Reviewerly/exHarmony huggingface dataset:
BF-detector: an automated tool for CI build failure detection

Continuous Integration (CI) aims at supporting developers in inte-grating code changes quickly through automated building. How-ever, there is a consensus that CI build failure is a major barrierthat developers face, which prevents them from proceeding furtherwith development. In this paper, we introduceBF-Detector, anautomated tool to detect CI build failure. Based on the adaptationof Non-dominated Sorting Genetic Algorithm (NSGA-II), our toolaims at finding the best prediction rules based on two conflictingobjective functions to deal with both minority and majority classes.We evaluated the effectiveness of our tool on a benchmark of 56,019CI builds. The results reveal that our technique outperforms state-of-the-art approaches by providing a better balance between bothfailed and passed builds.BF-Detectortool is publicly available,with a demo video, at: https://github.com/stilab-ets/BF-Detector.
Searching for an Optimal Hexagonal Shaped Enumeration Unit Size for Effective Spatial Pattern Recognition in Choropleth Maps

Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations.
Teaching software engineering: an active learning experience

Teaching has changed; students, too. Software engineering is always evolving. In this paper, the authors present their experience teaching an introductory course to software engineering applying active learning, flipped classroom, gamification, and WAC (Writing Across the Curriculum) at the University of Buenos Aires' School of Engineering.
Real-Time Deep Image Retouching Based on Learnt Semantics Dependent Global Transforms

Although artists' actions in photo retouching appear to be highly nonlinear in nature and very difficult to characterize analytically, we find that the net effects of interactively editing a mundane image to a desired appearance can be modeled, in most cases, by a parametric monotonically non-decreasing global tone mapping function in the luminance axis and by a global affine transform in the chrominance plane that are weighted by saliency. This allows us to simplify the machine learning problem of mimicking artists in photo retouching to constructing a deep artful image transform (DAIT) using convolutional neural networks (CNN). The CNN design of DAIT aims to learn the image-dependent parameters of the luminance tone mapping function and the affine chrominance transform, rather than learning the end-to-end pixel level mapping as in the mainstream methods of image restoration and enhancement. The proposed DAIT approach reduces the computation complexity of the neural network by two orders of magnitude, which also, as a side benefit, improves the robustness and generalization capability at the inference stage. The high throughput and robustness of DAIT lend itself readily to real-time video enhancement as well after a simple temporal processing. Experiments and a Turing-type test are conducted to evaluate the proposed method and its competitors.
Increasing Diversity through Dynamic Critique in Conversational Recipe Recommendations

Conversational recommender systems help to guide users to discover items of interest while exploring the search space. During the exploration process, the user provides feedback on recommended items to refine subsequent recommendations. On one hand, critiquing as a way of feedback has proven effective for conversational interactions. On the other hand, diversifying the recommended items during exploration can help increase user understanding of the search space, which critiquing alone may not achieve. Both aspects are important elements for recommender applications in the food domain. Conversational exploration can help to introduce new food items, and diversity in diet has been shown to predict nutritional health. This paper introduces a novel approach that combines critique and diversity to support conversational recommendation in the recipe domain. Our initial evaluation in comparison to a baseline similarity-based recommender shows that the proposed approach increases diversity during the exploration process.
Contrastive analysis for scatterplot-based representations of dimensionality reduction

Cluster interpretation after dimensionality reduction (DR) is a ubiquitous part of exploring multidimensional datasets. DR results are frequently represented by scatterplots, where spatial proximity encodes similarity among data samples. In the literature, techniques support the understanding of scatterplots' organization by visualizing the importance of the features for cluster definition with layout enrichment strategies. However, current approaches usually focus on global information, hampering the analysis whenever the focus is to understand the differences among clusters. Thus, this paper introduces a methodology to visually explore DR results and interpret clusters' formation based on contrastive analysis. We also introduce a bipartite graph to visually interpret and explore the relationship between the statistical variables employed to understand how the data features influence cluster formation. Our approach is demonstrated through case studies, in which we explore two document collections related to news articles and tweets about COVID-19 symptoms. Finally, we evaluate our approach through quantitative results to demonstrate its robustness to support multidimensional analysis.
Medical Image Enhancement with Brightness Preserving Based on Local Contrast Stretching and Global Dynamic Histogram Equalization

