Reviewerly/exHarmony huggingface dataset:
Promoting EFL learners’ exposure to multiculturalism using Skype in the classroom – a case study

The study aims at indicating the effectiveness of employing Skype in the classroom to promote chances for EFL learners to expose to multiculturalism. Action research was carried out via a one-year case study on a teacher-participant. The data was collected by observation during Skype connections, the participant’s reflections before and after the connections, and interview of the teacher-participant for his reflections for enhancing his learners’ chances for acquiring multiculturalism. The findings of the study showed that Skype benefited the teacher-participant in facilitating multiculturalism in EFL learning contexts, how Skype could be useful for multicultural connections, and the development of learners’ common curiosity about other cultures through the exposure to multiculturalism. /Keywords: multiculturalism, Skype in the classroom, language teaching and learning
Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples

Two-alternative forced-choice (2AFC) reader studies are useful for evaluating medical imaging devices because humans can rapidly make direct comparisons with high precision leading to low variability in study results. We propose a method for estimating the receiver operating characteristic (ROC) curve, reader performance (area under the ROC curve, AUC), and uncertainty on AUC from a series of 2AFC trials on a finite data set. Our method greatly reduces the number of 2AFC comparisons required by using an algorithm created for sorting, in this case Merge Sort. By altering the algorithm to work in discrete layers, we can make unbiased estimates as the study proceeds. Because the merging is pre-planned with a tree structure, we can use a Hanley-McNeil approximation to predict the reduction in variance in AUC from performing more 2AFC comparisons. The algorithm is also altered to increase the amount of time between the reader seeing the same image repeatedly thus decreasing potential learning. We compare our method with that of Massanes and Brankov (2016).
Modified Quick Fuzzy Hypersphere Neural Network for Pattern Classification Using Supervised Clustering

Mane, D. T. Kshirsagar, J. P. Kulkarni, U. V.Defining methods which map the features present in the input to a binomial class output is the core objective of pattern classification. The Modified Quick Fuzzy Hypersphere Neural Network (MQFHSNN) algorithm proposed is an extension of the existing Class-Specific Fuzzy Hypersphere Neural Network (CSFHSNN). Its membership function has been modified such that it outperforms the existing algorithms in the field of pattern classification. Fuzzy set hyperspheres are employed by MQFHSNN for pattern clustering and classes are represented by a combination of fuzzy set hypersphere. By using class labels for forming the clusters, the presented model gives 100% accuracy on the training data. The core contributors to facilitate learning in MQFHSNN are: creation of hyperspheres based on measurements provided by interclass and intraclass distance techniques as well as decision of patterns of individual classes by the fuzzy membership function. The proposed model is put into test on four standard datasets and the results obtained are more superior to the existing models. This leads us to the conclusion that MQFHSNN is being used for improvising model accuracy in pattern classification.
FORMATIVE EVALUATION IN THE SLOVAK REPUBLIC

Slovak education system is based on the normative evaluation forms favouring summative evaluation of education (Shewbridge, C., Bruggen, J., Nusche, D. and P. Wright. (2014), OECD Reviews of Evaluation and Assessment in Education: Slovak Republic 2014, OECD Reviews of Evaluation and Assessment in Education, OECD Publishing, Paris). The OECD evaluation team noted that summative evaluation prevails in Slovakia, such as quizzes, followed by formal tests at the end of a unit which main purpose is to provide the basis for the choice of marks awarded to pupils at the end of each term. Feedback is limited mostly to what the pupil should be better at, rather than on how the pupil can achieve this improvement. The OECD evaluation team pointed out that regular assessment of pupils in Slovakia is not formative enough, and that there also exists a strong need for feedback to pupils in order to improve their learning. The evaluation team suggests that "elements of formative assessment" should be introduced into the Slovak education system, since these elements can capture not only "what" and "with what result” pupils learn, but also "how" they learn.Therefore, the aim of the research was to map out the state of formative assessment and its use in teaching in Slovakia with teachers of Natural Sciences, Mathematics and Informatics, i.e. whether teachers use only traditional methods of evaluation leading to summative assessment, or also various forms of formative assessment. The research was conducted during the school year 2017/2018. The survey was conducted on a sample of more than 700 teachers from Slovak schools. Respondents were provided with an electronic questionnaire that had been created as a part of a national project called “IT Academy – education for the 21st century”. To test the relationship between variables in the set hypotheses, we used chi-squared test and we also examined the relationships between variables using the Spearman correlation coefficient (rho). The research results have pointed out that the teachers of Natural Sciences, Mathematics and Informatics in Slovakia use mainly summative assessment tools in teaching (tests, short quizzes, homework) as well as formative assessment tools. The very concept of "formative assessment" is new for many teachers – they do not understand it, they do not know formative assessment tools and their functions. The idea of some teachers about forms of formative assessment such as self-assessment and peer assessment – is wrong. All of this implies a need to not only educate teachers in the methods of active learning, but also to pay attention to strategies connected with evaluation, especially formative assessment of the pupils‘ learning process. This can be achieved by organizing trainings focused on formative assessment methods in teaching.Acknowledgements:This paper was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences – grant VEGA 1/0265/17 “Formative Assessment in Teaching of Natural Sciences, Mathematics, and Informatics.”
Intelligent Gender Recognition System for Classification of Gender in Malaysian Demographic

