Reviewerly/exHarmony huggingface dataset:
Revealing the Social Aspects of Design Decay

The pull-based development model is widely used in source-code environments like GitHub. In this model, developers actively communicate and share their knowledge or opinions through the exchange of comments. Their goal is to improve the change under development, including its positive impact on design structure. In this context, two central social aspects may contribute to combating or adversely amplifying design decay. First, design decay may be avoided, reduced or accelerated depending whether the communication dynamics among developers - who play specific roles - is fluent and consistent along a change. Second, the discussion content itself may be decisive to either improve or deteriorate the structural design of a system. Unfortunately, there is no study on the the role that key social aspects play on avoiding or amplifying design decay. Previous work either investigates technical aspects of design decay or confirms the high frequency of design discussions in pull-based software development. This paper reports a retrospective study aimed at understanding the role of communication dynamics and discussion content on design decay. We focused our analysis on 11 social metrics related to these two aspects as well as 4 control technical metrics typically used as indicators of design decay. We analyzed more than 11k pull request discussions mined from five large open-source software systems. Our findings reveal that many social metrics can be used to discriminate between design impactful and unimpactful pull requests. Second, various factors of communication dynamics are related to design decay. However, temporal factors of communication dynamics outperformed the participant roles' factors as indicators of design decay. Finally, we noticed certain social metrics tend to be indicators of design decay when analyzing both aspects together.
Quantum-inspired algorithms for solving low-rank linear equation systems with logarithmic dependence on the dimension

We present two efficient classical analogues of the quantum matrix inversion algorithm [Harrow et al., 2009] for low-rank matrices. Inspired by recent work of Tang [Tang, 2019], assuming length-square sampling access to input data, we implement the pseudoinverse of a low-rank matrix allowing us to sample from the solution to the problem Ax = b using fast sampling techniques. We construct implicit descriptions of the pseudo-inverse by finding approximate singular value decomposition of A via subsampling, then inverting the singular values. In principle, our approaches can also be used to apply any desired smooth function to the singular values. Since many quantum algorithms can be expressed as a singular value transformation problem [Andras Gilyen et al., 2019], our results indicate that more low-rank quantum algorithms can be effectively dequantised into classical length-square sampling algorithms.
UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of BERT-based Models

Offensive language detection is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media.This paper describes our Transformer-based solutions for identifying offensive language on Twitter in five languages (i.e., English, Arabic, Danish, Greek, and Turkish), which was employed in Subtask A of the Offenseval 2020 shared task.Several neural architectures (i.e., BERT, mBERT, Roberta, XLM-Roberta, and ALBERT), pre-trained using both single-language and multilingual corpora, were fine-tuned and compared using multiple combinations of datasets.Finally, the highest-scoring models were used for our submissions in the competition, which ranked our team 21st of 85, 28th of 53, 19th of 39, 16th of 37, and 10th of 46 for English, Arabic, Danish, Greek, and Turkish, respectively.
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning

Image paragraph captioning (IPC) aims to generate a fine-grained paragraph to describe the visual content of an image.Significant progress has been made by deep neural networks, in which the attention mechanism plays an essential role.However, conventional attention mechanisms tend to ignore the past alignment information, which often results in problems of repetitive captioning and incomplete captioning.In this paper, we propose an Interactive key-value Memoryaugmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions.In addition, we employ an adaptive attention mechanism to realize adaptive alignment from image regions to caption words, where an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions.Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate the effectiveness of our IMAP model.
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem.Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations.To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner.Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation; 2) a top-down classification strategy is introduced instead of training a set of local classifiers.The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem.
Attention Transfer Network for Aspect-level Sentiment Classification

