Reviewerly/exHarmony huggingface dataset:
Hierarchical Deep Multitask Learning With the Attention Mechanism for Similarity Learning

Similarity learning is often adopted as an auxiliary task of deep multitask learning methods to learn discriminant features. Most existing approaches only use the single-layer features extracted by the last fully connected layer, which ignores the abundant information of feature channels in lower layers. Besides, small cliques are the most commonly used methods in similarity learning tasks to model the correlation of data, which can lead to the limited relation learning. In this article, we present an end-to-end hierarchical deep multitask learning framework for similarity learning which can learn more discriminant features by sharing information from different layers of network and dealing with complex correlation. Its main task is graph similarity inference. We build focus graphs for each sample. Then, an attention mechanism and a node feature enhancing model are introduced into backbone network to extract the abundant and important channel information from multiple layers of network. In the similarity inference task, a relation enhancing mechanism is applied to graph convolutional network to leverage the crucial relation in channels, which can effectively facilitate the learning ability of the whole framework. The extensive experiments have been conducted to demonstrate the effectiveness of the proposed method on person reidentification and face clustering applications.
Graph Representation Learning with Adaptive Mixtures

Graph Neural Networks (GNNs) are the current state-of-the-art models in learning node representations for many predictive tasks on graphs. Typically, GNNs reuses the same set of model parameters across all nodes in the graph to improve the training efficiency and exploit the translationally-invariant properties in many datasets. However, the parameter sharing scheme prevents GNNs from distinguishing two nodes that are isomorphic and that the translation invariance property may not exhibit in real-world graphs. In this paper, we present Graph Representation Learning with Adaptive Mixtures (GRAM), a novel approach for learning node representations in a graph by introducing multiple independent GNN models and a trainable mixture distribution for each node. GRAM is a general framework that can be readily embedded into existing models for enhancing representation learning. It allows for adaptive node clustering with mixtures and joint optimization of all GNN models simultaneously. To achieve this, we develop an Expectation-Maximization algorithm with stochastic gradient descent in a scalable manner. Specifically, in the E-Step, we approximate the posterior probability distribution of the latent cluster membership based on the k-th independent GNN models in the last iteration. In the M-step, we update all GNN models and the mixture parameters based on the refined latent cluster membership for downstream tasks, e.g., node classification. To fully exploit the graph structural information, we further implement a regularization term on unlabeled nodes, which encourages adjacent nodes to have the same mixture distribution. By jointly optimizing the model in an end-to-end manner, GRAM learns to capture different abstract patterns on graphs for different node clusters, which yields discriminative representation for each node. We evaluate GRAM on five benchmark datasets with extensive experiments. GRAM is demonstrated to consistently boost state-of-the-art GNN variants in node classification tasks.
Cascaded Cross-Domain Fusion of Virtual Try-On

Image-based virtual try-on, aiming to fit new in-shop clothes into a person image, has gained extensive attention in the fields of computer vision and image process community. However, the existing methods are difficult to generate photo-realistic try-on images when large-scale deformations or large occlusions occur. To address this issue, we propose a novel two stage visual try-on network. Specifically, in the first stage, we used a shape matching model to learn the geometric transformation of in-shop clothes. For the second stage, an U-net with cascaded attention mechanism is presented to learn the composition mask which adjust the clothes and rendered persons. The adjusted clothes and the rendered person are combined by the composition mask to get the final try-on result. Experimental results have shown that our method can generate photo-realistic images with no occlusion.
GSNESR: A Global Social Network Embedding Approach for Social Recommendation

Recommender systems are extensively utilized on the internet for helping customers to pick on the items which strike his fancy. Along with the fast progress of online social networks, how to use the additional social information for recommendation has been intensively investigated. In this article, we devise a graph embedding technology to incorporate the customers' social network side information into conventional matrix factorization model. More specifically, first we introduce the graph embedding approach Node2Vec to obtain the customer social latent factor. Then we utilize the matrix factorization technique to find the customer scoring latent factor. Finally we think of recommendation problem as a successive task of social network embedding and integrate customer social latent factor and customer scoring latent factor into our recommendation model. We select the dominant scoring predict task as the evaluation scenario. The effectiveness for our proposed social recommendation (GSNESR) model is validated on three benchmark real world datasets. Experimental results indicate that our proposed GSNESR outperform other state-of-the-art methods.
Line-Like Object Detection in Corrupted Images by Averaging of Second Order Vector Fields

