Reviewerly/exHarmony huggingface dataset:
Textual Matching Framework for Measuring Similarity Between Profiles in E-recruitment

Finding the most relevant jobs for job-seeker is one of the ongoing challenges in the area of e-recruitment. Text mining, information retrieval and natural language processing are some of the key concepts for matching a number of profiles and find the most textually similar job profiles given the job-seeker profile. The mission of finding the relevant jobs that are textually similar to the job-seeker profile depends mainly on the quality of three mains phases. The first early phase is basically constructing the profiles that should be involved in the matching phase through gathering data from different sources and representing this data into a form of important keywords. The second phase is the matching phase which works on mining and analyzing the text of these constructed profiles to find the similar profiles. The final phase is ranking these produced similar profiles by relevance. We introduce our intelligent similarity computing engine which works on predicting the personalized jobs for each job-seeker. It aims to empower the recommendation effectiveness in the area of e-recruitment by applying the three aforementioned phases on the problem of job recommendation. Its purpose is to recommend the textually relevant jobs based on computing the textual similarity distance between jobs and job-seekers’ profiles.
A Detailed Survey Of The Security Issues And Defensive Tactic In Cloud Background

Cloud computing technology is a stretchy, price- efficient and provides a nice stand for the trade opportunities and the customer services via network. It is the main route to improve the capacity or else add the capability with dynamism without investing in new infrastructure. From the past few years cloud computing technology  was the fastest growing segment in the IT filed. Extreme plenty of information has been stored in the cloud because of its convenience. In the face of of the public relations neighboring in the cloud the consumers are unwilling towards make a  proposal for  their  business in the cloud. The growth of the cloud was deduced by the security issues in the cloud. The complications in the data privacy and the protection of the data will extending due to the clumpness in the market . The cloud architecture have several threatening issues the cloud service users need to be aware of the issues and the existing protective techniques. Hence in this paper a survey was held depends upon the different security issues belongs to the cloud and also its defensive methodology. There are several other existing methodologies are there which can improving the features in the cloud and also reduces the risk in the cloud. This survey was more specific to different safety problems also defensive mechanism in cloud computing.
JavaScript Implementation of Scagnostics and Its Applications

Scagnostics is a set of features that characterizes the 2D distributions in the underlying data. Various real-world applications have been using Scagnostics visual features to detect unusual bivariate data correlations. Concomitantly, many applications are required to be implemented on web platforms due to their accessibility and convenience. Therefore, this chapter discusses a recent JavaScript implementation of Scagnostics, an extension to higher dimensional data, and its applications in detecting abnormalities in bivariate and multivariate time series data. Its implementation in JavaScript supports the tremendous demand for visual features in the web environment. Likewise, its higher dimensional implementations allow generating Scagnostics features for the rapidly growing multivariate data. Finally, conventional ScagnosticsJS computations involve time-consuming algorithms, and they are sensitive to slight changes in the underlying data. Therefore, this chapter also discusses a recent attempt to tackle these issues using machine learning to estimate the Scagnostics scores.
What Questions Are You Inclined to Answer? Effects of Hierarchy in Corporate Q&A Communities

An increasing number of companies have started to implement corporate knowledge-sharing communities. Consistent with the observation in the offline setting, employees are less likely to share knowledge with individuals who have higher job ranks (i.e., higher-ups) in corporate communities such as online wikis and discussion groups. Given the importance of managers’ engagements in the community and the needs for knowledge sharing across the hierarchy, we examine whether such observation persists in the corporate question-and-answer (Q&A) community, another popular type of corporate knowledge-sharing community. On the one hand, as in the offline setting and other types of communities, employees can still be reluctant to share knowledge with the higher-ranked individuals in the Q&A community. On the other hand, a Q&A community has some unique attributes that can potentially motivate employees to engage more with the higher-ups. Using a unique dataset from a large corporate Q&A community and a potential-dyads approach, we find that a user is inclined to respond to a knowledge seeker whose job rank is higher than (vs. lower than or the same as) the user’s rank in the corporate Q&A community. We further show the causality of the result with a quasi-experiment that leverages the promotions announced in our study period. Since these promotions are based on employees’ performances before the existence of the community, the promotion announcements are largely exogenous to our research interest. We also find that knowledge providers exert greater effort when answering questions from the higher-ups. Finally, our analyses show that knowledge providers who post more answers to higher-ranked seekers and who display greater effort in those answers are more likely to get promoted in subsequent years. Given the critical role of knowledge sharing and the increasing prevalence of online communities, our study offers a better understanding of the knowledge-sharing pattern in the corporate Q&A community of the hierarchical organizations and delivers useful managerial implications.
Curvature Graph Neural Network

Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguishing the importance of nodes in the topology structure. We believe that the structural importance of neighboring nodes is closely related to their importance in aggregation. In this paper, we introduce discrete graph curvature (the Ricci curvature) to quantify the strength of structural connection of pairwise nodes. And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature. To improve the adaptability of curvature to various datasets, we explicitly transform curvature into the weights of neighboring nodes by the necessary Negative Curvature Processing Module and Curvature Normalization Module. Then, we conduct numerous experiments on various synthetic datasets and real-world datasets. The experimental results on synthetic datasets show that CGNN effectively exploits the topology structure information, and the performance is improved significantly. CGNN outperforms the baselines on 5 dense node classification benchmark datasets. This study deepens the understanding of how to utilize advanced topology information and assign the importance of neighboring nodes from the perspective of graph curvature and encourages us to bridge the gap between graph theory and neural networks.
Stepwise Diagnosis and Dynamic Change&amp;nbsp;Prediction AI System for COVID-19

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Research on Data Security Technology Based on Blockchain Technology

Blockchain started with Bitcoin, but it is higher than Bitcoin. With the deepening of applied research on blockchain technology, this new technology has brought new vitality to many industries. People admire the decentralized nature of the blockchain and hope to solve the problems caused by the operation of traditional centralized institutions in a more fair and effective way. Of course, as an emerging technology, blockchain has many areas for improvement. This article explains the blockchain technology from many aspects. Starting from the typical architecture of the blockchain, the data structure and system model of the blockchain are first introduced. Then it expounds the development of consensus algorithms and compares typical consensus algorithms. Later, the focus will be on smart contracts and their application platforms. After analyzing some of the challenges currently faced by the blockchain technology, some scenarios where the blockchain is currently developing well are listed. Finally, it summarizes and looks forward to the blockchain technology.
Perceptual losses for self-supervised depth estimation

Abstract Convolution neural network has shown excellent results in stereo and monocular disparity estimation, while most of the existing methods convert the image depth prediction problem into the image reconstruction problem, and calculate the depth of each pixel through the disparity between the generated left and right images. However, in the reconstruction task, the loss is still calculated at the pixel level when comparing the reconstructed picture with the original picture, which will greatly affect the estimation of picture depth due to the problems of illumination and occlusion. Therefore, when calculating the loss of image reconstruction, it is very important to compare the higher-level features extracted from the reconstructed image with the original image. In this paper, based on the existing methods, we have innovated the loss function and introduced perceptual loss, i.e., we use feedforward neural network to extract features to further evaluate the reconstructed image, to make the reconstruction loss of baseline more accurate and improve the accuracy and robustness of the depth prediction model. To compare the improved effect, we performed extensive experiments on KITTI driving data by the improved model set, and the experimental index obtains better performance than the original baseline model.
Classification and analysis of literary works based on distribution weighted term frequency-inverse document frequency

Abstract Term Frequency-Inverse Document Frequency (TF-IDF) is a commonly used data mining weighting technology for information retrieval. It can evaluate the importance of a word to a text and is widely used in Internet search engines. In order to improve the text analysis ability of different text types, a variety of weighting algorithms of TF-IDF have been developed. For the word analysis of literary works of various genres, this paper adopts Distribution Weighted Term Frequency-Inverse Document Frequency (TF-IDF-DW), which takes the distribution of feature items within and between classes into account, and can get better screening results. In the text classification part, via the comprehensive weight value obtained by TF-IDF-DW, and the classification results are obtained by class center vector algorithm and Bayesian algorithm. Finally, the classification performance of TF-IDF-DW algorithm is evaluated by comparison with the traditional TF-IDF method.
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TWAG: A Topic-Guided Wikipedia Abstract Generator

Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that \modelnames outperforms various existing baselines and is capable of generating comprehensive abstracts. Our code and dataset can be accessed at \url{https://github.com/THU-KEG/TWAG}
Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition

By implicitly recognizing a user based on his/her speech input, speaker identification enables many downstream applications, such as personalized system behavior and expedited shopping checkouts. Based on whether the speech content is constrained or not, both text-dependent (TD) and text-independent (TI) speaker recognition models may be used. We wish to combine the advantages of both types of models through an ensemble system to make more reliable predictions. However, any such combined approach has to be robust to incomplete inputs, i.e., when either TD or TI input is missing. As a solution we propose a fusion of embeddings network foenet architecture, combining joint learning with neural attention. We compare foenet with four competitive baseline methods on a dataset of voice assistant inputs, and show that it achieves higher accuracy than the baseline and score fusion methods, especially in the presence of incomplete inputs.
Maximum Entropy Inverse Reinforcement Learning Based on Behavior Cloning of Expert Examples

This study proposes a preprocessing framework for expert examples based on behavior cloning (BC) to solve the problem that inverse reinforcement learning (IRL) is inaccurate due to the noises of expert examples. In order to remove the noises in the expert examples, we first use supervised learning to learn the approximate expert policy, and then use this approximate expert policy to clone new expert examples from the old expert examples, the idea of this preprocessing framework is BC, IRL can obtain higher quality expert examples after preprocessing. The IRL framework adopts the form of maximum entropy, and specific experiments demonstrate the effectiveness of the proposed approach, in the case of expert examples with noises, the reward functions that after BC preprocessing is better than that without preprocessing, especially with the increase of noise level, the effect is particularly obvious.
Rich Common Crucial Feature for Crowdsourcing-Based Mobile Visual Location Recognition

