Reviewerly/exHarmony huggingface dataset:
Are Variable, Array, and Object Diagrams in Introductory Java Textbooks Explanative?

Diagrams in textbooks are essential tools in explaining concepts. An effective diagram helps a novice learner build a runnable mental model, increase recall and improve problem-solving skills. Richard Mayer suggests that for a diagram to be effective, it must beexplanative and, thus, document two major features: \textitsystem topology andcomponent behaviour. We surveyed diagrams of variables, arrays and objects in 15 commonly used introductory Java textbooks to investigate if the diagrams are explanative. This abstract presents the analysis of arrays.
Indian New Currency Denomination Identification System with Audio Feedback Using Convolution Neural Networks for Visually Challenged and Elderly People

The visually challenged people lack the ability to perform activities for daily living in general and instrumental daily living activities in particular. The major problems faced by visually challenged people and elderly people with low vision in pursuing money transactions are to distinguish between an original and fake currency note and identify the correct denomination of the given currency note. This paper proposes a hand held portable device that implements a currency recognition algorithm developed using customized Convolution neural networks. The proposed device has the ability to distinguish between original and fake currency notes and recognize the denomination of the currency notes. Further, it sends the information regarding the currency denomination by an audio feedback to the visually challenged user thus enabling him to do money transactions on his own. The performance of the proposed system is verified under real time by giving it to the actual user and the accuracy of the proposed system is found to be 97.4% executed within 10 s of time.
Semi-supervised Learning to Rank with Nonlinear Preference Model

The problem of learning to rank in static and dynamic formulation is considered, wherein expert or user’s preference function model is used as a ranking function. In order to reduce dimension of the problem, ranking learning problem on clusters in feature space is stated, while aggregated training dataset consist of estimates of clusters centers and average rank of the items inside each cluster. The problem of both linear and nonlinear models of preference functions building is considered, and in the latter case the method of kernel-based learning in pointwise and pairwise framework. is used. Estimates preference function models are found as solutions of corresponding regularized optimization problems, and a hierarchical regularization scheme is implemented with successive use of a priori estimates of feature weights and estimates of linear model parameters. Within semi-supervised learning concept, unlabeled dataset provides additional regularization for estimated preference model smoothing using the graph data model, considering data geometric structure. Online ranking learning is implemented using training dataset in the form of a sequence of identical items series, described by measured features and relative rank within the series. Recurrent algorithms for estimating the parameters of preference function model as well as recurrent ranking learning algorithm in the space of conjugate variables are developed.
Abstract Model Based on Location Attention and Competition Mechanism

It is a challenging task to obtain the key semantic information in sentences, which affects the accuracy of abstract generated. A summary model based on localization attention mechanism to extract key semantic information is designed. It mainly consists of three parts: a sequence-to-sequence network based on a location attention and a flashback structure, a selection gate encoder network and a competition mechanism. Firstly, keywords are used to increase the weights of words in key sentences by probability superposition of important information. Secondly, the attention mechanism allocates the weight of the every word in sentences to locate key semantic information. It is used by the decoder to generate the summary. At the same time, key sentences are used on the selection gate encoder network. The semantic allocation probability of words in the sentences is calculated by the key information, then it is decoded to generate a second version of the summary. Furthermore, a competition mechanism is designed to optimize abstract generated by both networks by calculating the cosine distance between both versions of abstract and key sentences. The experimental results show that the test Rouge-1, Rouge-2, Rouge-f are 38.17%, 22.24% and 34.97%, respectively. It indicates that the proposed model outperforms other existing models.
Information Security Issues For Travel Companies

The article deals with the theoretical and methodological foundations of scientific research of information aspects of tourism security. The place of the concepts of "tourism security" and the information component in the structure of the category "tourism security" are defined. The key problems of information security of travel companies due to information threats are identified. The essence and ways of providing certain components of information security of tourism are characterized. The prospects of further research of information aspects of tourism security are determined.
A Study on Vocal Tract Shape Estimation and Modelling of Vocal Tract

Nowadays, electronics is widespread and seeking its scope in medical and health sector enabling different tasks for persons with disability either partial or full. Diseases like asthma, Parkinson’s, etc., make the patient unable to speak. The person suffering from asthma losses muscle functionality, also unable to speak properly in the severe cases. In case of Parkinson’s diseases (PD) affected person, the voice is very vibrating in nature which is difficult to understand. From the ancient times till day, many researchers have provided different types of speech production systems that reproduce the speech very similar to the natural one. This paper reviews different speech models including some of the first mechanical model of speech synthesizer that produce five vowels, the first electrical model of vocal tract that produces continuous speech, an analogue integrated circuit vocal tract chip and many more.
Saliency Detection with Stepwise Aggregation Network

