Reviewerly/exHarmony huggingface dataset:
i-DARTS: Improving differentiable architecture search by using graph and few-shot learning

On the CIFAR-10 (Canadian Institute for Advanced Research-10), ImageNet, and Penn Treebank datasets, Neural Architecture Search (NAS) algorithms obtained better results by computerizing the process of architectural design on the CIFAR-10, ImageNet, and Penn Treebank datasets. Even though the search time has been simplified, search algorithms count on performance prediction or controllers. When used on a new task with a fresh dataset, this may necessitate optimal structuring. The problem of architecture search is not solved because this is done by hand. Using continuous relaxation and gradient descent methods, Differentiable Architecture Search (DARTS) [1] avoids this issue. There are, however, plenty of intriguing methods to make DARTS better. In this paper, we first split the DARTS’s supernet into three (03) sub-supernets and applied neural message passing so that each node in the graph has information from other nodes. The three (03) sub-supernets by using gradient descent to find the best graph represent the whole search space. By adding parameter sharing and transfer learning, our method enhances the final accuracy of one-shot-based DARTS systems consistently. On CIFAR-10, it achieves 98.25% test set accuracy, according to the results.
Abnormal analysis of moisture content of tobacco leaves by big data based on XGboost model

In order to solve the problem of traditional modeling cannot solve the problem of unstable moisture content at the outlet of Moisture machine, Select material flow rate, water addition ratio, circulating hot air temperature during operation, compensation fresh air temperature, circulation damper opening, compensation for fresh damper opening, discharge hood pressure, outlet tobacco temperature, outlet tobacco moisture content, steam application ratio as Process parameters; then the process parameter correlation analysis model and the water content anomaly marking model were determined, and 69 abnormal rules of moisture content of tobacco sheets and 120 normal rules were excavated by XGboost based on big data. The accuracy and recall rate of the model were 92.5% and 96.7%, respectively. The uniformity and accuracy of the moisture improves the stability of the moisture content of the tobacco sheet after regaining moisture.
Research on the Application of YOLO v3 in Railway Intruding Objects Recognition

In order to detect foreign objects intruding into the track and prevent foreign objects from causing railroad safety accidents, the track foreign object intrusion detection algorithm is investigated. For the specific application scenario of railway foreign object intrusion, the enhanced YOLOv3 high speed railway foreign object detection network is proposed to improve the ability of using picture features and detection effect, and the average detection accuracy reaches 79.2% with slightly reduced detection speed, which is 4.4% higher than the original network. The enhanced YOLOv3 railroad foreign object intrusion detection network can effectively improve the detection accuracy of targets at different scales.
Abnormal Behavior Recognition System based on Improved CRNN Model

Abnormal behavior recognition technology based on surveillance video has been an important direction of current research, with high research value and application demand. Due to the complexity and variability of crowd movement and external environment, the recognition of abnormal behavior is quite challenging, and there is still a need for further research on the recognition technology of human abnormal behavior in surveillance video. In this paper, we proposed an adaptive video frame extraction method, and after a series of experiments and comparisons with former methods, our model achieved 86.78% accuracy on the UCF-Crime dataset and 95.2% accuracy in the subsequent system development of the algorithm model for segment detection and localization of surveillance videos. And the maximum speed of recognition can reach 543. 7FPS. Data shows that the algorithm and system we designed can achieve better performance on Abnormal behavior recognition.
Extract Data Points from Invoices with Multi-layer Graph Attention Network and Named Entity Recognition

Extracting key information and data points from business documents, such as invoices and bank statements, is highly significant to office automation especially in accounting and financial areas. Compared with general documents, invoices are highly structured and usually with complex layouts like tables and text boxes, which makes typical Natural Language Processing models less effective when processing the plain texts that are directly converted from the original invoices. To address this issue, many approaches adopted Graph-based Networks to propagate node embeddings along with revealing latent connections between tokens or text fragments. In this work, we focused on the task of extracting data points from invoices as a sub-task of Information Extraction (IE), where the data point extraction is modelled as a node-level classification approach. We also proposed the Stacked Propagation Network to facilitate the propagation of node embeddings according to different edge maps, based on Graph Attention Network (GAT). In addition, Named Entity Recognition (NER) is adopted to improve the performance of data point extraction. Extensive experiments have been performed to show the effectiveness of the proposed approach, on a real-world dataset. Ablation studies are also conducted to evaluate the influences brought by each component.
Researches Advanced in Medical Image Analysis Based on Convolutional Neural Networks

