Reviewerly/exHarmony huggingface dataset:
A Fast and Efficient Segmentation of Soil-Transmitted Helminths Through Various Color Models and k-Means Clustering

Soil-transmitted helminths (STH) are one of the causes of health problems in children and adults. Based on a large number of helminthiases cases that have been diagnosed, a productive system is required for the identification and classification of STH in ensuring the health of the people is guaranteed. This paper presents a fast and efficient method to segment two types of STH; Ascaris Lumbricoides Ova (ALO) and Trichuris Trichiura Ova (TTO) based on the analysis of various color models. Firstly, the ALO and TTO images are enhanced using modified global contrast stretching (MGCS) technique, followed by the extraction of color components from various color models. In this study, segmentation based on various color models such as RGB, HSV, L*a*b and NSTC have been used to identify, simplify and extract the particular color needed. Then, k-means clustering is used to segment the color component images into three clusters region which are target (helminth eggs), unwanted and background regions. Then, additional processing steps are applied on the segmented images to remove the unwanted region from the images and to restore the information of the images. The proposed techniques have been evaluated on 100 images of ALO and TTO. Results obtained show saturation component of HSV color model is the most suitable color component to be used with the k-means clustering technique on ALO and TTO images which achieve segmentation performance of 99.06% for accuracy, 99.31% for specificity and 95.06% for sensitivity.
Learning Programming in Virtual Reality Environments

With the increasing popularity of Virtual Reality (VR), more and more studies have been undertaken to identify the applications of VR in different industries, especially in education. There are studies that have developed VR scenarios for training and education. However, few empirical research has been conducted on effects of VR games in higher education. This paper is to study students’ acceptance of VR games in higher education and their actual performance through VR learning games. We propose that learning in VR games will positively motivate students’ learning attitude in higher education, and learning in VR games will positively improve college students’ learning in computer programming.
Visualizing Progress Tracking for Software Teams on Large Collaborative Touch Displays

Keeping track of the progress on software projects can be difficult and time consuming. Progress tracking requires developers to track progress by hand or digitally neither of which have good support for collaborative team processes. In this paper we present DashVis a tool to help support teams to track progress more effectively using large touch displays and visualization techniques. We conducted a study and found the visualizations to be very effective in supporting teams to gain a more accurate way of keeping track of progress. With large touch displays becoming ubiquitous in the work place and the demand for software teams to understand their progress more effectively there is a need for tools like DashVis.
On the tracking of sensitive data and confidential executions

The production of large amounts of sensitive data raises growing concerns on confidentiality guarantees. Considering this, it is natural that data owners have an interest in how their data are being used. In this work, we propose Data aNd Application Tracking (DNAT), a trustworthy platform for tracking the executions of applications over sensitive data in untrusted environments. For traceability purposes, we use blockchain and smart contracts, and to guarantee execution confidentiality and, especially, enforce that operations are appropriately logged in the blockchain, we use Intel SGX. Experiments show that tracking costs on Ethereum varies from 1 to 61 cents of a US dollar, depending on the operation and urgency for consolidation. The time cost of confidential execution is associated with the SGX overhead. It increases non-linearly initially but has a linear growth rate when data and application size gets much higher than the available enclave page cache (≈ 93 MB).
Translation and Expansion: Enabling Laypeople Access to the COVID-19 Academic Collection

Academic collections, such as COVID-19 Open Research Dataset (CORD-19), contain a large number of scholarly articles regarding COVID-19 and other related viruses. These articles represent the latest development in combating COVID-19 pandemic in various disciplines. However, it is difficult for laypeople to access these articles due to the term mismatch problem caused by their limited medical knowledge. In this article, we present an effort of helping laypeople to access the CORD-19 collection by translating and expanding laypeople's keywords to their corresponding medical terminology using the National Library of Medicine's Consumer Health Vocabulary. We then developed a retrieval system called Search engine for Laypeople to access the COVID-19 literature (SLAC) using open-source software. Utilizing Centers for Disease Control and Prevention's FAQ questions as the basis for developing common questions that laypeople could be interested in, we performed a set of experiments for testing the SLAC system and the translation and expansion (T&E) process. Our experiment results demonstrate that the T&E process indeed helped to overcome the term mismatch problem and mapped laypeople terms to the medical terms in the academic articles. But we also found that not all laypeople's search topics are meaningful to search on the CORD-19 collection. This indicates the scope and the limitation of enabling laypeople to search on academic article collection for obtaining high-quality information.
Perceptions and Attitudes of Future Primary Education Teachers on Technology and Inclusive Education