Identification of a person gender as a man or woman based on the past experiences through features of face such as eyes, mouth, cheek can be obtained through an intelligent gender recognition system. Detection of a person’s gender can be difficult but important for security purposes, especially where safety issues concerning woman in public amenities. The objectives of this research are to identify the techniques for classifying features from man and woman facial images, through which embed as a system and validify using photos within Malaysian demographic. This research is focused on utilizing facial features for gender classification in real time, emphasizing on deep learning-based gender recognition and HAAR Cascade classifier using pre-trained caffe model in OpenCV library. Results show that under Malaysian demographic, probability of 86% accuracy of gender recognition were obtained.
Efforts towards Combining Graphics, Uncertainty, and Semantics: A Survey

Modeling the real world is a basic but vital task in computer science. Graphics, uncertainty, and semantics are three key aspects of understanding structure and "why" when handling complex relationships with imperfect or unknown information. The combination of the three aspects can provide a systematic and effective method for modeling the real world. This paper presents a survey of the efforts towards combining these aspects. One branch of the efforts is to combine graphics and uncertainty as probabilistic graphical models (PGMs), and then associate PGMs with semantics. The other branch is to combine graphics and semantics as graph-based knowledge representations, and then add the probability to handle uncertainty. We introduce the models and methods involved in these efforts and discuss the expressiveness, pros and cons of them. Finally, we suggest future work in this domain.
Digital literacy as a key factor in the successful adaptation of individuals and societies to digitalizing reality

The authors have summarized and systematized the most substantial global experience in the implementation of digital literacy as a factor in successful functioning of social subjects in the digitalizing world. They are offering an original interpretation of the concept of digital literacy as a sociological category, and outlined the ways of further research in this field.
<i>Flipgrid.com – An easy-to-use free classroom student video site (website and smartphone app)</i>

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Region Merging with Texture Pattern for Segmentation of SAR Images

This paper proposes a novel merging cost with texture pattern discrimination for region merging in the segmentation of SAR images, which integrates texture patterns, statistical characteristics, and shape prior of SAR images. By the initial partition based on multiscale Bhattacharyya distance, the input image is first over-segmented into region patches. Then, in the region merging process, a correlation measure of texture patterns between adjacent patches is constructed to obtain the proposed merging cost by fusing it with statistical similarity measure (SSM) and the relative common boundary length penalty (RCBLP) according to the pixel number of region patches. Finally, the segmentation result is output by iteratively merging the pair of adjacent regions with the smallest merging cost until the end condition is satisfied. Experimental results on real SAR images indicate that the proposed method is competitive in the segmentation of SAR images with complex scenes in comparison with several recent state-of-the-art ones.
Robust Feature Matching via Multi-Scale Grid Structure

Feature matching is a critical prerequisite of many computer vision tasks. This paper focuses on differentiating inliers and outliers from a set of putative feature correspondences to obtain a satisfactory matching result. In order to better exploit the spatial consistency of the true matches, we introduce the multi-scale grid structure into the feature matching problem, reducing the time complexity of neighborhood construction simultaneously. Furthermore, to make the motion statistics more reliable, the multi-neighbor structure is utilized to boost the robustness. Experimental results reveal that our method outperforms other state-of-the-art feature matching methods both in accuracy and efficiency.
An Efficient and Practical Quantum Blind Signature Protocol with Relaxed Security Model

