Reviewerly/exHarmony huggingface dataset:
Feature Comparison for Automatic Bug Report Classification

Nowadays, various bug tracking systems (BTS) such as Jira, Trace, and Bugzilla have been developed and proposed to gather the issues from users worldwide. This is because those issues, called bug reports, contain a significant information for software quality maintenance and improvement. However, many bug reports with poor quality might have been submitted to the BTS. In general, the reported bugs in the BTS are firstly analyzed and filtered out by bug triagers. However, with the increasing amount of bug reports in the BTS, manually classifying bug reports is a time-consuming task. To address this problem, automatically distinguishing of bugs and non-bugs is necessary. To the best of our knowledge, this task is never easy for bug reports classification because the problem of bug reports misclassification still occurs to date. The background of this problem may be arise from using inappropriate or confusing features. Therefore, this work aims to study and discover the most proper features for binary bug report classification. This study compares seven features such as unigram, bigram, camel case, unigram+bigram, unigram+camel case, bigram+ camel case, and all features together. The experimental results show that the unigram+camel case should be the most proper features for binary bug report classification, especially when using with the logistic regression algorithm. Consequently, the unigram+camel case should be the proper feature to distinguish bug reports from the non-bugs ones.
Digital Nudges for Encouraging Developer Actions

Researchers have examined a wide variety of practices to help software engineers complete different programming tasks. Despite the fact that studies show software engineering practices and tools created to improve the software development process are useful for preventing bugs, decreasing debugging costs, reducing debugging time, and providing additional benefits, software engineers rarely use them in practice. To persuade humans to alter and adopt new behaviors, psychologists have studied the concept of nudges. My research aims to investigate how digital nudges, or the process of using technology to automatically create nudges, can be beneficial in helping software developers and teams adopt software engineering activities and integrate them into their normal workflow.
Continual Learning for Robotics

Continual learning (CL) is a particular machine learning paradigm where thedata distribution and learning objective changes through time, or where all thetraining data and objective criteria are never available at once. The evolutionof the learning process is modeled by a sequence of learning experiences wherethe goal is to be able to learn new skills all along the sequence withoutforgetting what has been previously learned. Continual learning also aims atthe same time at optimizing the memory, the computation power and the speedduring the learning process. An important challenge for machine learning is not necessarily findingsolutions that work in the real world but rather finding stable algorithms thatcan learn in real world. Hence, the ideal approach would be tackling the realworld in a embodied platform: an autonomous agent. Continual learning wouldthen be effective in an autonomous agent or robot, which would learnautonomously through time about the external world, and incrementally develop aset of complex skills and knowledge. Robotic agents have to learn to adapt and interact with their environmentusing a continuous stream of observations. Some recent approaches aim attackling continual learning for robotics, but most recent papers on continuallearning only experiment approaches in simulation or with static datasets.Unfortunately, the evaluation of those algorithms does not provide insights onwhether their solutions may help continual learning in the context of robotics.This paper aims at reviewing the existing state of the art of continuallearning, summarizing existing benchmarks and metrics, and proposing aframework for presenting and evaluating both robotics and non roboticsapproaches in a way that makes transfer between both fields easier.
Sets of Robust Rules, and How to Find Them

Association rules are among the most important concepts in data mining. Rules of the form $$X \rightarrow Y$$ are simple to understand, simple to act upon, yet can model important local dependencies in data. The problem is, however, that there are so many of them. Both traditional and state-of-the-art frameworks typically yield millions of rules, rather than identifying a small set of rules that capture the most important dependencies of the data. In this paper, we define the problem of association rule mining in terms of the Minimum Description Length principle. That is, we identify the best set of rules as the one that most succinctly describes the data. We show that the resulting optimization problem does not lend itself for exact search, and hence propose Grab, a greedy heuristic to efficiently discover good sets of noise-resistant rules directly from data. Through extensive experiments we show that, unlike the state-of-the-art, Grab does reliably recover the ground truth. On real world data we show it finds reasonable numbers of rules, that upon close inspection give clear insight in the local distribution of the data.
Information Design for XR Immersive Environments: Challenges and Opportunities