Recently, benefiting from the progress of deep learning, salient object detection based on Convolutional Neural Networks (CNNs) has achieved significant improvement. For most methods that build upon Fully Convolutional Neural Network (FCN), how to utilize information from hierarchical features is a research hotspot. In this paper, we propose a novel aggregation module to integrate information through a step-by-step fusion mechanism. First, we construct a widely used encoder-decoder network as our framework and combine the features from encoder and decoder in the first step of our Stepwise Aggregation Module (SAM) to obtain a more comprehensive feature. Then, we feed the new feature map and the saliency map generated by previous stage into the Recurrent Integration Layer (RIL) to fetch information. Compared with previous methods, the proposed mechanism is simpler and more efficient. Experiments verify the effectiveness of our method. On five benchmarks, our model achieves the state-of-art performance.
Research on the Current Status of Sparse Neural Network Acceleration Processing Technology in Deep Learning

With the development of neural network, the demand of storage and memory bandwidth is increasing rapidly. At the same time, most of the data in the neural network can be reduced and compressed. This paper analyzes various techniques of neural network acceleration, compares the advantages and disadvantages of these techniques, and looks forward to some applicable methods of sparse neural network acceleration.
What’s the body got to do with it? Exploring embodied, political readings in the classroom through poetry

This essay attempts to explore the links between politics, poetry, and collective, embodied readings and discussion in the classroom. When my Year 12 class were asked ‘What is Poetry?’, their answers suggested something in the Romantic tradition – of poetry as expressive, individual and emotional. My experience studying poetry with my Year 9 class suggests something altogether different – that studying poetry allows students to have an embodied and collective reading. In the sense that politics concerns the control of bodies, poetry allows the class to read in an embodied sense, thus politically, in a way that assessment criteria and the demands of high-stakes testing, with their focus on individual understanding, do not. Thus, I argue for the study of poetry as essential in terms of recognising students’ political agency – not primarily as exam-takers but as citizens of a country and an Earth they will, to put it grandly, inherit.
On Sensing Principles Using Temporally Extended Bar Codes

The detection of ionic variation patterns could be a significant marker for the diagnosis of neurological and other diseases. This paper introduces a novel idea for training chemical sensors to recognise patterns of ionic variations. By using an external voltage signal, a sensor can be trained to output distinct time-series signals depending on the state of the ionic solution. Those sequences can be analysed by a relatively simple readout layer for diagnostic purposes. The idea is demonstrated on a chemical sensor that is sensitive to zinc ions with a simple goal of classifying zinc ionic variations as either stable or varying. The study features both theoretical and experimental results. By extensive numerical simulations, it has been shown that the proposed method works successfully in silico. Distinct time-series signals are found which occur with a high probability under only one class of ionic variations. The related experimental results point in the right direction.
Two-Way Sequence Modeling for Context-Aware Recommender Systems with Multiple Interactive Bidirectional Gated Recurrent Unit

For modeling the user behavior in recommender systems, the task of combining the contexts of interactions corresponds to the sequential item history has inevitable role in improving the quality of recommendations. The resort of existing recommendation models is the left-to-right autoregressive training approach. While training a certain model at a specific time step, both future (right) context/data along with the past (left) is always available in the given training set sequences. It is intuitive that the current behavior of the user has certain connections with their future actions too. Future behaviors of users can boost the quality of recommendations. In this paper, two-way sequence modeling technique is proposed for concatenating both left-to-right (past) and right-to-left (future) dependencies in a user interaction sequence. Inspired from the text modeling techniques, a Multiple interactive Bidirectional Gated Recurrent Unit (MiBiGRU) architecture is proposed to model the two-way dependencies in recommender systems. Modeling future contexts along with past contexts is an auspicious way for attaining better recommendation accuracy.
Very Robust Direct Solver of the P3P Problem

