Reviewerly/exHarmony huggingface dataset:
Defect Detection Method of Trash Trap Based on Deep Learning Semantic Segmentation

Due to the fact that the image information of trash trap is usually collected underwater, the quality of the image is low, which affects the reliability of the results of trash trap defect detection. Therefore, a trash trap defect detection method based on deep learning semantic segmentation is proposed. The threshold segmentation in semantic segmentation is used to realize the division of the target part and the background part, and the valley gray value between the corresponding wave peak in the mesh rope area and the corresponding wave peak in the mesh area is taken as the segmentation threshold value. When the gray value of the pixel in the trash net bag image is less than the threshold value, its gray value is set to 0, otherwise it is set to 255, so as to realize the segmentation of the target image. The convolution neural network in deep learning is used to recognize and detect the segmented target image. After the convolution neural network is preprocessed by Xavier initialization, the segmented target image of the trash trap is propagated forward in the convolution neural network to extract the characteristics of the image, which is normalized by softmax, and then the corresponding defects are identified by back propagation. In the comparison test results, regarding the detection of the defects of the trash trap, the design method witnesses the error rate of lower than 15.00%, the accuracy rate of more than 85.00%, the effective detection rate of more than 85.00%, and the maximum value of 95.00%, which is significantly better than the comparison method.
The Stable Method of UAV Landing on the Moving Platform

This paper addresses the multi-rotor unmanned aerial vehicle (UAV) landing problem. Aiming at the problem that the UAV is easy to roll over when landing on a mobile platform, this paper proposes a method of adding electromagnets on the landing gear, and quantitatively calculates the required electromagnet parameters. This method enables the UAV to land stably on a mobile platform and take off repeatedly. To demonstrate the performance of the proposed method, we present and analyze both simulation and experimental results, showing that this method enables the UAV to land smoothly and has good stability.
A web service selection model based on DeepFM and hash space segment matching

QoS is an important indicator to measure the quality of web services. How to choose the most suitable web service is an urgent problem to solve. Factors such as independent QoS attributes and data sparseness may cause poor service selection accuracy, making it impossible to select matching web services for users. Therefore, this paper proposes an algorithm for service selection based on DeepFM model and hash list. By learning the interaction relationship between low-order features and high-order features of QoS attributes based on the DeepFM model and DeepFM model can effectively solve the cold start problem caused by data sparseness. To improve the accuracy of QoS prediction, the hash-based service matching algorithm is used to quickly select service resources. Experiments show that the proposed model is helpful to improve the effect of service selection.
UGG-DA:Uncertainty-Guided Gradual Distribution Adaptation and Dynamic Prediction with Streaming Data

Streaming data prediction has had a great deal of success in a variety of situations as sensor technology has advanced. However, efficacy declines due to the typical evolution of data distribution over time. Traditional domain adaptation entails only a minor transition. We propose a novel progressive domain adaptation method to address this issue. Our method can reduce domain discrepancy for both large and minor shifts that have accumulated. Our approach designs a dynamic alignment strategy for pseudo labels based on uncertainty and confidence. Then, we provide a new theoretical bound for a scenario of gradual domain adaptation that is more stringent than traditional DA. The experimental results confirm the superiority of our method in the wind field dataset, demonstrating its viability and potential.
A Lightweight and Dynamic Convolutional Network for Real-time Semantic Segmentation

Semantic segmentation is a difficult task that satisfies most of the demands of autonomous driving and drone aerial photography in a unified fashion. Convolutional neural networks can properly categorize image pixels via end-to-end model training. However, achieving the optimal trade-off between segmentation precision and the number of network parameters while maintaining a suitable inference time has become a challenging task. In this paper, we propose a lightweight dynamic convolutional semantic segmentation network, LDCNet, which belongs to the asymmetric network architecture. First, we designed a coding module that includes dynamic convolution: DDAB. The success of this module is attributed to the use of dynamic convolution, which increases the utilization of local and contextual information of the features. We also designed the decoding module containing feature pyramids and hybrid attention: HA-FP,which performs a multi-scale fusion of features accompanied by feature selection. On the Cityscapes and Camvid datasets, LDCNet obtains 73.5 mIoU and 69.4 mIoU accuracy with 78.4 FPS and 91.3 FPS, respectively, without pre-training or post-processing. Our experimental findings reveal that LDCNet achieves an outstanding balance between segmentation accuracy and network parameters with just 0.96 M.
Real-time Omnidirectional Depth Perception Based on Multi-view Wide-angle Vision System

