Reviewerly/exHarmony huggingface dataset:
A Multinomial Naïve Bayesian (MNB) Network to Automatically Recommend Topics for GitHub Repositories

GitHub has become a precious service for storing and managing software source code. Over the last year, 10M new developers have joined the GitHub community, contributing to more than 44M repositories. In order to help developers increase the reachability of their repositories, in 2017 GitHub introduced the possibility to classify them by means of topics. However, assigning wrong topics to a given repository can compromise the possibility of helping other developers approach it, and thus preventing them from contributing to its development.
Enhanced automatic twin support vector machine for imbalanced data classification

Most of the classification approaches assume that the sample distribution among classes is balanced. Still, such an assumption leads to biased performance over the majority class. This paper proposes an enhanced automatic twin support vector machine – (EATWSVM) to deal with imbalanced data, which incorporates a kernel representation within a TWSVM-based optimization. To learn the kernel function, we impose a Gaussian similarity, ruled by a Mahalanobis distance, and couple a centered kernel alignment-based approach to improving the data separability. Besides, we suggest a suitable range to fix the regularization parameters concerning both the dataset’ imbalance ratio and overlap. Lastly, we adopt One-vs-One and One-vs-Rest frameworks to extend our EATWSVM formulation for multi-class tasks. Obtained results on synthetic and real-world datasets show that our approach outperforms state-of-the-art methods concerning classification performance and training time.
Diagnosing the Environment Bias in Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models.
Improving Mispronunciation Detection of Arabic Words for Non-Native Learners Using Deep Convolutional Neural Network Features

Computer-Aided Language Learning (CALL) is growing nowadays because learning new languages is essential for communication with people of different linguistic backgrounds. Mispronunciation detection is an integral part of CALL, which is used for automatic pointing of errors for the non-native speaker. In this paper, we investigated the mispronunciation detection of Arabic words using deep Convolution Neural Network (CNN). For automated pronunciation error detection, we proposed CNN features-based model and extracted features from different layers of Alex Net (layers 6, 7, and 8) to train three machine learning classifiers; K-nearest neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). We also used a transfer learning-based model in which feature extraction and classification are performed automatically. To evaluate the performance of the proposed method, a comprehensive evaluation is provided on these methods with a traditional machine learning-based method using Mel Frequency Cepstral Coefficients (MFCC) features. We used the same three classifiers KNN, SVM, and RF in the baseline method for mispronunciation detection. Experimental results show that with handcrafted features, transfer learning-based method and classification based on deep features extracted from Alex Net achieved an average accuracy of 73.67, 85 and 93.20 on Arabic words, respectively. Moreover, these results reveal that the proposed method with feature selection achieved the best average accuracy of 93.20% than all other methods.
An Efficient Human Computer Interaction through Hand Gesture Using Deep Convolutional Neural Network

This paper focuses on the achievement of effective human–computer interaction using only webcam by continuous locating or tracking and recognizing the hand region. We detected the region of interest (ROI) in the captured image range and classify hand gestures for specific tasks. Firstly, background subtraction is used based on the main frame captured by webcam, and some preprocessing are done, and then YCrCb skin segmentation is used on RGB subtracted image. The ROI is detected using Haar cascade classifier for hand palm detection. Next, kernelized correlation filters tracking algorithm is used to avoid noise or background influences for tracking the ROI, and the median-flow tracking algorithm is used for depth tracking. The ROI is converted to a binary channel (black and white), resized to 54 × 54. Then gesture recognition is done using a 2D convolutional neural network (CNN) by entering the preprocessed ROI on the architecture. Two predictions are made based on skin segmented frame and image dilated frame, and gesture is recognized from the maximum value of those two predictions. The tracking and recognition process is continued until the ROI is presented on the frames. Finally, after validation, the proposed system has successfully obtained a recognition rate of 98.44%, which is usable for the practical and real-time application.
Stimulus Sampling With 360-Videos: Examining Head Movements, Arousal, Presence, Simulator Sickness, and Preference on a Large Sample of Participants and Videos

As the public use of virtual reality (VR) scales, understanding how users engage across various sources of VR content is critical. 360-video is popular due to its ease of both creation and access. There are, however, few studies of 360-videos, and they suffer from three limitations. First, most studies rely on small and homogeneous samples of participants. Second, they tend to examine only a single 360-video, or a handful of them in a few exceptional cases. Third, very few studies trace participants’ VR use over multiple experiences. The current study examined a large sample of participants (511) and a large set of 360-videos (80). Each participant experienced 5 of the videos, and we tracked head movement in addition to self-report data on presence, arousal, simulator sickness, and future use intention for each video. This design allowed us to answer novel questions relating to individual differences of participants and changes in experience over time, and in general to present results of VR use at a scale not seen before in the literature. Moreover, the results suggest that looking at patterns across stimuli provide unique insights which are missed when looking only within a single piece of content.
A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring

Image Deblurring is a very popular area of research in all over the world.It is an illposed problem which still does not have an ideal solution.Therefore, in order to analyse the research problems and to understand the statement of image deblurring we look in to the state-of-the-art methods proposed in various recent publications.Hence, the present study is focused on the overall review of the techniques used in image deblurring and their solutions to tackle the illposed problem using mathematical model.Based on the overall technique simple MAP model falls short in either deriving accurate convergence to the global optimum or computational implementation.Convergence of the algorithm is the most important factor for the effective performance of Lo and L1 norms for regularizers for theoretical applications.The importance of the choice of the correct estimator and the role of various priors in solving the imaging inverse problem using the MAP estimation method is discussed.The consequence of different priors to the rate of convergence of the MAP algorithm and its computational complexity are studied and tabulated.
A fuzzy inference system (FIS) to evaluate the security readiness of cloud service providers

Abstract Cloud computing is a model for on-demand delivery of IT resources (e.g., servers, storage, databases, etc.) over the Internet with pay-as-you-go pricing. Although it provides numerous benefits to cloud service users (CSUs) such as flexibility, elasticity, scalability, and economies of scale, there is a large trust deficit between CSUs and cloud service providers (CSPs) that prevents the widespread adoption of this computing paradigm. While some businesses have slowly started adopting cloud computing with careful considerations, others are still reluctant to migrate toward it due to several data security and privacy issues. Therefore, the creation of a trust model that can evolve to reflect the true assessment of CSPs in terms of either a positive or a negative reputation as well as quantify trust level is of utmost importance to establish trust between CSUs and CSPs. In this paper, we propose a fuzzy-logic based approach that allows the CSUs to determine the most trustworthy CSPs. Specifically, we develop inference rules that will be applied in the fuzzy inference system (FIS) to provide a quantitative security index to the CSUs. One of the main advantages of the FIS is that it considers the uncertainties and ambiguities associated with measuring trust. Moreover, our proposed fuzzy-logic based trust model is not limited to the CSUs as it can be used by the CSPs to promote their services through self-evaluation. To demonstrate the effectiveness of our proposed fuzzy-based trust model, we present case studies where several CSPs are evaluated and ranked based on the security index.
Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images

Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.
VIVO: Surpassing Human Performance in Novel Object Captioning with Visual Vocabulary Pre-Training.

It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no additional image-caption training data, other than COCO Captions, is allowed for model training. Thus, conventional Vision-Language Pre-training (VLP) methods cannot be applied. This paper presents VIsual VOcabulary pretraining (VIVO) that performs pre-training in the absence of caption annotations. By breaking the dependency of paired image-caption training data in VLP, VIVO can leverage large amounts of paired image-tag data to learn a visual vocabulary. This is done by pre-training a multi-layer Transformer model that learns to align image-level tags with their corresponding image region features. To address the unordered nature of image tags, VIVO uses a Hungarian matching loss with masked tag prediction to conduct pre-training. We validate the effectiveness of VIVO by fine-tuning the pre-trained model for image captioning. In addition, we perform an analysis of the visual-text alignment inferred by our model. The results show that our model can not only generate fluent image captions that describe novel objects, but also identify the locations of these objects. Our single model has achieved new state-of-the-art results on nocaps and surpassed the human CIDEr score.
A conceptual model for unifying variability in space and time

Software engineering faces the challenge of developing and maintaining systems that are highly variable in space (concurrent variations of the system at a single point in time) and time (sequential variations of the system due to its evolution). Recent research aims to address this need by managing variability in space and time simultaneously. However, such research often relies on nonuniform terminologies and a varying understanding of concepts, as it originates from different communities: software product-line engineering and software configuration management. These issues complicate the communication and comprehension of the concepts involved, impeding the development of techniques to unify variability in space and time. To tackle this problem, we performed an iterative, expert-driven analysis of existing tools to derive the first conceptual model that integrates and unifies terminologies and concepts of both dimensions of variability. In this paper, we present the unification process of concepts for variability in space and time, and the resulting conceptual model itself. We show that the conceptual model achieves high coverage and that its concepts are of appropriate granularity with respect to the tools for managing variability in space, time, or both that we considered. The conceptual model provides a well-defined, uniform terminology that empowers researchers and developers to compare their work, clarifies communication, and prevents redundant developments.
Train No Evil: Selective Masking for Task-Guided Pre-Training

