Reviewerly/exHarmony huggingface dataset:
Unsupervised local cluster-weighted bootstrap aggregating the output from multiple stochastic simulators

In the present work, we consider the problem of combining the output from multiple stochastic computer simulators to make inference on a quantity of interest, as a means of reducing the inherent model-form uncertainty in the absence of any measurements. In most real-world situations, judging an individual stochastic simulator to be the "best" for any given point in the input space is highly doubtful. Thus, making inference by relying on the so-deemed best simulator may not be adequate, especially when the sampled data is limited. To this end, we propose an ensemble learning method based on local Clustering and bootstrap aggregation (Bagging), which rather than treating the stochastic predictions of the simulators as competing individual information sources, treats those as part of an ensemble, thus diversifying the hypothesis space. We call the proposed method: unsupervised local cluster-weighted bootstrap aggregation. Variational Bayesian Gaussian mixture clustering is the first step in this ensemble learning approach for discriminating the outputs, and deriving the probability map (weights) of the clustered simulators output. Clustering is performed on the stochastic output corresponding to the binned input space. Performing the clustering independently and deriving the probability map for each local region of the binned input space is a novelty that guarantees an adaptive solution, whereby certain simulators are potentially more fitting than others in corresponding regions of the input space. The second step consists in a local cluster-weighted Bootstrap Aggregation, which serves the purpose of weighted combination of the clustered ensemble of outputs from the individual simulators. Based on simulations, we demonstrate how the input bin size, sample size, output dispersion and level of agreement amongst the simulators affect the performance of the proposed method. We compare the unsupervised local cluster-weighted bootstrap aggregation method to classical Bagging, Bayesian Model Averaging and Stacking of predictive distributions. Finally, we demonstrate the method by evaluating the fatigue damage equivalent load on a wind turbine blade, using 10 finite element based simulators. The results point to the need for practitioners to consider this as a useful method, when model-form uncertainty is of concern and when output from multiple stochastic simulators are available.
Korean Part-of-speech Tagging Based on Morpheme Generation

Two major problems of Korean part-of-speech (POS) tagging are that the word-spacing unit is not mapped one-to-one to a POS tag and that morphemes should be recovered during POS tagging. Therefore, this article proposes a novel two-step Korean POS tagger that solves the problems. This tagger first generates a sequence of lemmatized and recovered morphemes that can be mapped one-to-one to a POS tag using an encoder-decoder architecture derived from a POS-tagged corpus. Then, the POS tag of each morpheme in the generated sequence is finally determined by a standard sequence labeling method. Since the knowledge for segmenting and recovering morphemes is extracted automatically from a POS-tagged corpus by an encoder-decoder architecture, the POS tagger is constructed without a dictionary nor handcrafted linguistic rules. The experimental results on a standard dataset show that the proposed method outperforms existing POS taggers with its state-of-the-art performance.
Combination of AHP and TOPSIS methods for the ranking of information security controls to overcome its obstructions under fuzzy environment

The organizations utilizing the cloud computing services are required to select suitable Information Security Controls (ISCs) to maintain data security and privacy. Many organizations bought popular products or traditional tools to select ISCs. However, selecting the wrong information security cont rol without keeping in view severity of the risk, budgetary constraints, measures cost, and implementation and mitigation time may lead to leakage of data and resultantly, organizations may lose their user's information, face financial implications, even reputation of the organization may be damaged. Therefore, the organizations should evaluate each control based on certain criteria like implementation time, mitigation time, exploitation time, risk, budgetary constraints, and previous effectiveness of the control under review. In this article, the authors utilized the methodologies of the Multi Criteria Decision Making (MCDM), Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to help the cloud organizations in the prioritization and selection of the best information security control. Furthermore, a numerical example is also given, depicting the step by step utilization of the method in cloud organizations for the prioritization of the information security controls.
Evaluating new requirements to pen-centric intelligent user interface based on end-to-end mathematical expressions recognition