Aspect-level sentiment classification (ASC) aims to detect the sentiment polarity of a given opinion target in a sentence.In neural network-based methods for ASC, most works employ the attention mechanism to capture the corresponding sentiment words of the opinion target, then aggregate them as evidence to infer the sentiment of the target.However, aspect-level datasets are all relatively small-scale due to the complexity of annotation.Data scarcity causes the attention mechanism sometimes to fail to focus on the corresponding sentiment words of the target, which finally weakens the performance of neural models.To address the issue, we propose a novel Attention Transfer Network (ATN) in this paper, which can successfully exploit attention knowledge from resource-rich document-level sentiment classification datasets to improve the attention capability of the aspect-level sentiment classification task.In the ATN model, we design two different methods to transfer attention knowledge and conduct experiments on two ASC benchmark datasets.Extensive experimental results show that our methods consistently outperform state-of-the-art works.Further analysis also validates the effectiveness of ATN.
A Theoretical Analysis of the Repetition Problem in Text Generation

Text generation tasks, including translation, summarization, language models, and etc. see rapid growth during recent years. Despite the remarkable achievements, the repetition problem has been observed in nearly all text generation models undermining the generation performance extensively. To solve the repetition problem, many methods have been proposed, but there is no existing theoretical analysis to show why this problem happens and how it is resolved. In this paper, we propose a new framework for theoretical analysis for the repetition problem. We first define the Average Repetition Probability (ARP) to characterize the repetition problem quantitatively. Then, we conduct an extensive analysis of the Markov generation model and derive several upper bounds of the average repetition probability with intuitive understanding. We show that most of the existing methods are essentially minimizing the upper bounds explicitly or implicitly. Grounded on our theory, we show that the repetition problem is, unfortunately, caused by the traits of our language itself. One major reason is attributed to the fact that there exist too many words predicting the same word as the subsequent word with high probability. Consequently, it is easy to go back to that word and form repetitions and we dub it as the high inflow problem. Furthermore, we extend our analysis to broader generation models by deriving a concentration bound of the average repetition probability for a general generation model. Finally, based on the theoretical upper bounds, we propose a novel rebalanced encoding approach to alleviate the high inflow problem and thus reducing the upper bound. The experimental results show that our theoretical framework is applicable in general generation models and our proposed rebalanced encoding approach alleviates the repetition problem significantly in both the translation task and the language modeling task. The source code of this paper can be obtained from https://github.com/fuzihaofzh/repetition-problem-nlg.
Learning with Contrastive Examples for Data-to-Text Generation

Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., "Nikkei gains" is generated when "Nikkei drops" is expected.We investigate models trained on contrastive examples, that is, incorrect sentences or terms, in addition to correct ones to reduce such errors.We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as "gain" or "drop".We then use learning methods with several losses that exploit contrastive examples.Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability to generate sentences with better lexical choices, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances of different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance.Human evaluation also supports the effectiveness of using contrastive examples.
Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations

Interlinear Glossed Text (IGT) is a widely used format for encoding linguistic information in language documentation projects and scholarly papers.Manual production of IGT takes time and requires linguistic expertise.We attempt to address this issue by creating automatic glossing models, using modern multi-source neural models that additionally leverage easy-to-collect translations.We further explore cross-lingual transfer and a simple output length control mechanism, further refining our models.Evaluated on three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on overall accuracy as well as lemma and tag recall.
A Conceptual Model for Data Collection and Analysis for AR-based Remote Collaboration Evaluation

A significant effort has been devoted to the creation of the enabling technology and in the proposal of novel methods to support remote collaboration using Augmented Reality (AR), given the novelty of the field. As the field progresses to focus on the nuances of supporting collaboration and with the growing number of prototypes mediated by AR, the characterization and evaluation of the collaborative process becomes an essential, but difficult endeavor. Evaluation is particularly challenging in this multifaceted context involving many aspects that may influence the way collaboration occurs. Therefore, it is essential the existence of holistic evaluation strategies that monitor the use and performance of the proposed solutions regarding the team, its members, and the technology, allowing adequate characterization and report of collaborative processes. As a contribute, we propose a conceptual model for multi-user data collection and analysis that monitors several collaboration aspects: individual and team performance, behaviour and level of collaboration, as well as contextual data in scenarios of remote collaboration using AR-based solutions.
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking

Generic visual object tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracker, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of trackers, reliable and quantifiable ways for studying the per factor tracking behavior remain barely available. Addressing this issue, in this paper we contribute to the community a tracking diagnosis toolkit, TracKlinic, for diagnosis of challenge factors of tracking algorithms.TracKlinic consists of two novel components focusing on the data and analysis aspects, respectively. For the data component, we carefully prepare a set of 2,390 annotated videos, each involving one and only one major challenge factor. When analyzing an algorithm for a specific challenge factor, such one-factor-per-sequence rule greatly inhibits the disturbance from other factors and consequently leads to more faithful analysis. For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically. With TracKlinic, a thorough study is conducted on ten state-of-the-art trackers on nine challenge factors (including two compound ones). The results suggest that, heavy shape variation and occlusion are the two most challenging factors faced by most trackers. Besides, out-of-view, though does not happen frequently, is often fatal. By sharing TracKlinic <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we expect to make it much easier for diagnosing tracking algorithms, and to thus facilitate developing better ones.
An effective assessment of cluster tendency through sampling based multi-viewpoints visual method

Social networks are the rich sources to people for sharing the knowledge on health-related issues. Nowadays, Twitter is one of the great significant social platforms to the people for a discussion on topics. Analyzing the clusters for the tweets concerning terms is a complex process due to the sparsity problem. Topic models are useful or avoiding this problem with derivations of topic clusters. Finding pre-cluster tendency is the major problem in many clustering methods. Existing methods, such as visual access tendency (VAT), cosine-based VAT (cVAT), multi viewpoints-based cosine similarity VAT (MVS-VAT) majorly used to access the prior information about clusters tendency problem. Solution of cluster tendency indicates the tractable number of clusters. The MVS-VAT enables the cluster tendency for the tweet documents effectively than other visual methods. However, it takes a higher number of viewpoints, thus requiring more computational time for the clustering of tweets data. Therefore, sampling-based visual methods are proposed to overcome the computational problem. Several standard health keywords are used for the extraction of health tweets to illustrate the effectiveness of proposed work in the experimental study.
Texture classification based on image (natural and horizontal) visibility graph constructing methods

Texture classification is widely used in image analysis and some other related fields. In this paper, we designed a texture classification algorithm, named by TCIVG (Texture Classification based on Image Visibility Graph), based on a newly proposed image visibility graph network constructing method by Lacasa et al. By using TCIVG on a Brodatz texture image database, the whole procedure is illustrated. First, each texture image in the image database was transformed to an associated image natural visibility graph network and an image horizontal visibility graph network. Then, the degree distribution measure [P(k)] was extracted as a key characteristic parameter to different classifiers. Numerical experiments show that for artificial texture images, a 100% classification accuracy can be obtained by means of a quadratic discriminant based on natural TCIVG. For natural texture images, 94.80% classification accuracy can be obtained by a linear SVM (Support Vector Machine) based on horizontal TCIVG. Our results are better than that reported in some existing literature studies based on the same image database.
Google Translate vs. DeepL

The present research analyses the performance of two free open-source neural machine translation (NMT) systems —Google Translate and DeepL— in the (ES&gt;EN) translation of somatisms such as tomar el pelo and meter la pata, their nominal variants (tomadura/tomada de pelo and metedura/metida de pata), and other lower-frequency variants such as meter la pata hasta el corvejón, meter la gamba and metedura/metida de gamba. The machine translation outcomes will be contrasted and classified depending on whether these idioms are presented in their continuous or discontinuous form (Anastasiou 2010), i.e., whether different n-grams split the idiomatic sequence (or not), which may pose some difficulties for their automatic detection and translation. Overall, the insights gained from this study will prove useful in determining for which of the different scenarios either Google Translate or DeepL delivers a better performance under the challenge of phraseological variation and discontinuity.
Implementation-Independent Representation for Deep Convolutional Neural Networks and Humans in Processing Faces

