Reviewerly/exHarmony huggingface dataset:
Categorizing Online Harassment on Twitter

Harassment on social media is a hard problem to tackle since those platforms are virtual spaces in which people enjoy the liberty to express themselves with no restrictions. Furthermore, a large amount of users generating publications on online media like Twitter contributes to the hardness of controlling sexism and sexual harassment content, requesting robust methods of Machine Learning (ML) to be applied in this task. To do so, this work aims at comparing the performance of supervised ML algorithms to categorize online harassment in Twitter posts. We tested Logistic Regression, Gaussian Naïve Bayes, Decision Trees, Random Forest, Linear SVM, Gaussian SVM, Polynomial SVM, Multi-Layer Perceptron, and AdaBoost methods on the SIMAH Competition benchmark data, using TF-IDF vectors and Word2Vec embeddings as features. As results, we reached scores above 0.80% of accuracy for all the harassment types in the data. We also showed that, when using TF-IDF vectors, Linear and Gaussian SVM are the best methods to predict harassment content, while Decision Trees and Random Forest better categorize physical and sexual harassment. Overall, by using TF-IDF vectors presented higher performance on these data, suggesting that the training corpus for Word2Vec influenced negatively on the classification task outcomes.
Polynomial fitting techniques applied to opto-mechanical properties of PDMS Sylgard 184 for given curing parameters

Abstract Mechanical and optical properties of Polydimethylsiloxane (PDMS) have been measured and reported for different applications, however, a full analysis and a compendium of its tension and compression moduli behaviour have not been carried out, nor of its refractive index, for several mixture ratios, temperature and curing time. In this work, samples of PDMS were manufactured and tested to know tension and compression moduli and refractive index as a function of fabrication parameters; Minitab ® , Matlab ® ’s Least-squares fitting in Curve Fitting Toolbox™ and genetic algorithms were employed to yield functional dependencies to describe PDMS’s behavior. The obtained fitting polynomials are shown to have large agreement with experimental data. Finally, a potential application in the design of a gradient index lens for use in artificial vision is presented.
Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy

Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models-Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)-we, first of all, show that the minimum of Renyi entropy coincides with the "true" number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.
A Multi-task Learning Model for Daily Activity Forecast in Smart Home

Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R2 are improved by at least 1.542%, 7.79% and 1.69%, respectively.
Associating Code Clones with Association Rules for Change Impact Analysis

When a programmer makes changes to a target program entity (files, classes, methods), it is important to identify which other entities might also get impacted. These entities constitute the impact set for the target entity. Association rules have been widely used for discovering the impact sets. However, such rules only depend on the previous co-change history of the program entities ignoring the fact that similar entities might often need to be updated together consistently even if they did not co-change before. Considering this fact, we investigate whether cloning relationships among program entities can be associated with association rules to help us better identify the impact sets. In our research, we particularly investigate whether the impact set detection capability of a clone detector can be utilized to enhance the capability of the state-of-the-art association rule mining technique, Tarmaq, in discovering impact sets. We use the well known clone detector called NiCad in our investigation and consider both regular and micro-clones. Our evolutionary analysis on thousands of commit operations of eight diverse subject systems reveals that consideration of code clones can enhance the impact set detection accuracy of Tarmaq with a significantly higher precision and recall. Micro-clones of 3LOC and 4LOC and regular code clones of 5LOC to 20LOC contribute the most towards enhancing the detection accuracy.
Enhancing Source Code Refactoring Detection with Explanations from Commit Messages

