Reviewerly/exHarmony huggingface dataset:
Approach for improving efficiency of three-dimensional object recognition in light-field display

With the development of light-field acquisition technology from two-dimensional (2-D) to three-dimensional (3-D) sensors, point clouds are currently being used in the 3-D reconstruction of light-field imaging. The computational requirements of point cloud processing decrease the efficiency of 3-D object recognition in a light field reconstruction. An optimization strategy is proposed to improve the efficiency of object feature recognition in a 3-D light field reconstruction. The proposed method involves a normal estimation, uniform keypoint sampling, random Monte Carlo sampling, signature of histograms of orientations descriptor extraction, <italic>k</italic>-dimensional tree matching, and geometric consistency clustering estimation. During the experiments, all scenarios corresponding to each model are tested, 2711 times in three virtual and real international standard databases (i.e., Kinect, Mian, and Clutter). The experimental results indicate that the efficiency of the proposed method is improved by 9.26% on an average with the same accurate recognition rate of 84.67%.
Class Name Recommendation Based on Graph Embedding of Program Elements

In software development, the quality of identifier names is important because it greatly affects program comprehension for developers. However, naming identifiers that appropriately represent the nature or behavior of program elements such as classes and methods is a difficult task requiring rich development experience and software domain knowledge. Although several studies proposed techniques for recommending identifier names, there are few studies targeting class names and they have limited availability. This paper proposes a novel class name recommendation approach widely available in software development. The key idea is to represent quantitatively the nature or behavior of classes by leveraging embedding technology for heterogeneous graphs. This makes it possible to recommend class names even where a previous approach cannot work. Experimental results suggest that the proposed approach can produce more accurate class name recommendation regardless of whether classes are used. In addition, a further experiment reveals a situation where the proposed approach is particularly effective.
Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field

Uncertainty analysis of a time-varying ensemble vector field is a challenging topic in geoscience. Due to the complex data structure, the uncertainty of a time-varying ensemble vector field is hard to quantify and analyze. Measuring the differences between pathlines is an effective way to compute the uncertainty. However, existing metrics are not accurate enough or are sensitive to outliers; thus, a comprehensive tool for the further analysis of the uncertainty of transport patterns is required. In this paper, we propose a novel framework for quantifying and analyzing the uncertainty of an ensemble vector field. Based on the classical edit distance on real sequence (EDR) method, a robust and accurate metric was proposed to measure the pathline uncertainty. Considering the spatial continuity, we computed the transport variance of the neighborhood of a location, and evaluated the uncertainty correlation between each location and its neighborhood by using the local Moran’s I. Based on the proposed uncertainty measurements, a visual analysis system called UP-Vis (uncertainty pathline visualization) was developed to interactively explore the uncertainty. It provides an overview of the uncertainty and supports detailed exploration of transport patterns at a selected location, and allows for the comparison of transport patterns between a location and its neighborhood. Through pathline clustering, the major trends of the ensemble pathline at a location were extracted. Moreover, a glyph was designed to intuitively display the transport direction and diverging degree of each cluster. For the uncertainty analysis of the neighborhood, a comparison view was designed to compare the transport patterns between a location and its neighborhood in detail. A synthetic data set and weather simulation data set were used in our experiments. The evaluation and case studies demonstrated that the proposed framework can measure the uncertainty effectively and help users to comprehensively explore uncertainty transport patterns.
Fast Traffic Accident Identification Method Based on SSD Model

Traditional traffic accident identification methods have the problems of complex detection process, poor detection performance and poor real-time performance so far. In this paper, we propose a new type of traffic accident identification method based on target detection algorithm Single Shot MultiBox Detector (SSD). We collect and simulate traffic accident data sets in different scenarios and compare the detection performance of different target detection algorithms, aiming at the problems of traffic accident detection existing in the original SSD, the idea of multi-feature fusion and adaptive default box selection algorithm are proposed to improve it. Finally, we present an evaluation on the collected data, the improved SSD_A method shows considerable performance, which can reach 97% mAP (mean average precision) at the speed of 32 FPS (frames per second).
An Incremental Identification Method for Fraud Phone Calls Based on Broad Learning System