Medical image analysis has gradually become one of the new hot applications in computer vision community, whose goal is to realize disease diagnosis and detailed analysis of lesion area through the related computer vision technology. Thanks to the rapid development of convolutional neural network, deep learning has been widely used in medical image analysis, which greatly improves the analysis accuracy and speed. In this paper, through in-depth literature research, we introduce the existing research progress of medical image analysis from classification, detection and segmentation. We further analyze the performance of representative methods on common data sets, and finally summary the existing problems in deep learning-based medical image analysis and predict the solutions to these problems in the future.
Research on the Construction Method of Standardized Cost Breakdown Structure based on Equipment Feature Information Extraction

A method of constructing a standardized cost breakdown structure based on equipment feature information extraction is briefly introduced. Based on the historical equipment project data, this method abstracts the common features of the product breakdown structure of equipment types, and is oriented to the equipment component objects at different levels in the equipment composition structure to match the cost subjects. Type and, build equipment standardized cost decomposition structure. To effectively regulate heterogeneous and complex equipment data resources are regulated effectively. At the same time, it needs to adapt to the compatibility and inclusiveness of data content of different levels and granularities, Besides, the problems of fast and convenient template generation and data collection specification in the process of cost data collection are solved.
Research on Personalized Recommendation Service of Mobile Library Based on User Portrait

Embedding user portraits into the personalized recommendation service of mobile libraries will solve the problems of information overload and semantic gap, and the retrieval results cannot meet the personalized needs of readers. Based on user portrait technology, the mobile library’s recommendation service has built a personalized recommendation service model through the processes of data collection, data preprocessing, portrait modeling and effect evaluation. It analyzes the key issues involved in user privacy protection, dynamic changes in user portraits, and service performance optimization.
Flappy Bird: Optimization of Deep Q-Network by Genetic Algorithm

DQN is a classical algorithm in reinforcement learning, combining traditional Q-learning with neural network. In previous researches, DQN has been used to implement Atari Game, and other games including Flappy Bird. However, the convergence rate of DQN is unacceptable. In this paper, by utilizing a genetic algorithm, the convergence of neural network in DQN is accelerated. In the end, we evaluate the proposed method can achieve better convergence rate with the environment of flappy bird, and the results demonstrate the superiority of our method. Moreover, this optimization of Deep Q-Network is an application of genetic algorithm on convolution neural network (CNN), which provides more ideas and possibilities for the combination of genetic algorithm and CNN.
Multi-Mode Integrated Forecast of Surface Temperature based on Random Forest

Due to the influence of global climate change, extreme weather events such as high temperature and severe cold occur frequently in recent years. Traditional numerical prediction has been unable to meet the requirement of accuracy of temperature prediction. To improve the accuracy of forecast, single deterministic forecast has been gradually changing into multi-mode integrated forecast. Random forest has strong adaptability to data sets, without standardized training but fast training speed. It can be applied to large-scale data sets and is suitable for solving nonlinear data regression analysis problems such as meteorological factors. In this paper, the multi-mode integrated forecast of 2m surface temperature based on random forest algorithm is studied by using the forecast results of ECMWF, JMA and NCEP models in TIGGE data set and compared with the multi-mode integrated forecast of CART algorithm. The results show that the prediction performance of multi-mode integrated forecasting is significantly improved compared with the single mode forecast of each center, meanwhile the prediction performance of multimode forecast based on random forest is significantly improved compared with the existing CART multi-mode forecast.
ResNet Does Not Perform Intelligent Understanding of Picture in Image Recognition

We present failure cases of ResNet-50 on car damage detection, damage location classification, and bear image classification. When trained on images from the Internet, we find that 1) close-ups and cropped photos of undamaged cars are classified as damaged, 2) damage location classification may depend more on the photo angle than the actual damage location, 3) images with bears cropped out are classified as containing bears, and 4) bear images cut into squares and randomly reshuffled are still classified as containing bears. We deduce from these experimental results and a theoretical consideration on convolutions that ResNet does not take into account global relative positions of patterns. In other words, there is no recognition of a global shape. Instead, ResNet relies on spurious correlations between certain features and the associated classes. For example, in our car damage recognition experiment, we see that ResNet never truly recognizes a car damage or its location but bases its prediction on features such as how close to the car and from which angle the photo was taken. We explain that, depending on the task, removal of such spurious correlations can be nearly impossible. From our experience, the methods presented here are common in the industry. Therefore, we want to caution practitioners who believe ResNet and possibly other CNN architectures are intelligently understanding an image when in fact they are not.
MCCA: Multimodal clothing compatibility learning based on channel attention