Blind signature has a wide range of applications in the fields of E-commerce and block-chain because it can effectively prevent the blind signer from getting the original message with its blindness. Owing to the potential unconditional security, quantum blind signature (QBS) is more advantageous than the classical ones. In this paper, an efficient and practical quantum blind signature scheme relaxed security model is presented, where quantum superposition, decoy qubits and hash function are used for the purpose of blindness. Compared with previous QBS scheme, the presented scheme is more efficient and practical with a relaxed security model, in which the signer’s dishonest behavior can be detected other than being prevented as in other QBS schemes.
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation

The most recent fast and accurate image segmentation methods are built upon fully convolutional deep neural networks. In this paper, we propose new deep learning strategies for DenseNets to improve segmenting images with subtle differences in intensity values and features. We aim to segment brain tissue on infant brain MRI at about 6 months of age where white matter and gray matter of the developing brain show similar T1 and T2 relaxation times, thus appear to have similar intensity values on both T1- and T2-weighted MRI scans. Brain tissue segmentation at this age is, therefore, very challenging. To this end, we propose an exclusive multi-label training strategy to segment the mutually exclusive brain tissues with similarity loss functions that automatically balance the training based on class prevalence. Using our proposed training strategy based on similarity loss functions and patch prediction fusion we decrease the number of parameters in the network, reduce the complexity of the training process focusing the attention on less number of tasks, while mitigating the effects of data imbalance between labels and inaccuracies near patch borders. By taking advantage of these strategies we were able to perform fast image segmentation (90 seconds per 3D volume), using a network with less parameters than many state-of-the-art networks, overcoming issues such as 3Dvs2D training and large vs small patch size selection, while achieving the top performance in segmenting brain tissue among all methods tested in first and second round submissions of the isointense infant brain MRI segmentation (iSeg) challenge according to the official challenge test results. Our proposed strategy improves the training process through balanced training and by reducing its complexity while providing a trained model that works for any size input image and is fast and more accurate than many state-of-the-art methods.
Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models

Most existing methods of effort estimations in software development are manual, labor-intensive and subjective, resulting in overestimation with bidding fail, and underestimation with money loss. This paper investigates effectiveness of sequence models on estimating development effort, in the form of man-months, from software project data. Four architectures; (1) Average word-vector with Multi-layer Perceptron (MLP), (2) Average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model are compared in terms of man-months difference. The approach is evaluated using two datasets; ISEM (1,573 English software project descriptions) and ISBSG (9,100 software projects data), where the former is a raw text and the latter is a structured data table explained the characteristic of a software project. The LSTM sequence model achieves the lowest and the second lowest mean absolute errors, which are 0.705 and 14.077 man-months for ISEM and ISBSG datasets respectively. The MLP model achieves the lowest mean absolute errors which is 14.069 for ISBSG datasets.
3D registration of multiple surface measurements using projected random patterns

When repairing worn components it is crucial to have detailed knowledge of the current object’s state. For this purpose a multi-sensor system was developed to measure objects in different scales and modalities. This work focuses on the 3-D measurement of worn turbine blades using a fringe projection system. The 3-D geometry of turbine blades is crucial for the overall performance and safety of an engine. Therefore it is not sufficient to rely on single fringe projection measurements for a functional evaluation. To obtain a 3-D model the blade has to be measured from multiple directions. Gathered data are combined to form the model. This process is called registration or stitching. To reduce uncertainties during the process markers can be applied on or near the measurement object. However, common methods using markers are insufficient in automatability and feature density and therefore are not applicable in this case. In this work a novel registration strategy based on projected random patterns is developed. Multiple projectors are placed around the object to illuminate its geometry. Keypoints are identified by capturing additional grayscale images and applying state-of-the art feature detection algorithms. Feature matching is performed on consecutive measurements. Matches are preprocessed and a random sample consensus approach is chosen to calculate the rigid body transformation. Multiple measurements of the turbine blade and other geometries have been successfully aligned using the proposed strategy. Beyond that the high density of features allows the alignment of measurements with different scales and resolutions.
Hash Algorithm Optimization for Long-Span Digital Currency Transactions Based on Multi-Constraint Optimization

Block chain technology and satellite integration to achieve wide-area security transactions has become a hot topic in the field of information security. It will face two major problems: the real-time transmission of large-span digital currency and the credibility of digital currency transmission under complex interference. In order to solve the above problems, this paper proposes a multi-constrained optimization-based hashing algorithm for long-span digital currency transactions. By analyzing satellite communication means and one-way hashing operation, the relationship between time delays under long-span digital currency transmission is obtained. By optimizing the hashing operation of constraints, the algorithm is improved largely. The adaptive ability of bit error rate (BER) and the construction of digital token based on multi-constraint enhanced hash algorithm provide a reliable authentication benchmark for digital currency transactions, design a large-span digital currency transaction process, and effectively suppress the information leakage caused by interference in the transmission process. The simulation results show that the performance of the proposed algorithm is greatly improved in security and complexity.
Author Correction: Self-verifying variational quantum simulation of lattice models