Cross Reality (XR) immersive environments offer challenges and opportunities in designing for cognitive aspects (e.g. learning, memory, attention, etc.) of information design and interactions. Information design is a multidisciplinary endeavor involving data science, communication science, cognitive science, media, and technology. In the present paper the holodeck metaphor is extended to illustrate how information design practices and some of the qualities of this imaginary computationally augmented environment (a.k.a. the holodeck) may be achieved in XR environments to support information-rich storytelling and real life, face-to-face, and virtual collaborative interactions. The Simulation Experience Design Framework & Method is introduced to organize challenges and opportunities in the design of information for XR. The notion of carefully blending both real and virtual spaces to achieve total immersion is discussed as the reader moves through the elements of the cyclical framework. A solution space leveraging cognitive science, information design, and transmedia learning highlights key challenges facing contemporary XR designers. Challenges include but are not limited to interleaving information, technology, and media into the human storytelling process, and supporting narratives in a way that is memorable, robust, and extendable.
Survey and Analysis of Computing Education at Japanese Universities: Non-IT Departments and Courses

We conducted the first national survey of computing education at Japanese universities in 2016. In this paper, we report the survey result of the computing education at non-IT departments and faculties whose major subject is not computing. The survey covers various aspects of computing education including program organization, quality and quantity of educational achievement, students, teaching staff and computing environment. We collected 994 answers through the survey. At least 87,000 non-ICT students are taking computing education in Japan. Although computing education is carried out at every major academic discipline, teaching effort greatly differs depending on the academic discipline. We also find shortage of teaching staff for computing education. The analysis result will be an essential input to develop reasonable curriculum guidelines and accreditation criteria to improve computing education at non-IT departments.
Real-time feedback query expansion technique for supporting scholarly search using citation network analysis

Scholars routinely search relevant papers to discover and put a new idea into proper context. Despite ongoing advances in scholarly retrieval technologies, locating relevant papers through keyword queries is still quite challenging due to the massive expansion in the size of the research paper repository. To tackle this problem, we propose a novel real-time feedback query expansion technique, which is a two-stage interactive scholarly search process. Upon receiving the initial search query, the retrieval system provides a ranked list of results. In the second stage, a user selects a few relevant papers, from which useful terms are extracted for query expansion. The newly expanded query is run against the index in real time to generate the final list of research papers. In both stages, citation analysis is involved in further improving the quality of the results. The novelty of the approach lies in the combined exploitation of query expansion and citation analysis that may bring the most relevant papers to the top of the search results list. The experimental results on the Association of Computational Linguistics (ACL) Anthology Network data set demonstrate that this technique is effective and robust for locating relevant papers regarding normalised discounted cumulative gain (nDCG), precision and recall rates than several state-of-the-art approaches.
TEST

Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified into the relevant semantic labels and AI domains. Our proposed tool is based on a two-stage cascading model--the first stage classifies if the sentence contains a technology term or not; and the second stage identifies the technology keyword in the sentence. We obtain a competitive accuracy for both tasks of sentence classification and text identification.
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs

Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data have been successfully applied to infer the parameters of a dynamical system without explicitly solving it. While the benefits in computational cost are well established, the theoretical foundation has been criticized in the past. We offer a novel interpretation which leads to a better understanding, improvements in state-of-the-art performance in terms of accuracy and robustness and a decrease in run time due to a more efficient setup for general nonlinear dynamical systems.
Why is logic so likeable? A single-process account of argument evaluation with logic and liking judgments.

