Reviewerly/exHarmony huggingface dataset:
Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

Abstract The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole‐session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed‐query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole‐session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole‐session performance.
From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

The quality of the data is directly related to the quality of the models drawn from that data. For that reason, many research is devoted to improve the quality of the data and to amend errors that it may contain. One of the most common problems is the presence of noise in classification tasks, where noise refers to the incorrect labeling of training instances. This problem is very disruptive, as it changes the decision boundaries of the problem. Big Data problems pose a new challenge in terms of quality data due to the massive and unsupervised accumulation of data. This Big Data scenario also brings new problems to classic data preprocessing algorithms, as they are not prepared for working with such amounts of data, and these algorithms are key to move from Big to Smart Data. In this paper, an iterative ensemble filter for removing noisy instances in Big Data scenarios is proposed. Experiments carried out in six Big Data datasets have shown that our noise filter outperforms the current state-of-the-art noise filter in Big Data domains. It has also proved to be an effective solution for transforming raw Big Data into Smart Data.
HOW KNOWLEDGEABLE ARE APPLIED LINGUISTICS AND SLA RESEARCHERS ABOUT BASIC STATISTICS?

Abstract Despite the prevalence of quantitative approaches in applied linguistics (AL) and second language acquisition (SLA) research (Gass, 2009), evidence indicates a need for improvement in analyzing and reporting SLA data (e.g., Larson-Hall &amp; Plonsky, 2015). However, to improve quantitative research, researchers must possess the statistical knowledge necessary to conduct quality research. This study assesses AL and SLA researchers’ knowledge of key statistical concepts on a statistical knowledge test. One hundred and ninety-eight AL and SLA researchers from North America and Europe responded to 26 discipline-specific questions designed to measure participants’ ability to (a) understand basic statistical concepts and procedures, (b) interpret statistical analyses, and (c) critically evaluate statistical information. Results indicate that participants generally understood basic descriptive statistics, but performance on items requiring more advanced statistical knowledge was lower. Quantitative research orientation, number of statistics courses taken, and frequent use of statistics textbooks had positive influences on researchers’ statistical knowledge.
Virtual Mouse Implementation using Open CV

Hand Gesture Recognition plays a key role in human-computer interactions. As we can see that there are so many new Technological advancements happening such as biometric authentication which we can see frequently in our smart phones, similarly hand gesture recognition is a modern way of human-computer interaction i.e., we can control our system by showing our hands in front of webcam and hand gesture recognition can be useful for all kinds of people. Based upon this idea this paper is presented. This paper provides a detailed explanation to the algorithms and methodologies for the color detection and virtual mouse.
Autonomous Driving System with Road Sign Recognition using Convolutional Neural Networks

According to statistics, most road accidents take place due to lack of response time to instant traffic events. With the self-driving cars, this problem can be addressed by implementing automated systems to detect these traffic events. To design such recognition system in self-driving automated cars, it is important to monitor and manoeuvre through real-time traffic events. This involves correctly identifying the traffic signs that can be faced by an automated vehicle, classifying them, and responding to them. In this paper, an attempt is made to design such system, by applying image recognition to capture traffic signs, classify them correctly using Convolutional Neural Network, and respond to it in real-time through an Arduino controlled autonomous car. To study the performance of this road sign recognition system, various experiments were conducted using Belgium Traffic Signs dataset and an accuracy of 83.7% has been achieved by this approach.
Implementation of digital pathology into diagnostic practice: perceptions and opinions of histopathology trainees and implications for training

There is increasing interest in the utility of digital pathology in the diagnostic setting. Successful transition requires guidance and training, but additionally an understanding of opinions and attitudes of histopathologists to ensure that potential barriers are addressed. Histopathology trainees as a group are likely to be at the forefront of this revolution, and have specific and as yet largely neglected training needs in this context. We designed an online survey for trainees within our region to capture their opinions and attitudes to digital pathology in the diagnostic setting, and to assess their perceived training needs. This survey indicates overall that these trainees have similar aspirations with regard to the predicted utility of digital pathology and the challenges faced as have been recognised among consultant histopathologists. While their training needs are also largely similar, there are specific additional considerations based around training in multiple centres with varying exposure to digital pathology.
Automated Classification of Software Bug Reports

