Miracle-dz/UniFAR-FTUs huggingface dataset:
Deformable convolution-based motion target detection algorithm

For motion target detection in dynamic backgrounds, most traditional methods have drawbacks, such as long computation time or high limitation on the background. This paper proposes a deformable convolution-based motion target detection algorithm by replacing part of the C3 module in the YOLOv5 feature extraction layer with deformable convolution (DCNv3) to introduce long distance dependence and adaptive spatial aggregation, and adding an ECA attention mechanism to reduce the effect of background variation. The the accuracy, recall and mAP of the improved YOLOv5s algorithm increases by 1.6%, 3.8% and 2.3% respectively, and is able to identify moving targets in dynamic backgrounds more accurately than the original algorithm.
Application development and technical analysis of rider helmet wearing monitoring system

More and more people are choosing two-wheeled motor cars as their primary mode of transportation as society develops quickly. As a result, concerns about rider safety have grown over time. Using a helmet may successfully protect the rider's head, lower their chance of suffering a head injury, and lessen their exposure to traffic accidents. The electric bike riders in the images without helmets were found using detection and recognition techniques, and their license plate data was gathered. This technique can successfully identify motorcyclists who disobey the law and give the appropriate authorities crucial information to develop more exact safety standards and enforcement actions. Monitoring the level of helmet use among drivers of motor vehicles is therefore crucial. This study compares and contrasts the key technologies and provides an overview of the current helmet-wearing monitoring systems. The rider helmet wearing monitoring system is first introduced, along with its use cases and importance. The approaches are then distilled into two groups in accordance with the detection techniques' underlying operating principles. The benefits and drawbacks of the two common approaches are then compared, along with potential adaptations. The prospect and summary of the rider helmet wearing monitoring system's use and research directions follows.
The proposal and application of an intelligent algorithm - lion swarm algorithm

The core of future energy Internet is new energy as the main body. With the access of large-scale nonlinear photovoltaic (PV) systems, a phenomenon has been found in recent years: when some PV modules are obstructed, it can cause a "mismatch" in the photovoltaic array. Even if very few components are obstructed, the amount of electricity generated will decrease significantly. Moreover, the photovoltaic output characteristics will show multi-peak characteristics. The traditional tracking algorithm can not effectively track the maximum power. Therefore, this paper proposes a new intelligent algorithm: Lions algorithm. The convergence performance of the algorithm and the influence of each parameter on the convergence are analyzed. Compared with particle swarm optimization (PSO) algorithm, the actual optimization ability of lions algorithm is verified. The lions algorithm is applied to partially occlusive PV maximum power point tracking (MPPT) to verify the effect. The algorithm can also be adopted in other fields that need optimization.
Prediction model of dissolved oxygen concentration in sewage treatment based on CNN-LSTM network and DBSCAN

The water consumption of urban users is complex, and there are great differences between residential water and industrial wastewater. Real-time monitoring of water quality is an important task of urban water management. Among the water quality parameters, dissolved oxygen concentration (DO) is an important index to evaluate the quality of water. Therefore, the accuracy of dissolved oxygen prediction results is very important for the management of water bodies in sewage treatment. In this paper, a model based on DBSCAN-CNN-LSTM is proposed to solve the problems of inaccurate prediction accuracy and poor data convergence in the existing water quality prediction model in the sewage treatment process. Firstly, the obtained water quality data are preprocessed to obtain water quality factors with high correlation with DO.Secondly, the input data are predicted and output by establishing a hybrid model based on DBSCAN-CNN-GRU. The DBSCAN algorithm can cluster the sewage data and reduce the influence of local minima on the prediction results, so as to improve the prediction accuracy and data convergence. Compared with the traditional neural network models GRU, LSTM and CNN-LSTM, the predicted values is closer to the real values.
A high-performance bone age assessment neural network for x-ray images

Although deep learning based X-ray bone age detection algorithms have achieved good results, the complexity of these SOTA methods is also increasing. In this article, we propose a new lightweight network that exceeds the accuracy of the SOTA algorithm, but significantly reduces computational complexity. Our method has an average error of 0.18 years and a parameter quantity of only 0.9M, which outperforms the existing best methods in terms of computational performance and accuracy.
Research on lightweight detection model of spontaneous combustion of electric vehicles