Four experiments examined the claims that people can intuitively assess the logical validity of arguments, and that qualitatively different reasoning processes drive intuitive and explicit validity assessments. In each study participants evaluated arguments varying in validity and believability using either deductive criteria (logic task) or via an intuitive, affective response (liking task). Experiment 1 found that people are sensitive to argument validity on both tasks, with valid arguments receiving higher liking as well as higher deductive ratings than invalid arguments. However, the claim that this effect is driven by logical intuitions was challenged by the finding that sensitivity to validity in both liking and logic tasks was affected in similar ways by manipulations of concurrent memory load (Experiments 1 and 2) and variations in individual working memory capacity (Experiments 3 and 4). In both tasks better discrimination between valid and invalid arguments was found when more working memory resources were available. Formal signal detection models of reasoning were tested against the experimental data using signed difference analysis (Stephens, Dunn, & Hayes, 2018b). A single-process reasoning model which assumes that argument evaluation in both logic and liking tasks involves a single latent dimension for assessing argument strength but different response criteria for each task, was found to be consistent with the data from each experiment (as were some dual-process models). The experimental and modeling results confirm that people are sensitive to argument validity in both explicit logic and affect rating tasks, but that these results can be explained by a single underlying reasoning process. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Gaussian quadrature inference for multicarrier continuous-variable quantum key distribution

A multicarrier continuous-variable quantum key distribution (CVQKD) protocol utilizes Gaussian subcarrier quantum continuous variables (CV) for information transmission. Here, we propose the Gaussian quadrature inference (GQI) method for multicarrier continuous-variable quantum key distribution. The GQI framework provides a minimal error estimate of the quadratures of the CV quantum states from the measured noisy subcarrier variables. GQI utilizes the fundamentals of regularization theory and statistical information processing. We characterize GQI for multicarrier CVQKD, and define a method for the statistical modeling and processing of noisy Gaussian subcarrier quadratures. We introduce the terms statistical secret key rate and statistical private classical information. The framework can be established in an arbitrary CVQKD protocol and measurement setting, and implementable by standard low-complexity statistical functions.
Hybrid Model of Correlation Based Filter Feature Selection and Machine Learning Classifiers Applied on Smart Meter Data Set

Feature selection is referred to the process of obtaining a subset from an original feature set according to certain feature selection criterion, which selects the relevant features of the dataset. It plays a role in compressing the data processing scale, where the redundant and irrelevant features are removed. Feature selection techniques show that more information is not always good in machine learning applications. Apply different algorithms for the data at hand and with baseline classification performance values we can select a final feature selection algorithm. In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and Machine learning as classifiers. The objective of this study is to select relevant features and analyze the outperform machine learning algorithms in order to train our model, predict and compare their classification performance. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. This proposed classifier model is applied to our smart meter datasets. To measure the performance of these selected features; seven benchmark classifier are used; Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbor (kNN), Naïve Bayes (NB), Decision Tree (DT), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). This paper then analyzes the performance of all classifiers with feature selection in term of accuracy, sensitivity, F-Measure, Specificity, Precision, and MCC. From our experiment, we found that Random Forest classifier performed higher than other used classifiers.
Fast Color Quantization by K-Means Clustering Combined with Image Sampling

Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.
Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks

We transfer the notion of structural symmetries to lifted planning task representations, based on abstract structures which we define to model planning tasks. We show that symmetries are preserved by common grounding methods and we shed some light on the relation to previous symmetry concepts used in planning. Using a suitable graph representation of lifted tasks, our experimental analysis of common planning benchmarks reveals that symmetries occur in the lifted representation of many domains. Our work establishes the theoretical ground for exploiting symmetries beyond their previous scope, such as for faster grounding and mutex generation, as well as for state space transformations and reductions.
Virtual Reality for Deeper Learning: An Exemplar from High School Science

This paper uses qualitative data, from a study which embedded virtual reality (VR) in high school science classes, to produce an exemplar designed to illustrate how the technology can promote engagement and Deeper Learning for students. Application of the Deeper Learning conceptual framework to the exemplar provides unique insights into how VR can be deployed in curriculum-aligned ways to develop and combine content mastery, self-directed, collaboration and, perhaps most importantly, creative endeavour in high school science, especially in low income school communities.
End-to-End Ensemble Learning by Exploiting the Correlation Between Individuals and Weights

