Reviewerly/exHarmony huggingface dataset:
Multidimensional assessment of daily living activities in a shared Augmented Reality environment

This paper presents an Augmented Reality (AR) framework able to measure, assess and train end-user performances in occupational therapy and/or home living environment taking into account a set of multidimensional performance indicators in a shared AR environment between a therapist and an end-user. The framework can be used for rehabilitation purposes or in Ambient Assisted Living (AAL) context where fragile users can be guided and monitored (i.e., their caregiver). The system includes a set of distributed sensors that are preprocessed and proposed in AR to the therapist/caregiver who can interact in the same real and virtual environments during patient/end-user assessment/support. The framework was designed in collaboration with the clinical experts of Villa Rosa rehabilitation hospital in Pergine (Italy) to meet all the relevant requirements from the point of view of the therapist (such as interfaces options, evaluation parameters) and a fragile end-user (such as characteristics and positions of objects with respect to the field of view). We developed a specific activity of daily living (ADL) of setting a table in a kitchen. Both the therapist and the user wear a Microsoft HoloLens 2. The therapist is asked to set the table with virtual objects. The user next has to set the table, this time with real objects, trying to match them as closely as possible to the corresponding virtual models. Once the exercise is completed, thanks to a developed computer vision-based algorithm for object segmentation, localization, and identification, the therapist can automatically perceive in AR the error made by the user for each object and the elapsed time. The localization algorithm performance was optimized through a calibration process that achieved a level of accuracy of 5 mm with a confidence level of 95% and residuals between the rotations of the objects estimated by the algorithm and the reference rotations with values less than 1°. In conclusion, the proposed framework is suitable for the metrological assessment of ADLs in living environments.
Understanding Accessibility Issues in China's Contact Tracing Systems During the COVID-19 Pandemic for Visually Impaired People

To fight against the COVID-19 pandemic, China has deployed the Health Code Apps and the Trajectory Code Apps as the front-end of the national contact tracing system. To date, accessibility issues for visually impaired people using these apps have not been studied. In this paper we use a mixed-method approach, including qualitative and quantitative studies, to explore these accessibility issues. The qualitative study included interviews and observations with visually impaired users, while quantitative analysis was conducted through surveys. We found that the current contact tracing systems are inaccessible and that most visually impaired people can only use the app with other people's help. The findings serve as a reference point for the potential large-scale adoption of digital contact tracing systems.
ServiceNavigator - A Bayesian Assistance System for Diagnosing Industrial Production Systems

Service technicians servicing machines in small-and medium-sized enterprises face the challenge to diagnose machines with increasing complexity in less time. To help them cope with the task of diagnosis (i.e. finding faults) this article introduces a novel fault diagnosis algorithm, and a web-based implementation for industrial fault diagnosis. When a fault occurs the algorithm proposes observations for the service technicians and generates likely causes according to the observations taken. This helps technicians to find faults faster, facilitates management of expert knowledge, and ultimately decreases system downtime. We have evaluated our approach with a Monte Carlo simulation of an industrial packaging machine and through the implementation of some prototype software. Both evaluations show that our approach is usable for real-world service technicians and operators of production machinery.
A Fusion Neural Network Incorporating Attention for Sensor-Based Human Activity Recognition

Even though the RNN, LSTM, and other networks are used to extract dependencies in time series, sensor-based human behavior recognition (HAR) still faces some difficulties, and the ability of deep learning (DL) networks to extract features still needs to be improved. We propose a fusion neural network in which an optimized small optimized Convolutional Block Attention Module (MP-CBAM) is suitable for HAR tasks to extract samples. The MP-CBAM is added to two branches Convolutional Neural Network (CNN) with different convolution kernel sizes, and the fused features are labeled with GRU for temporal dependence. Then the softmax function is used for classification. We validate on benchmark datasets UCI-HAR and WISDM and accuracies of 97.15% and 9S.9S% are obtained, respectively, demonstrating the adaptation of our framework to both single-sensor and multi-sensor devices.
A Dual Knowledge Aggregation Network for Cross-Domain Sentiment Analysis

