Reviewerly/exHarmony huggingface dataset:
Classification of Hadith According to Its Content Based on Supervised Learning Algorithms

Given the importance of the Prophet's Hadith for Muslims all over the world, where it is the second source of Islam after the Qur'an and the fundamental resource of legislation in the Islam community.This study is focused on the Classification of hadith automatically into different categories according to its content, based on Hadith text.The objective of this study is to build a classifier model can classify and differentiate hadith categories, to predict its topic like prayer, fasting, and zakat; using data mining and machine learning techniques.In this study, many supervised learning algorithms plus combination methods such as the stacking algorithm was used to improve classification accuracy.The best three classifiers were evaluated mainly: the Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB), which achieved higher accuracy reached up to 0.965%, 0.956, and 0.951% respectively.Also, Binary (Boolean algebra) and TF-IDF methods as term weighting was applied to determine the frequency of each word in the hadith text, and identify the most significant features in training dataset using Information Gain (IG), and Chi-square (CHI).The experimental results showed that re-train these classifiers after applying IG and CHI as features selection; gave better accuracy compared to the previous results.Additional to, the best classifier gave high accuracy was DT, it has achieved higher accuracy in most test cases whether in the Boolean algebra or TF-IDF because it can deal with missing values and identifying the most essential features from the training dataset, known as features engineering.
Cell Counting Algorithm Based on YOLOv3 and Image Density Estimation

Cell counting is an important branch of medical image processing research. One of the important tasks is to count red cells and white cells in microscopic images. In an image, the distribution of white blood cells is relatively sparse and easy to count, while the distribution of red blood cells is relatively dense, which is prone to overlap adhesion. In this paper, we proposed a new algorithm for counting red blood cells and white blood cells in microscopic images. Firstly, we use YOLOv3 network to detect discrete red cells, white blood cells and aggregated red blood cells in microscopic image. Then we count aggregated red blood cells by image density estimation method. In this way, we can provide more accurate counting results than YOLOv3 and image density estimation method.
View-Dependent Video Textures for 360° Video

A major concern for filmmakers creating 360° video is ensuring that the viewer does not miss important narrative elements because they are looking in the wrong direction. This paper introduces gated clips which do not play the video past a gate time until a filmmaker-defined viewer gaze condition is met, such as looking at a specific region of interest (ROI). Until the condition is met, we seamlessly loop video playback using view-dependent video textures, a new variant of standard video textures that adapt the looping behavior to the portion of the scene that is within the viewer's field of view. We use our desktop GUI to edit live action and computer animated 360° videos. In a user study with casual viewers, participants prefer our looping videos over the standard versions and are able to successfully see all of the looping videos' ROIs without fear of missing important narrative content.
Qsearch: Answering Quantity Queries from Text

Quantities appear in search queries in numerous forms: companies with annual revenue of at least 50 Mio USD, athletes who ran 200 m faster than 19.5 s, electric cars with range above 400 miles, and so on. Processing such queries requires the understanding of numbers present in the query to capture the contextual information about the queried entities. Modern search engines and QA systems can handle queries that involve entities and types, but they often fail on properly interpreting quantities in queries and candidate answers when the specifics of the search condition (less than, above, etc.), the units of interest (seconds, miles, meters, etc.) and the context of the quantity matter (annual or quarterly revenue, etc.). In this paper, we present a search and QA system, called Qsearch, that can effectively answer advanced queries with quantity conditions. Our solution is based on a deep neural network for extracting quantity-centric tuples from text sources, and a novel matching model to retrieve and rank answers from news articles and other web pages. Experiments demonstrate the effectiveness of Qsearch on benchmark queries collected by crowdsourcing.
Policy-Driven Blockchain and Its Applications for Transport Systems

Blockchains offer opportunities for developing advanced digital services. While current research on this topic is still growing, various security concerns have been raised and the security of blockchains has been becoming the most important issue which must be well addressed. Blockchain transactions are based on digital signatures, where the public key is associated with the ownership of the digital coin. User management of public or permissionless blockchain is ad hoc, i.e., any user can join and leave the blockchain network and participate in the Proof of Work (PoW). However, in a private blockchain and a permissioned blockchain, there are usually some constraints for users. In this paper, we investigate a scenario which provides a blockchain network with a set of policies where every user's signing key is associated with a policy set. It is particularly interesting while users are working in different sectors. We call our scheme as “policy-driven”, since policies in our scheme restrict users' rights. Our system is featured with a novel and lightweight policy-driven signature (PDS) scheme whose security has been proven formally. To justify our scenario, we provide experimental results and an example for the railway management services.
Eye localization based on weight binarization cascade convolution neural network