Advances in deep learning end-to-end recognition systems allow moving forward intelligent user interfaces. At the same time, they lead to new implications and restrictions in the UI design. In this paper, we discuss new requirements for the pen-centric intelligent user interface for operating with mathematical expressions. We argue that the following features are necessary for such an interface: use of variables (both automatically assigned and defined by the user), quick editing, and connected writing support. We demonstrate and evaluate features in SMath - a solution for iterative input and automatic calculation of handwritten mathematical expressions. It allows pen-based input and editing of two-dimensional mathematical expressions in various scenarios with a possibility to reuse the results of previous calculations. Through the evaluation study, we justify the proposed requirements from the point of user needs and preferences. In addition to the automatic metrics that were collected during test tasks execution using the application, we gathered and summarized users' feedback, which can be further used to formulate functional requirements and design the user interface. We also provide the evaluation of the recognition performance of the system on an open-source dataset of handwritten mathematical expressions.
High Quality Image Processing System for ADAS

Image processing technology enables capturing high quality images and video from raw image sensors. Recently, the advent of Advanced Driver Assistance Systems (ADAS) present new challenges on image processing due to their requirement of concurrent visual and analytics use cases. This paper introduces the requirements arising from ADAS systems along with a proposed Imaging sub-system (ISS) to address them. The proposed ISS solution is integrated in the `TDA3' family of processors from Texas Instruments (TI). The paper delves into detail of the software and tool set provided to enable tuning for high image quality in a wide variety of illumination conditions. The paper discusses objective image quality measurement and comparison with a couple of existing solutions in market. The results clearly demonstrate the advantages of the proposed solution in terms of feature set and captured image/video quality.
Navigation-by-preference

We present Navigation-by-Preference, n-by-p, a new conversational recommender that uses what the literature calls preference-based feedback. Given a seed item, the recommender helps the user navigate through item space to find an item that aligns with her long-term preferences (revealed by her user profile) but also satisfies her ephemeral, short-term preferences (revealed by the feedback she gives during the dialog). Different from previous work on preference-based feedback, n-by-p does not assume structured item descriptions (such as sets of attribute-value pairs) but works instead in the case of unstructured item descriptions (such as sets of keywords or tags), thus extending preference-based feedback to new domains where structured item descriptions are not available. Different too is that it can be configured to ignore long-term preferences or to take them into account, to work only on positive feedback or to also use negative feedback, and to take previous rounds of feedback into account or to use just the most recent feedback.
Feature Weighting in Finding Feedback Documents for Query Expansion in Biomedical Document Retrieval

Finding good feedback documents for query expansion is a well-known problem in the field of information retrieval. This paper describes a novel approach for finding relevant documents for feedback in query expansion for biomedical document retrieval. The proposed approach relies on a small amount of human intervention to find good feedback documents and tries to learn the relation between query and documents in terms of usefullness of document for query expansion. This proposed approach uses an NLP-based feature weighting technique with classification and clustering method on the documents and identifies relevant documents for feedback. The documents are represented using term frequency and inverse document frequency (TF–IDF) features and these features are weighted according to the type of query and type of the terms. The experiments performed on CDS 2014, 2015 and 2016 datasets show that the feature weighting in finding feedback documents for query expansion approach gives good results as compared to the results of pseudo-relevance feedback, relevance feedback and the results of TF–IDF features without weighting.
Development of Augmented Reality application for learning the concept of molecular geometry

Abstract This research aimed to develop and measure the usability of the Augmented Reality App used for learning Chemistry, focusing on the concept of molecular geometry. AGILE model of development was employed to step by step development, starting from the analysis of the curriculum and need assessment, followed by design, development, deployment and evaluation. Some computer software such as Vuforia (database), unity (App generator) and blender (3D object maker) used at the development stage of the app. 17 students took part in the testing phase of the app. The students evaluated the ease of use of the app using the System Usability Scale (SUS). The SUS score of the app was 92,7, which means the app is easy to use, and most of them would recommend the app for other students. The result indicated that the app is user-friendly, light, and useful to learn the concept of molecular geometry.
Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