Medical images such as MRI image, mammogram images, X-ray images, CT-scan images provide appropriate information regarding human brain soft tissue, cancer, stroke and various other diseases. These images help doctors to identify the diseases easily. But these images may suffer from very low contrast ratio and to improve contrast of such image we used hybrid technique which is the combination of contrast stretching (CS) and Brightness Preserving Dynamic Histogram Equalization (BPDHE). Our proposed method work as follows: In the first step, we applied pre-processing to remove noises present in images then we estimated accurate background surface and obtained new foreground image by subtracting this estimated background from the original image. We carried out local contrast stretching method on new image which containing only foreground object. Finally, after combining the background and enhanced foreground image we apply global BPDHE method to enhance image. The experiments were performed on various low contrast medical images. Experiments show more accurate results than other contrast enhancement methods.
What do incident response practitioners need to know? A skillmap for the years ahead

Digital forensics incident response (DFIR) specialists are expected to possess multidisciplinary skills including expert knowledge of computer-related principles and technology. On the other hand, recent studies suggest that existing training and study programs may not fully address the needs of future DFIR professionals. To reveal possible gaps in practitioners education and identify the most needed skills, we built a skillmap for DFIR where we followed a threefold approach: (1) an online survey among DFIR experts; (2) a review of training programs; and (3) an analysis of job listings on LinkedIn. Each source was first analyzed on its own and the findings were merged into a DFIR skillmap which is the main contribution of this article. The results show that network forensics and incident handling are the most demanded domains of skills. While these are covered by existing courses the newly desired skills, in particular, cloud forensics and encrypted data, need to get more space in training and education. We hope that this article provides educators with information on ways to improve in the years ahead.
Implicit Aspect Extraction from Online Clothing Reviews with Fine-tuning BERT Algorithm

Abstract In the era of e-commerce, tremendous product reviews can provide quick and valuable insight into the market trends. Aspect-based sentiment analysis (ABSA) on product reviews is becoming increasingly important for both companies and consumers. Implicit aspect extraction, a subtask of ABSA, aims to identify the aspect of a review sentence without explicit aspect terms. For Chinese online reviews, this paper proposes a fine-tuning BERT based method to extract implicit aspects. Fine-tuning BERT performs well on a variety of natural language processing tasks. Using labeled data, the hidden layer parameters of pre-training BERT are fine-tuned in a supervised manner to generate fine-tuning BERT. Through experiments on Chinese online reviews of clothing, it is believed that fine-tuning BERT can improve the effect of implicit aspects identification.
Automatic Modulation Recognition Through Wireless Sensor Networks in Aeronautical Wireless Channel

The Doppler effect causes frequency offset and spectrum spread of communication signals, which increases the difficulty of feature extraction when identifying the modulation mode in aeronautical wireless channels. To improve the signal modulation recognition capability of the single monitoring node in Wireless Sensor Networks (WSN), a method based on the bidirectional convolution gated recurrent deep neural network (Bi-CGDNN) with an attention mechanism is proposed in this paper. We design the framework with different types of network layers, realizing frequency offset suppression and feature mapping. In addition, quadrature sampling signals and instantaneous feature statistics are collected to generate the dataset. Thus, the waveform features and phase-frequency information of the signals can be used for combinatorial analysis. In particular, the attention mechanism is introduced to optimize the framework for better feature perception and higher recognition rates. Experimental results show that our method achieves great recognition effects at different maximum Doppler frequencies. The recognition rate reaches 98.1% at the signal-to-noise ratio (SNRs) of 8dB with the Doppler frequency of 200Hz.
A Probabilistic Inference Based Approach for Querying Associative Entities in Knowledge Graph

Querying associative entities is to provide top ranked entities in knowledge graph (KG). Many entities are not linked explicitly in KG but actually associated when incorporating outside user-generated data, which could enrich entity associations for the query processing of KG. In this paper, we leverage user-entity interactions (called user-entity data) to improve the accuracy of querying associative entities in KG. Upon the association rules obtained from user-entity data, we construct the association entity Bayesian network (AEBN), which facilitates the representation and inference of the dependencies among entities. Consequently, we formulate the problem of querying associative entities as the probabilistic inferences over AEBN. To rank the associative entities, we propose the approximate method to evaluate the association degree between entities. Extensive experiments on various datasets verify the effectiveness and efficiency of our method. Experimental results show that our proposed method outperforms some state-of-the-art competitors.
Constructing Knowledge Graphs for Online Collaborative Programming

This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class achieved 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.
Schematic memory persistence and transience for efficient and robust continual learning