In this paper, we propose a robust and novel direct solver for solving the Perspective-Three-Point (P3P) problem, which is called RP3P by us. Although the P3P has been studied for many years and achieved great success, it still faces the following three limitations at least: (1) The instability of solutions caused by different ordering of vertices, (2) geometric degradation in the singular case, (3) depending on the specific equation solver. The main method of the proposed solver is to use two auxiliary variables to simplify the calculation process. Additionally, the depth of the three-dimension control points is normalized to simplify the process of derivation, and then the depth of three-dimension control points can be obtained in a simple fashion. RP3P does not depend on particular equation solver and has high stability in image noise. Moreover, in “singular cylinder”, even if all other classic direct solutions are seriously degraded, the proposed solution can still obtain reliable solution.
Online Heterogeneous Mixture Learning for Big Data

We propose the online machine learning for big data analysis with heterogeneity. We performed an experiment to compare the accuracy of each iteration between batch one and online one. It is possible to converge quickly with the same accuracy as the batch one.
Profile Expression Recognition with Head-Mounted Spherical Camera

How to recognize the face expression of a user moving freely is new problem to be coped with. In this paper, we propose a novel method of face expression recognition via the profile image captured by a head-mounted spherical camera. First, we created a fisheye profile image dataset. Then, we trained a 3D convolutional neural network proposed by DU Tran [1] for dynamic expression recognition. As shown in the experimental results, we achieved as high as 72.2% face expression recognition rate.
Stabilizing Neural Search Ranking Models

Neural search ranking models have been not only actively studied in the information retrieval community, but also widely adopted in real-world industrial applications. However, due to the non-convexity and stochastic training of neural model formulations, the obtained models are unstable in the sense that model predictions can vary a lot for two models trained with the same configuration. In practice, new features are continuously introduced and new model architectures are explored to improve model effectiveness. In these cases, the instability of neural models leads to unnecessary document ranking changes for a large portion of queries. Such changes not only lead to inconsistent user experience, but also add noise to online experimentation and can slow down model improvement cycles. How to stabilize neural search ranking models during model update is an important but largely unexplored problem. Motivated by trigger analysis, we suggest balancing the trade-off between performance improvement and the number of affected queries. Concretely, we formulate it as an optimization problem with the objective as maximizing the average effect over the affected queries. We propose two heuristics and one theory-guided stabilization method to solve the optimization problem. Our proposed methods are evaluated on two of the world’s largest personal search services: Gmail search and Google Drive search. Empirical results show that our proposed methods are very effective in optimizing the proposed objective and are applicable to different model update scenarios.
외국인 학부 신입생의 학문적 문식력 변화 양상 연구

The purpose of this study was to examine the academic literacy change of foreign undergraduate students. It analyzed the compositions of foreign undergraduates who took the course ‘Thinking and Writing’ over two semesters at K university. Twelve students participated in this study, and they each wrote about four topics. Each composition was more than 600 characters, and a total of 48 compositions were analyzed. Academic literacy is “the ability to understand and produce text in a shared way in an academic community”. This study evaluated academic literacy using the categories of linguistic literacy, critical literacy, cultural literacy, and information literacy. After evaluating the academic literacy in the 48 compositions, we concluded that undergraduates progressively changed their academic literacy over a year, even though the rate had fallen to a specific point. The foreign undergraduate freshmen students in our study received relatively high scores for their cultural literacy and the content aspects of their linguistic literacy, while the expression aspects of their linguistic literacy received low scores. Although the improvement in cultural literacy was somewhat high through a communication course, such as a first year college writing class, the improvement in the expression aspects of linguistic literacy was somewhat low. It was apparent that the improvement in academic literacy was evenly achieved. In addition, when we looked at the changes in the academic literacy of upper, middle, and lower-level students, we saw different aspects of individual change. (Korea University)
Investigating Software Testing Practices in Software Development Organizations

Software testing, which is a knowledge-intensive and collaborative activity, is a sub-area of software engineering. Software testing knowledge can be applied to different testing tasks and purposes. Since software development is an error-prone task, in order to achieve quality software products, validation and verification should be carried throughout the development. This study, using qualitative methods, investigates the current practice of software testing practices in two software companies on the basis that they both claimed to apply software testing practices in their software development work. Interview results revealed some interesting latest trends in software testing from both companies.
A Method for Human Parsing Based on Deep Learning and Attention Mechanism

Human parsing is thought as a specific image semantic segmentation task. Most existing methods adopt encoder- decoder framework, and make full use of global context to achieve better image segmentation effect. A model is proposed in this paper which uses Convolutional Block Attention Module to select more discriminate feature and refinement residual learning to learn residual representation between input and output. The experiment results shows that our proposed model can achieve better performance on our dataset than other networks. From the generated human body segmentation images, the model can achieve more details and semantic consistency.
Awareness Logic: an Epistemological Defence Correlations between Awareness Logic and Epistemology