The data sequence compressed using the Lempel-Ziv-77 (LZ77) algorithm comprises a series of fixed-length tuples.To decompress this data, it is essential to know the encoding parameters such as the tuple length used for the compression.If this essential information becomes unavailable owing to conditions such as loss of the header, it is difficult to accurately determine the parameters using the compressed data.In this study, we investigate the blind estimation of tuple length from the LZ77-compressed data when the header is unavailable.To this end, we propose a novel idea of utilizing the LZ77 image generated from the LZ77-compressed data.The LZ77 image exhibits unique patterns based on the image size.The correlation between the image size and tuple length is indicated by different patterns of vertical lines in the LZ77 image.By exploiting a convolutional neural network (CNN), we develop an iterative algorithm while generating LZ77 images with different size.The results of the experiment on a public database show that the LZ77 image plays the role of an extremely powerful visual feature descriptor, and the proposed iterative algorithm estimates the tuple length with 100% accuracy.
The Impact of Lexicon Adaptation on the Emotion Mining From Software Engineering Artifacts

Sentiment analysis and emotion mining techniques are increasingly being used in the field of software engineering. However, the experiments conducted so far have not yielded high accuracy results. Researchers indicate a lack of adaptation of the methods of emotion mining to the specific context of the domain as the main cause of this situation. The article describes research aimed at examining whether the adaptation of the lexicon with emotional intensity of words in the context of software engineering improves the reliability of sentiment analysis. For this purpose, a new lexicon is developed in which words are evaluated as if they were used in the field of software engineering. A comparative experiment of emotion mining based on a generic and a software engineering specific lexicon does not reveal any significant differences in the results.
Code Reviews With Divergent Review Scores: An Empirical Study of the OpenStack and Qt Communities

Code review is a broadly adopted software quality practice where developers critique each others' patches. In addition to providing constructive feedback, reviewers may provide a score to indicate whether the patch should be integrated. Since reviewer opinions may differ, patches can receive both positive and negative scores. If reviews with divergent scores are not carefully resolved, they may contribute to a tense reviewing culture and may slow down integration. In this paper, we study patches with divergent review scores in the OPENSTACK and QT communities. Quantitative analysis indicates that patches with divergent review scores: (1) account for 15%-37% of patches that receive multiple review scores; (2) are integrated more often than they are abandoned; and (3) receive negative scores after positive ones in 70% of cases. Furthermore, a qualitative analysis indicates that patches with strongly divergent scores that: (4) are abandoned more often suffer from external issues (e.g., integration planning, content duplication) than patches with weakly divergent scores and patches without divergent scores; and (5) are integrated often address reviewer concerns indirectly (i.e., without changing patches). Our results suggest that review tooling should integrate with release schedules and detect concurrent development of similar patches to optimize review discussions with divergent scores. Moreover, patch authors should note that even the most divisive patches are often integrated through discussion, integration timing, and careful revision.
Estimation affects of formats and resizing process to the accuracy of convolutional neural network

image quality, formatting, resizing and compression processes affect on deep neural network performance. These affects are investigated in many papers. However, signal represented images do not look like images which were captured by a camera, because they were plotted in a computer, which let to omit some noises. So formatting and resizing processes are important parameters that affect on network accuracy. In this work, ECG signal representation in different domains were saved in different image formats and CNN trained on these images. Obtained results were compared and showed that JPG image format best fits for training ECG images on CNN.
A Comparison of Strategies for Missing Values in Data on Machine Learning Classification Algorithms