Omnidirectional depth matters significantly to the field of environmental 3D perception. Currently, the learning methods for inferring depth from visual information primarily focus on accuracy improvement at the expense of computational speed and cost, which makes them unsuitable for some real-time applications like robotics.In this paper, we present a real-time and lightweight deep learning network to predict omnidirectional depth from four wide-angle camera images, which includes a multi-scale feature extractor and a two-stage cost aggregation architecture to effectively integrate feature information. Moreover, we propose a synthetic urban dataset for learning omnidirectional depth. It relies on four lenses, each with the field-of-view (FOV) of 180°, and a panoramic lens to render multi-view images and ground-truth depth maps on the Blender platform. Plus, the GAN network is employed to enhance the dataset diversity by producing different weather samples. Finally, the whole network is trained and evaluated on our dataset, and experimental results demonstrate the effectiveness of the dataset and the feasibility of the proposed method. It can achieve a trade-off between model accuracy, computational speed and complexity, making it useful for real-time applications.
Cognitive Skills in Software Engineering: Operating Complex, Adaptive Systems at Speed and Scale

Technical coding and troubleshooting skills are recognized as being critical skills for software developers, but these are insufficient for successfully managing reliability in continuous deployment environments. Broader cognitive skills such as anomaly detection, continuous model updating, cognitive flexibility, and learning from incidents enable software engineers to cope with complexity, the challenges of operating systems at speed and scale, and continuing to meet the demands of modern software engineering work.
Web service classification based on self-attention mechanism

With the prosperity of service-related technologies, web services have exploded. It is also a widely used technology to automatically classify web services through similar functions. The automatic classification of services also plays an important role in service discovery and service composition, and it can also help to quickly discover required web services in a large repository. However, most web service classification methods can not extract more useful features due to the limitations of the models used. In this paper, we propose a new type of deep neural network based on self-attention mechanism (SAM-Net). First, we add a self-attention mechanism in the process of dealing with service description to focus on more important information. Second, information gain theory is used to extract gain words from service descriptions to increase features. To prove the effectiveness of the method, we compared nine popular web service classification methods through 10943 data sets collected from the real world, and the experiments verified that our method obtained a higher accuracy rate.
BoxNet: Fine-grained Action Classification in Boxing

The skeleton data is widely used in general action recognition since it is robustly distinguish the action operator and complex background. When it comes to more fine-grained scenario like those in sports competitions, such characteristic of skeleton data could further enlarge differences of actions among varied classes and reduce negative effects from background to achieve better recognition result. However, the problem of coarse video record which severely deteriorate the validity of neural network is still to be solved. In this work, we present an improved graph convolutional network(GCN) methods of fine-grained technical action classification based on previous works, where early fusion and parameterized adjacency matrix are also adopted. An interpolation method is firstly applied in action classification to elevate the performance of networks with coarse input. Moreover, a new dataset about boxing established for fine-grained action classification task. Our model is verified on NTU-RGB+D and tested on established boxing dataset, and exceeds common GCN methods.
Detection of dangerous water area during UAV autonomous landing

Aiming at the problem of water dangerous area detection faced by UAV during emergency autonomous landing, the features of water dangerous area are extracted from the image by neural network, the texture features of the image are obtained by HOG algorithm, and the features extracted by neural network and texture features are classified by support vector machine method (SVM). Then, the classifier is trained based on color features and regional texture features to detect the specific location of water hazard areas in the image. The experiment shows that the method has a good result in detecting the dangerous area of water during UAV autonomous landing, and the detection accuracy can reach more than 90%.
Unbiased minimum-variance simultaneous input and state estimation based on maximum correntropy criterion

The existing unbiased minimum-variance (UMV) filter with simultaneous input and state estimation (SISE) is based on the classical minimum mean square error (MMSE) criterion of the optimal Gaussian system. Nevertheless, when the system is in non-Gaussian noise measurement system, the performance of existing SISE algorithms will deteriorate significantly, and the maximum correntropy criterion (MCC) can solve this problem commendably. In this paper, maximum correntropy filter based unbiased minimum-variance simultaneous input and state estimation method (MCF-UMVSISE) is proposed, SISE filter is transformed into standard form firstly, in this case the input estimation is not required when deriving this estimator. Then the maximum correntropy criterion is used for derivation, which will make the performance better under non-Gaussian noise. In the end, experiments and simulations are carried out under Gaussian and non-Gaussian observation noises respectively to prove the effectiveness of the algorithm.
An RGB-T Object Tracking Method for Solving Camera Motion Based on Correlation Filter