Cross-domain sentiment analysis (CDSA) is an essential subtask of sentiment analysis. It aims to utilize rich source domain data to conquer the data-hungry problem on target domain. Most existing approaches depending on deep learning mainly concentrate on common features or pivots. However, few of them consider the effect of external Knowledge Graph (KG). In this paper, we propose a Dual Knowledge Aggregation Network for Cross-Domain Sentiment Analysis (DKAN), which leverages prior knowledge from two external KGs. Specifically, DKAN comprises two main parts. One is extracting sentence representation features. The other aims to introduce external knowledge better. Also, we use SenticNet to avoid noise from KG by selecting top-n words and inserting special tokens in sentences. We also conduct empirical analyses on the effectiveness of our model on the Amazon reviews dataset. DKAN achieves promising performance compared with other methods.
Spectral-Pointer Network : Pre-sort leads the Pointer Network to elude the TSP vortex

TSP is an important research field in mathematics. It has many vital applications in reality, such as robot path planning, order distribution optimal path, etc. The existing Pointer Network based on the attention mechanism often consumes a lot of computing power to solve the TSP problem with a large number of node inputs, and it is still difficult to obtain satisfactory route planning. Therefore, this work proposes a Spectral Pointer Network, a Pointer Network with pre-classification ability, to optimize the performance of the current Pointer Network. This work proves that Spectral-Point Network has more robust adaptability and more reasonable planning than the existing Pointer Network and Kmeans-Pointer Network. This work also proves that Spectral-Pointer Network has the model generalization ability far beyond the training length with meager computational cost, which greatly optimizes the contradiction between the generalization ability problem under inadequate training and the high computational cost problem of the Pointer Network.
Human phone usage recognition based on OpenPose

With the development of science and technology, more and more people use mobile phones outdoors, which will cause danger to pedestrians using mobile phones and drivers on the road. This paper uses OpenPose method to detect whether pedestrians on the road are using mobile phones. We describe an approach to recognize and classify pedestrian posture in an individual context, more precisely in open door environment. The posture belongs into two main groups: phone usage and not. This paper uses 15 points OpenPose model to estimate human key points. Then select and calculate features using the estimated coordinates. Four neural network models are trained and tested using different feature combinations to classify the posture. The best model achieves 89.66% accuracy in classification.
Blockchain-based Federated Learning with Limited Resources

Multiple clients involved in federated learning upload learning model parameters to a central server instead of raw data, which enhances the privacy of individual nodes and reduces the communication stress. However, the traditional architecture relies on a reliable and stable central server with crises of trust and single point failure. We merge the federated learning architecture with the blockchain to replace the centralized server and achieve a secure, fully decentralized blockchain federated learning. Focus on training scenarios with limited resources, we analyse system delays and design an algorithm adjusting local iterations to achieve better model performance. The performance of the proposed algorithm is evaluated in a simulated environment compared with training results for fixed local iterations. The results show that our method is well adapted in dynamic environments.
Context-aware Transformer Model for Crowd Localization

Because crowd density varies greatly in real scenes, detection-based methods are less reliable in crowded areas. Existing methods of applying detection-based transformer models to complete crowd localization are also subject to the same constraints. Moreover, there are many small targets in the scene of dense crowds, which is even more obvious. To address this issue, our model employs context-aware module to extract information that fuses different scales, thereby addressing the potential rapid scale change, and uses transformer to build an end-to-end crowd localization model. Extensive experiments show that our model adaptively learns contextual information for crowd localization, significantly outperforming previous more advanced models.
A Hierarchical Path-based and SSIM-motivated Method for Cartoon Image Interpolation

In cartoon industry, hand drawing of a cartoon image is costly and time-consuming. Thus, it is desirable to develop computational technics that can synthesize in-between cartoon frames automatically therefore release animators from repetitive work. However, existing studies more focus on natural images. Different from natural images, cartoons have two unique features: textual-less due to smooth color pieces, large and often non-linear motions, which are both hard to handle for many approaches. To address these challenges, we proposed a hierarchical framework based on path-based method to resolve large displacements in a coarse-to-fine manner, a SSIM-motivated loss to resolve textual-less problem by exploiting structural similarity between two path regions which compensate the lack of textures. The qualitative and quantitative evaluation shows efficiency and favorable results of our method.
The application of white-box encryption algorithms for distributed devices on the Internet of Things

With the rapid development of the Internet of Things and the exploration of its application scenarios, embedded devices are deployed in various environments to collect information and data. In such environments, the security of embedded devices cannot be guaranteed and are vulnerable to various attacks, even device capture attacks. When embedded devices are attacked, the attacker can obtain the information transmitted by the channel during the encryption process and the internal operation of the encryption. In this paper, we analyze various existing white-box schemes and show whether they are suitable for application in IoT. We propose an application of WBEAs for distributed devices in IoT scenarios and conduct experiments on several devices in IoT scenarios.
Phishing website identification based on double weight random forest

