Reviewerly/exHarmony huggingface dataset:
PRIME: A Personalized Recommender System for Information Visualization Methods via Extended Matrix Completion

Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human–computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called P ersonalized R ecommender System for I nformation visualization M ethods via E xtended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users’ states. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts.
An approach to generate the bug report summaries using two-level feature extraction

Bug report is one of the major software artifact which is generated during the software development process. Changing requirements in the software development process leads to the continuous evolution of bugs which give challenges to the project management task. Bug Reports are the most consulted artifact by the software community. A Bug Report not only contains the information about the bug but also includes information like the resolution process, the enhancements by other persons, and the suggestions from the users if there are any. During the software evolution and maintenance phase, a developer spends a lot of effort and time searching for the appropriate bug report for resolving the bug quickly. Automatic Bug Report Summarization is one approach to solve the issue of time and effort. Bug report summarization helps developers not only find the appropriate bug report quickly but also assists in managing many tasks related to Bug Report Maintenance. In this paper, we have developed a two-level approach to generate the Bug Report summaries where the title, the description and the comments of a resolved bug report are considered for the summary. We find the entities in the title to create a template-based sentence for describing what the bug report is about. We use the PageRank algorithm along with the cosine similarity measure to find the summary of the Description field of bug report. The two level feature-based approach is used to find the relevant comments and the sentences from the comments. We have used the BRC dataset which has been used by most of the research community in this field. Finally the summaries from title, description and comments are merged together to create a final summary. The approach uses the features which have been successfully used by other researchers for the text summarization and especially to the domain of the meeting conversation like data. Empirical results shows that our approach works equally well with the other supervised and unsupervised approaches in terms of ROUGE Scores.
Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse

Recently, feature extraction from user reviews has been used for requirements reuse to improve the software development process. However, research has yet to use sentiment analysis in the extraction for it to be well understood. The aim of this study is to improve software feature extraction results by using sentiment analysis. Our study’s novelty focuses on the correlation between feature extraction from user reviews and results of sentiment analysis for requirement reuse. This study can inform system analysis in the requirements elicitation process. Our proposal uses user reviews for the software feature extraction and incorporates sentiment analysis and similarity measures in the process. Experimental results show that the extracted features used to expand existing requirements may come from positive and negative sentiments. However, extracted features with positive sentiment overall have better values than negative sentiments, namely 90% compared to 63% for the relevance value, 74–47% for prompting new features, and 55–26% for verbatim reuse as new requirements.
An intermodal approach to cohesion in constrained and unconstrained language

Abstract This article investigates cohesion in the spoken and written registers of constrained language varieties to highlight the similarities and differences in the cohesion patterns of mediated (i.e., interpreted and translated) and non-native texts with respect to original texts produced by native speakers. In particular, it examines how different types of cohesive devices are distributed across spoken and written native, non-native, and mediated speeches originally delivered impromptu and read out at the plenary sessions of the European Parliament. The dataset comes from the European Translation and Interpreting Corpus (EPTIC) ( Ferraresi and Bernardini 2019 ). The context provides a rare opportunity to examine the spoken and written registers of professional communication, both mono- and multilingual, in a relatively homogenous setting. First, in the exploratory analysis, I investigate the distribution of different types of cohesive devices across the investigated varieties drawing on mosaic plots and correspondence analysis. Thereafter, I make use of regression modelling of the overall frequency of cohesive devices across the examined varieties to evaluate the effect of constrainedness, mode of delivery, and individual variation. The results indicate that non-native and mediated texts do diverge from native production in the use of cohesive devices, but in different ways.
A non-factoid question answering system for prior art search

A patent gives the owner of an invention the exclusive rights to make, use and sell their invention. Before a new patent application is filed, patent lawyers are required to engage in Prior Art Search to determine the likelihood that an invention is novel, valid or to make sense of the domain. To perform this search, existing platforms utilize keywords and Boolean Logic, which disregards the syntax and semantics of natural language and thus, making the search extremely difficult. Consequently, studies regarding semantics using neural embeddings exist, but these only consider a narrow number of unidirectional words. In this study, we propose an end-to-end framework to consider bidirectional semantics, syntax and the thematic nature of natural language for prior art search. The proposed framework goes beyond keywords as input queries and takes a patent as the input. The contributions of this paper is twofold; adapting pre-trained embedding models (e.g., BERT) to address the semantics and syntax of language, followed by the second component, which exploits topic modeling to build a diversified answer that covers all themes across domains of the input patent. We evaluate the performance of the proposed framework on the CLEF-IP 2011 benchmark dataset and a real-world dataset obtained from Google patent repository and show that the proposed framework outperforms existing methods and returns meaningful results for a given patent.
On the correlation between human fixations, handcrafted and CNN features

