Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Federated Cross-Domain Social Recommendation

By combining user feedback on items with social networks, cross-domain social recommendations provide users with more accurate recommendation results. However, traditional cross-domain social recommendations require holding both data of ratings and social networks, which is not easy to achieve for both information-oriented and social-oriented websites. To promote cross-domain social network collaboration among the institutions holding different data, this chapter proposes a federated cross-domain social recommendation (FCSR) algorithm. The main innovation is applying Random Response mechanism to achieve sparsely maintained differential privacy for user connections and proposing Matrix Confusion Method to achieve efficient encrypted user feature vector updates. Our experiments on three datasets show the practicality of FCSR in social recommendation and significantly outperforms baselines.
Monitoring Technology Around Trains Using AI and Image Processing

In recent years, the development of AI technology has led to rapid improvements in various fields, such as image recognition, language processing, and time series analysis. In the automotive field, driver assistance systems using cameras and sensors have already been put to practical use, and research on automatic driving is also active. On the other hand, some operators have just started to conduct tests of forward monitoring using cameras and sensors in railway. In this paper, we introduce our proposed research on AI application and anomaly detection using cameras and sensors in trains to improve train safety.
Textual Concept Expansion with Commonsense Knowledge to Improve Dual-Stream Image-Text Matching

We propose a Textual Concept Expansion (TCE) approach for creating joint textual-visual embeddings. TCE uses a multi-label classifier that takes a caption as input and produces as output a set of concepts that are used to expand, i.e., enrich the caption. TCE addresses the challenge of the limited number of concepts common between an image and its caption by leveraging general knowledge about the world, i.e., commonsense knowledge. Following a recent trend, the commonsense knowledge is acquired by creative use of the training data. We test TCE within a popular dual-stream approach, Consensus-aware Visual-Semantic Embedding (CVSE). This popular approach leverages a graph that encodes the co-occurrence of concepts, which it takes to represent a consensus between the textual and visual modality that captures commonsense knowledge. Experimental results demonstrate an improvement of image-text matching when TCE is used for the expansion of the background collection and the query. Query expansion, not possible in the original CVSE, is particularly helpful. TCE can be extended in the future to make use of data that is similar to the target domain, but is drawn from an additional, external data set.
Binary Neural Network for Video Action Recognition

In the typical video action classification scenario, it is critical to extract the temporal-spatial information in the videos with complex 3D convolution neural networks, which significantly expand both the computation cost and memory costs. In this paper, we propose a novel 1-bit 3D convolution block named 3D BitConv, capable of compressing the 3D convolutional networks while maintaining a high level of accuracy. Due to its high flexibility, the proposed 3D BitConv block can be directly embedded in the latest 3D convolutional backbone. Consequently, we binarize two representative 3D convolutional neural networks (C3D and ResNet3D) and validate the accuracy of action recognition tasks. The results of two widely used datasets demonstrate that the two proposed binary 3D networks achieve impressive performance at a lower cost. Furthermore, we carry out an extensive ablation analysis to test and verify the efficacy of the components in 3D BitConv.
Research on Image Determination Method of Hydrophobicity Distribution of Composite Insulator Based on Convolutional Neural Network

In the process of composite insulator hydrophobicity detection, in view of the limitations that the grading results of composite insulator hydrophobicity visual detection based on spray method are easily disturbed by subjective factors and can’t continuously detect and mark the hydrophobicity distribution characteristics of insulators, a method for composite insulator hydrophobicity distribution image determination based on convolution neural network is proposed. Firstly, a hydrophobicity grading model based on convolutional neural network is built, and then an image determination method of hydrophobicity distribution based on sliding window is proposed. Through experiments on the images of the verification set, it is concluded that the accuracy of the hydrophobicity grading model can reach 90%; Three samples were used to verify the method. The results show that the contour of the measured image is similar to the actual state of the sample, and the normalized error of the calculated hydrophobicity measurement of each sample is less than 0.03, which proves the high accuracy of this method. The hydrophobicity distribution image determination method provides an accurate, intuitive and efficient new digital detection method for analyzing the surface aging of composite insulators.
Application of Deep Learning in Rolling Bearing Fault Diagnostics

This paper presents a diagnostic method for rolling bearings using deep learning instead of conventional rule-based diagnostic methods which requires human and time costs. However, deep learning has two major problems. One is difficulty in acquiring large amount of data under the operation with damaged rolling bearings for training, and the other is difficulty in interpreting the diagnostic results. As a solution to these problems, this paper introduces transfer learning and a method to visualize the input data points contributing to the diagnostic results.
Lightweight Object Detection Algorithm Based on Hybrid Pruning Strategy