The RGB-T tracking based on correlation filter frame is widely studied because of its high efficiency in most complex scenes. However, the performance of these trackers is limited when facing some specific challenges, such as camera motion and background clutter. This paper focuses on how to solve the camera motion in the framework of correlation filter. First, given the input infrared and RGB images, we extract different features and use multi-expert systems to select the experts, and then conduct decision fusion tracking. Secondly, we first design a feature matching algorithm to locate the target that shows excellent performance. Comprehensive experimental results show that the proposed tracker has better performance in both accuracy and robustness. Our results on VOT-RGBT2019 dataset also demonstrate that it solves the common camera motion challenges in RGB-T tracking.
Education Application Usability Assessments: Comparison Between Blackboard and Canvas Based on App Reviews

A Learning Management System, or LMS, is a software that helps educators and students increase and improve their educational involvement and learning. Two excellent examples are Canvas and Blackboard. Different course materials are organized and readily accessible by LMS, which incorporates social learning engagement. This research aims to assess the usability of the two learning management systems, compare the usability of Blackboard and Canvas based on student satisfaction, and calculate the usability scores of the two leaning management tools. Reviews of apps are crucial for understanding user input. To evaluate the usability of software, we analyze app reviews to categorize the usability issues based on prior research in usability engineering. Apps stakeholders are students, teachers, and system administrators. In this study, we investigate the issues with the Blackboard and Canvas mobile apps for education. We group them according to Jacob Neilson's heuristics [14]; ten general guidelines for creating user interfaces. Because they are more comparable to general rules of thumb than precise usability requirements, they are known as “heuristics.” Breaking down app reviews using Natural Language Processing (NLP), also sometimes referred to as text preparation, is an essential process that involves getting text data ready for analysis. Text cleaning is the process of removing noise and unimportant material from text data so that it can be more effectively examined. In fact, the annotator would find it easier to connect the review with the right heuristic [13].
Internet of Secure Elements: A New Direction for Secure Elements Use in 6G Landscape: Invited Talk

This talk presents a new and emerging open technology named Internet of Secure Elements (IOSE), whose goal is to enforce trust and security for digital platforms such as Internet of Things (IoT), blockchain, cloud computing or edge computing, thanks to on-line secure elements identified by Uniform Resource Identifiers (URIs). A Secure Element is a tamper resistant microcontroller equipped with host interfaces such as ISO7816, I2C, or SPI. Most of secure elements include a Java Virtual Machine (JVM) and execute embedded program written in the JAVACARD language. More than 10 billion secure elements are manufactured every year.
Single Image Super-resolution Method for Electrical Equipment Images Based on Deep Learning

Image super-resolution (SR) has achieved wide interest in the field of computer vision since it was proposed. Single image super-resolution (SISR) refers to obtaining a corresponding high-resolution (HR) image from a low-resolution image (LR). High-resolution images have more information and thus help to improve the training accuracy of neural network models. High-resolution images are often lacking in electrical engineering due to the limitation of sensor and optical device manufacturing process and cost. Therefore, it is of great practical importance to obtain HR images by super-resolution techniques. In this paper, we propose a novel approach based on generative adversarial network (GAN). Our proposed method is better than existing methods with respect to accuracy, and the visual improvement in our performance is evident.
A Flexible Monotone Neural Network for Data Mining

Incorporating the monotonicity prior into deep learning models that are used for data mining in domains like finance, e-commerce marketing might be crucial at times. One class of existing methods uses monotonicity regularization to train monotonic neural networks, but not all of them are assured to provide monotonic predictors and others require additional validation procedures. By altering the network structure, a different class of approaches, including Deep Lattice Networks (DLN), can ensure monotonicity. The link between the inputs and outputs of the DLN, however, is step-wise and counterintuitive due to its complicated structure. In this study, a monotonous network called the Deep Isotonic Embedding Network (DIEN) is developed. Different modules are used by DIEN to address monotonic and non- monotonic features individually, and the outputs of these modules are then linearly merged to get the prediction result. We demonstrate how DIEN can ensure that the learned model is monotonic in relation to particular features. In addition, compared to DLN, DIEN does not need to construct intricate structures like lattice. Results from experiments on both synthetic and real-world datasets demonstrate that DIEN outperforms existing methods.
Leveraging Ultrasound Sensing for Virtual Object Manipulation in Immersive Environments