We target the problem of software bug reports classification. Our main aim is to build a classifier that is capable of classifying newly incoming bug reports into two predefined classes: corrective (defect fixing) report and perfective (major maintenance) report. This helps maintainers to quickly understand these bug reports and hence, allocate resources for each category. For this purpose, we propose a distinctive feature set that is based on the occurrences of certain keywords. The proposed feature set is then fed into a number of classification algorithms for building a classification model. The results of the proposed feature set achieved high accuracy in classification with SVM classification algorithm reporting an average accuracy of (93.1%) on three different open source projects.
Natural Language Contents Evaluation System for Detecting Fake News using Deep Learning

This Recently, a lot of information is spreading rapidly on SNS. Inaccurate communication of news media includes fears about unreliable sources and fake news that lacks confirmation of facts. Fake news is spread through SNS, causing social confusion and further economic loss. The purpose of the news is accurate information transmission. In this regard, it is very important to judge the discrepancies in the contents of the text and the distorted reports. We try to solve the problem of judging whether the sentence to be verified is correct after collecting the facts. This paper defines the problem of extracting the related sentences from the input sentence in Fact Data Corpus which is assumed to be fact and judging whether the extracted sentence and the input sentence are true or false. In the various NLP tasks, we create a Korean-specific pre-training model using state-of-the-art BERT. Using this model, fine-tuning is performed to match the data set detected by Korean fake news. The AUROC score of 83.8% is derived from the test set generated using the fine-tuned model.
SIKE Round 2 Speed Record on ARM Cortex-M4

We present the first practical software implementation of Supersingular Isogeny Key Encapsulation (SIKE) round 2, targeting NIST's 1, 2, and 5 security levels on 32-bit ARM Cortex-M4 microcontrollers. The proposed library introduces a new speed record of SIKE protocol on the target platform. We achieved this record by adopting several state-of-the-art engineering techniques as well as highly-optimized hand-crafted assembly implementation of finite field arithmetic. In particular, we carefully redesign the previous optimized implementations of filed arithmetic on 32-bit ARM Cortex-M4 platform and propose a set of novel techniques which are explicitly suitable for SIKE/SIDH primes. Moreover, the proposed arithmetic implementations are fully scalable to larger bit-length integers and can be adopted over different security levels. The benchmark result on STM32F4 Discovery board equipped with 32-bit ARM Cortex-M4 microcontrollers shows that the entire key encapsulation over p434 takes about 326 million clock cycles (i.e. 1.94 s @168 MHz). In contrast to the previous optimized implementation of the isogeny-based key exchange on low-power 32-bit ARM Cortex-M4, our performance evaluation shows feasibility of using SIKE mechanism on the target platform. In comparison to the most of the post-quantum candidates, SIKE requires an excessive number of arithmetic operations, resulting in significantly slower timings. However, its small key size makes this scheme as a promising candidate on low-end microcontrollers in the quantum era by ensuring the lower energy consumption for key transmission than other schemes.
Primate Markerless Pose Estimation and Movement Analysis Using DeepLabCut

The analysis and understanding of primate behavior play a fundamental role in fields such as neuroscience, medicine, psychology, genetics, and more. This paper demonstrates an automatic detection of primate features by using an open-source deep learning toolset, DeepLabCut. We trained the deep neural network to locate 17 features and extract the monkey pose by relating the set of features detected. The model is trained with 5,967 manually annotated monkey images, which achieved train and test set errors of 3.61 and 19.72 unit pixels respectively. We also plotted the feature trajectories across multiple frames to show that the trained model can be used for analyzing behavior.
Secure and Private Function Evaluation with Intel SGX

Secure function evaluation (SFE) allows two parties to jointly evaluate a publicly known function without revealing their respective inputs. SFE can be realized via well-known cryptographic protocols, such as Yao's garbled circuits (GC) and the protocol of Goldreich, Micali, and Wigderson (GMW), which obliviously evaluate a Boolean circuit representation of the function. Unfortunately, even with the most recent optimizations that touch known lower bounds, these protocols incur an impractical high communication overhead.
A robust dimensionality reduction and matrix factorization framework for data clustering

