Reviewerly/exHarmony huggingface dataset:
Same Same but Different: Finding Similar User Feedback Across Multiple Platforms and Languages

Users submit feedback about the software they use through application distributions platforms, i.e., app stores, and social media.Previous research has found that this type of feedback contains valuable information for software evolution, such as bug reports, or feature requests.However, popular applications receive thousands of feedback entities per day, making their manual analysis unrealistic.In this work, we present an approach to automatically identify similar user feedback across different languages and platforms.At the core of the approach is a word aligner that aligns words based on their semantic similarity and the similarity of their local semantic contexts.Additionally, we make use of machine translation, sentiment analysis, and text classification, to extract the sentiment polarity and content nature of user feedback written in different languages.We use the results of these components to compute a similarity score between user feedback pairs.We evaluated our approach on user feedback entities written in four different languages, and retrieved from five different mobile applications obtained from four different app stores and social networking sites.The obtained results are encouraging.Compared to human assessment, the overall performance for monolingual user feedback pairs yielded a strong correlation of 0.79.For the crosslingual feedback pairs the correlation was also strong, with a value of 0.78.
Artificial intelligence speech recognition model for correcting spoken English teaching

Artificial intelligence speech recognition technology is an important direction in the field of human-computer interaction. The use of speech recognition technology to assist teachers in the correction of spoken English pronunciation in teaching has certain effects and can help students without being constrained by places, time and teachers. Based on artificial intelligence speech recognition technology, this paper improves and analyzes speech recognition algorithms, and uses effective algorithms as the system algorithms of artificial intelligence models. Meanwhile, based on phoneme-level speech error correction, after introducing the basic knowledge, construction and training of acoustic models, the basic process of speech cutting, including the front-end processing of speech and the extraction of feature parameters, is elaborated. In addition, this study designed a control experiment to verify and analyze the artificial intelligence speech recognition correction model. The research results show that the method proposed in this paper has a certain effect.
First- And Third-Person Video Co-Analysis By Learning Spatial-Temporal Joint Attention

Recent years have witnessed a tremendous increase of first-person videos captured by wearable devices. Such videos record information from different perspectives than the traditional third-person view, and thus show a wide range of potential usages. However, techniques for analyzing videos from different views can be fundamentally different, not to mention co-analyzing on both views to explore the shared information. In this paper, we take the challenge of cross-view video co-analysis and deliver a novel learning-based method. At the core of our method is the notion of "joint attention", indicating the shared attention regions that link the corresponding views, and eventually guide the shared representation learning across views. To this end, we propose a multi-branch deep network, which extracts cross-view joint attention and shared representation from static frames with spatial constraints, in a self-supervised and simultaneous manner. In addition, by incorporating the temporal transition model of the joint attention, we obtain spatial-temporal joint attention that can robustly capture the essential information extending through time. Our method outperforms the state-of-the-art on the standard cross-view video matching tasks on public datasets. Furthermore, we demonstrate how the learnt joint information can benefit various applications through a set of qualitative and quantitative experiments.
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such judgements. The present work focuses on the impact of uncertainty communication when judging bivariate visualizations. Specifically, we model how users update their beliefs about variable relationships after seeing a scatterplot with and without uncertainty representation. To model and evaluate the belief updating, we present three studies. Study 1 focuses on a proposed “Line + Cone” visual elicitation method for capturing users' beliefs in an accurate and intuitive fashion. The findings reveal that our proposed method of belief solicitation reduces complexity and accurately captures the users' uncertainty about a range of bivariate relationships. Study 2 leverages the “Line + Cone” elicitation method to measure belief updating on the relationship between different sets of variables when seeing correlation visualization with and without uncertainty representation. We compare changes in users beliefs to the predictions of Bayesian cognitive models which provide normative benchmarks for how users should update their prior beliefs about a relationship in light of observed data. The findings from Study 2 revealed that one of the visualization conditions with uncertainty communication led to users being slightly more confident about their judgement compared to visualization without uncertainty information. Study 3 builds on findings from Study 2 and explores differences in belief update when the bivariate visualization is congruent or incongruent with users' prior belief. Our results highlight the effects of incorporating uncertainty representation, and the potential of measuring belief updating on correlation judgement with Bayesian cognitive models.
Convolutional Neural Network based Bidirectional Sign Language Translation System