Eye localization is a key step in the field of face recognition and analysis, which is the premise and breakthrough of drowsiness estimation and auxiliary driving. An eye localization method based on Weight Binarization Cascade Convolution Neural Network (WBCCNN) is proposed, in which the WBCCNN is composed of four levels and the weight is constrained by binarization. It predicts eye positions from coarse-to-fine to improve the performance of eye localization, and binary network not only helps to reduce the storage size of the model, but also speeds up the operation. Experiments on eye localization are performed using Labeled Faces in the Wild (LFW), BioID, and Labeled Face Parts in the Wild (LFPW) Databases, from which the results show that the average detection errors of left eye and right eye by our method are 0.66% and 0.71% on LFW, respectively. The operation speed of binary network is approximately as twice as that of non-binary. In addition, our method requires less storage capacity, which maintains higher performance on BioID and LFPW, compared to some state-of-the-art works.
Detecting Financial Statement Fraud Using Random Forest with SMOTE

Abstract The study explores the comparison of various classification models in detecting fraudulent financial statements (FFS). Due to the high-class imbalance in this unique domain, the samples chosen in existing researches tend to be processed not so realistically. Therefore Random Forest is adopted to learn imbalanced data, in addition, sampling with SMOTE. Some more effective measure metrics of performance are also added. The experimental dataset includes 11726 publicly available Chinese financial disclosures from 2007 to 2017, of which 1314 financial statements were accused of fraud by CSRC. The result shows that the Random Forest outperforms other algorithms: Artificial Neural Network (ANN), Logistics Regression (LR), Support Vector Machines (SVM), CART, Decision Trees, Bayesian Networks, Bagging, Stacking and Adaboost.
Enhancing Python Compiler Error Messages via Stack

Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the Python programming language. Pycee automatically queries Stack Overflow to provide customised and summarised information within the IDE. We evaluated two Pycee variants through a think-aloud user study during which 16 programmers completed Python programming tasks while using Pycee.Results: The majority of participants agreed that Pycee was helpful while completing the study tasks. When compared to a baseline relying on the official Python documentation to enhance compiler error messages, participants generally preferred Pycee in terms of helpfulness, citing concrete suggestions for fixes and example code as major benefits.Conclusions: Our results confirm that data from online sources such as Stack Overflow can be successfully used to automatically enhance compiler error messages. Our work opens up venues for future work to further enhance compiler error messages as well as to automatically reuse content from Stack Overflow for other aspects of programming.
Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions

Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, leading to a limited understanding of the internal decision process, impossibility to gain insights, and ultimately to skepticism from clinicians. We present a visual analytics approach to uncover the decision-making process of a CAD system for classifying pancreatic cystic lesions. This CAD algorithm consists of two distinct components: random forest (RF), which classifies a set of predefined features, including demographic features, and a convolutional neural network (CNN), which analyzes radiological (imaging) features of the lesions. We study the class probabilities generated by the RF and the semantical meaning of the features learned by the CNN. We also use an eye tracker to better understand which radiological features are particularly useful for a radiologist to make a diagnosis and to quantitatively compare with the features that lead the CNN to its final classification decision. Additionally, we evaluate the effects and benefits of supplying the CAD system with a case-based visual aid in a second-reader setting.
Camera calibration using a planar target with pure translation

This paper presents a novel camera calibration method using a planar target with pure translation and a known translation distance. It only requires the straightness and position accuracy of the one-axis translational platform. There are theoretically no constraints among the normal direction of the planar target, the optical axis direction of the camera, and the moving direction of the translational platform. The paper analyzes the closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data are implemented to verify the effectiveness of the proposed method. Compared with Zhang’s method, the proposed method realizes the camera calibration process automatically and evaluates the calibration process via the measurement accuracy of the calibrated camera, so it is a key factor to advance 3D computer vision one more step from expert to novice use.
Enriching Variety of Layer-Wise Learning Information by Gradient Combination