Aiming at the problems of insufficient detection accuracy and high misjudgment rate caused by a large amount of redundant data, a random forest algorithm based on the combination of feature weight selection and decision tree weight was proposed to construct a phishing website detection model. The feature data uses the clustering algorithm to form clusters, selects the features inside and at the edge of the cluster to train the decision tree, inputs the test data set to calculate the weight of each decision tree, and combines the improved Bayesian formula to determine each decision tree. The weight of the decision tree is finally formed into a double-weight random forest algorithm, which can improve the accuracy of phishing website detection.
Design of Mixed Reality Assembly System Based on Improved ORB Algorithm

This paper studies the natural feature point detection algorithm in a virtual assembly system. To address the problems of unsatisfactory detection effect and poor matching accuracy of the ORB algorithm, a feature uniform distribution algorithm based on pyramidal grid division is proposed, and a strategy of retaining feature points according to Harris response values is formulated to solve the problem of feature point aggregation while ensuring extraction speed. Hamming distance and the RANSAC algorithm are used to constrain the matching results. The robot assembly system based on this algorithm overlays the 3D model of the spraying robot and virtual information in the mobile device to improve the assembly efficiency of the staff. The experimental results show that the mixed reality(MR) assembly system based on the algorithm of this paper performs well, and the real-time performance and alignment accuracy are improved.
Exploiting Cross-scale Consistency for Object Detection in Aerial Images

It is challenging for convolution neural networks (CNN) to handle aerial images with extremely small objects. At each layer of the CNN, the limited receptive field leads to the contradiction between learning detailed features of the small objects and the global features of the large objects. Moreover, it is difficult to learn the feature representation for small objects occupying quite a few pixels. In this letter, we exploit the cross-scale consistency to enhance the feature representation of the objects at variant scales. We devise a double-stream training pipeline to capture the correspondence between objects at different scales and improve the adaptation of receptive field to scale variation. To explicitly formulate the model adaptability to object scales, we propose a novel scale consistency loss to learn the scale-invariant feature representation for object detection. To verify the effectiveness of our method, extensive experiments are conducted on the VisDrone-DET dataset which has large variance in object scale and quite small objects. Our method achieves state-of-the-art performance, outperforming existing methods by about 2.50%.
Aggregating color and absolute difference for CFA interpolation

Reconstructing images from raw pictures taken by digital cameras has always been a hot topic in the computer vision community, which aims to transform the intensity of light captured by the single charge-coupled device(CCD) to pixel color information of all channels. As a commonly used technique, interpolation has been proven to be an effective solution for this challenging task, but its performance is still limited especially when dealing with the edges of images. In this paper, we propose a three-stage color reconstruction solution for digital cameras, which allocates weights on neighboring pixels using aggregated color difference on stage one and aggregated absolute difference on the last two stages. Experimental results show that our proposed interpolation method outperforms existing interpolation methods on most images in both subjective and objective measurements.
Parameter selection and application on the smoothly clipped absolute deviation method

With the development of science and technology, high-dimensional data has become a popular topic in scientific research. However, most variables are meaningless in high-dimensional data. Therefore, the sparsity problem in high-dimensional data is worth to be focus in the statistical methodology and application in decades. Smoothly clipped absolute deviation (SCAD) method is often used to solve the problem of high-dimensional variable selection. The selection of regular parameters is obtained by generalized cross-validation, and the coordinate descent algorithm is often used to estimate the model coefficients in the past. In this paper, AIC and BIC are used to find the optimal regularization parameters, and the path calibration sparse shooting algorithm is used to estimate the model coefficients. Our algorithm has a strong guarantee in theory. We can achieve global linear convergence to the only sparse local optimal, and the convergence speed is fast. It is found that residual sum of squares of model obtained by path calibration sparse shooting algorithm combined with BIC, which is the smallest in solving SCAD problem by numerical experiments.
Scheme design of one-stop tracing service center

The one-stop tracking service platform is a production and marketing service center established in a completely decentralized network environment. Based on scattered enterprise information applications, it provides enterprises with a complete set of PAAS platform solutions of cloud application development platform and cloud application governance platform through container technology and micro service architecture
Heterogeneous IoT and data fusion communication algorithms for power distribution station areas