In order to slove the problems of incomplete feature extraction of clothing images and difficult modeling of clothing compatibility, a model named ECA-RMCN (a multimodal clothing compatibility model based on channel attention) is proposed. The ECA-Net(a high-efficiency channel attention module) is introduced on the residual module of the feature extraction network CNN to enhance important features such as low-level and high-level clothing, and suppress invalid features; the combined loss function is used to deal with the problem of unbalanced between positive samples and negative samples of clothing to achieve better matching effect. Comparative experiments are performed on the public Polyvore dataset to validate whether the effective of the model. The experiment data illustrate that the algorithm is excelled than else algorithm in the compatibility prediction and matching performance of clothing, and has good application value.
Application of seismic image enhancement technology based on single-frame super-resolution reconstruction

The clarity of seismic images has a great impact on the results of seismic data interpretation; therefore, high-resolution seismic images can provide finer geological information for seismic exploration. Coordinate-based Multilayer Perceptron(MLP) is easy for gradient-based optimization and machine learning, and represents a more compact order of magnitude than grid sampling. In this paper, we propose a combination of Random Fourier Features(RFF) and coordinate based MLP method to achieve super-resolution reconstruction of seismic images, taking one image coordinate as input and the output is the RGB value of the pixel at that location. Preprocessing the spatial coordinates of the image with Fourier feature mapping enables the MLP to learn higher frequency detail information and achieve super-resolution reconstruction of a single seismic image. Experiments show that the texture and edge details of the final processed seismic image are much clearer.
Research on Spectrum Perception Based on Evolutionary Game Theory

Aiming at the problem that the information of cognitive terminals in mining areas is mixed and occupies the butter, a spectrum sensing evolutionary game scheme based on cognitive nodes and fusion nodes was proposed. The fusion nodes and cognitive nodes are regarded as bounded rational participants. Power is used to normalize the regulatory behavior of the fusion nodes, the improved behavior of the cognitive nodes, and the collaborative benefits of caching information. An evolutionary game model is built to analyze the situation of the balance points under the two sides’ action strategies, and a stable and evolutionary phase map is obtained. The influence of spectral factors on behavioral strategies is also analyzed. When the regulation cost is 0. 05mW, the improvement cost is 0. 02mW, and the proportion distribution is 0.4, the collaboration benefits should be at least greater than 0. 125mW, and the greater the collaboration benefits, the faster the strategy of regulation improvement is realized. The results show that the collaboration benefit as an incentive form will increase the probability of regulation improvement and the perception process will converge rapidly; The adjustment of cost can promote the development of spectrum sensing to the trend of stable and high efficiency.
Action Recognition based on Video Spatio-Temporal Transformer

Video action recognition has become a very important research hotspot in the field of computer vision. At present, the methods of action recognition using deep learning, such as C3D networks and 3D ResNet networks, lack attention mechanism, and are not cost-effective due to the high cost when using GPU graphics cards for training. The study proposes a new R-TST network structure, which first uses the LSTM module to correlate the frames of the video to maximize the preservation of the information features of the video action. The TST module structure contains temporal attention and spatial attention to enhance the features’ expressive ability of action recognition. The experiment results show that the R-TST network structure can outperform other network structures and improve utilization rate while saving GPU hardware costs, but shows a slight decrease in accuracy on the UCFIOI and HMDB51 datasets.
Agent-Based Crop Model for Smart Irrigation: Design of a State Estimator

In the design of smart irrigation systems, there are several open challenges, among which: i) the modeling of heterogeneity of cropping land, and ii) the estimation of non-measured state variables to control crop development. This work addresses both challenges by an agent-based model (ABM) of a discretized field and by using state estimation techniques. For the last challenge, two software sensors, i.e., an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are used and compared to estimate on-line the states of homogeneous portions of land assigned to the agents of an ABM model. The agent-based crop model is presented and simulated under two different climatic scenarios to assess the performance of the estimation techniques. Simulation results of a testbed in Colombia shows the advantages of UKF over the EKF.
Learning-based Local Path Planning for UAV in Unknown Environments