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Investigation of Side Channel Leakage of FeRAM Using Discrete Wavelet Transform

Ferroelectric RAM (FeRAM) is a Non-Volatile Memory (NVM) which offers high endurance, fast speed, low power and high density. Thus, they are suitable to replace conventional memories such as Dynamic RAM (DRAM). However, FeRAM are vulnerable to side channel attack. An adversary can measure the current drawn by the memory during read and write operation and find corresponding data being written to it or being read from the memory. This is a major problem for FeRAM as it would compromise the security of data being transmitted over communication networks. This work, for the first time, investigates the vulnerability of FeRAMs with noise. We have used Discrete Wavelet Transform (DWT) to reduce measurement noise since it is widely used for reducing noise from different type of signals. SureShrink operation is performed for noise reduction of write and read current. The analysis of noise filtered write and read current reveals that, FeRAM write operation is vulnerable to side channel attack whereas its read operation is resilient to such attacks since the data signature in the read current is minimal and vanishes due to measurement/environment noise.
Robo-Advisors: Exploring and Leveraging the Competition

There are lot of talks about robo-advisors taking the jobs of human financial advisors. Amid the excitement or anxiety about robots doing better than humans in many aspects, there is little discussion on the potential limitations of machines in taking over certain aspects of the financial planning process. What are the technologies behind robo-advisory systems, what are the pitfalls, and will they really replace human advisors? In this paper, I review recent literature on robo-advice and conclude that human advisors adopting robo-advisors into their practices will enhance their practices even further and deliver better outcomes than either standalone robo-advisors or human advisors.
Providing Reusability-Aware Recommendations

As contemporary software development relies more on software reuse, several systems have been designed to automate the process of finding reusable software components from online sources and integrating them to one’s source code. However, these systems focus on whether the proposed components cover the desired functionality, without assessing also their reusability. In this chapter, we present a recommendation system for source code components that covers both the functional and the quality aspects of component reuse. Our system, which is named QualBoa, retrieves components from online repositories and reports their functional matching to the query as well as their reusability score, which is based on configurable thresholds of source code metrics. Upon providing an example usage scenario and evaluating QualBoa, we conclude that it is effective for recommending reusable source code.
Automatic Identification of Speakers From Head Gestures in a Narration

In this work, we focus on quantifying speaker identity information encoded in the head gestures of speakers, while they narrate a story. We hypothesize that the head gestures over a long duration have speaker-specific patterns. To establish this, we consider a classification problem to identify speakers from head gestures. We represent every head orientation as a triplet of Euler angles and a sequence of head orientations as head gestures. We use a database having recordings from 24 speakers where the head movements are recorded using a motion capture device, with each subject narrating ten stories. We get the best speaker identification accuracy of 0.836 using head gestures over a duration of 40 seconds. Further, the accuracy increases by combining decisions from multiple 40 second windows when a recording is available with duration more than the window length. We achieve an average accuracy of 0.9875 on our database when the entire recording is used. Analysis of the speaker identification performance over 40 second windows across a recording reveals that the speaker-identity information is more prevalent in some parts of a story than others.
Investigation of Requirements Interdependencies in Existing Techniques of Requirements Prioritization

Requirements prioritization (RP) is considered as a key role in producing a successful system by selecting the most important requirements to be released. Requirements interdependencies (RI) is one of the crucial aspects that need to be addressed in RP, since most of the requirements in reality are not independent and have dependencies between each other. Thus, ignoring RI in RP process may lead to produce inaccurate prioritization result which directly impacts the system’s success. In spite of this, little is known about the impact of RI, and obviously further research is urgently required to measure the RI in the RP techniques. Hence, this study aims to investigate and analyze the existence and the execution steps of handling RI in the existing RP techniques to improve the performance of techniques in generating accurate result and assist the researchers and practitioners to select the appropriate technique that can handle RI in prioritization process. The findings indicate that, out of 65 techniques, there are only 4 techniques that handle the RI. The result reveals that these four techniques still suffer from issues of manual process and heavily rely on the experts’ participation. Proposing a new technique is recommended to overcome the identified limitations.