In this paper the possibility of improving signal-to-noise ratio (SNR) of detected line-like objects (LLOs) by summing together second order vector fields (2Φs) acquired by processing the line images with added noise or other random disturbances are investigated. A 2Φ can be obtained by processing the input image with a LLO detection filter. The resulting vector field can be used for the extraction of the image features and enables detection of more complex objects. When adding many 2Φs together, persistent LLOs will be amplified, whereas randomly occurring LLOs will tend to mutually attenuate. Images with added Gaussian white noise, Salt and Pepper noise, line and LLO disturbances are examined and SNR values examined statistically. The resulting SNR values are compared with the classical image averaging techniques.
Steampunk Machine Learning

Fitting models to data is all the rage nowadays but has long been an essential skill of engineers. Veterans know that real-world systems foil textbook techniques by interleaving routine operating conditions with bouts of overload and failure; to be practical, a method must model the former without distortion by the latter. Surprisingly effective aid comes from an unlikely quarter: a simple and intuitive model-fitting approach that predates the Babbage Engine. The foundation of industrial-strength decision support and anomaly detection for production datacenters, this approach yields accurate yet intelligible models without hand-holding or fuss. It is easy to practice with modern analytics software and is widely applicable to computing systems and beyond.
IoT Health Status Monitoring System for Elderly People

The Internet of Things (IoT) is one of the most promising technologies with highly application in healthcare. In this context the work presents a mobile IoT compatible solution to optimize the remote assessment process using non-intrusive measurement devices embedded on the level of Fitbit smartwatch. The developed system that includes the smartwatch provides information about health status and motor activity of the patients that is available and accessible anywhere and anytime. A framework for continuous monitoring of physical activity based on smartwatch devices is described in the paper. A hybrid mobile application and a software structure assembled to support the processes of data collection, processing, storage, and display are part of developed framework. Preliminary results are included in the paper.
Self-Supervised Multi-Scale Pyramid Fusion Networks for Realistic Bokeh Effect Rendering

To balance the aesthetic requirements on photo quality and expensive high-end SLR cameras, synthetic bokeh effect rendering has emerged as an attractive machine learning topic. However, most of bokeh rendering models either heavily relied on prior knowledge or were topic-irrelevant data-driven networks without task-specific knowledge, which restricted models' training efficiency and testing accuracy. Therefore, in this paper, following the principle of bokeh generation, a novel self-supervised multi-scale pyramid fusion network for bokeh rendering has been proposed. Structure consistencies are employed to emphasize the importance of bokeh components. Task-specific knowledge which mimics the "circle of confusion" phenomenon through disk blur convolutions is utilized as self-supervised information for network training. The proposed network has been evaluated with several state-of-the-art methods on a public large-scale bokeh dataset- the "EBB!" Dataset. The experiments demonstrates that it has much better processing efficiency and realistic bokeh effect with much less parameters size and running time.
Data Analysis on Library Entry Behavior of University Library

With the development of information technology, the data processing of University Libraries in China has been greatly promoted. At the same time, in the massive data resources, how to improve the utilization rate of university library collection resources, meet the needs of readers, and enhance the core competitiveness of the library has become a problem that must be solved in the development process of university library. This paper puts forward the data analysis and Research on the library entry behavior of university library. In this paper, according to the current situation of university library users’ library entry behavior, through data analysis, this paper investigates the library entry behavior of University Library and analyzes the numerical changes of school library management after the input of data analysis. According to the analysis results, more than 90% of teachers and students think that the data analysis of library entry behavior can promote the management of university library. And through the analysis and research of library entry behavior by data analysis technology, it improves the scientificity and pertinence of university library management. The data analysis of university library entry behavior provides technical support for the management of university library.
For-Loops and Pattern Matching

This chapter presents problem solving using for-loops and pattern matching.
Comparative Research on Several Denoising Methods for Pepper and Salt Noise Images

Salt and pepper noise is a kind of noise often seen in the image. In this paper, five commonly used denoising algorithms are selected: mean filtering, median filtering, Wiener filtering, low-pass filtering and wavelet denoising. For the image of pepper and salt noise, the horizontal and vertical comparison of the advantages and disadvantages of each algorithm at different noise intensities is compared. The experiment results show that the median filtering method improves the PSNR value by 150%, which has a very good performance in the salt and pepper noise denoising experiment. In the high noise level, the noise image can be restored well. However, a little image information disappears in the image with noise coefficient of 0.4, which shows that the same algorithm has a limit of denoising, even if the low noise coefficient can be processed well, once reaching a critical value of noise coefficient, the denoising effect will gradually decline. Therefore, it is necessary to optimize the algorithm continuously to achieve the denoising requirements even in the high noise coefficient environment.
Entity-Aware Graph Convolution Networks for Event Detection