Hand gesture recognition is a fundamental component of intuitive and immersive user interfaces in virtual reality (VR) applications. This paper presents a data-driven approach utilizing ultrasound data and deep learning techniques for hand gesture recognition in VR interfaces. The proposed methodology involves acquiring data from a subject, training a model using the acquired data, and evaluating the model’s performance on both the training data and during real-time inference. The evaluation metrics primarily focus on accuracy percentage: measuring the classifier’s performance in correctly classifying hand gestures. 4 hand gestures were primarily considered for the study and demonstration. For offline evaluation with a 20% test-train split, an accuracy percentage of 91% was observed. For online evaluation, an accuracy percentage of 92% was achieved. Results on the classification of 7 hand gestures were also analyzed for both online and online evaluation due to the promising results from the 4 gesture classification. The latency of the pipeline, from ultrasound data acquisition using screenshots to sending commands for VR object manipulation, was measured to be 59.48 milliseconds. The results demonstrate the effectiveness of the approach in accurately recognizing hand gestures, both during training and in real-time inference. We supplement our results with a video of the forearm ultrasound data being used to control a custom-designed VR game in a low-latency fashion. This research provides valuable insights into the performance and applicability of ultrasound-based hand gesture recognition techniques in VR interfaces. By employing deep learning and leveraging real-time data acquisition, this approach paves the way for intuitive and immersive interactions in various VR applications. The study contributes to the field of body sensor networks, highlighting the potential of forearm ultrasound based data-driven techniques for enhancing user interaction and immersion in VR environments.
Construction Vehicle Detection Method Based on Multi-Scale Residual Network

In the process of urbanization, construction vehicles such as cranes pose a significant threat to the safe operation of overhead transmission lines. It is helpful to reduce the risk of lines by collecting images in the construction area through cameras, monitors, and other equipment, and detecting the construction vehicle targets that may pose a threat in real-time. Aiming at the problems of large scale variation and poor detection accuracy of construction vehicles, a multi-scale residual construction vehicle detection network (MSRCVD-Net) is designed based on the residual network. By introducing a hierarchical residual connection structure into the residual blocks in the feature extraction network, the model can express coded multi-scale features at a more refined level, enhance the receptive field range of each network layer, and improve the feature extraction capability. In addition, by adding a receptive field enhancement network after the feature extraction network, the problem of information loss caused by the network in the process of sampling under feature extraction is alleviated, and the detection capability of construction vehicles is further improved. The experiment shows that the MAP value of MSRCVD-Net is 71.8%. The effect of detection is effectively improved.
VirtualIMU: Generating Virtual Wearable Inertial Data from Video for Deep Learning Applications

In the era of deep learning, accessibility to a large amount of wearable Inertial Measurement Unit (IMU) data plays a crucial role in various biomedical and health applications. However, collection of ‘big’ IMU data is extremely challenging due to its cost and time requirements. To address this, researchers have explored using publicly available videos, such as those on YouTube, to extract human skeletal models and synthesize IMU data. However, existing methods for converting skeletal models to virtual IMU data are oversimplified and lack systematic data augmentation capabilities. In this study, we propose a systematic approach to synthesize realistic and diverse IMU data, including three-axis accelerometer and gyroscope measurements, from video-based skeleton representations. Through experiments involving seven healthy individuals, we demonstrate that our method can accurately synthesize accelerometer and gyroscope data with a normalized root mean square error of 14.4 % and 16.0 %, respectively. Furthermore, we qualitatively evaluate the algorithm’s ability to generate a large volume of diverse IMU data. Our findings affirm the potential of obtaining diverse synthetic IMU data from videos, offering a promising solution to reduce the costs associated with collecting IMU data in deep learning-based applications.
Mastering Bidding in Fight the Landlord with Perfect Information Distillation

The popular card game DouDizhu in China has become a research hotspot of computer game because of its unique characteristics. For the bidding stage of DouDizhu game, accurate evaluation of hand cards strength is the key to mastering accurate bidding. In this paper, we propose a method combining convolutional neural network and reinforcement learning, and add the feature of winning distance. Supervised learning is used to train agent, and perfect information distillation technology is used to optimize bidding methods.The experimental results show that the bidding network model proposed in this paper is effective, and by comparing with some open source DouDizhu AI, it proves that the bidding method in this paper is effective.
A Fast Predictive Learner Model of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process

To predict the oxygen content in flue gas quickly and accurately during the municipal solid waste incineration (MSWI), the dynamic pruning strategy based on mutual information (MI) is used to build a learner model of deep stochastic configuration network (DSCN) in this paper. This learning model consists of two parts. One is to select the characteristic variables of the oxygen content in flue gas through the improved sailfish optimizer (SFO) with t-distribution. The other is to use MI to prune hidden nodes dynamically in a layer-by-layer manner during the training of DSCN, and then obtain a prediction model of the oxygen content in flue gas. The experimental results show that the improved SFO can select the optimal characteristic variables, MI can effectively reduce the complexity of the DSCN after pruning and achieve the rapid and accurate prediction of oxygen content in flue gas, which lays a foundation for timely optimization and adjustment of incineration conditions.