy in each model. Additionally, the visualized feature map also demonstrated that salient region extracted in specified feature layer had good consistency with purity value. As a result, the proposed feature purity can serve as a quantitative metric representing the degree of feature extraction in CNN without the dependency of label or specific network structure; and has significant interpretability and versatility across models.
A Research on Advanced Technology of Target Detection in Unmanned Driving

Unmanned driving leads the development of smart cities and safe transportation. It relies on a large amount of complex data generated during driving. This paper reviews the state-of-the-art research status of data collection and analysis techniques. We divide the data required for unmanned driving into two types: traffic scene data and driving behavior data. Firstly, by fully considering the different needs of enterprises, universities and related institutions, we have fully studied the algorithms proposed by scholars all over the world. Then we introduce mainstream 2D and 3D target detection algorithms based on RGBD and laser point clouds. Finally, the article introduces some of the most widely used urban road data sets.
Proximal Policy Optimization with Future rewards

Among the current reinforcement learning algorithms, the Policy Gradient algorithm (PG)[7] is one of the traditional and most widely used algorithms, but it has the disadvantage of unstable gradient estimation, and the newly Proximal Policy Optimization algorithm (PPO) [8]solves the problem. It solves the stability problem, but the update policy is slow, and it is easy to produce over-fitting when the training times are too many. In this article, a new method is proposed, referring to Asynchronous Advantage Actor-Critic (A3C)[9], the basic PPO algorithm is trained in parallel, and a method that considers future rewards is introduced, and the future rewards are also calculated to the current. In the reward, through the OPEN GYM platform and the experimental results of capturing at any position of the robotic arm, our actions can ensure a faster training speed, while also avoiding overfitting during long-term training.
Computational simulation and theoretical comparative study for the complexes of heme-iron and heme-zinc

By using computer technology, molecular simulation and analysis of the coordination structure of heme iron and complex formed by the heme ligand and Zn are carried out under the same level of calculation. Studies have found that the ability of heme ligands to capture Fe is significantly stronger than that of Zn. However, the binding energy and the stability of the complex of heme ligand and Zn are stronger than those of the complex of heme ligand and Fe, and at the same concentration, the complex absorbs light in the near-ultraviolet region to a greater degree.
A malicious code family classification method based on self-attention mechanism

Malicious code families have become a major threat to network security. Many current methods convert malicious code into images and use deep learning to classify malicious code families. However, the family classification method based on deep learning incorporates the overall characteristics of the malicious code into the classification model, which may cause interference with redundant information in the malicious code. This paper proposes a malicious code family classification method based on a self-attention mechanism. When analysing a noisy data structure such as malicious code images, the attention mechanism is introduced to filter the interference information in the malicious code. The experimental results show that the classification accuracy rate of this method for malicious code families is 99.56%, and the recall rate is 98.06%. Rigorous theoretical analyses and numerous experiments prove our method is efficient and reliable.
Modeling of specific safety-critical driving scenarios for data synthesis in the context of autonomous driving software

Autonomous driving will be one of the key characteristics for modern vehicles in the future, especially with the objective of saving more people’s lives on the roads due to signiﬁcant reductions in the number of traﬃc accidents. One of the most challenging aspects of autonomous cars are the safety-critical driving scenarios, such as emergency braking. Their criticality has seldom been measured in terms of further forensic analysis or software solutions in the ﬁeld of artiﬁcial intelligence. As a consequence, this paper answers the following scientiﬁc question: How to provide alternative data about some rarely recorded scenarios of safety-critical driving so as to achieve improved training and validation of machine learning algorithms in the autonomous driving context?