Recently, pre-trained language models mostly follow the pre-train-then-fine-tuning paradigm and have achieved great performance on various downstream tasks.However, since the pretraining stage is typically task-agnostic and the fine-tuning stage usually suffers from insufficient supervised data, the models cannot always well capture the domain-specific and task-specific patterns.In this paper, we propose a three-stage framework by adding a task-guided pre-training stage with selective masking between general pre-training and finetuning.In this stage, the model is trained by masked language modeling on in-domain unsupervised data to learn domain-specific patterns and we propose a novel selective masking strategy to learn task-specific patterns.Specifically, we design a method to measure the importance of each token in sequences and selectively mask the important tokens.Experimental results on two sentiment analysis tasks show that our method can achieve comparable or even better performance with less than 50% of computation cost, which indicates our method is both effective and efficient.The source code of this paper can be obtained from https://github.com/thunlp/SelectiveMasking.
Breast Cancer Prediction Using Deep Learning and Machine Learning Techniques

Breast Cancer is mostly identified among women and is a major reason for increasing the rate of mortality among women. Diagnosis of breast cancer is time consuming and due to the lesser availability of systems it is necessary to develop a system that can automatically diagnose breast cancer in its early stages. Various Machine Learning and Deep Learning Algorithms have been used for the classification of benign and malignant tumours. The Wisconsin Breast Cancer Dataset has been used which contains 569 samples and 30 features. The paper emphasises on various models that is implemented such as Logistic Regression, Support Vector Machine (SVM) and K Nearest Neighbour (KNN), Multi-Layer perceptron classifier, Artificial Neural Network(ANN)) etc. on the dataset taken from the repository of Kaggle. Each of these algorithms has been measured and compared with respect to accuracy and precision obtained. All the techniques are coded in python and executed in Google Colab, which is a Scientific Python Development Environment. The experiments have shown that SVM and Random Forest Classifier are the best for predictive analysis with an accuracy of 96.5%. To increase the accuracy of prediction, deep learning algorithms such as CNN and ANN have been implemented. The maximum accuracy obtained in the case of ANN and CNN are 99.3% and 97.3% respectively. Activation functions such as Relu and sigmoid have been used to predict the outcomes in terms of probabilities.
A pragmatic and intelligent model for sarcasm detection in social media text

The world has now become an ecumenical village because of the Internet. Online platforms like e-commerce sites, search engines, social media have convoluted with the general routine of daily life. Social sites such as Twitter and Facebook have a user population larger than most of the countries, due to which communication is now largely shifted to text-based communication from verbal communication. This research investigates a common yet crucial problem of sarcasm detection in text-based communication. To prevent this problem a novel model has been proposed based on Google BERT (Bidirectional Encoder Representations from Transformers) that can handle volume, velocity and veracity of data. The performance of the model is compared with other classical and contemporary approaches such as Support Vector Machine, Logistic Regression, Long Short Term Memory and Convolutional Neural Network, BiLSTM and attention-based models which have been reported to be used for such tasks. The proposed model establishes its competence by evaluation on different parameters such as precision, recall, F1 score and accuracy. The model is built with the hope that it may help not only the government but also the general public to build a safer and technologically advanced society.
A survey on monocular 3D human pose estimation

Recovering human pose from RGB images and videos has drawn increasing attention in recent years owing to minimum sensor requirements and applicability in diverse fields such as human-computer interaction, robotics, video analytics, and augmented reality. Although a large amount of work has been devoted to this field, 3D human pose estimation based on monocular images or videos remains a very challenging task due to a variety of difficulties such as depth ambiguities, occlusion, background clutters, and lack of training data. In this survey, we summarize recent advances in monocular 3D human pose estimation. We provide a general taxonomy to cover existing approaches and analyze their capabilities and limitations. We also present a summary of extensively used datasets and metrics, and provide a quantitative comparison of some representative methods. Finally, we conclude with a discussion on realistic challenges and open problems for future research directions.
Improved Black Hole optimization algorithm for data clustering

Algorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization algorithm is a nature-inspired algorithm that belongs to the meta-heuristic category. The Black-Hole algorithm (BH) simulates the black hole phenomenon which is formed from a star with massive size and very high gravitational power. The algorithm starts with a population of a specific size of possible solutions and then gets evaluated by selecting the best one as a black hole. In the suggested modifications, we have introduced a new idea for generating the stars absorbed by the black hole. The star movement towards the black hole has also been modified to increase exploration capabilities. The modified algorithm was used to prove its effectiveness in data clustering without any prior knowledge about the nature of the provided data. Several benchmark datasets and statistical techniques have been used to evaluate the performance of suggested modification. The experiment results promised that the improved algorithm can overcome popular optimization algorithms.
Investor attention and cryptocurrency: Evidence from the Bitcoin market

This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.
A Survey on Concept Factorization: From Shallow to Deep Representation Learning

The quality of obtained features by representation learning determines the performance of a learning algorithm and subsequent application tasks (e.g., high-dimensional data clustering). As an effective paradigm for learning representations, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Moreover, lots of effective CF-based methods have been proposed based on different perspectives and properties, but it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from current studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing current methods. Specifically, we first review the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.