Deep convolutional neural networks (DCNN) nowadays can match human performance in challenging complex tasks, but it remains unknown whether DCNNs achieve human-like performance through human-like processes. Here we applied a reverse-correlation method to make explicit representations of DCNNs and humans when performing face gender classification. We found that humans and a typical DCNN, VGG-Face, used similar critical information for this task, which mainly resided at low spatial frequencies. Importantly, the prior task experience, which the VGG-Face was pre-trained to process faces at the subordinate level (i.e., identification) as humans do, seemed necessary for such representational similarity, because AlexNet, a DCNN pre-trained to process objects at the basic level (i.e., categorization), succeeded in gender classification but relied on a completely different representation. In sum, although DCNNs and humans rely on different sets of hardware to process faces, they can use a similar and implementation-independent representation to achieve the same computation goal.
A Self-Adaptive Differential Evolution Algorithm Using Oppositional Solutions and Elitist Sharing

Differential evolution (DE) has been applied to solve complex optimization problems. An effective DE algorithm should be convergent and able to jump out of the local optimal solution. Motivated by these considerations, an improved differential evolution is proposed, which is based on the oppositional solution, elite sharing schemes, the heuristic crossover operator and combined with a self-adaptive parameter setting strategy. The algorithm is denoted as SOSESDE. First, in the early stage of evolution, the disturbance oppositional strategy is applied to the worse individuals to increase the search space since the oppositional search strategy will generate redundant offspring by the same genetic operation, which can be avoided by random perturbation. Then, an elite sharing scheme is used for information exchange. In this scheme, the K-means is first used to divide the present population into several subpopulations, and then, the elitist in each subpopulation is taken for mutation operation. In addition, the related parameters F and CR are self-adaptively adjusted based on the results of the Wilcoxon signed-rank test and the probability that a parent is selected for the next generation. Besides, the heuristic crossover operator is constructed by using uniform design method. Finally, 53 benchmark functions are optimized using SOSESDE, and the results are compared with those of various state-of-the-art algorithms. The experiments show that compared to these algorithms, SOSESDE exhibits better performance.
Digital Campus – a future former investment in education for a sustainable society

Intelligent learning and research are happening everywhere, nevertheless, conventional teaching and learning in universities, based on face-to-face or in person approach is still the basic environment. Actual movements in socio-economic life around the world impose e-learning and e-presence. However, moving digital is more than online teaching, is changing the environment. The present research paper aims to demonstrate how a digital campus, with all aspects, can perform a crucial role in enriching the university campus’s structure and culture to ensure the quality assurance of teaching, research and administrative management using actual innovative technologies. Moreover, digital campus changes the perspective of social and physical environment, and the present paper found out that Digital campus provide anytime and anywhere access to university facilities (maintaining the environment clean in the same time), learning, research, and meeting the industry as well.
Quality assessment for color correction-based stitched images via bi-directional matching

With the deepening of social information, the panoramic image has drawn a significant interest of viewers and researchers as it can provide a very wide field of view (FoV). Since panoramic images are usually obtained by capturing images with the overlapping regions and then stitching them together, image stitching plays an important role in generating panoramic images. In order to effectively evaluate the quality of stitched images, a novel quality assessment method based on bi-directional matching is proposed for stitched images. Specifically, dense correspondences between the testing and benchmark stitched images are first established by bi-directional SIFT-flow matching. Then, color-aware, geometric-aware and structure-aware features are respectively extracted and fused via support vector regression (SVR) to obtain the final quality score. Experiments on our newly constructed database and ISIQA database demonstrate that the proposed method can achieve comparable performance compared with the conventional blind quality metrics and the quality metrics specially designed for stitched images.
Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

Information used in existing ontology matching solutions are usually grouped into four categories: lexical information, structural information, semantic information, and external information, respectively. By summarizing and analyzing the approaches for utilizing the same kind of information, this paper finds that lexical information is mainly analyzed based on text and dictionary similarity. Similarly, structural information and semantic information are mainly analyzed based on graph structure and reasoner, respectively. The approaches for aggregating information analysis results are discussed. Challenges in the analysis of various types of information for existing ontology matching solutions are also described, and insights into directions for future research are provided.