Traffic sign detection can provide important judgment information for the automatic driving system and the intelligent navigation system. In order to deploy the traffic sign detection model on edge equipment and have better detection performance, a lightweight object detection algorithm based on hybrid pruning strategy was proposed. Firstly, to reduce the interference of background noise and enhance the important features of the detected object, an attention-based fusion module (AFM) is proposed to fuse the feature maps of different layers. Secondly, in order to reduce the number of model parameters and ensure real-time detection, a hybrid pruning scheme is proposed to compress the network structure. Experimental results on Tsinghua-Tencent 100K (TT 100K) China traffic signs dataset show that the mAP of the improved model is 90.58%, the model size is 12.4 MB, which verifies that the proposed algorithms can effectively compress the model and improve the real-time detection on the premise of ensuring the accuracy.
Image Super-Resolution Using Mixed Attention Module

Most of super-resolution methods based on CNN ignore the differences between features and treat all positions equally. Therefore, most of the existing methods have the problem of feature redundancy, which limits the performance of super-resolution methods. This article proposes a novel approach to solve feature redundancy with mixed attention block (MAB). The MAB will learn to recognize important regions in space, and distinguish important channels of input features. The MAB module extracts the important spatial and channel parts of image features, which better reflects image and enhances the effect of the SR model. Experiment uses the Flickr2K and DIV2K data sets for training, and is evaluated under the standard test sets. Experimental results show that the MAB block is very helpful to enhance the effect of SR model, and the method achieves good results with smaller memory space of parameter.
On the Effects of Collaborators Selection and Aggregation in Cooperative Coevolution: An Experimental Analysis

Cooperative Coevolution is a way to solve complex optimization problems by dividing them in smaller, simpler sub-problems. Those sub-problems are then tackled concurrently by evolving one population of solutions—actually, components of a larger solution—for each of them. However, components cannot be evaluated in isolation: in the common case of two concurrently evolving populations, each solution of one population must be coupled with another solution of the other population (the collaborator) in order to compute the fitness of the pair. Previous studies have already shown that the way collaborators are chosen and, if more than one is chosen, the way the resulting fitness measures are aggregated, play a key role in determining the success of coevolution. In this paper we perform an experimental analysis aimed at shedding new light on the effects of collaborators selection and aggregation. We first propose a general scheme for cooperative coevolution of two populations that allows to (a) use different EAs and solution representations on the two sub-problems and to (b) set different collaborators selection and aggregation strategies. Second, we instantiate this general scheme in a few variants and apply it to four optimization problems with different degrees of separability: two toy problems and two real prediction problems tackled with different kinds of model (symbolic regression and neural networks). We analyze the outcomes in terms of (a) effectiveness and efficiency of the optimization and (b) complexity and generalization power of the solutions. We find that the degree to which selection and aggregation schemes differ strongly depends on the interaction between the components of the solution.
A Multilevel Cognitive Diagnosis Model with Time Influencing Factors

Knowledge assessment is an effective means to further improve the level and quality of intelligent learning. Cognitive diagnosis aims to detect the potential knowledge structure and knowledge status of subjects, and is a necessary condition for realizing intelligent learning and intelligent knowledge assessment. The widely used DINA model ignores the phenomenon that the subjects’ cognitive ability will gradually decline over time and knowledge forgetting, and the classification of original knowledge states is too extreme, the existing classification methods are complicated, and the diagnostic performance is low. In this paper, an improved N-DINA model is presented, which adds the time influence factor to the correct answer rate calculation based on the original model and introduces the multi-level classification of knowledge status into some mastery parameters to improve the accuracy and interpretability of intelligent knowledge assessment. Sex. The experimental results show that the model outperforms the existing models on the two experimental indicators of PMR and RMSE.
Directional DCT Based Depth Image Coding for 3-D Video

In three-dimensional (3-D)/stereoscopic video, the multi-view plus depth (MVD) representation is one of the most recognized data formats. The MVD encodes and transmits several video/texture views and their corresponding depth views, and at the decoder side, a number of desired virtual views can be synthesized by the received texture and depth views. The depth view/image should be carefully investigated, as its quality is important to the synthesized view in MVD. An efficient coding method for the depth image is presented in this paper, which adopting the directional DCT (DDCT) to substitute the widely used 2-D DCT. Experimental results tested on the Middlebury stereo datasets show that the presented method maintains higher accuracy of the depth image and thus improves the quality of the synthesized view.
Research Progress of Improved Conventional Image Edge Detection Algorithm