A considerable amount of gap in communication exists amongst the speech and hearing-impaired individuals with the other people; which is of paramount importance to be bridged. The aim is to study various methods for effective intercommunication between Sign language and the English language. Initially, a Hardware glove is implemented which has of flex sensors whose accuracy is proved to be not very high. To further improve the accuracy, a model using a convolutional neural network was trained on an existing data-set. Since the data-sets were not versatile and the scope was narrow, a new diversified data-set was created and the model was further improved. The new model has very high accuracy and it can predict almost every alphabet. Various other gestures having facial features and gestures including both the hands were added to our data-set. This model has a huge potential as it can interpret any gesture of various sign languages if provided in the data-set. The user can also add extra gestures in the data-set, making it highly customized. Further, the data is sent to an application which will convert the received text to speech. To reduce the communication gap, the system is made wholly bidirectional i.e. speech can also be converted to the sign language. Initially, the speech is taken at the input and is converted to the text which acts as the input for the next step in which the converted text is directly taken as the input to be converted to the corresponding gesture according to the convenient sign language. Thus, the input speech is translated into a video consisting of a sequence of gestures of the American sign language which can be extended to other languages as well. Bidirectional Sign Language Translating system consists of a software system. It is named as a bidirectional system as it not only converts the sign language to speech via text conversion but also incorporates a system which translates the speech to the prescribed sign language with text conversion as the mediator. The methodology has been explained in the further sections.
Laprob: A Label propagation-Based software bug localization method

Bug localization, which locates suspicious snippets related to the bugs mentioned in the bug reports, is time-consuming and laborious. Many automatic bug localization methods have been proposed to speed up the process of bug fixing and reduce the burden on developers. However, these methods have not fully utilized the intra-relations and inter-relations among the bug reports and the source files (i.e., call relationships between the source files). In this paper, we propose a novel method LaProb (a label propagation-based software bug localization method) that makes full use of the intra-relations and inter-relations among the bug reports and the source files. LaProb transforms the problem of bug localization into a multi-label distribution learning problem. LaProb first constructs a BHG (Biparty Hybrid Graph) by analyzing the structures and contents of bug reports and source files, and calculates the intra-relations between pairs of bug reports and source files, as well as the inter-relations between bug reports and source files. Based on BHG, LaProb then predicts the label distribution on source files by using the label propagation algorithm for the target bug report. Finally, LaProb finishes the bug localization task by sorting the results of label propagation. The experimental results on nine open-source software projects (i.e., SWT, AspectJ, Eclipse, ZXing, SEC, HIVE, HBASE, WFLY and ROO) show that compared with several state-of-the-art methods (including BugLocator, BRTracer, BLUiR, AmaLgam, Locus and BLIZZARD), LaProb performs the best in terms of all five metrics on average. For MAP performance measure, LaProb achieves an improvement of 30.9%, 36.6%, 28.0%, 22.2%, 20.1% and 53.5%, respectively. LaProb is capable of making full use of the intra-relations and inter-relations among the bug reports and the source files and achieves better performance than seven state-of-the-art methods.
Clustering Analysis via Deep Generative Models With Mixture Models

Clustering is a fundamental problem that frequently arises in many fields, such as pattern recognition, data mining, and machine learning. Although various clustering algorithms have been developed in the past, traditional clustering algorithms with shallow structures cannot excavate the interdependence of complex data features in latent space. Recently, deep generative models, such as autoencoder (AE), variational AE (VAE), and generative adversarial network (GAN), have achieved remarkable success in many unsupervised applications thanks to their capabilities for learning promising latent representations from original data. In this work, first we propose a novel clustering approach based on both Wasserstein GAN with gradient penalty (WGAN-GP) and VAE with a Gaussian mixture prior. By combining the WGAN-GP with VAE, the generator of WGAN-GP is formulated by drawing samples from the probabilistic decoder of VAE. Moreover, to provide more robust clustering and generation performance when outliers are encountered in data, a variant of the proposed deep generative model is developed based on a Student's-t mixture prior. The effectiveness of our deep generative models is validated though experiments on both clustering analysis and samples generation. Through the comparison with other state-of-art clustering approaches based on deep generative models, the proposed approach can provide more stable training of the model, improve the accuracy of clustering, and generate realistic samples.
CMeIE: Construction and Evaluation of Chinese Medical Information Extraction Dataset