Crowdsourcing provides an effective way to construct a location recognition image database. Comparing with traditional location image database construction, crowdsourced image database has massive advantages, e.g., much richer information for location, with various angles, timestamps, distances and weather information, providing useful potential for high recognition precision. However, when capturing the crowdsourced images, it is inevitable to have various disturbances on these location images, for example, moving vehicles and pedestrians, hindering the realization of potential. To address this challenge, we first propose a Rich Common Crucial Feature (RCCF) detection framework to exclude unimportant visual SURF features from crucial features. To achieve a good balance between the efficiency and accuracy, we further propose an RCCF based Visual Hash Bits (VHB) scheme to encode RCCF features into hash bits to vote for most matching images. Furthermore, deep feature extraction is also utilized with visual search architecture MobileNet. Extensive experiments are conducted on a crowdsourced dataset with 9,064 location images, demonstrating that our scheme outperforms other state-of-the-art schemes.
REDS: Rule Extraction for Discovering Scenarios

Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e., inputs of the simulation model, where the system is unstable. Subgroup discovery methods are commonly used for scenario discovery. They find scenarios in the form of hyperboxes, which are easy to comprehend. Given a computational budget, results tend to get worse as the number of inputs of the simulation model and the cost of simulations increase. We propose a new procedure for scenario discovery from few simulations, dubbed REDS. A key ingredient is using an intermediate machine learning model to label data for subsequent use by conventional subgroup discovery methods. We provide statistical arguments why this is an improvement. In our experiments, REDS reduces the number of simulations required by 50--75\% on average, depending on the quality measure. It is also useful as a semi-supervised subgroup discovery method and for discovering better scenarios from third-party data, when a simulation model is not available.
IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition

Existing classification-based face recognition methods have achieved remarkable progress, introducing large margin into hypersphere manifold to learn discriminative facial representations. However, the feature distribution is ignored. Poor feature distribution will wipe out the performance improvement brought about by margin scheme. Recent studies focus on the unbalanced inter-class distribution and form a equidistributed feature representations by penalizing the angle between identity and its nearest neighbor. But the problem is more than that, we also found the anisotropy of intra-class distribution. In this paper, we propose the `gradient-enhancing term' that concentrates on the distribution characteristics within the class. This method, named IntraLoss, explicitly performs gradient enhancement in the anisotropic region so that the intra-class distribution continues to shrink, resulting in isotropic and more compact intra-class distribution and further margin between identities. The experimental results on LFW, YTF and CFP-FP show that our outperforms state-of-the-art methods by gradient enhancement, demonstrating the superiority of our method. In addition, our method has intuitive geometric interpretation and can be easily combined with existing methods to solve the previously ignored problems.
Formal Semantics of a Classical-Quantum Language

We investigate the formal semantics of a simple imperative language that has both classical and quantum constructs. More specifically, we provide an operational semantics, a denotational semantics and two Hoare-style proof systems: an abstract one and a concrete one. The two proof systems are satisfaction-based, as inspired by the program logics of Barthe et al for probabilistic programs. The abstract proof system turns out to be sound and relatively complete, while the concrete one is sound only.
A Graph Data Augmentation Strategy with Entropy Preserving.

The Graph Convolutional Networks (GCNs) proposed by Kipf and Welling are effective models for semi-supervised learning, but facing the obstacle of over-smoothing, which will weaken the representation ability of GCNs. Recently some works are proposed to tackle with above limitation by randomly perturbing graph topology or feature matrix to generate data augmentations as input for training. However, these operations have to pay the price of information structure integrity breaking, and inevitably sacrifice information stochastically from original graph. In this paper, we introduce a novel graph entropy definition as an quantitative index to evaluate feature information diffusion among a graph. Under considerations of preserving graph entropy, we propose an effective strategy to generate perturbed training data using a stochastic mechanism but guaranteeing graph topology integrity and with only a small amount of graph entropy decaying. Extensive experiments have been conducted on real-world datasets and the results verify the effectiveness of our proposed method in improving semi-supervised node classification accuracy compared with a surge of baselines. Beyond that, our proposed approach significantly enhances the robustness and generalization ability of GCNs during the training process.
Preprocessing and Characteristic Statistics of Blood Cell Images Based on Delphi

Aiming at how to effectively extract characteristic indicators from the blood cell morphology map, so as to determine the nature of such cell morphology, and distinguish it from other cell morphology patterns to estimate whether the cell has pathological changes. Cell image is preprocessed by chromaticity interval method, binarization, contour acquisition, cell segmentation, defect filling and noise removal, and then the region marker of cell color difference is used to calculate the compactness of its characteristic parameters and the position of its center of gravity. Through automatic processing of Delphi program, statistical data can be provided as an important basis for pathological diagnosis.
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification

Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.