Image edges contain most of the information in an image, which is one of the most basic and important features in an image. Image edge extraction occupies a special place in computer vision and image processing, which is the basis for implementing boundary-based image segmentation and target recognition. In order to make more researchers understand the research status of image edge detection, this paper introduces the implementation principles and methods of several traditional image edge detection algorithms, summarizes the new edge detection methods that appear on this basis, analyzes the characteristics and processing performance of various algorithms, and also compares the excellence among different algorithms to provide reference and guidance for researchers to further improve and innovate the algorithms.
Neural Network Ensemble With Evolutionary Algorithm for Highly Imbalanced Classification

Imbalanced data is a major challenge in classification tasks. Most classification algorithms tend to be biased toward the samples in the majority class but fail to classify the samples in the minority class. Recently, ensemble learning, as a promising method, has been rapidly developed in solving highly imbalanced classification. However, the design of the base classifier for the ensemble is still an open question because the optimization problem of the base classifier is gradientless. In this study, the evolutionary algorithm (EA) technique is adopted to solve a wide range of optimization design problems in highly imbalanced classification without gradient information. A novel EA-based classifier optimization design method is proposed to optimize the design of multiple base classifiers automatically for the ensemble. In particular, an EA method with a neural network (NN) as the base classifier termed NN ensemble with EA (NNEAE) is developed for highly imbalanced classification. To verify the performance of NNEAE, extensive experiments are designed for testing. Results illustrate that NNEAE outperforms other compared methods.
Cascade Detector Based on Multi-scale Features for Small Objects in Various Backgrounds of Remote Sensing

Different from natural images, remote sensing images in complex backgrounds have the challenges of small object feature extraction with small size, few available features, and high localization accuracy requirements. Consequently, it is difficult to detect these small objects with mainstream object detection methods. To enhance the retention of small object feature details during feature extraction and characteristics enhancement, this paper improves the Cascade R-CNN algorithm to solve the above problems of small object detection. Res2Net was used to enhance feature representation and feature fusion to extract features with fine-grained and rich semantic information. PAFPN was used for learning high-resolution features with large perceptual fields to perceive contextual information from the area around the object for inference. Cascade R-CNN was used as a high-quality detector by continuously increasing the threshold of IOU. Experiments show that the detection performance of the method proposed in this paper is 4.3% higher than that of the current mainstream deep learning methods, which is significantly better than other mainstream detection methods.
IoTBeholder

With the deployment of a growing number of smart home IoT devices, privacy leakage has become a growing concern. Prior work on privacy-invasive device localization, classification, and activity identification have proven the existence of various privacy leakage risks in smart home environments. However, they only demonstrate limited threats in real world due to many impractical assumptions, such as having privileged access to the user's home network. In this paper, we identify a new end-to-end attack surface using IoTBeholder, a system that performs device localization, classification, and user activity identification. IoTBeholder can be easily run and replicated on commercial off-the-shelf (COTS) devices such as mobile phones or personal computers, enabling attackers to infer user's habitual behaviors from smart home Wi-Fi traffic alone. We set up a testbed with 23 IoT devices for evaluation in the real world. The result shows that IoTBeholder has good device classification and device activity identification performance. In addition, IoTBeholder can infer the users' habitual behaviors and automation rules with high accuracy and interpretability. It can even accurately predict the users' future actions, highlighting a significant threat to user privacy that IoT vendors and users should highly concern.
Narrative-Based Visual Feedback to Encourage Sustained Physical Activity

Stories are a core way human beings make meaning and sense of the world and our lived experiences, including our behaviors, desires, and goals. Narrative structures, both visual and textual, help us understand and act on information, while also evoking strong emotions. Focusing on the health context, this research examines the effectiveness of narrative-based feedback in motivating physical activity behaviors and underlying attitudes over longitudinal periods. After collecting two weeks of baseline physical activity levels, N=39 participants installed our smartphone application, WhoIsZuki. The WhoIsZuki app supports goal setting and semi-automated activity tracking, and it provides an ambient display that visually encodes these tracked activities as well as progress toward goals. Half of participants received a version of the interface that supplied behavioral feedback in the form of a multi-chapter episodic narrative, while the other half received a control condition version that provided an aesthetically-similar visualization but without any characterization, episodic structure, dramatic effect, or other narrative elements. After interacting with these versions for four months, our analysis showed that participants receiving the multi-chapter narrative feedback performed more physical activity, achieved more goals, experienced more positive psychological shifts, and overall engaged more meaningfully with the digital intervention.
Image-Based Self-attentive Multi-label Weather Classification Network