In this paper, we present the Chinese Medical Information Extraction (CMeIE) dataset, consisting of 28, 008 sentences, 85, 282 triplets, 11 entities, and 44 relations derived from medical textbooks and clinical practices, constructed by several rounds of manual annotation. Additionally, we evaluate performances of the most recent state-of-the-art frameworks and pre-trained language models for the joint extraction of entities and relations task on the CMeIE dataset. Experiment results show that even these most advanced models still have a large space to improve on our dataset; currently, the best F1 score on the dataset is 58.44%. Our analysis points out several challenges and multiple potential future research directions for the task specialized in the medical domain.
Non-Additive Cost Functions for JPEG Steganography Based on Block Boundary Maintenance

Recent advances show that a reasonable non-additive cost function can significantly improve the security level of additive cost based steganography. So far, there is only one principle, called block boundary continuity (BBC), that has been proposed to define the non-additive cost function for JPEG steganography, and it aims at synchronizing the modification direction of inter-block boundaries in the spatial domain. In this article, we found that JPEG steganography usually introduces more and larger modifications on the boundary than on the inside of each intra-block in the spatial domain, which is another important factor affecting security. Therefore, we present a new principle, called block boundary maintenance (BBM), to minimize the modifications on the spatial block boundaries. In theory, we deduce the BBM principle on how to modify a pair of DCT coefficients of the intra-block to reduce the modifications on the spatial block boundary. According to the BBM principle, we design a new strategy to define non-additive cost functions for JPEG steganography by exploiting the coefficient correlation of the intra-block in the DCT domain. The experimental results show that the BBM-based strategy can minimize modifications on the spatial block boundaries and thus achieve a high-security level when resisting modern JPEG steganalysis. Furthermore, the two principles of BBC and BBM can be fused to further improve the empirical security.
The influence of human factors on 360∘ mulsemedia QoE

Quality of Experience (QoE) is indelibly linked to the human side of the multimedia experience. Surprisingly, however, there is a paucity of research which explores the impact that human factors has in determining QoE. Whilst this is true of multimedia, it is even more starkly so as far as mulsemedia - applications that involve media engaging three or more of human senses - is concerned. Hence, in the study reported in this paper, we focus on an exciting subset of mulsemedia applications - 360∘ mulsemedia - particularly important given that the upcoming 5G technology is foreseen to be a key enabler for the proliferation of immersive Virtual Reality (VR) applications. Accordingly, we study the impact that human factors such as gender, age, prior computing experience, and smell sensitivity have on 360∘ mulsemedia QoE. Results showed insight into the potential of 360∘ mulsemedia to inspire and to enrich experiences for Generation Z - a generation empowered by rapidly advancing technology. Patterns of prior media usage and smell sensitivity play also an important role in influencing the QoE evaluation - users who have a preference for dynamic videos enjoy and find realistic the 360∘ mulsemedia experiences.
Spam Detection on Arabic Twitter

Twitter has become a popular social media platform in the Arab region. Some users exploit this popularity by posting unwanted advertisements for their own interest. In this paper, we present a large manually annotated dataset of advertisement (Spam) tweets in Arabic. We analyze the characteristics of these tweets that distinguish them from other tweets and identify their targets and topics. In addition, we analyze the characteristics of Spam accounts. We utilize Support Vector Machines (SVMs) and contextual embedding based models to identify these Spam tweets with macro averaged F1 score above 98%.
Region of Interest Based Graph Convolution

Machine vision of human facial expressions has been studied for decades, from prototypical expressions to Action Units (AUs), from hand-crafted to deep features, from multi-class to multi-label classifications. Since the widely adopted deep networks lack interpretation on learnt representations, human prior knowledge cannot be effectively imposed and examined. On the other hand, AU is a human defined concept. In order to align with this idea, a finer level of network design is desired. In this paper, we first extend the heatmaps to ROI maps, encoding the location of both positive and negative occurred AUs, then employ a well-designed backbone network to regress it. In this way, AU detection is performed in two stages, key regions localization and occurrence classification. To prompt the spatial dependency among ROIs, we utilize graph convolution for feature refinement. The decomposition of similarity matrix is supervised by AU labels. This novel framework is evaluated on two benchmark databases (BP4D and DISFA) for AU detection. The experimental results are superior to the state-of-the-art algorithms and baseline models, demonstrating the effectiveness of our proposed method.
Down to the Last Detail