Most existing Non-negative Matrix Factorization (NMF) related data clustering techniques directly decompose the original feature space while have not well considered the fact that the low dimensional feature space is always embedded in the high dimensional feature space and can better reveal the spatial distribution of data. In this letter, we propose a new matrix factorization model, which unites the objectives of clustering and dimensionality reduction simultaneously. In the proposed framework, the clustering based on matrix factorization is actually executed on the embedded subspace which may provide more accurate and reasonable solutions. Furthermore, we use the l2,1-norm instead of the conventional l2-norm to enhance the clustering results and make the clustering framework more robust to the noises and outliers. Meanwhile, in order to preserve as much as possible local similarity of the data, we have also employed an affinity matrix with special learning to introduce the manifold learning into the cluster indicator matrix. An optimization procedure based on Augmented Lagrangian Method (ALM) is devised to effectively solve the proposed problem and explicitly show the clustering results. Experimental results on the benchmark datasets with different proprieties exhibit the superior performance of the proposed method.
A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging

Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is a non-invasive technique for quantifying cerebral blood flow (CBF). Limited by the T1 decay rate of the labeled spins, very short time is available for data acquisition after one spin labeling cycle, resulting in a low spatial resolution. The traditional strategy to achieve high spatial resolution in ASL MRI is to add more labeling cycles. However, the total acquisition time is exponentially prolonged, making it highly sensitive to motions. Moreover, signal-to-noise-ratio (SNR) drops as spatial resolution increases. There needs an alternative approach to improve spatial resolution and SNR for ASL MRI without increasing scan time. Therefore, we propose a novel two-stage multi-loss super-resolution (SR) network (TSMLSRNet) for reconstruction of high resolution ASL images. Specifically, the first stage network uses the mean squared error (MSE) loss function to produce a first SR estimate, while the second stage network adopts the gradient sensitive (GS) loss function to further improve high-frequency details for the output SR image. The multi-loss joint training strategy is finally used to preserve both the low-frequency and high-frequency information of the ASL images. Moreover, the noise in ASL images is simultaneously reduced. Validation results using in-vivo data clearly show the effectiveness of the proposed ASL SR algorithm that outperforms state-of-the-art image reconstruction algorithms.
Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features

Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against the state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images.
Moving vehicle tracking based on improved tracking–learning–detection algorithm

This study addresses the tracking–learning–detection (TLD) algorithm for long‐term single‐target tracking of moving vehicle from video streams. The problems leading to tracking failures in existing TLD methods are discovered, and an improved TLD (ITLD) tracking algorithm is proposed which is more robust to object occlusion and illumination variation. A square root cubature Kalman filter (SRCKF) is employed in the tracker of TLD to predict the position of the object when occlusion occurs. Besides, this study introduces fast retina keypoint (FREAK) feature into the tracker to alleviate the instability caused by illumination variation or scale variation. The overlap comparison and the normalised cross‐correlation coefficient (NCC) are introduced to the integrator of the TLD to obtain reliable bounding boxes with improved tracking precision. Experiments are conducted to compare the performance of the state‐of‐the‐art trackers and the proposed method, using the object tracking benchmark that includes 50 video sequences (OTB‐50) and TLD datasets. The experimental results show that the proposed ITLD outperforms on both tracking accuracy and robustness. The proposed method can track a moving vehicle even when it is temporally totally occluded.
Deep Instance-Level Hard Negative Mining Model for Histopathology Images

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i.e., patches) and the task is to predict a single class label to the WSI. However, in many real-life applications such as computational pathology, discovering the key instances that trigger the bag label is of great interest because it provides reasons for the decision made by the system. In this paper, we propose a deep convolutional neural network (CNN) model that addresses the primary task of a bag classification on a histopathology image and also learns to identify the response of each instance to provide interpretable results to the final prediction. We incorporate the attention mechanism into the proposed model to operate the transformation of instances and learn attention weights to allow us to find key patches. To perform a balanced training, we introduce adaptive weighing in each training bag to explicitly adjust the weight distribution in order to concentrate more on the contribution of hard samples. Based on the learned attention weights, we further develop a solution to boost the classification performance by generating the bags with hard negative instances. We conduct extensive experiments on colon and breast cancer histopathology data and show that our framework achieves state-of-the-art performance.
Text Document Clustering: Issues and Challenges