Weather condition has a significant impact on production and operations in all areas. The timely recognition of weather conditions through image information helps the work platform to adjust accordingly in a timely or adaptive manner in order to reduce non-essential losses. Early convolutional neural networks were able to perform multiple classifications of weather states based on images. In practice, multiple weather conditions often co-exist and multi-label classification networks are more suitable for this context. In this paper, we propose a self-attentive multi-classification network that uses convolutional networks to extract image features; the self-attentive mechanism is used to obtain the dependency relationships between different weather states. The experiments show that the method in this paper has good accuracy for weather classification.
Research on the Influence Path of Organizational Resilience on Innovation Performance of Start-up Enterprises Based on Machine Learning Algorithm

Under the impact of the epidemic on the economy, start-ups are facing enormous pressure and challenges.Based on machine learning algorithm, this study obtains business indicators such as registration time, registered capital, number of employees, operating income and R&D investment of start-up enterprises through data mining to construct a Bayesian network model.The purpose of this study is to explore the impact of organizational resilience on innovation performance of start-up enterprises before and after the impact, so as to help start-up enterprises achieve transformation and upgrading and reduce innovation costs.The results show that organizational resistance factors (executive compensation, number of employees, ownership concentration, etc.) have no significant impact on innovation performance, while organizational resilience factors (operating revenue, operating profit, asset-liability ratio, etc.) have a significant impact on innovation performance.
Somatosensory Illusions

Illusions arouse interest in the layperson and researcher alike. The layperson learns that perception is fallible, and the researcher wants to better understand mechanisms of perception and implement illusions in various applications. We are accustomed to visual illusions, but less so to their somatosensory sisters, some of which summon fresh amazement bordering on disbelief. What can possibly make us feel that our tongue is split in two, or that a brush touching an artificial hand touches us? How do we study such experiences? This chapter directs the reader to sources describing a variety of somatosensory illusions. We also outline methodological issues in studying them, and describe methods used to study the well-known rubber hand illusion, and the recently described whose hand illusion.
Semi-supervised Person Re-identification Method Based on Voting Algorithm

Semi-supervised person re-ID (RE-ID) learning discriminant information across irrelevant camera views is very challenging problem in the case of no paired tag. A deep model for the pseudo label evaluation for semi-supervised Re-ID is created. The method is to use multiple similar or identical sub-models to vote on the pseudo-labels of unlabeled person pictures, and increase confidence level of the pseudo-labels, so as to iterate model. We propose feature mining guided by a multi-voting model to improve the credibility of pseudo-labels. Because most target pairs are cross-view pairs, we developed a cross-view consistent voting mechanism to achieve a good state of pseudo-labels between different camera views. In order to realize an effective and stable voting algorithm, we introduce a new loss constraint to obtain a stable pseudo-label through the idea of entropy reduction. Our model is significantly better than the latest semi-supervised RE-ID method in effect.
Bendlets and Sparse Representation for Biological Image Inpainting

During the preparation of biological image, some defects are inevitable, such as some tissues are lost due to the rolling deformation of the frozen tissue. To solve this problem, a new inpainting method is proposed based on a sparse representation of the bendlet domain. The biological images are insisted of closed contours and linear textures, which is suitable to be represented sparsely with the bendlet proposed in recent years. In this method, we build a bendlet system which can be used to recognize the curvature and orientation parameters of the closed contours and linear texture appeared in the images. For the inpainting of missing regions, the bendlet transform is applied to the broken image, and the resulting coefficients are regularized using a prior penalty to constrain them. By linearly reducing the threshold constants, a final set of sparse coefficients is fitted, which is the inpainting image. In this paper, we use locust slice image as an example, through numerical experiments, the following conclusions are obtained: the PSNR values of our method are 12.4%–27% higher than other methods, SSIM values were 1.5%–10.8% higher.
Automated Identification for High Voltage Isolation Switch via Generative Adversarial Network

The detection and recognition for different states of high voltage isolation switch plays a key role in the quality management process of the electrical power systems. In the literature, most of the related techniques focus on the classification of deterministic states and ignore the identification of non-deterministic ones. Bearing this in mind, we propose a generative adversarial network (GAN) based approach to detect and recognize various cases of the state of high voltage isolation switch. To note that the presented GAN is supposed to increase the number of images since insufficient-sample is frequent in a majority of machine vision-oriented tasks. Meanwhile, we manually collected the data required for the experiment to build a data set, and compared the experiment with the most advanced technology methods. The final result shows that the superiority of our presented method in classification accuracy.