The goal of the current research was to explore the perceptions of the students in the last year of a degree in Primary Education towards technology and inclusive education. It employed a mixed method research with a combined use of qualitative and quantitative methods. The research population was comprised of 153 learners, and the quantitative sample consisted of 104 participants and the qualitative one 57. Quantitative data collection was conducted using a validated questionnaire, while the qualitative data were collected through individual semi-structured interviews. On analysing the results, it was emphasized the effectiveness of technology to design and implementation of inclusive practices. However, the results also pointed out the necessity to train students technically as well as pedagogically in the use of technological tools. Moreover, it was highlighted the lack of training in inclusive education and inclusive practices during the degree, training that students considered essential to be ready for their future careers.
Bias-Variance Trade-Off in Continuous Test Norming

In continuous test norming, the test score distribution is estimated as a continuous function of predictor(s). A flexible approach for norm estimation is the use of generalized additive models for location, scale, and shape. It is unknown how sensitive their estimates are to model flexibility and sample size. Generally, a flexible model that fits at the population level has smaller bias than its restricted nonfitting version, yet it has larger sampling variability. We investigated how model flexibility relates to bias, variance, and total variability in estimates of normalized z scores under empirically relevant conditions, involving the skew Student t and normal distributions as population distributions. We considered both transversal and longitudinal assumption violations. We found that models with too strict distributional assumptions yield biased estimates, whereas too flexible models yield increased variance. The skew Student t distribution, unlike the Box-Cox Power Exponential distribution, appeared problematic to estimate for normally distributed data. Recommendations for empirical norming practice are provided.
Variational Auto-Encoder for text generation

Many different methods to text generation have been introduced in the past. Recurrent neural network language(RNNLM) is powerful and scalable for text generation in unsupervised generative modeling. We extended the RNNLM and propose the Variational Auto-Encoder Recurrent Neural Network(VAE-RNNLM), which designed to explicitly capture such global features as continuous latent variable. Maximum likelihood learning in such a model presents an intractable inference problem. VAE-RNNLM circumvents these difficulties by using the architecture of the latest advance in variational inference to introduce a practical training technique for powerful neural network generative models with latent variables. In this paper, we using VAE-RNNLM for text generation and achieved good performance.
Novelty Discovery with Kernel Minimum Enclosing Balls

We introduce the idea of utilizing ensembles of Kernel Minimum Enclosing Balls to detect novel datapoints. To this end, we propose a novelty scoring methodology that is based on combining outcomes of the corresponding characteristic functions of a set of fitted balls. We empirically evaluate our model by presenting experiments on synthetic as well as real world datasets.
Text Duplicated-checking Algorithm Implementation Based on Natural Language Semantic Analysis

Natural language is a tool and way to express the interaction between computer and human (NATURAL) language. At present, the analysis ability of text semantics is relatively mature. The clustering algorithm, probability graph model algorithm, text mining algorithm and other processing methods have been successfully implemented. This paper is based on natural language processing text, combined with word2vec word vector conversion technology, through similarity calculation, using its semantic analysis ability to construct an optimized LDA model which refers to an importance sampling idea to extract topic words and use cosine similarity to calculate the repetition rate. We can achieve an ideal semantic text duplicate analysis effect by comparing it with the results of the LDA model before and after optimization.
Speaker Verification Using Cosine Distance Scoring with i-vector Approach

In this paper, a robust yet simple speaker verification system is implemented. The speaker verification system is investigated employing the i-vector approach with the Cosine Distance Scoring (CDS) for system classification. In addition, to measure the system performance, Equal Error Rate (EER), Detection Error Trade-off (DET) Curve, Receiver Operating Characteristic (ROC) curve as well as Detection Cost Function (DCF) were utilized. Experimental results are conducted on the TMIT database using 64 randomly selected speakers. The proposed system utilizes the Mel Frequency Cepstral Coefficients (MFCC) and Power Normalized Cepstral Coefficients (PNCC) for feature extraction. In addition, features normalization methods such as Feature Warping (FW) and Cepstral Mean-Variance Normalization (CMVN) are used in order to mitigate channel effect noise. The speakers are modeled with the i-vector while CDS is used for classification. Experimental results demonstrate that the proposed system achieved promising results while being computationally efficient.
Alleviating Load Imbalance in Data Processing for Large-Scale Deep Learning