The article addresses the problems of large models, high cost, high false detection rate, low detection accuracy, and insufficient computing power of edge computing platforms that are common in current smoke and fire detection systems for parking lot fire monitoring. In this paper, we propose a lightweight self-ignition detection improvement model that is easy to use on monitoring devices and edge computing platforms. The model uses the YOLOv5 model as the base network, introduces an adaptive co-concern module, enhances the smoke and fire feature extraction capability of the backbone network, and uses sub-pixel sampling for semantic enhancement of the depth feature map. The size of the improved model is only 3.8MB, which can be more lightly deployed in edge surveillance environment for monitoring. The simulation comparison experiments before and after the model improvement are conducted in the self-combustion dataset under the self-built parking lot surveillance scenario with 3977 images in the training set and 896 images in the test set. The experimental results show that the error rate of the improved model for self-combustion smoke and fire detection is 8.1%, which is 2.7% lower than the error detection rate before the improvement; the detection accuracy reaches 82.0%, which is 5.2% higher than the accuracy before the improvement.
Dual-stream high-dimensional features implicit augmentation for micro-expression recognition

Aiming at the problem that micro-expression (ME) participants have their own expressions that interfere with microexpression recognition (MER), a dual-stream high-dimensional features implicit augmentation for MER is proposed. The innovative fusion method of adaptive face mask and Action Units (AUs) is used to reduce the interference of the participants' own expressions, and combined with optical flow information to form more focused high-dimensional spatiotemporal information. Enhance high-dimensional spatio-temporal features through an improved implicit semantic data enhancement algorithm to alleviate the problem of small sample data. Using high-dimensional spatiotemporal information to focus the network model on ME active areas. Using a two-stream hybrid convolutional network to extract features from temporal and spatial domain information respectively to improve the representation ability of the network. The experimental results show that under the evaluation criteria of MEGC2019, UF1 and UAR are improved by 5.5% and 6.8% respectively compared with the best method.
A malicious programs detection method incorporating transformer and co-occurrence matrix

To address the problem that traditional malicious program detection algorithms require a lot of specialized domain knowledge and convolutional neural networks for detecting malicious programs that are difficult to capture global features and long-range dependencies, we propose a malicious program detection method based on Transformer architecture and innovatively use the program assembly opcode frequency to construct a co-occurrence matrix, which in turn generates images as model inputs. Using the multi-headed attention mechanism of Transformer, we extract the malicious program opcode invocation pattern implied behind the co-occurrence matrix image and thus achieve the detection of malicious programs. The method can adapt to PE files of different sizes with fewer parameters and outperforms ViT-B/16 with an accuracy of 0.9887, precision of 0.9873, and F1 score of 0.9849.
The inversion of ocean current extraction by along-track interferometric SAR based on sea surface model

It is challenging to estimate ocean surface currents from along-track interferometric (ATI) data due to the complexity of the ocean environment. In order to complete the process from modeling the ocean surface signals to extracting the ocean surface currents, this research integrates dynamic sea surface modeling imagery with an iterative technique to inverse the currents. To complete the simulation analysis of time-varying ocean surface imaging, ocean surface backscatter coefficients and ocean surface echoes are constructed using the wave spectrum model as the basis. The ATI data from the simulation modeling are inverted using the iterative inversion algorithm of the Synthetic Aperture Radar (SAR) ocean surface currents field, and the accuracy of the inverse ocean surface currents field obtained reaches 0.1 m/s, proving the viability of the whole system.
Deep learning algorithm of clustering analysis in IUR teaching system

Optimized data analysis methods can enhance teaching system efficiency. The optimization algorithm has spontaneous recognition of high-dimensional data sparsity in the density peak clustering information. This study proposed a deeplearning algorithm in the density peak clustering analysis of the IUR teaching system, which has the advantages of fast calculation speed and dynamic data update. The research is based on the k-means clustering method to select IUR teaching system loads, and the pheromone parameter is used as an influencing factor in the clustering process. The proposed algorithm can better implement teaching data analysis across systems. In addition, the improved algorithm has low complexity and strong operability. The innovative elements of the IUR teaching systems such as renewable subjects, R&D resource input, and innovative teaching management can be controlled collaboratively. In the experimental analysis, the proposed algorithm can further strengthen the quantitative analysis of the IUR teaching system configuration, improve the refinement method for the effective control and distribution of innovative teaching systems, and fully exploit the data resources for the combination of innovative applications and teaching systems development.
Area localization of leaf diseases under complex environments