This study proposes to use the combination of gradient concept to enhance the learning capability of Deep Convolutional Networks (DCN), and four Partial Residual Networks-based (PRN-based) architectures are developed to verify above concept. The purpose of designing PRN is to provide as rich information as possible for each single layer. During the training phase, we propose to propagate gradient combinations rather than feature combinations. PRN can be easily applied in many existing network architectures, such as ResNet, feature pyramid network, etc., and can effectively improve their performance. Nowadays, more advanced DCNs are designed with the hierarchical semantic information of multiple layers, so the model will continue to deepen and expand. Due to the neat design of PRN, it can benefit all models, especially for lightweight models. In the MSCOCO object detection experiments, YOLO-v3-PRN maintains the same accuracy as YOLO-v3 with a 55% reduction of parameters and 35% reduction of computation, while increasing the speed of execution by twice. For lightweight models, YOLO-v3-tiny-PRN maintains the same accuracy under the condition of 37% less parameters and 38% less computation than YOLO-v3-tiny and increases the frame rate by up to 12 fps on the NVIDIA Jetson TX2 platform. The Pelee-PRN is 6.7% mAP@0.5 higher than Pelee, which achieves the state-of-the-art lightweight object detection. The proposed lightweight object detection model has been integrated with technologies such as multi-object tracking and license plate recognition, and it used in a commercial intelligent traffic flow analysis system as its edge computing equipment. There are already three countries and more than ten cities have deployed this technique into their traffic flow analysis systems.
The "Least-Adult" Role in Participatory Design with Children

Child participation in design is a central focus of Child Computer Interaction (CCI) research, however, examples of participatory research with children are primarily situated in adult-led contexts (e.g. design lab, classroom, museum) where design objectives, activities and tools are devised and facilitated by adults. In this paper, we contribute to current discussions by describing a participatory study situated within the "child-led nature-play contexts" of nine children (7-11 years). By adapting the role of "least-adult" originally described in the childhood studies literature, we describe how this role can be established to access these exclusive play places and maintained through co-inquiry into each child's unique play practice. This research contributes to current discussions of child participation in CCI by (i) introducing the role of least-adult as an approach to engaging with children through participatory research, (ii) recognising the influence of place in shaping child participation, and (iii) pointing to spatial-temporal contextual factors as an important factor for enabling and shaping participatory research.
A New PHP Web Application Development Framework Based on MVC Architectural Pattern and Ajax Technology

Hypertext Preprocessor (PHP) web application frameworks have become significantly popular in web development, by providing built-in libraries which could help developers to write codes without writing them from scratch. Because of their popularities, most developers are required to have solid knowledge on using the available frameworks. To create a dynamic and real-time web application, most of the developers use Ajax technology due to what it is capable of. However, most of the existing PHP frameworks do not have built-in Ajax libraries where users need to implement their own Ajax request. Therefore, in this paper, we develop a new PHP web application development framework based on the Model View Controller (MVC) architectural pattern and Ajax technology. The framework itself implements Ajax technology with its built-in library. After conducting this research, we concluded that the new PHP web application framework could be used as a web application development tool to help users to create a dynamic and real-time web application.
Predicting and Understanding News Social Popularity with Emotional Salience Features

This paper studies the properties of socially popular news with a focused interest on the emotions conveyed through their headlines. We delve deeply into the notion of emotional salience in news values and extract the emotion intensities features across the valence, joy, anger, fear and sadness dimensions. A novel dataset consisting of 47,611 English news headlines from six publishers that received more than 17 million shares and likes were retrieved using Facebook APIs over ten consecutive months in 2018. In contrast with the conventional knowledge that only high-arousal, negative emotions are associated with viral news, the data revealed that headlines with higher intensities across all five emotion dimensions (including positive, joyful news) are significantly associated with social popularity, though the emotion-popularity correlation patterns differ for different publishers (e.g., daily broadcast vs. politics-slanted publishers). From the predictive experiments, we found that the emotion features had complimentary benefits to existing features, which included strong baselines features and word embedding. The final hybrid model achieved the highest predictive performance (R^2 = .54, tau = .53; F1 = .44, AUC = .85). Using two additional publishers' data, robustness tests further showed the advantage of the proposed model against a state-of-the-art method: The Guardian (tau = .45 vs. .37) and The New York Times (tau = .46 vs. .32).
Deep Spatial Pyramid Features Collaborative Reconstruction for Partial Person ReID