Scalable deep learning remains an onerous challenge, as it is constrained by many factors, including those related to load imbalance. For many deep-learning software systems, multiple data-processing components-including neural network training, graph scheduling, input pipeline, and gradient synchronization-execute simultaneously and asynchronously. Such execution can cause the various data-processing components to contend with one another for the hardware resources, leading to severe load imbalance and, in turn, degraded scalability. In this paper, we present an in-depth analysis of state-of-the-art deep-learning software, TensorFlow and Horovod, to understand their scalability limitations. Based on this analysis, we propose four novel solutions that minimize resource contention and improve deep-learning performance by up to 35% for training various neural networks on 24,576 GPUs of the Summit supercomputer at Oak Ridge National Laboratory.
Data Comets: Designing a Visualization Tool for Analyzing Autonomous Aerial Vehicle Logs with Grounded Evaluation

Abstract Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of D ata C omets , an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g
Instructional media for space geometry based on augmented reality to improve students’ spatial reasoning

Abstract Technology development is growing rapidly in the 21st century. It takes a thorough preparation in various fields to deal with this condition, including in education field. Technology can be used to help learning process in the classroom particularly as an instructional media. This research aims to determine the effectiveness of instructional media of space geometry based on augmented reality in improving students’ spatial reasoning. The data were collected in one junior high schools in Yogyakarta-Indonesia. Thirty-three students of grade 8th were involved in this study. The results show that learning media is feasible to use and effective to improve students’ spatial reasoning as shown from the spatial test in which most of the students can solve the spatial reasoning tests and the spatial score were increase from 49 to 79.35 from the maximum score 100. This finding implies that AR instructional media could enhance spatial skill as it provides better visualization.
Efficient Secure Pattern Matching with Malicious Adversaries

In a secure pattern matching scheme, a client learns only the locations where his private pattern matches a server’s private text, while server learns nothing. In this article, we propose a secure pattern matching protocol for the semi-honest setting which is then enhanced to guarantee full simulation-based security in the presence of malicious parties. The proposed protocol supports exact pattern matching, approximate pattern matching and pattern matching with wildcards. It is analytically shown that the proposed protocol is considerably more efficient in the approximate matching with at most <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> permitted mismatches while it has the same speed in the exact matching case comparing with the recent work in the literature. The achievements are also experimentally evaluated on a case of secure Desoxyribo-Nucleic Acid (DNA) search over the NCBI dataset of the United States national library of medicine. The results show efficiency of the proposed protocol and particularly confirm low computation overhead for the client.
Facial Features Extraction Using LBP for Human Age Estimation Based on SVM Classifier

Research on age estimation witnessed increasing attention due to the demand for its applications. The age estimation has an essential role in preventing under-age persons from performing adult activities. The proposed age estimation technique is carried out through several stages; preprocessing, feature extraction and then age classification. In this paper, the Local Binary Pattern (LBP) algorithm is adopted to extract the face features focusing on selecting the best possible combination among all the features produced from the LBP algorithm. Feature Selection Method (FSM) is employed to increase the accuracy. FSM yields better results compared to other techniques' results. Support Vector Machine (SVM) is used to classify the tested person image and assign that person to the related age. Results conducted using MATLAB produced accuracy of 93.81% with FSM technique compared to 81.61% without it. When damaged images are excluded from the database used for training, the accuracy is increased to 94.57%.
Tired of Wake Words?

In this paper, we aim to draw attention towards wake words. Wake words are an integral part of every request addressed to Intelligent Personal Assistants (IPAs). Currently, a request made to an IPA is led by wake words, making a conversation with an IPA more tiresome than a conversation with a human being. The main question we pose in this paper is, whether we can eliminate the use of wake words at least in specific contexts. Based on our experience with IPAs we propose three less burdensome alternatives that avoid the need for speaking wake words in some cases. Based on these approaches we discuss how to design seamless conversations with IPAs.
Speaker Identification in Multi-Talker Overlapping Speech Using Neural Networks