Continual learning is considered a promising step toward next-generation Artificial Intelligence (AI), where deep neural networks (DNNs) make decisions by continuously learning a sequence of different tasks akin to human learning processes. It is still quite primitive, with existing works focusing primarily on avoiding (catastrophic) forgetting. However, since forgetting is inevitable given bounded memory and unbounded task loads, 'how to reasonably forget' is a problem continual learning must address in order to reduce the performance gap between AIs and humans, in terms of (1) memory efficiency, (2) generalizability, and (3) robustness when dealing with noisy data. To address this, we propose a novel ScheMAtic memory peRsistence and Transience (SMART)1 framework for continual learning with external memory that builds on recent advances in neuroscience. The efficiency and generalizability are enhanced by a novel long-term forgetting mechanism and schematic memory, using sparsity and 'backward positive transfer' constraints with theoretical guarantees on the error bound. Robust enhancement is achieved using a novel short-term forgetting mechanism inspired by background information-gated learning. Finally, an extensive experimental analysis on both benchmark and real-world datasets demonstrates the effectiveness and efficiency of our model.
Indian Masked Faces in the Wild Dataset

Due to the COVID-19 pandemic, wearing face masks has become a mandate in public places worldwide. Face masks occlude a significant portion of the facial region. Additionally, people wear different types of masks, from simple ones to ones with graphics and prints. These pose new challenges to face recognition algorithms. Researchers have recently proposed a few masked face datasets for designing algorithms to overcome the challenges of masked face recognition. However, existing datasets lack the cultural diversity and collection in the unrestricted settings. Country like India with attire diversity, people are not limited to wearing traditional masks but also clothing like a thin cotton printed towel (locally called as “gamcha”), “stoles”, and “handkerchiefs” to cover their faces. In this paper, we present a novel Indian Masked Faces in the Wild (IMFW) dataset which contains images with variations in pose, illumination, resolution, and the variety of masks worn by the subjects. We have also benchmarked the performance of existing face recognition models on the proposed IMFW dataset. Experimental results demonstrate the limitations of existing algorithms in presence of diverse conditions.
A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification

Abstract Objective With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution. Materials and Methods Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger. Results This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems. Discussion Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime. Conclusion This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.
ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap (Property Graph to Ontology Mapper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.
A Grover Search-Based Algorithm for the List Coloring Problem

Graph coloring is a computationally difficult problem, and currently the best known classical algorithm for $k$-coloring of graphs on $n$ vertices has runtimes $\Omega(2^n)$ for $k\ge 5$. The list coloring problem asks the following more general question: given a list of available colors for each vertex in a graph, does it admit a proper coloring? We propose a quantum algorithm based on Grover search to quadratically speed up exhaustive search. Our algorithm loses in complexity to classical ones in specific restricted cases, but improves exhaustive search for cases where the lists and graphs considered are arbitrary in nature.
Analyzing clarification in asynchronous information‐seeking conversations

Abstract This research analyzes human‐generated clarification questions to provide insights into how they are used to disambiguate and provide a better understanding of information needs. A set of clarification questions is extracted from posts on the Stack Exchange platform. Novel taxonomy is defined for the annotation of the questions and their responses. We investigate the clarification questions in terms of whether they add any information to the post (the initial question posted by the asker) and the accepted answer, which is the answer chosen by the asker. After identifying, which clarification questions are more useful, we investigated the characteristics of these questions in terms of their types and patterns. Non‐useful clarification questions are identified, and their patterns are compared with useful clarifications. Our analysis indicates that the most useful clarification questions have similar patterns, regardless of topic. This research contributes to an understanding of clarification in conversations and can provide insight for clarification dialogues in conversational search scenarios and for the possible system generation of clarification requests in information‐seeking conversations.
Data-driven extract method recommendations: a study at ING

The sound identification of refactoring opportunities is still an open problem in software engineering. Recent studies have shown the effectiveness of machine learning models in recommending methods that should undergo different refactoring operations. In this work, we experiment with such approaches to identify methods that should undergo an Extract Method refactoring, in the context of ING, a large financial organization. More specifically, we (i) compare the code metrics distributions, which are used as features by the models, between open-source and ING systems, (ii) measure the accuracy of different machine learning models in recommending Extract Method refactorings, (iii) compare the recommendations given by the models with the opinions of ING experts. Our results show that the feature distributions of ING systems and open-source systems are somewhat different, that machine learning models can recommend Extract Method refactorings with high accuracy, and that experts tend to agree with most of the recommendations of the model.