The existing event detection models based on graph convolutional networks only consider the syntactic structure from the dependency tree of the sentence, and ignore the importance of the entity to the event in the syntactic structure, which is used for event detection task. In this paper, we propose a Entity-Aware Convolutional Networks (EAGCN) which exploits adding entity information directly to the syntactic structure by dynamically modifying the dependency graph of the sentence in the convolution operation. Besides, we followed EAGCN with a Bi-directional Long-Short Term Memory to import sequence information into structure information, which is an indispensable part of the model. The sufficient experimental results show that our model achieves the best F1 score for the event detection task on the ACE 2005 dataset.
Construction of Mobile Service Platform in University Library

In recent years, with the popularity of wireless networks, mobile library as a quick and convenient service is used in the construction of university library, but its service mode is single and hard to better play the advantages of mobile service. In this paper, a kind of based on the integration of functions, basic services, extended services and personalized service composition of mobile service platform in university libraries. At last, the case of mobile service platform construction of Huazhong Agricultural University Library is introduced.
DFNM: Dynamic Fusion Network of Intra- and Inter-modalities for Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to leverage multiple modalities involving language, vision and acoustic to analyze the sentiment polarity tendency expressed in text or video. In practice, we usually receive the multimodal information at the same time. For example, when we watch a video, we usually listen to the sound and watch the subtitles in sync. This indicates that different modalities should be synchronously fused in a way of acrossing different timestamps (Inter-modal fusion). Previous studies investigate different methods to obtain the good fusion of multiple modalities by improving the LSTM structure. However, due to the characteristics of LSTM structure, each temporal information of one modality cannot be fully fused with all the temporal information of all other modalities at the same time. Moreover, each temporal information of the same modality also need to be fused (Intra-modal fusion). To fill this gap, in this paper, we design a novel multimodal heterogeneous graph structure to obtain full dynamic fusion of different temporal information from intra- and inter-modalities effectively. Experimental results on public datasets demonstrate the effectiveness of our proposed method.
Remote Sensing Image Recommendation Using Multi-attribute Embedding and Fusion Collaborative Filtering Network

With the popularization of remote sensing applications, the number of remote sensing users and their application requirements have increased explosively. Introducing recommendation systems into the distribution of remote sensing images can lower the threshold to obtain images for the public and subvert the traditional image service pattern. Different from other industries, users in the remote sensing field do not purchase data frequently in most cases, making it difficult to rely solely on order information to make recommendations. Therefore, how to use the semantic information of users and images as a foundation is an urgent problem. In this paper, we propose a feasible framework for personalized remote sensing image recommendation. We first describe remote sensing users and images through user duties, image semantics and order information; then, we complete the information modeling process via a knowledge graph. Next, we extract multidimensional features from the knowledge graphs of users and images with the help of knowledge representation learning and quantifiable features. Finally, using the high-dimensional spatial modeling capabilities of deep neural networks to perform a deep interaction exploration, we propose a Multi-attribute Fusion-based Collaborative Filtering Network, called MaF-CFNet. The model effectively fuses the multidimensional attribute features of users and images and obtains a recommendation score for each candidate image. The experimental results on our datasets demonstrate the effectiveness of MaF-CFNet compared to traditional methods.
On LCD, self dual and isodual cyclic codes over finite chain rings

In this paper, LCD cyclic, self dual and isodual codes over finite chain rings are investigated. It was proven recently that a non-free LCD cyclic code does not exist over finite chain rings. Based on algebraic number theory, we introduce necessary and sufficient conditions for which all free cyclic codes over a finite chain ring are LCD. We have also obtained conditions on the existence of non trivial self dual cyclic codes of any length when the nilpotency index of the maximal ideal of a finite chain ring is even. Further, several constructions of isodual codes are given based on the factorization of the polynomial xn−1 over a finite chain ring.
A Novel View Attention Network for Skeleton based Human Action Recognition