Abstract Traditional local image descriptors such as SIFT and SURF are based on processings similar to those that take place in the early visual cortex. Nowadays, convolutional neural networks still draw inspiration from the human vision system, integrating computational elements typical of higher visual cortical areas. Deep CNN’s architectures are intrinsically hard to interpret, so much effort has been made to dissect them in order to understand which type of features they learn. However, considering the resemblance to the human vision system, no enough attention has been devoted to understand if the image features learned by deep CNNs and used for classification correlate with features that humans select when viewing images, the so-called human fixations, nor if they correlate with earlier developed handcrafted features such as SIFT and SURF. Exploring these correlations is highly meaningful since what we require from CNNs, and features in general, is to recognize and correctly classify objects or subjects relevant to humans. In this paper, we establish the correlation between three families of image interest points: human fixations, handcrafted and CNN features. We extract features from the feature maps of selected layers of several deep CNN’s architectures, from the shallowest to the deepest. All features and fixations are then compared with two types of measures, global and local, which unveil the degree of similarity of the areas of interest of the three families. From the experiments carried out on ETD human fixations database, it turns out that human fixations are positively correlated with handcrafted features and even more with deep layers of CNNs and that handcrafted features highly correlate between themselves as some CNNs do.
Foundational ontologies, <scp>ontology‐driven</scp> conceptual modeling, and their multiple benefits to data mining

Abstract For many years, the role played by domain knowledge in all stages of knowledge discovery has been recognized. However, the real‐world semantics embedded in data is often still not fully considered in traditional data mining methods. In this article, we argue that the quality of data mining results is directly related to the extent that they reflect important properties of real‐world entities represented therein. Analyzing and characterizing the nature of these entities is the very business of the area of formal ontology. We briefly elaborate on two particular types of artifacts produced by this area: foundational ontologies and ontology‐driven conceptual modeling languages grounded on them. We then elaborate on the benefits they can bring to several activities in a data mining process. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Knowledge Representation Fundamental Concepts of Data and Knowledge &gt; Data Concepts
Hybrid condition monitoring of nonlinear mechatronic system using biogeography-based optimization particle filter and optimized extreme learning machine

This paper proposes a hybrid condition monitoring approach, which integrates bond graph model-based diagnostic technique and data-driven remaining useful life (RUL) prediction, for a nonlinear mechatronic system. In this approach, various degrading faults can be considered and the physical degradation model is not required for RUL prediction. Firstly, an integrated fault signature matrix is proposed by the causal path of bicausal-bond graph model to improve fault isolation performance. After that, a biogeography-based optimization (BBO)-particle filter is developed for fault identification. For prognosis, an optimized extreme learning machine (OELM) is proposed where the hidden layer biases and input weights are optimized by BBO. The fault identification results provide data set to train the OELM for prognosis. Finally, the effectiveness of the approach is verified by simulation and experiment results.
Fearful faces modulate spatial processing in peripersonal space: An ERP study

Peripersonal space (PPS) represents the region of space surrounding the body. A pivotal function of PPS is to coordinate defensive responses to threat. We have previously shown that a centrally-presented, looming fearful face, signalling a potential threat in one's surroundings, modulates spatial processing by promoting a redirection of sensory resources away from the face towards the periphery, where the threat may be expected – but only when the face is presented in near, rather than far space. Here, we use electrophysiological measures to investigate the neural mechanism underlying this effect. Participants made simple responses to tactile stimuli delivered on the cheeks, while watching task-irrelevant neutral or fearful avatar faces, looming towards them either in near or far space. Simultaneously with the tactile stimulation, a ball with a checkerboard pattern (probe) appeared to the left or right of the avatar face. Crucially, this probe could either be close to the avatar face, and thus more central in the participant's vision, or further away from the avatar face, and thus more peripheral in the participant's vision. Electroencephalography was continuously recorded. Behavioural results confirmed that in near space only, and for fearful relative to neutral faces, tactile processing was facilitated by the peripheral compared to the central probe. This behavioural effect was accompanied by a reduction of the N1 mean amplitude elicited by the peripheral probe for fearful relative to neutral faces. Moreover, the faster the participants responded to tactile stimuli with the peripheral probe, relative to the central, the smaller was their N1. Together these results, suggest that fearful faces intruding into PPS may increase expectation of a visual event occurring in the periphery. This fear-induced effect would enhance the defensive function of PPS when it is most needed, i.e., when the source of threat is nearby, but its location remains unknown.
Not all that glitters is gold: Political stability and trade in Sub-Saharan Africa

This study examines linkages between political stability and trade openness dynamics in a panel of 44 countries in SSA from 1996 to 2016. The empirical evidence is based on the generalized method of moments. From the findings, the negative relationship between political stability and merchandise trade is not significant while the negative relationship between political stability and trade openness (exports plus imports) is significant. Hence, the findings do not validate the tested hypothesis that political stability/no violence increases trade in the sub-region. The perspective that some forms of political stability can slow down and prevent international trade is consistent with Oslon in Rise and Decline of Nations (RADON) and recent contributions to the economic development literature which have shown that not all forms of political stability are development friendly because much depends on the extent to which stability translates into, inter alia, good governance. The principal policy implication is that standards of political governance need to be boosted in order to improve the anticipated effects of political stability on trade, especially in the light of the ambitious African Continental Free Trade Area (AfCFTA). Other policy implications are discussed.
Accelerating the Bron-Kerbosch Algorithm for Maximal Clique Enumeration Using GPUs