Regional location of plant diseases helps to develop targeted prevention and control strategies to improve crop production efficiency and reduce agricultural losses. Recently, some important advances have been made in computer vision-based methods. However, most of the current mainstream methods are based on target detection or rotation detection, which always include environmental noise in the results of locating disease areas. In this paper, we revisit the task from the perspective of segmentation tasks and propose a feature reinforcement module. Specifically, we effectively focus on the shortcomings of previous methods where neighbourhood features cannot interact by looking at the shortcomings of the Feature Pyramid Network (FPN) in the feature extraction process through multi-scale features repetitive sampling. In addition, we compared the dataset annotation methods under different annotations such as target detection, rotation detection, and segmentation, and the visualisation well demonstrates the need to use segmentation methods. In the final experimental results, our method is proved to improve mAP by 1.3% and mIOU by 1% over state-of-the-art methods. A large number of methods have demonstrated the superiority and reliability of our method.
Fake account detection based on Node2Vec and node feature merging

Online social network has become an indispensable place for People's Daily life and social communication. Billions of people around the world are more or less active on online social networks every day. While it brings us the convenience of real-time communication and obtaining information, it also hides the threat of fake users in the network. These fake users use malicious comments and spread fake links,seriously affecting the normal experience of users in social networks. The article proposed a fake account detection model based on node2vec and node feature merging that can help accurately identify fake users in online social networks. Experiments showed that the model proposed in this paper has some improvements in accuracy, precision, recall and F1 value compared with other models in fake account detection tasks.
Wireless sensor network coverage optimization strategy based on improved cuckoo search algorithm

Efficient deployment of nodes inside the Wireless Sensor Network contributes to achieving maximum network coverage and minimum energy consumption. Aiming at the problems of low sensor node coverage and high redundancy of Sink nodes in the convergence layer in wireless sensor networks, we propose the WSN coverage optimization method based on the improved cuckoo search algorithm. First, the qubit Bloch spherical coordinates are introduced in the population initialization process to maintain better population diversity and wider coverage space. Secondly, in the Levy flight optimization stage of the cuckoo search algorithm, the residual operation inside the candidate solution matrix is used to generate long-distance dependency information of sensor nodes and new candidate solutions. Finally, a greedy evaluation strategy of dimension-by-dimension update is added to improve the random walk selection process to avoid the interference of node information between the same dimensions, so as to improve the iteration speed and accuracy of cuckoo search algorithm. Here, we use Matlab to build the coding environment and simulate the sensor nodes. The simulation results show that, compared with the leading methods in the current field, the improved cuckoo search algorithm can achieve a higher coverage ratio, and the redundancy ratio of Sink nodes in the aggregation layer is lower. Our proposed ICS model has better robustness and higher accuracy.
Understanding method of power dispatch professional language based on multi-model fusion

In order to improve the accuracy of power dispatch professional language understanding, the professional language understanding method of power dispatching based on multi-model fusion is proposed. First, the dispatch professional language is represented as a low-dimensional feature vector based on the pre-trained word vector model. Then the mapping relationship between scheduling professional language and scheduling intention is trained based on text convolutional neural network (TextCNN). The relation relationship between professional language slot feature and information labels are trained based on the bidirectional long-term short-term memory network-conditional random field (BiLSTM-CRF), the dispatch professional language understanding is realized by the joint multi-model recognition results. Finally, through the verification of power dispatch professional language of a control center, compared with other methods, the proposed professional language understanding method has higher accuracy.
Perspectives on limiting tobacco access and supporting access to nicotine vaping products among clients of residential drug and alcohol treatment services in Australia

Introduction Tobacco endgame strategies aim to drive down population smoking rates, the success of which can be improved with public buy-in, including from populations with high smoking rates such as alcohol and other drug (AOD) service clients. This study aimed to explore acceptability of tobacco retail and nicotine reduction, and subsidised nicotine vaping to support AOD service clients following a smoking cessation attempt. Methods We interviewed 31 Australian AOD service clients who currently or previously smoked, following a 12-week randomised trial comparing nicotine replacement therapy with nicotine vaping product (NVP) for smoking cessation. Participants were asked how effectively three scenarios would support tobacco cessation: tobacco retailer reduction, very low-nicotine cigarette standard and subsidised NVP access. We thematically analysed participant views on how each approach would support tobacco abstinence. Results Tobacco retailer reduction raised concerns about increasing travel and accessing cigarettes from alternate sources, with generally lower acceptability, though a range of perspectives were provided. Reducing nicotine in tobacco products was described as reducing appeal of smoking and potentially increasing illicit purchases of non-reduced nicotine products. Clients of AOD services were highly accepting of subsidised NVP access for tobacco cessation, as this would partly address financial and socioeconomic barriers. Conclusions Australian tobacco control policy should consider how these approaches impact ease and likelihood of tobacco access by AOD service clients in relation to the general population. Understanding clients’ acceptability of tobacco control and endgame measures can inform how to avoid potential unintended consequences for these clients.
Effects of combined physical and cognitive program performed in socially enriched environment on older people’s cognitive abilities and quality of life (StimCoAPS): study protocol for a randomized controlled trial