Dealing with missing values in data is an important feature engineering task in data science to prevent negative impacts on machine learning classification models in terms of accurate prediction. However, it is often unclear what the underlying cause of the missing values in real-life data is or rather the missing data mechanism that is causing the missingness. Thus, it becomes necessary to evaluate several missing data approaches for a given dataset. In this paper, we perform a comparative study of several approaches for handling missing values in data, namely listwise deletion, mean, mode, k-nearest neighbors, expectation-maximization, and multiple imputations by chained equations. The comparison is performed on two real-world datasets, using the following evaluation metrics: Accuracy, root mean squared error, receiver operating characteristics, and the F1 score. Most classifiers performed well across the missing data strategies. However, based on the result obtained, the support vector classifier method overall performed marginally better for the numerical data and naïve Bayes classifier for the categorical data when compared to the other evaluated missing value methods.
Single-pass randomized algorithms for LU decomposition

In this paper, we present some single-pass randomized algorithms to compute LU decomposition. These algorithms need only one pass over the original matrix and hence are very suitable for extremely large and high-dimensional matrix stored outside of core memory or generated in a streaming fashion. Rigorous error bounds and complexity of these algorithms are provided. Numerical experiments show that these single-pass algorithms have the similar accuracy and runtime (excluding the cost of matrix transfer) compared with the state-of-the-art randomized algorithms for LU decomposition.
A systematic mapping study for blockchain based on complex network

Summary Blockchain has started to appear as a potentially reliable and underlying technology for various fields. There have been lots of surveys focusing on blockchain with respect to specific topics, such as security, architecture, applications, and so on. However, a systematic mapping study, including all related fields about blockchain, has been largely ignored. In this article, we revisit the problem of complex networks in the form of scientific collaboration networks. More specifically, we utilize the method of systematic mapping and implement them into blockchain technology. We collect 233 articles by searching Baidu scholar with the keyword “blockchain,” then construct two complex networks according to the relationship of keywords and authors, respectively. The keywords' complex network is a small‐world network while the authors' complex network is not. Furthermore, the tool of Netdraw provides a visualized graph for the complex network. Meanwhile, we find some subgroups in the network, which may highlight the future direction of blockchain.
Fast Object Detection Using Dimensional Based Features for Public Street Environments

Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.
Semisupervised sentiment analysis method for online text reviews

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.
Efficient Keyword Spotting through Hardware-Aware Conditional Execution of Deep Neural Networks

Keyword spotting is a task that requires ultra-low power due to its always-on operation. State-of-the-art approaches achieve this by drastically pruning model size, yet often at the expense of accuracy. This work tackles this fundamental conflict between operating efficiency and accuracy in three ways: 1.) Exploiting dynamic neural network cascades for keyword spotting using an end-to-end hardware-aware training; 2.) Deriving the optimal number of stages and stage dimensions in function of the input class distributions; 3.) Using the low-latency response of the first stage for speculative execution of the later stages, training the dynamic cascade through a hardware-aware cost function. Results show the framework can generate cascade models optimized in function of the class distribution (background noise, target keywords and other-speech), reducing computational cost by 87% for always-on operation while maintaining the baseline accuracy of the most complex model of the cascade. On top of this, the hardware-aware speculative execution provides an additional 2x energy savings over the non-speculative case.
Investigating the Use of Deep Neural Networks for Software Defect Prediction

Many software projects are shipped to customers containing defects. Defective software cost money, time, and lives. To reduce this harm, software companies allocate testing and quality assurance budgets. The enormous sizes of modern software pose challenges to traditional testing approaches due to the need for scalability. Defect prediction models have been used to direct testing efforts to probable causes of defects in the software. Early approaches for software defect prediction relied on statistical approaches to classify software modules and decide whether each module is a defect-prone module or not. Lately, many researchers used machine learning techniques to train a model that can classify software modules to defect-prone modules and not defect-prone modules. Starting from the new millennium, neural networks and deep learning won many competitions in machine learning applications. However, the use of deep learning to build a software defect prediction model was not investigated thoroughly. In this paper, we used a deep neural network to build a software defect prediction model and compared our proposed model with other machine learning algorithms like random forests, decision trees, and naive Bayesian networks. The result shows small improvement over the other learning models in most of the comparisons. These results prove the value of using deep learning for defect prediction and open the door for more experiments.