Maximal clique enumeration (MCE) is a classic problem in graph theory to identify all complete subgraphs in a graph. In prior MCE work, the Bron-Kerbosch algorithm is one of the most popular solutions, and there are several improved algorithms proposed on CPU platforms. However, while few studies have focused on the related issue of parallel implementation, recently, there have been numerous explorations of the acceleration of general purpose applications using a graphics processing unit (GPU) to reduce the computing power consumption. In this article, we develop a GPU-based Bron-Kerbosch algorithm that efficiently solves the MCE problem in parallel by optimizing the process of subproblem decomposition and computing resource usage. To speed up the computations, we use coalesced memory accesses and warp reductions to increase bandwidth and reduce memory latency. Our experimental results show that the proposed algorithm can fully exploit the resources of GPU architectures, allowing for the vast acceleration of operations to solve the MCE problem.
Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases

We detail the challenges and barriers in applying natural language processing techniques to a collection of medical examiner case investigation notes related to fatal opioid poisonings. Major advances in biomedical informatics have made natural language processing (NLP) of medical texts both a realistic and useful task. Biomedical NLP tools are typically designed to process documents originating from biomedical libraries or electronic health records (EHRs). The usefulness of biomedical NLP tools on texts authored outside of EHRs is unclear, despite an abundance of medicolegal documents existing at the intersection of medicine and law. In particular, we detail our experiences processing unstructured text and extracting semantic concepts using case investigation notes; these notes were authored by trained investigative professionals working in a medical examiner's office and describe cases containing deaths related to fatal opioid poisonings. Applying NLP to case notes is a particularly important step in generalizing the advances of biomedical NLP for other related domains and giving guidance to data scientists working with unstructured data generated outside of EHRs.
An Industry 4.0-Ready Visual Analytics Model for Context-Aware Diagnosis in Smart Manufacturing

The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.
Parallel orthogonal deep neural network

Ensemble learning methods combine multiple models to improve performance by exploiting their diversity. The success of these approaches relies heavily on the dissimilarity of the base models forming the ensemble. This diversity can be achieved in many ways, with well-known examples including bagging and boosting. It is the diversity of the models within an ensemble that allows the ensemble to correct the errors made by its members, and consequently leads to higher classification or regression performance. A mistake made by a base model can only be rectified if other members behave differently on that particular instance, and provide the aggregator with enough information to make an informed decision. On the contrary, lack of diversity not only lowers model performance, but also wastes computational resources. Nevertheless, in the current state of the art ensemble approaches, there is no guarantee on the level of diversity achieved, and no mechanism ensuring that each member will learn a different decision boundary from the others. In this paper, we propose a parallel orthogonal deep learning architecture in which diversity is enforced by design, through imposing an orthogonality constraint. Multiple deep neural networks are created, parallel to each other. At each parallel layer, the outputs of different base models are subject to Gram–Schmidt orthogonalization. We demonstrate that this approach leads to a high level of diversity from two perspectives. First, the models make different errors on different parts of feature space, and second, they exhibit different levels of uncertainty in their decisions. Experimental results confirm the benefits of the proposed method, compared to standard deep learning models and well-known ensemble methods, in terms of diversity and, as a result, classification performance.
Spatiotemporal Saliency Representation Learning for Video Action Recognition

Deep convolutional neural networks (CNNs) have achieved great success in human action recognition, however they are still limited in understanding complex and noisy videos owing to the difficulties of exploiting appearance and motion information. Most existing works have been devoted to designing CNN architectures, which overlook the quality of network inputs that is of great importance. This paper provides an alternative solution of action recognition improvement by focusing on the quality of network inputs. A multi-task video salient object detection approach with object-of-interest segmentation scheme, which takes into account both human and action-relevant cues, is proposed to immunize the input video from background clutter. Further, a simple spatiotemporal residual network architecture is presented, which operates on multiple high-quality inputs for long-term action representation learning. Empirical evaluations on various challenging datasets demonstrate that the proposed framework can perform competitively against state-of-the-art. Besides better performance, learning representations of saliency can help prevent the action recognition model from overfitting and speed up the convergence of training.
Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.
Text Summarization Techniques and Applications

Abstract A person does not need to go through pages of articles for a given topic to understand the gist; a mere summary is more than sufficient in many cases. This has given rise to many apps that crunch through hundreds of articles to generate a personalized feed of summaries that a user can go through. With more and more people having access to the internet, lots of information is being created and shared online. This gives us the luxury of having it just a click away from consumption. However, not all of this information is filtered and cleared from the noise. This work aims to explore different techniques of text summarization and evaluate them on different parameters such as the extent of compression/summarization, retention of meaning/gist, and grammatical errors.