Ensemble learning performs better than a single classifier in most tasks due to the diversity among multiple classifiers. However, the enhancement of the diversity is at the expense of reducing the accuracies of individual classifiers in general and, thus, how to balance the diversity and accuracies is crucial for improving the ensemble performance. In this paper, we propose a new ensemble method which exploits the correlation between individual classifiers and their corresponding weights by constructing a joint optimization model to achieve the tradeoff between the diversity and the accuracy. Specifically, the proposed framework can be modeled as a shallow network and efficiently trained by the end-to-end manner. In the proposed ensemble method, not only can a high total classification performance be achieved by the weighted classifiers but also the individual classifier can be updated based on the error of the optimized weighted classifiers ensemble. Furthermore, the sparsity constraint is imposed on the weight to enforce that partial individual classifiers are selected for final classification. Finally, the experimental results on the UCI datasets demonstrate that the proposed method effectively improves the performance of classification compared with relevant existing ensemble methods.
Image Enhancement Using Modified Histogram and Log-Exp Transformation

An effective method to enhance the contrast of digital images is proposed in this paper. A histogram function is developed to make the histogram curve smoother, which can be used to avoid the loss of information in the processed image. Besides the histogram function, an adaptive gamma correction for the histogram is proposed to stretch the brightness contrast. Moreover, the log-exp transformation strategy is presented to progressively increase the low intensity while suppressing the decrement of the high intensity. In order to further widen the dynamic range of the image, the nonlinear normalization transformation is put forward to make the output image more natural and clearer. In the experiment on non-uniform illumination images, the average contrast per pixel (CPP), root mean square (RMS), and discrete entropy (DE) metrics of the developed approach are shown to be superior to selected state-of-the-art methods.
Rain Streaks Removal for Single Image Via Directional Total Variation Regularization

Images captured in rainy conditions are often corrupted by unexpected rain streaks, which severely degrade the performance of subsequent processes in outdoor computer vision systems. In this paper, we exploit the directional smoothness of rain streaks for the single-image rain streaks removal and propose a convex model that uses the directional total variation (DTV) to characterize the smoothness of rain streaks in arbitrary orientations. The proposed model consists of four terms: the fidelity term, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm for the sparsity of rain streaks, and two DTV regularization terms for the directional smoothness and the piecewise smoothness of rain streaks and rain-free backgrounds, respectively. To solve the proposed model, we develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework. Extensive experimental results on both synthetic and real rainy images show that our method outperforms the recent state-of-the-art methods visually and quantitatively.
The University of Helsinki Submission to the WMT19 Parallel Corpus Filtering Task

This paper describes the University of Helsinki Language Technology group's participation in the WMT 2019 parallel corpus filtering task.Our scores were produced using a two-step strategy.First, we individually applied a series of filters to remove the 'bad' quality sentences.Then, we produced scores for each sentence by weighting these features with a classification model.This methodology allowed us to build a simple and reliable system that is easily adaptable to other language pairs.
Direct Adversarial Attack on Stego Sandwiched Between Black Boxes

Due to the amazing progresses in deep learning techniques, steganography has now been challenged to tackle not only artificial feature-based but also effective deep-learning-based steganalysis. Recent steganographers have tried to conduct adversarial attacks to defend the steganalysis networks by fine-tuning the embedding details with the help of adversarial information, which, however, mostly are white-box attacks. This research studies a novel method to conduct stegano-graphic adversarial attacks in practical scenario where stegos are sandwiched between black boxes. In our case, the toolboxes to generate stegos are steganographic black boxes where embedding adjustments are prohibited, and networks to detect stegos are semi-black boxes where most of the steganalysis networks' details are unavailable. By reforming few-pixel-attack into the form of extraction conservation noises and add them directly onto stegos, we ensure the message extraction and launch the attack in practical scenario. Experiments show that the proposed method can significantly boost the error rate of the deep-learning-based steganalysis and at the same time keep a comparable error rate when facing artificial feature-based steganalysis.