This paper develops a novel learning-based local path planning method for Unmanned Aerial Vehicles (UAVs) in unknown environments. We establish a neural network (NN) with two fully connected hidden layers, where the distances from the UAV to the hit points of the locally detected obstacles, a strategic temporary goal and the direction to the final destination are selected as the input of the NN, and the reference velocity for the UAV to track is chosen as the output. To collect the training data, we propose a local path planning method, which repeatedly constructs a local Laplacian Potential Field (LPF) only based on the UAV's real-time obstacle detections of limited scope, and requires the UAV to track the negative gradient direction of the resulting potential function. Then, the UAV follows the reference velocity generated by the trained NN path planner to safely approach the final destination. Simulations demonstrate the effectiveness, adaptability, and efficiency of the proposed learning-based path planning method, which outperforms the above LPF -based path planning method and, unlike many other learning-based methods, does not need to re-train the NN parameters when changed to new maps.
Risk-attitudes, Trust, and Emergence of Coordination in Multi-agent Reinforcement Learning Systems: A Study of Independent Risk-sensitive REINFORCE

Trust facilitates collaboration and coordination in teams and is paramount to achieving optimality in the absence of direct communication and formal coordination devices. We investigate the influence of agents' risk-attitudes on trust and the emergence of coordination in multi-agent environments. To that end, we consider Independent Risk-sensitive Policy Gradient, Risk-sensitive REINFORCE, RL-agents in repeated 2-agent coordination games (Stag-Hunt). We experimentally validate our hypothesis that the agents' risk-attitudes influence coordination and collaboration by influencing the agents' learning dynamics and can lead to efficient learning of Pareto optimal policies. This suggests that risk-sensitive agents could achieve better results in multi-agent task environments.
Kinodynamic planning for robotic manipulators using set-based methods

We revisit the trajectory planning problem for general N-link robotic manipulators. Our approach focuses on characterising the whole set of states that can be transferred to a target set of velocities without violating constraints. To achieve this, we work in the commonly utilized two dimensional projected space of the Lagrangian dynamics on a specific predefined path. The produced dynamics is a double integrator, with nonlinearities being pushed into a non-convex and state-dependent input constraint set. We approach the problem as a special reach-avoid set computation problem using ordering properties of the trajectories generated by a suitably parame-terised state feedback control law. Our results are illustrated in simulation using a realistic model of the UR5™ commercial robot.
Investigating Augmented Reality as a mode of representation for hearing and hearing-impaired preschool children

This paper presents an empirical study of 33 hearing preschool children and 22 preschool children with a hearing impairment, examining the effects of Augmented Reality (AR) as a mode of representation to support perceptions of abstract time concepts in the solving of mathematical problems. The findings indicate that an AR representation mode has the potential to present more comprehensive information, enhance cognitive understanding and encourage the active learning experiences of preschool children. The effects were especially significant for those children with a hearing impairment. We have discussed how presenting information through an AR mode can aid the perception of time and sequence by preschool children, especially hearing-impaired children, resulting in improved skills for solving mathematical problems.
Metro Futures 2020: Enabling Participation at Varying Depths and Scales via Digital Technology

Large scale urban planning consultations can elicit public contributions with limited reflectivity and scope in comparison with participatory projects in HCI. We therefore investigated how digital technology can be configured to enable participation at greater depth and at scale in Metro Futures 2020 – an online public consultation on light rail trains. Drawing on participatory methods used in HCI, we devised experience-centered and creative activities at different depths and scales – from “shallow and wide” social media polls to “deep and narrow” online workshops – and a website with three ways of exploring a digital train mock-up. Participation on the website was both at scale and to notable depth according to our analyses of data entry behavior and website comments. Our findings show that the value of online public consultations can be increased through providing experiential context for design proposals, supporting exploration of design details via immersion, and providing interconnected activities of varying depths and scales.
Optimization assisted framework for thyroid detection and classification: A new ensemble technique

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like "(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification". Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as" Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)" are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of "curse of dimensionality". Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.