Partial person re-identification (ReID) is a hot research problem in computer vision. Accurate partial ReID is very challenging due to the common occlusion problem. To address this problem, in this paper, we propose a novel D eep spatial pyramid feature C ollaborative R econstruction approach (DCR ) for partial person ReID, which can effectively and efficiently tackle the occlusion in arbitrary sizes. Specifically, a fully convolutional network (FCN) is first leveraged to extract feature maps of an arbitrary-size image, and then the spatial pyramid pooling (SPP) is adopted to obtain spatial pyramid features. Thereafter, our DCR method is designed to efficiently solve the matching problem between the partial person and the holistic person in the partial person ReID task where the occlusion problem often occurs. Experiments on two partial person ReID datasets demonstrate the efficiency and efficacy of the proposed method by comparing to several state-of-the-art partial person ReID approaches. Our method outperforms all the competitors with a large margin and can achieve an improvement of 9.07% and 5.95% over the DSR method on the Partial REID and Partial-iLIDS Person ReID datasets in terms of the Rank-1 accuracy, respectively.
High-dimensional causal discovery under non-Gaussianity

Summary We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, $\sigma$, of the variables such that each observed variable $Y_v$ is a linear function of a variable-specific error term and the other observed variables $Y_u$ with $\sigma(u) &lt; \sigma (v)$. The causal relationships, i.e., which other variables the linear functions depend on, can be described using a directed graph. It has previously been shown that when the variable-specific error terms are non-Gaussian, the exact causal graph, as opposed to a Markov equivalence class, can be consistently estimated from observational data. We propose an algorithm that yields consistent estimates of the graph also in high-dimensional settings in which the number of variables may grow at a faster rate than the number of observations, but in which the underlying causal structure features suitable sparsity; specifically, the maximum in-degree of the graph is controlled. Our theoretical analysis is couched in the setting of log-concave error distributions.
PCRB and IMM for Target Tracking in the Presence of Specular Multipath

This article considers the general problem of tracking a noncooperative target in the presence of specular multipath. We revisit a novel posterior Cramér-Rao bound (PCRB) methodology for this problem [1] and develop a robust interacting multiple model (IMM) extended Kalman filter (EKF) implementation. Both the PCRB and the IMM-EKF augment the target state to include the multipath parameters. The approaches are demonstrated via a simulated scenario, in which an airborne radar tracks a low-altitude airborne target. It is shown that the PCRB increases rapidly subsequent to the multipath effect occurring, indicating that tracking performance is significantly degraded at such times. Furthermore, it is shown that the IMM-EKF offers near-optimal performance, with target state estimation errors consistently within 15% of the PCRB. The IMM-EKF also consistently provides credible estimates. It is concluded that the PCRB methodology and the IMM-EKF tracker provide powerful tools for performance assessment and target state estimation in challenging multipath scenarios.
Face Inpainting via Nested Generative Adversarial Networks

Face inpainting aims to repaired damaged images caused by occlusion or cover. In recent years, deep learning based approaches have shown promising results for the challenging task of image inpainting. However, there are still limitation in reconstructing reasonable structures because of over-smoothed and/or blurred results. The distorted structures or blurred textures are inconsistent with surrounding areas and require further post-processing to blend the results. In this paper, we present a novel generative model-based approach, which consisted by nested two Generative Adversarial Networks (GAN), the sub-confrontation GAN in generator and parent-confrontation GAN. The sub-confrontation GAN, which is in the image generator of parent-confrontation GAN, can find the location of missing area and reduce mode collapse as a prior constraint. To avoid generating vague details, a novel residual structure is designed in the sub-confrontation GAN to deliver richer original image information to the deeper layers. The parent-confrontation GAN includes an image generation part and a discrimination part. The discrimination part of parent-confrontation GAN includes global and local discriminator, which benefits the reconstruction of overall coherency of the repaired image while obtaining local details. The experiments are executed over the publicly available dataset CelebA, and the results show that our method outperforms current state-of-the-art techniques quantitatively and qualitatively.