The fusion of heterogeneous data from multiple sensors is a key issue in the field of smart grids. With the rapid development of computer technology and multi-source sensing, the scale and structure of distributed IoT has become increasingly complex. It generates massive, multi-source, heterogeneous and sparse diverse data sets. The types of this data are constantly changing and the form of access is shifting from static to dynamic. This network heterogeneity poses a significant challenge in handling large volumes of heterogeneous data and maintaining the operational status of sensors in real time. In this paper, a two-channel CNN-based data fusion scheme for multiple sources of information is proposed to address the management of heterogeneous networks and data fusion communication issues. Among them, an improved data collection platform achieves more comprehensive and accurate monitoring and estimation of the network operation status. Finally, the scheme proposed in this paper is subjected to simulation experiments, which prove that the data fusion algorithm not only ensures real-time efficiency but also guarantees high accuracy.
Incentive Mechanism Of Distributed Electric Energy Transaction Based On Blockchain

The centralized electric energy transaction has deficiencies of large network overhead and high transaction cost, however, users trading electric energy in the distributed mode characterized by inactivity. Simultaneously, information disclosure easily occurs under complex transaction environment. Therefore, a distributed multi-party electric energy transaction incentive mechanism based on blockchain is proposed in this paper. According to the supply and demand relationship for electric energy, the transaction pricing model among multiple parties is established based on the auction theory, and the genetic algorithm is adopted to maximize the social welfare of participants in the distributed electric energy transaction. Meanwhile, the anonymous electric energy transaction is realized based on the blockchain, which can ensure the security of user transaction information to the greatest extent. The simulation results proved that the multi-party transaction incentive mechanism proposed in this paper can protect users’ privacy and maximize social welfare of the energy transaction system.
Single-pixel imaging encryption based on 2D coupled Logistic mapping

A secure single-pixel imaging (SPI) method which based on 2D Logistic map is proposed in this paper. A 2D Logistic map combined with its key and the asymmetric ratio is utilized to generate the asymmetric measurement matrices and encrypt the measurements. Compared with the existing secure SPI methods, the proposed method achieves the same security and the asymmetric measurement matrices suppress the noise effectively with a better imaging quality. The key of the chaos system combined with the asymmetric ratio in the asymmetric measurement matrices are transmitted in the secret channel instead of the measurement matrices. Based on the proposed strategy, transmitted data size is reduced. Simulation results show that our method achieves advantages in image quality and security. The proposed strategy can also drop the transmitted data size and computational complexity.
Indoor Monocular Image Depth Estimation Based on Semantic Information of Tree-shaped ASPP Structure

ASPP (Atrous Spatial Pooling Pyramid) has the advantage that it can expand the receptive field and extract multiscale features without changing the image resolution. We introduce it into the depth estimation task to improve the problems of inaccurate depth estimation, blurred edges, and loss of depth information details on the current unsupervised depth estimation methods for indoor monocular images. However, the ASPP module does not consider the relationship between different pixel features, resulting in inaccurate extraction of scene features in the depth estimation task. Therefore, we propose a Tree-shaped ASPP structure for this drawback, combined with the SC-SfMLearner network using the NYUv2 dataset, adding the spatial semantic information pool formed by the ASPP tree structure between the encoder and decoder structures of the depth estimation network, which can not only expand the receptive field without losing resolution but also capture and fuse multi-scale context information, so that different pixels establish connections between features. The results show that, compared with the original method, the improved method has stronger network feature extraction ability, clearer contours of each target in the scene, more distinct layers, and more accurate depth estimation results.
An Adaptive Gradient Descent Optimization Algorithm Based on Stratified Sampling

A necessary part of deep learning is the adjustment of hyperparameters, which is also one of the most expensive parts of deep learning. The current mainstream adaptive learning rate algorithms include AdaGrad, RMSProp, Adam, and AdamW. AdaGrad can adapt to different learning rates for different parameters. However, its adaptive learning rate is monotonically reduced, which will lead to a weak ability to update parameters in the later stage. Although RMSProp, Adam, and AdamW solved the problem of gradually decreasing the adaptive learning rate of AdaGrad, they all introduced a hyperparameter—the momentum coefficient. In this paper, a new optimization algorithm SAdam is proposed. SAdam uses the stratified sampling technique to combine the windows with fixed first-order and second-order gradient information, which not only solves the problem that the adaptive learning rate of AdaGrad is constantly decreasing but also does not introduce additional hyperparameters. Moreover, experiments show that the test accuracy of SAdam is no less than Adam.