Skeleton based human action recognition is becoming increasingly popular nowadays thanks to the development of low-cost depth sensors and pose estimation techniques. To enrich the expression of human skeletal characteristics and enhance generalization ability of models, new approaches are proposed to utilize human skeletons observed by multiple viewpoints for feature extracting. Despite the significant progress on these multi-view skeletons based approaches, the intrinsic correlation among views and fusion form of features have not been extensively investigated. In order to tackle these problems, we proposed a novel View Attention Network (VANet), which can learn the relationship of different views and fuse the multi-view features effectively. First, the spatio-temporal dynamics of human skeletons are encoded in the multi-view skeletal arrays. Then, a multi-branch Convolutional Neural Network (CNN) is adopted for extracting features from multiple views. Moreover, we design a view attention module to capture the correlation across different views. Particularly, we expend the module to a multi-head format to increase the feature spaces and enhance the robustness of entire network. Finally, an aggregated feature is learned from the module for final recognition. Extensive experiments on public NTU RGB+D 60 and SBU Kinect Interaction datasets show that our approach can achieve state-of-the-art results.
Enhanced Medical Dialogue Diagnosis with Intra-inter Window Attention Encoder

Information extraction from clinical dialogue to build a knowledge graph that can generate electronic medical charts has been an important application of NLP in the medical industry. Although substantial research has been carried out on such information extractor, existing models underperform due to lack of contextual background between dialogue windows. This study proposes a model that can extract relevant medical items from the doctor-patient dialogues by leveraging a multi-layer encoder-decoder framework. The experimental results on a well-studied dataset shows that our model outperform the current baseline models, which prove the model effectiveness.
Data Breach on Consumer Behavior

Big data is now affecting the daily lives in many different areas, such as payment system, online shopping, health services and so forth. There has no doubt that big data is able to make the lives of people more convenient to a certain extent, but it can also threaten privacy security in the meantime. In order to explore the hazardous effects of data breach on consumer behavior and understand how netizens act and feel when experiencing it, a questionnaire was completed by 110 participants. This article will demonstrate the primary issues on potential security risks on big data, especially the effects of data breach on consumer behavior by discussing the causes, solutions and ethical concerns.
Left dihedral codes over finite chain rings

Let R be a finite commutative chain ring, D2n be the dihedral group of size 2n and R[D2n] be the dihedral group ring. In this paper, we completely characterize left ideals of R[D2n] (called left D2n-codes) when gcd(char(R),n)=1. In this way, we explore the structure of some skew-cyclic codes of length 2 over R and also over R×S, where S is an isomorphic copy of R. As a particular result, we give the structure of cyclic codes of length 2 over R. In the case where R=Fpm is a Galois field, we give a classification for left D2N-codes over Fpm, for any positive integer N. In both cases we determine dual codes and identify self-dual ones.
Visual Study of Multi-Dimensional Data in Remote Earth Sensing Problems

The article deals with the problem of multidimensional data presentation based on the application of the scientific visualization method. A method of visual-interactive animation based on dynamic representation of images in the form of 2D cross-sectional models is proposed. The effect of multidimensionality is achieved through the choice of 2D models, the variability of the directions of dynamic sections and the use of stereoscopic vision capabilities.
A Novel Sentence Completion System for Punjabi Using Deep Neural Networks

Sentence completion systems are actively studied by many researchers which ultimately results in the reduction of cognitive effort and enhancement in user-experience. The review of the literature reveals that most of the work in the said area is in English and limited effort spent on other languages, especially vernacular languages. This work aims to develop state-of-the-art sentence completion system for the Punjabi language, which is the 10th most spoken language in the world. The presented work is an outcome of the results of the experimentation on various neural network language model combinations. A new Sentence Search Algorithm (SSA) and patching system are developed to search, complete and rank the completed sub-string and give a syntactically rich sentence(s). The quantitative and qualitative evaluation metrics were utilized to evaluate the system. The results are quite promising, and the best performing model is capable of completing a given sub-string with more acceptability. Best performing model is utilized for developing the user-interface.
Multi-cloud Privacy-Preserving Logistic Regression

Clouds can significantly reduce the cost and time of business solutions. However, cloud services introduce significant security and privacy challenges when they process sensitive information. For instance, a dataset for machine learning could contain delicate information that traditional encryption approaches cannot protect during data analysis. Homomorphic Encryption (HE) schemes and secure Multi-Party Computation (MPC) are considered solutions for privacy protection in third-party infrastructures. In this paper, we propose a Multi-Cloud Logistic Regression based on Residue Number System (MC-LR-RNS) that provides security, parallel processing, and scalability. To validate the efficiency and practicability of the solution, we provide its analysis with different configurations, datasets, and cloud service providers. We use six available datasets from medicine (diabetes, cancer, drugs, etc.) and genomics. The analysis shows that MC-LR-RNS provides the same levels of quality as non-HE solutions and improved performance due to multi-cloud parallel computations.