The number of older people worldwide is constantly increasing. However, ageing is accompanied by a decline in cognitive ability that can affect quality of life. Cognitive, physical and social activities can all slow this decline but social factors and their contribution to the well-being remain under-researched. The aim of this study is to analyse how the practice environment can foster these social relations and thus enhance well-being and to identify the psychological dimensions that are activated by social relations. This study is a 3-year randomised controlled trial designed to assess the effects on participants’ cognitive abilities and quality of life of a combined (ie, physical and cognitive) training programme in different social practice environments. A total of 159 older people (≥65 years old) will be recruited and randomly assigned to one of three practice environments: individual practice at home (n=53), group practice in a gymnasium (n=53) and group practice in an enriched environment (n=53). All participants will complete 12-week combined training sessions and will be assessed four times: before the start, in the middle, at the end and 6 months after the end of the programme. They will undergo cognitive function (episodic memory and executive functions), physical capacity (aerobic fitness, muscle strength and dynamic balance) and psychosocial assessments (indicators of psychological well-being, social support, self-esteem, anxiety, depression and achievement goal strategies), as well as semistructured interviews. Statistical analyses will be conducted to assess the effect of the practice environment on the perceived benefits of this programme in terms of cognitive abilities and quality of life, and to determine the role of psychosocial factors in this relationship. This protocol has been approved by an institutional review board (CERSTAPS: IRB00012476-2022-20-01-146). Trials registration number: NCT05721508.
Interactive Exercises for Computer-based Work Using a Webcam

Sedentary behavior in office environments has become a widespread concern due to its negative impact on individuals’ health and well-being. This study not only addresses this issue by providing details about the musculoskeletal disorders pertinent to the wrist, shoulders, and neck that can develop due to immobility or prolonged sitting in front of a computer workstation, but also promotes the regular incorporation of three specific exercises for office workers. In particular, this study contains a comprehensive literature review covering the trade-offs between wearable devices and computer vision techniques in monitoring and counting the repetitive movements of various physical activities. Moreover, this study utilizes the Mediapipe pose estimation technique to track exercise performance and develops algorithms using a state machine for accurately counting repetitions during active breaks in an office environment. The dataset used to evaluate the methods employed consisted of a total of 36 videos and was gathered by engaging the employees working at Fraunhofer IGD and the University of Rostock. The findings of the research validated that the state machine could count the interventions with a mean accuracy of 92. This suggests its incorporation in the future on a larger scale by selecting more exercises, a larger dataset, and various environmental settings.
THE LOCATION AND ORIGIN OF GLACIAL MELTWATER PHENOMENA IN THE EASTERN CHEVIOT HILLS

Summary Meltwater channels are sparse where ice movement was parallel with the valleys but abundant where it was across the trend of the ridges and valleys. Most channels were formed subglacially as englacial meltwater streams became superimposed. The location of complex systems of interconnected eskers, kames and kame terraces is related chiefly to (a) relief features, especially lowland basins, which impeded the flow of the wasting ice sheet and ultimately rendered large portions of it stagnant; (b) areas in which the abundance of till suggests a former ice sheet heavily charged with basal and englacial debris. Where factors (a) and (b) occurred together, as on the northern and eastern sides of the Cheviot Hills, upward curving shear planes induced by impeded flow may have carried large quantities of englacial debris from basal layers of the ice sheet up into the zone where systems of englacial and subglacial streams ultimately reworked the detritus into bedded sands and gravels.
Using Deep Learning to Identify Persons by their Movement on a Sensor Floor

We present an approach to identify persons based on their movement on a sensor floor. Three types of deep learning neural networks were trained on five subjects’ sensor data collected during ordinary working days in a test room. A Transformer network architecture proved to be the most successful, achieving a recognition rate of over 90% in the task of assigning just one minute of movement data to the correct person. Since the sensor floor can be installed invisibly under normal flooring, the findings result in new applications, e.g. for security systems or in the early detection of health problems that are reflected in the gait pattern.