Virtual try-on has attracted lots of research attention due to its potential applications in e-commerce, virtual reality and fashion design. However, existing methods can hardly preserve the fine-grained details (e.g., clothing texture, facial identity, hair style, skin tone) during generation, due to the non-rigid body deformation and multi-scale details. In this work, we propose a multi-stage framework to synthesize person images, where fine-grained details can be well preserved. To address the long-range translation and rich-details generation, we propose a Tree-Block (tree dilated fusion block) to replace standard ResNet-block where applicable. Notably, multi-scale feature maps can be smoothly fused for fine-grained detail generation, by incorporating larger spatial context at multiple scales. With a delicate end-to-end training scheme, our whole framework can be jointly optimized for results with significantly better visual fidelity and richer details. Moreover, we also explore the potential application in video-based virtual try-on. By harnessing the well-trained image generator and an extra video-level adaptor, a model photo can be well animated with a driving pose sequence. Extensive evaluations on standard datasets and user study demonstrate that our proposed framework achieves the state-of-the-art results, especially in preserving visual details in clothing texture and facial identity. Our implementation is publicly available via https://github.com/JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving.
Quaternion-Based Knowledge Graph Network for Recommendation

Recently, to alleviate the data sparsity and cold start problem, many research efforts have been devoted to the usage of knowledge graph (KG) in recommender systems. It is common for most existing KG based models to represent users and items using real-valued embeddings. However, compared with complex or hypercomplex numbers, these real-valued vectors are of less representation capacity and no intrinsic asymmetrical properties, thus may limit the modeling of interactions between entities and relations in KG. In this paper, we propose Quaternion-based Knowledge Graph Network (QKGN) for recommendation, which represents users and items with quaternion embeddings in hypercomplex space, so that the latent inter-dependencies between entities and relations could be captured effectively. In the core of our model, a semantic matching principle based on Hamilton product is applied to learn expressive quaternion representations from the unified user-item KG. On top of this, those embeddings are attentively updated by a customized preference propagation mechanism with structure information concerned. Finally, we apply the proposed QKGN to three real-world datasets of music, movie and book, and experimental results show the validity of our method.
The pricing of bad contagion in cryptocurrencies: A four-factor pricing model

We examine as if the incorporation of the contagion risk, which is found significant in cryptocurrencies, can make the resulting four-factor pricing model offers an improved explanatory power. We estimate contagion measure for the large left-tail events in the idiosyncratic disturbances of cryptocurrencies and then incorporate it into the three-factor pricing model. Using data of 1,967 cryptocurrencies from January 1, 2015 to September 26, 2019, we show that the four-factor pricing model outperforms both the cryptocurrency-CAPM and three-factor models. Our findings are useful to researchers of cryptocurrency anomalies and those applying quantitative strategies in the cryptocurrency market.
Research on Optimization of VR Welding Course Development with ANP and Satisfaction Evaluation

The purpose of this study is to explore the ability indices of VR (virtual reality) technology when it is applied to assist the teaching of a welding practice course, develop a VR welding course as the basis of course planning and teaching design, and implement experimental teaching to verify its effectiveness. On the basis of a literature review and focus group interviews, initial ability indices of a VR welding course were proposed. Then, 15 experts from VR- and welding-related specialties were invited to form a consulting team to determine “the ability indices of the VR welding course” according to the results of a Fuzzy Delphi expert questionnaire. Moreover, the results of an ANP (Analytic Network Process) expert questionnaire were used to understand the relative importance of the ability indices of the VR welding course, as well as the relative feasibility of VR-assisted welding teaching, in order to develop a “VR welding course”, in which 34 first-grade students of the welding practice course were taken as the research objects during the implementation of experimental teaching. The qualitative research and analysis results are as follows: (1) the VR welding course includes 8 ability indices and 30 evaluation indices; (2) the item with the highest feasibility in VR-assisted welding teaching is “welding construction”, followed by “map reading and drawing”; (3) best feasibility of VR technology “Interaction” in assisting welding teaching; (4) the relative importance of the ability indices of the VR welding course is the greatest for “welding construction”, followed by “welding inspection”; (5) the VR welding course students express significant positive responses to the learning of ability indices and ability demonstration; (6) the majority of students express significant positive learning satisfaction with VR-assisted welding course teaching. This study puts forward a set of rigorous models for the construction of ability indices for a VR course and course development. It can provide a reference for introducing VR-assisted teaching to related welding courses that are run by universities of science and technology in Taiwan. Furthermore, such VR courses can offer students a safe, diversified, and efficient learning environment.