With the continuous development of the communication industry, more and more fraud calls appear in the user's daily life and the crime of telecom fraud is growing rapidly, causing huge losses every year. Traditional fraud detection methods are less flexible and they all belong to passive interception and rely on intelligent terminals. At present, a more accurate and timely method is needed to deal with the evolving fraud. Therefore, this paper proposes an identification method for fraud phone calls based on Broad Learning System (BLS). We processed the text data of fraud phone calls through the first 15s of the call content identification monitoring, constructed the TF-IDF model, then converted it into a neural network based on the BLS and identified the fraud phone calls on this model. At the same time, the model can be updated quickly by corresponding incremental learning algorithm without retraining based on the BLS, which is suitable for fraud identification systems with few data features but high real-time prediction requirements. The method mentioned above is experimented and analyzed in detail. The results show that this method has higher accuracy and excellent training speed on fraud data. Compared with the original fraud identification methods, it can actively intercept and has higher accuracy. Compared with other neural network algorithms used in fraud system, the method has better training speed, can ensure the accuracy and timeliness of online fraud identification and help quickly identify fraud phone calls.
Predicting the Writer's Gender Based on Electronic Discourse

The present study compared three methods aimed at predicting the writer's gender based on writing features manifested in electronic discourse. The compared methods included qualitative content analysis, statistical analysis, and machine learning. These methods were further combined to create a mixed methods model. The findings showed that the machine learning model combined with qualitative content analysis produced the best prediction accuracy. Including qualitative content analysis was able to improve accuracy rates even when the training set for machine learning was relatively small. Thus, this study presented a concise model that can be fairly reliable in predicting gender based on electronic discourse with high accuracy rates and such accuracy was consistently found when the model was tested by two separate samples.
PTAOD: A Novel Framework for Supporting Approximate Outlier Detection Over Streaming Data for Edge Computing

Outlier detection over sliding window is a fundamental problem in the domain of streaming data management, which has has been studied over 10 years. The key to supporting outlier detection is to construct a neighbour list for each object, which is used for predicting which objects may become outliers or are impossible to become outliers. However, existing work ignores the fact that, outliers amount is usually small, in which it is unnecessary to construct neighbour-list for all objects when they arrive in the window. It causes both high space and computational cost, which turns the solution infeasible for working under edge computation environment. In this paper, we propose a novel framework named PTAOD (Probabilistic Threshold-based Approximate Outlier Detection). Firstly, we propose an algorithm for evaluating the probability of a newly arrived object becoming an outlier before it expires from the window, using evaluating result for avoiding unnecessary candidate maintenance. In addition, we introduce a novel index namely ZHB-Tree (Z-order-based Hash B-Tree) to maintain streaming data. Last of all, we propose a novel algorithm to maintain candidate outliers. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.
Real-time Face Recognition based on Deep neural network methods to solve occlusion problems

With massive amounts of crime around the world, the government must guarantee high security by using different methods to track and identify people in public places. Facial recognition is considered as the most effective tech for analyzing and identifying individuals' videos scenes or during authentication systems, but it has many difficulties. To address this issue, this paper presents all my comprehensions and my critical synthesis about methods and algorithms applied in face recognition and detection to solve occlusion problems.
Quality Assessment for Large-Scale Industrial Software Systems: Experience Report at Alibaba

To assure high software quality for large-scale industrial software systems, traditional approaches of software quality assurance, such as software testing and performance engineering, have been widely used within Alibaba, the world's largest retailer, and one of the largest Internet companies in the world. However, there still exists a high demand for software quality assessment to achieve high sustainability of business growth and engineering culture in Alibaba. To address this issue, we develop an industrial solution for software quality assessment by following the GQM paradigm in an industrial setting. Moreover, we integrate multiple assessment methods into our solution, ranging from metric selection to rating aggregation. Our solution has been implemented, deployed, and adopted at Alibaba: (1) used by Alibaba's Business Platform Unit to continually monitor the quality for 60+ core software systems; (2) used by Alibaba's R&D Efficiency Unit to support group-wide quality-aware code search and automatic code inspection. This paper presents our proposed industrial solution, including its techniques and industrial adoption, along with the lessons learned during the development and deployment of our solution.
Document Vector Extension for Documents Classification