We investigate the extent to which code commit summaries provide rationales and descriptions of code refactorings. We present a refactoring description detection tool CMMiner that detects code commit messages containing refactoring information and differentiates between twelve different refactoring types. We further explore whether refactoring information mined from commit messages using CMMiner, can be combined with refactoring descriptions mined from source code using the well-known RMiner tool. For six refactoring types covered by both CMMiner and RMiner, we observed 21.96% to 38.59% overlap in refactorings detected across four diverse open-source systems. RMiner identified approximately 49.13% to 60.29% of refactorings missed by CMMiner, primarily because developers often failed to describe code refactorings that occurred alongside other code changes. However, CMMiner identified 10.30% to 19.51% of refactorings missed by RMiner, primarily when refactorings occurred across multiple commits. Our results suggest that integrating both approaches can enhance the completeness of refactoring detection and provide refactoring rationales.
Blockchain in WSNs, VANets, IoTs and Healthcare: A Survey

Recently, blockchain technology has become one of the most groundbreaking technology. Blockchain was introduced to support digital transactions and, provide access to the distributed ledger in a secure and trusted way. Blockchain with smart contracts has the ability to provide secure transaction among users without the need of trusted third party or intermediary. In this paper, we presented a comprehensive survey on blockchain applications for Wireless Sensor Networks (WSNs), Vehicular Adhoc Networks (VANets), Internet of Things (IoTs) and healthcare. We review the problems addressed, their proposed solutions and, experimental evaluation security analysis. We also spot and review future challenges or open research issues of utilizing blockchain for WSNs, VANETs, IoTs, and healthcare.
CrossPatch-Based Rolling Label Expansion for Dense Stereo Matching

We present a novel algorithm called crosspatch-based rolling label expansion for accurate stereo matching. This optimization-based approach can effectively estimate the 3D label of each pixel from huge and infinite label space and then generate a continuous disparity map. The algorithm has two obvious characteristics when compared with the traditional label expansion algorithms. The first feature is the cross-based multilayer structure, where each layer contains a series of cross patches with adaptive shapes, reflecting the edge structure of objects on the image. Besides, such cross patches are non-overlapping and independent, satisfying the submodular property for employing graph cuts. The second feature is the rolling optimization, that firstly generates new label proposal by expanding candidate labels within cross patches, then globally updates labels for the whole image using a proposed rolling move. The experimental results show the high matching accuracy of our method, both in pixel level and subpixel level. According to the latest ranking list of Middlebury 3.0 benchmark, our method is one of the best stereo matching algorithms.
Applying deep learning algorithm to automatic bug localization and repair

Owing to the increasing size and complexity of software, large/small bugs have become inevitable. To fix software bugs in some cases, developers may need to spend a considerable amount of time debugging. Some studies have reported that typographical errors in natural and programming languages are nearly identical. We herein propose a method to solve these mistakes automatically. We perform bug localization using an autoencoder and CNN to compute a rank score. In details, we extract features from bug reports and program source code. Then, we input these features into the autoencoder. Next, the output of autoencoder applies to the CNN. Finally, we compute a rank score between the bug report and program source code. Regarding bug repair, we utilize Seq-GAN algorithm. In details, first, we convert program source code into multiple lines with tokens. Then, we apply the Seq-GAN algorithm to generate the candidate buggy patches. To evaluate the effectiveness of the proposed method, performance comparisons with similar related studies were conducted. The comparison shows that our approach produces better results compared to other studies.
A comparative evaluation of aggregation methods for machine learning over vertically partitioned data