Although numerous works have studied the problem of automatic speaker identification (SID), there are only few works on the SID for overlapping speech, and none of them consider the case of more than two simultaneous speakers. Recognizing that overlapping speech occurs frequently in real-life scenarios, such as in meetings or debates, this work investigates the methods for overlapping SID (OSID) that can determine identities in the overlapping speech from up to five simultaneous speakers. We propose two deep-learning OSID systems, one is two-stage and the other is single-stage. The two-stage system determines the number of simultaneous speakers firstly, followed by identifying the speaker(s). The single-stage system uses a single classifier to perform OSID directly, which is slightly more computationally efficient than the two-stage system. Our experiments show that the two-stage OSID system achieves better identification accuracy than that of the single-stage system. In addition, both the OSID systems based on one-dimensional convolutional neural networks (1DCNN) perform better than the systems based on multilayer perceptron (MLP) and Gaussian mixture models (GMMs). The proposed 1DCNN-based two-stage OSID system achieves 98.55% OSID accuracy for the clean audio data containing up to five simultaneous speakers. In more challenging experimental conditions involving both background noises and high overlapping energy ratios, the system still attained accuracies of above 90%.
Let's Go There

Hand-tracking has been advertised as a natural means to engage with a virtual environment that also enhances the feeling of presence in and lowers the barriers to entry to virtual reality. We seek to explore combining hand-tracking with voice input (which is then processed with automatic speech recognition) for a novel multimodal experience. Thus, we created Let's Go There, which explores this joint-input method for four functions in virtual reality environments: positioning, object identification, information mapping, and disambiguation. This combination may serve as a more intuitive means for users to communicate and navigate in virtual environments. We expect there to be multiple potential applications of this multimodal form of interaction across numerous domains including training, education, teamwork, and games.
Gazing Without Eyes: A “Stare-in-the-Crowd” Effect Induced by Simple Geometric Shapes

Of the many effects that eye contact has, perhaps the most powerful is the stare-in-the-crowd effect, wherein faces are detected more readily when they look directly toward you. This is commonly attributed to others' eyes being especially salient visual stimuli, but here we ask whether stares-in-the-crowd might arise instead from a deeper property that the eyes (but not only the eyes) signify: the direction of others' attention and intentions. In fact, even simple geometric shapes can be seen as intentional, as when numerous randomly scattered cones are all consistently pointing at you. Accordingly, we show here that cones directed at the observer are detected faster (in fields of averted cones) than are cones averted away from the observer (in fields of directed cones). These results suggest that perceived intentionality itself captures attention-and that even in the absence of eyes, others' directed attention stands out in a crowd.
Nurses’ Participation in the Design of an Intensive Care Unit: The Use of Virtual Mock-Ups

Co-design with multiple tools is useful when end users' knowledge is important, especially when designers work with people unfamiliar with design. Many studies have highlighted the importance of nurses' participation in design, and such participation requires the development of techniques and tools to facilitate collaboration. This article analyzes how nurses participated in designing a general intensive care unit in a walk-in virtual environment (VE) and examines how their work-related knowledge can be transferred to the design process of spaces.In this action research study, the design process was conducted by using virtual mock-ups, which were evaluated by multi-occupational groups in a walk-in VE. Nurses were the largest occupational group. Their work processes were under modification, since existing multi-patient rooms were being redesigned as single-patient rooms. The design of single-patient rooms was performed in three iterative cycles in the walk-in VE.The nurses could specify their requirements in the walk-in VE, and their suggestions were incorporated into the architectural design process. The nurses were satisfied with their role in the design process.Co-design with virtual mock-ups in walk-in VE is appropriate when designing new healthcare facilities and when the opinions of workers are important. Virtual mock-ups in walk-in VE can be used collaboratively, facilitating simultaneous feedback from multiple users. Virtual reality (VR) technology has evolved, and changes can be made rapidly and at a lower cost. Another advantage of VR is that it allows one to design larger spaces, thus providing larger layouts of facilities for evaluation.
Resolving the imbalance issue in short messaging service spam dataset using cost-sensitive techniques

Mobile spam messages have become one of the main concerns in the field of short messaging service (SMS) due to its negative impact on mobile users and networks. The current literature lacks effective solutions for this issue. In this study, the negative impacts of SMS spam were thoroughly analysed, and the existing techniques for SMS spam detection were investigated through two experiments. The first experiment was performed to test and compare the current data mining and cost-sensitive techniques, whereas the second experiment was conducted to test the performance of the proposed technique. Based on the experimental results of the first phase, the most optimal non-cost classifier is a Bayesian network classifier, which is well behaved under the cost-sensitive classifier and obtained the lowest rate of false negative and an acceptable false positive rate. The proposed strategy achieves the best performance in terms of false negative SMS spam classification, obtaining the smallest total expenses and highest precision amongst the compared strategies.