Simple linear models, which usually learn word-level representations that are later combined to form document representations, have recently shown impressive performance. To improve the performance of document-level classification, it is crucial to explore the factors affecting the quality of the document vector. In this paper, we propose the concept of containers and further explore the properties of word containers and document containers by experiments and theoretical demonstrations. We find that the document container has a fixed capacity and that the document vector obtained by a simple average of too many word embeddings undoubtedly cannot be fully loaded by the container and will lose some semantic and syntactic information on very large text datasets. We also propose an efficient approach for document representation, using clustering algorithms to divide a document container into several subcontainers and establishing the relationship between the subcontainers. We additionally report and discuss the properties of two methods of clustering algorithms, DVEM-Kmeans and DVEM-Random, on large text datasets by sentiment analysis and topic classification tasks. Compared to simple linear models, the results show that our models outperform the existing state-of-the-art in generating high-quality document representations for document-level classification relatedness tasks. Our approaches can also be introduced to other models based on neural networks, such as convolutional neural networks, recurrent neural networks and generative adversarial networks, in supervised or semisupervised settings.
Vertex-weighted realizations of graphs

Given a degree sequence d¯ of length n, the degree realization problem is to decide if d¯ has a realization, namely, an n-vertex graph whose degree sequence is d¯, and if so, to construct one such realization. The problem was well researched over the recent decades and plays an important role in the field of Social Networks. In this paper, we consider the following natural generalization of the problem: Let G=(V,E) be a simple undirected graph on V={1,2,…,n}. Let f¯∈R+n be a vector of requirements of the vertices, and let w¯∈R+n be a vector of provided services at the vertices. The provided services vector w¯ satisfies the requirements vector f¯ on G if the constraints ∑j∈Γ(i)wj=fi are satisfied for all i∈V, where Γ(i) denotes the neighborhood of i. We study the following weighted graph realization problem. Given a requirements vector f¯, the goal is to find a suitable graph G and a vector w¯ of provided services that satisfy f¯ on G. In the original degree realization problem, all the provided services must be equal to one. For even n, we show that every requirement vector is realizable. For odd n, the picture is more complicated, as certain requirement vectors are non-realizable. We provide a complete characterization for n=3 and n=5, and give (non-matching but close) necessary and sufficient conditions for realizability for odd n≥7. We provide a complete characterization for the variant in which the constraints that should be satisfied are: maxj∈Γ(i)⁡wj=fi, for all i∈V. As before, we show that every requirement vector can be realized if n is even. For odd n, we show that a vector is realizable if and only if not all requirements are distinct.
A New Evaluation of Video Encryption Security with a Perceptual Metric

Abstract Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) are metrics initially used to evaluate the visual quality of compressed images or sequences compared to the original ones. By analogy to compressed sequences, researchers use these metrics to evaluate the degradation of encrypted sequences. Video encryption algorithms target a maximum scrambling so that their contents become imperceptible to the human visual system. The distortion of PSNR and SSIM values comes from both compression and encryption. The use of these metrics to measure the degradation of joint compressed and encrypted sequences cannot give us a precise evaluation of the distortion. For a better evaluation, a Contrast Sensitivity Function (CSF) metric was used. This article aims to provide a perceptual evaluation of the encryption effect for the H.264 Advanced Video Coding compressed and encrypted sequences, using the CSF metric. The visual quality of the encrypted video is degraded and proven bad from different viewing distances.
Deep Neural Networks Ensemble with Word Vector Representation Models to Resolve Coreference Resolution in Russian

In this paper we present a novel neural networks ensemble to solve the task of coreference resolution in Russian texts. The ensemble consists of several neural networks, each based on recurrent Bidirectional long short-term memory layers (BiLSTM), attention mechanism, consistent scoring with selection of probable mentions and antecedents. The applied neural network topology has already shown state-of-the-art results in English for this task, and is now adapted for the Russian language. The resulting coreference markup is obtained by aggregating output scores from several blocks of independently trained neural network models. To represent an input source text, a combination of word vectors from two language models is used. We study the dependence of the coreference detection accuracy on various combinations of models of vector representation of words along with two tokenization approaches: gold markup or UDpipe tools. Finally, to show the improvement made by our ensemble approach, we present the results of experiments with both RuCor and AnCor datasets.
Coordinate: A Spreadsheet-Programmable Augmented Reality Framework for Immersive Map-Based Visualizations

Augmented reality devices are opening up a new design space for immersive visualizations. 3D spatial content can be overlaid onto existing physical visualizations for new insights into the data. We present Coordinate, a collaborative analysis tool for augmented reality visualizations of map-based data designed for mobile devices. Coordinate leverages a spreadsheet-programmable web interface paired with a contemporary augmented reality infrastructure for an easy-to-use tool that can provide spatial information to multiple users. It also offers an immersive visualization experience that seeks to enrich presentations for business, educational, and scientific discussions.
Recognizing Human Emotions from Facial Images by Landmark Triangulation: A Combined Circumcenter-Incenter-Centroid Trio Feature-Based Method