The text document clustering has become one of the foremost research areas in the field of data mining. The exponential growth of the textual data in today's digital world has bound the organizations to confront several challenges in organizing and browsing process. Although laborious work is done to make the process of clustering the text documents effortless, yet there are certain difficulties that emerge while dealing with a high volume of text documents. In this paper, the problems and challenges that come across while clustering a huge amount of text data are discussed and explained in detail.
Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification

The application of machine learning algorithms on text data is challenging in several ways, the greatest being the presence of sparse, high dimensional feature set. Feature selection methods are effective in reducing the dimensionality of the data and helps in improving the computational efficiency and the performance of the learned model. Recently, evolutionary computation (EC) methods have shown success in solving the feature selection problem. However, due to the requirement of a large number of evaluations, EC based feature selection methods on text data are computationally expensive. This paper examines the different evaluation classifiers used for EC based wrapper feature selection methods. A two-stage feature selection method is applied to twitter data for sentiment classification. In the first stage, a filter feature selection method based on Information Gain (IG) is applied. During the second stage, a comparison is made between 4 different EC feature selection methods, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search (CS) and Firefly Search, with different classifiers as subset evaluators. LibLinear, K Nearest neighbours (KNN) and Naive Bayes (NB) are the classifiers used for wrapper feature subset evaluation. Also, the time required for evaluating the feature subset for the chosen classifiers is computed. Finally, the effect of the application of this combined feature selection approach is evaluated using six different learners. Results demonstrate that LibLinear is computationally efficient and achieves the best performance.
Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review

The purpose of this study is to identify, summarize, and systematically compare various clustering and association rule techniques for web service discovery and recommendation, identify the most common data sets used in the extant literature, and highlight current trends and future research directions. Following the methodology of Kitchenham and Charters (Guidelines for performing Systematic Literature reviews in Software Engineering, 2007) for a systematic literature review (SLR), a set of research questions are designed. Six digital databases are searched. A total of 4581 papers were initially retrieved, and a rigorous two-stage scanning process resulted in 66 relevant papers. Based on the selection criteria and data extraction, 57 final studies were selected. These papers are summarized and compared, and the relevant information is extracted to answer the research questions. The synthesis resulted in knowledge of currently proposed methods for web service discovery and recommendation based on clustering and association rule techniques. Furthermore, it identifies algorithms, similarity measures, evaluation metrics, and data sets. Also identifies challenges, research gaps, trends, and future directions. We propose a classification of web service discovery and recommendation methods and map the 57 final selected papers into these classes. This review will help researchers to understand the current state-of-the-art in clustering and association rules techniques for web service discovery and recommendation, and also recognize trends and future directions for improvement. Future studies should broaden the basis of discovery and recommendation by including various types of web service descriptions including plain text that are currently used in web APIs. An opportunity for improvement by utilizing modern techniques based on big data analytics and social network analysis.
Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty

We present a novel approach to probabilistic image registration that leverages the strengths of deep-learning for modeling agreement between images. We use a deep multi-class classifier trained on different classes of patch pairs, including unrelated, registered, and a collection of discrete displacements between patches. The displacement classes alleviate the need for registration-time optimization by gradient descent; instead, posterior probabilities are used to directly predict expected values of displacements on the lattice of sampled locations. These, in turn, are used to update transformation parameters and the process is iterated. We empirically demonstrate the accuracy of our proposed method on deformable cross-modality registrations of brain MRI, and show improved results compared to Mutual Information based method on challenging data that includes simulated resections. Our approach enables local predictions of registration uncertainty and diagnostics that can indicate areas that seem unrelated in the two images. Uncertainty estimates provide end-users with intuitively actionable information on the quality of registration in interventional and surgical settings.