It is increasingly common applications where data are naturally generated in a distributed fashion, especially after the emergence of technologies like the Internet of Things (IoT). In sensor networks, in collaborative health or genomic projects, in credit risk analysis, among other domains, distinct features are collected from multiple sources, including the use of social media and mobile applications, and due to privacy concerns or communication costs, may not be shared among sites. This scenario of vertical data partitioning poses challenges to traditional machine learning (ML) approaches, as classical algorithms are designed to learn from the complete set of features. A common strategy is to combine predictions from local models trained at each site into a global model, and for this purpose, several aggregation methods have been proposed. In this work we tackle a gap within the related literature, performing a comparative evaluation of elementary and meta-learning-based aggregation methods to reveal their strengths and weakness for 46 datasets with varied characteristics. We show that no method outperforms its counterparts in all domains, emphasizing the need for experimental comparison to ensure a good choice in the domain of interest. Moreover, our experiments provide the first insights into the relations between datasets' properties and aggregators' performance. We show that for low class imbalance and a good instance-to-feature ratio, almost all aggregation methods tend to perform well. The silhouette coefficient (reflecting class separability) and class imbalance coefficient are the most influential properties on aggregators' performance, thus we recommend their analysis in the first step of the methodological design. We found that arithmetic-based methods are not suitable for datasets with poor class separability and a large number of classes, whereas meta-learning approaches are less sensitive for datasets with silhouette coefficient close to 0. Our analyses were summarized as classification and regression trees, which have the impact to serve as practical tools for future research. Taken together, our findings give rise to interesting applications in the domain of intelligent systems, especially regarding their potential to reduce the burden of vast experimental comparisons when training ML models with feature-partitioned data.
Exploring Appropriate Acoustic and Language Modelling Choices for Continuous Dysarthric Speech Recognition

There has been much recent interest in building continuous speech recognition systems for people with severe speech impairments, e.g., dysarthria. However, the datasets that are commonly used are typically designed for tasks other than ASR development, or they contain only isolated words. As such, they contain much overlap in the prompts read by the speakers. Previous ASR evaluations have often neglected this, using language models (LMs) trained on non-disjoint training and test data, potentially producing unrealistically optimistic results. In this paper, we investigate the impact of LM design using the widely used TORGO database. We combine state-of-the-art acoustic models with LMs trained with data originating from LibriSpeech. Using LMs with varying vocabulary size, we examine the trade-off between the out-of-vocabulary rate and recognition confusions for speakers with varying degrees of dysarthria. It is found that the optimal LM complexity is highly speaker dependent, highlighting the need to design speaker-dependent LMs alongside speaker-dependent acoustic models when considering atypical speech.
Multi-task Deep Learning for Colon Cancer Grading

Automated cancer grading is an important subject of study in digital pathology. In this paper, we introduce a multi-task learning approach to analyze digitized pathology images. The approach performs both classification and regression tasks in combination with a deep convolutional neural network to predict the tumor grade. Employing tissue microarrays (TMAs) and whole slide images (WSI), the proposed method achieved an accuracy of 85.91% in classifying colon tissues into four distinctive pathology classes, including benign and well differentiated, moderately differentiated, and poorly differentiated tumors.
Aceso: PICO-Guided Evidence Summarization on Medical Literature

Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso system, which automatically generates PICO-based evidence summaries from medical literature. In Aceso 1, we adopt an active learning paradigm, which helps to minimize the cost of manual labeling and to optimize the quality of summarization with limited labeled data. An UMLS2Vec model is proposed to learn a vector representation of medical concepts in UMLS 2, and we fuse the embedding of medical knowledge with textual features in summarization. The evaluation shows that our approach is better on identifying PICO sentences against state-of-the-art studies and outperforms baseline methods on producing high-quality evidence summaries.
Grouping sentences as better language unit for extractive text summarization

Most existing methods for extractive text summarization aim to extract important sentences with statistical or linguistic techniques and concatenate these sentences as a summary. However, the extracted sentences are usually incoherent. The problem becomes worse when the source text and the summary are long and based on logical reasoning. The motivation of this paper is to answer the following two related questions: What is the best language unit for constructing a summary that is coherent and understandable? How is the extractive summarization process based on the language unit? Extracting larger language units such as a group of sentences or a paragraph is a natural way to improve the readability of summary as it is rational to assume that the original sentences within a larger language unit are coherent. This paper proposes a framework for group-based text summarization that clusters semantically related sentences into groups based on Semantic Link Network (SLN) and then ranks the groups and concatenates the top-ranked ones into a summary. A two-layer SLN model is used to generate and rank groups with semantic links including the is-part-of link, sequential link, similar-to link, and cause–effect link. The experimental results show that summaries composed by group or paragraph tend to contain more key words or phrases than summaries composed by sentences and summaries composed by groups contain more key words or phrases than those composed by paragraphs especially when the average length of source texts is from 7000 words to 17,000 words which is the usual length of scientific papers. Further, we compare seven clustering algorithms for generating groups and propose five strategies for generating groups with the four types of semantic links.
Configuring differential evolution adaptively via path search in a directed acyclic graph for data clustering