Nandi, Avishek Dutta, Paramartha Nasir, MdHuman emotion reflected in facial expression is generated by coordinated operation of muscular movement of facial tissue which associates with the emotional state of the human subject. Facial expression is one of the most significant non-articulated forms of social communication and it is highly adopted by scientific community for the purpose of automated emotion analysis. In the present scope, a triangular structure is induced with three points, viz., circumcenter, incenter, and centroid are considered as the geometric primitive for extraction of relevant features. Information extracted from such features is utilized for the purpose of discrimination of one expression from another using MultiLayer Perceptron (MLP) classifier in images containing facial expressions available in various benchmark databases. Results obtained by applying this method found to be extremely encouraging.
Economic African Development in the Context of FinTech

The purpose of this chapter is to analyze the effect of FinTech and political incertitude on economic growth through a multiple regression. Thus, the authors employ the method of generalized least square (GLS) with panel data. The sample concerns 21 African countries during (2001-2014-2017). The authors use a wide range of measures from Global Findex Database 2017, the World Bank platform, the World Bank national accounts data, and the OECD National Accounts data files base in the context of Africa. Empirical results show that FinTech is a driver of economic growth unless it is actively used in a developed digital infrastructure. In fact, the authors prove that, when financial technologies are used in both transactions (receive and made digital payment), they significantly contribute to the economic cycle. Passive use like simple consumption actions are not a significant lever for the economy. The principal contribution is to highlight that the active use of financial innovations and not passive one and the developed digital infrastructure do promote economic growth in African countries.
Deep Multiresolution Cellular Communities for Semantic Segmentation of Multi-Gigapixel Histology Images

Tissue phenotyping in cancer histology images is a fundamental step in computational pathology. Automatic tools for tissue phenotyping assist pathologists for digital profiling of the tumor microenvironment. Recently, deep learning and classical machine learning methods have been proposed for tissue phenotyping. However, these methods do not integrate the cellular community interaction features which present biological significance in tissue phenotyping context. In this paper, we propose to exploit deep multiresolution cellular communities for tissue phenotyping from multi-level cell graphs and show that such communities offer better performance compared to the deep learning and texture-based methods. We propose to use deep features extracted from two distinct layers of a deep neural network at the cell-level, in order to construct cellular graphs encoding cellular interactions at multiple scales. From these graphs, we extract cellular interaction-based features, which are then employed to construct patch-level graphs. Multiresolution communities are detected by considering the patch-level graphs as layers of multi-level graphs, and also by proposing novel objective function based on non-negative matrix factorization. We report results of our experiments on two datasets for colon cancer tissue phenotyping and demonstrate excellent performance of the proposed algorithm as compared to current state-of-the-art methods.
Estimation of Gaussian mixture models via tensor moments with application to online learning

In this paper, we present an alternating gradient descent algorithm for estimating parameters of a spherical Gaussian mixture model by the method of moments (AGD-MoM). We formulate the problem as a constrained optimisation problem which simultaneously matches the third order moments from the data, represented as a tensor, and the second order moment, which is the empirical covariance matrix. We derive the necessary gradients (and second derivatives), and use them to implement alternating gradient search to estimate the parameters of the model. We show that the proposed method is applicable in both a batch as well as in a streaming (online) setting. Using synthetic and benchmark datasets, we demonstrate empirically that the proposed algorithm outperforms the more classical algorithms like Expectation Maximisation and variational Bayes.
Effective Technique to Reduce the Dimension of Text Data

In this article, features are selected using feature clustering and ranking of features for imbalanced text data. Initially the text documents are represented in lower dimension using the term class relevance (TCR) method. The class wise clustering is recommended to balance the documents in each class. Subsequently, the clusters are treated as classes and the documents of each cluster are represented in the lower dimensional form using the TCR again. The features are clustered and for each feature cluster the cluster representative is selected and these representatives are used as selected features of the documents. Hence, this proposed model reduces the dimension to a smaller number of features. For selecting the cluster representative, four feature evaluation methods are used and classification is done by using SVM classifier. The performance of the method is compared with the global feature ranking method. The experiment is conducted on two benchmark datasets the Reuters-21578 and the TDT2 dataset. The experimental results show that this method performs well when compared to the other existing works.