As an efficient data mining technique, data clustering has been widely-used for data analysis and extracting valuable hidden information. Leveraging the simplicity and effectiveness, the evolutionary optimization-driven clustering algorithms have exhibited promising performance and attracted tremendous attention. Up to the present, how to enable these algorithms to escape from local optima and accelerate convergence rates is an ongoing challenge. In this paper, we propose a novel adaptive Differential Evolution (DE) variant to deal with the above challenge when clustering data. In the improved DE algorithm, the four interdependent components, including mutation strategy, crossover strategy, scaling factor value, and crossover rate, are adaptively configured in an integrated manner via ant colony optimization (ACO) during the problem-solving process. To be specific, the relationships of four components in the DE algorithm are modeled as a directed acyclic graph, and a path in the graph exactly corresponds to a configuration for DE. During the optimization process, ant colony optimization is employed to search for a reasonable path for each individual of DE in terms of pheromones on arcs. In this manner, the configuration of the four interdependent components of DE will be generated dynamically, which is then used to guide the successive search behaviors of individuals in DE. Each individual has a path, representing a configuration for each component. After each iteration, individuals that generate promising solutions are allowed to deposit pheromone on the paths, resulting in more pheromones on the arcs appearing in better algorithm configurations (paths) more frequently. Through this manner, the search strategies and parameters of DE are comprehensively adapted by ACO. The proposed algorithm is named ACODE for short. To verify its effectiveness, the proposed ACODE is compared with four representative data clustering algorithms on eight widely-used benchmark datasets. The experimental results demonstrate the advantages of ACODE over half of the datasets.
A method for automated digital image‐based tracking of delamination fronts in translucent glass fibre‐laminated composite materials

Abstract A new automated digital image‐based method for tracking both the position and shape of the crack front of delaminations in translucent glass fibre‐laminated test specimens is proposed. The method is applicable to both fracture toughness tests and fatigue tests. The digital image‐based algorithm identifies the newly formed crack area by the change in reflected light and then extracts both the crack front shape and the position. The method is used in a standard double cantilevered beam specimen test setup with the addition of light sources and a camera. The method does not rely on subjective test operator interpretations or calibrations. A reference pattern on the specimen is used to correctly map between the camera coordinate system and the specimen coordinate system, which makes the method insensitive to the camera position and camera or specimen movement during the test. Experimental test examples are provided, which shows that the whole crack front can be tracked with significantly higher accuracy than possible with existing methods.
Towards Relevance and Sequence Modeling in Language Recognition

The task of automatic language identification (LID) involving multiple dialects of the same language family in the presence of noise is a challenging problem. In these scenarios, the identity of the language/dialect may be reliably present only in parts of the temporal sequence of the speech signal. The conventional approaches to LID (and for speaker recognition) ignore the sequence information by extracting long-term statistical summary of the recording assuming an independence of the feature frames. In this paper, we propose a neural network framework utilizing short-sequence information in language recognition. In particular, a new model is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task. This relevance weighting is achieved using the bidirectional long short-term memory (BLSTM) network with attention modeling. We explore two approaches, the first approach uses segment level i-vector/x-vector representations that are aggregated in the neural model and the second approach where the acoustic features are directly modeled in an end-to-end neural model. Experiments are performed using the language recognition task in NIST LRE 2017 Challenge using clean, noisy and multi-speaker speech data as well as in the RATS language recognition corpus. In these experiments on noisy LRE tasks as well as the RATS dataset, the proposed approach yields significant improvements over the conventional i-vector/x-vector based language recognition approaches as well as with other previous models incorporating sequence information.