Abstract with this paper, we intend to clarify some of the central notions that are commonly used in contemporary developments of Epistemic Logic, which lack a proper theoretical foundation. We want to follow the steps of some prominent epistemologists and epistemic logicians, who advocate for a correlation between their respective fields of study. We will proceed with a first small step that will consist in adapting one contemporary version of Epistemic Internalism to the framework of Awareness Logic, such that the key concepts in this logic can be sustained by an epistemological view, which, in turn, can work as a theoretical foundation for Awareness Logic.
A Procedure for Solving a Problem of Classification with Functions for Comparing Multi-Type Features

This paper presents an approach for classifying objects describing via multi-type feature. A problem of classification on based (precedents of) training set. Multi-type features divide several groups, and for each group of features special elementary logical classifiers will be built. Here, the focus is on features expressed by fuzzy sets. Part of the result is a comparison of the values of one fuzzy sign.
The Human Factor: The Most Important Asset for Successful Integration of Information and Communication Technologies in Education

The human factor may be the key to success as far as effectively integrating information and communication technologies into education are concerned.It can also represent the most unavoidable obstacle that prevents to reach this mostly aimed at achievement.As a matter of fact, to attain a satisfactory situation of ICT equipment with favorable indicators such as an ideal computer / student ratio, an internet connection available in all schools, teachers with private facilities, certainly does not mean that the result will be a thriving integration of ICT into education.This contribution provides a theoretical and empirical perspective concerning this issue with a case study of Algerian schools, revealing that the human factor represented by school directors, teachers, technical support staff, staff to monitor students using computers, is the most important asset for successful integration of ICT in education.
A Method for Content-Based Image Retrieval with a Two-Stage Feature Matching

Content-based image retrieval is an active area of research where image content is used to guide the search of relevant images from a dataset. Given a query image, the images in the dataset are ranked in terms of their scores of similarity to this image based on their visual appearance. Many existing algorithms are based on either single feature or the fusion of multi-features with a one-step search method, which may lead to undesirable results due to the mismatch between low-level features and high-level semantics. To address this issue, we propose a two-stage sequential search algorithm where the color feature, represented by a color histogram in the HSV space, is used to form an image set containing images of similar color distributions to that of the query image, then a second stage of search is performed via the matching of feature points, in terms of discrete wavelet transform (DWT), and the scale invariant feature transform (SIFT) feature, extracted from a low-frequency subgraph. Experiments are performed on the ZuBuD dataset and UKBench dataset. Compared to some state-of-the-art algorithms, the proposed algorithm gives higher precision score.
The Importance of the Human-Centric Approach in Combating Cyber Threats

The world faces increasing prevalence of cyber threats and cyberattacks despite advancements in technological defences against them. Cyber perpetrators are constantly looking to exploit any vulnerabilities in the computer and network systems, which humans are unfortunately the weakness link. Although they are aware of the threats and know what should be done in order to protect themselves and their organisations, people are still not engaging enough in these cyber hygiene practices. This article consequently attempts to understand human behaviours in cyberspace, providing insights to the reasons for this phenomenon and what can be done to improve it.
Dynamic Image Quality Analysis and Comparison between 6MHz and 8MHz Bandwidths for DVB-C Digital TV System

The object of this research is to analyze and compare the dynamic image quality between the DVB-C digital TV systems with bandwidth of 8 MHz and 6 MHz, in Europe and in Taiwan, respectively. Under the RF output level from -50dBm to-79dBm, with carrier modulation of either 64QAM or 256QAM, we simulate the dynamic image transmission over the digital TV system with standard measuring equipment on the following criteria, namely, the picture quality rating (PQR), the differential mean opinion score (DMOS), and the peak signal to noise ratio (PSNR). The standard measuring equipment, which is the property of the Telecommunication Technical Center (TTC), is composed of the instruments from the Tektronix and the Rode & Swazi (R & S). Based on our experiment setup, under the 64QAM carrier modulation at -78dBm level, the scores for the above three criteria, i.e., PQR, DMOS, and PSNR, are 4, 11, and 33, respectively for the European DTV system. Under the 64QAM carrier modulation at -72dBm level, the scores for the above three criteria, are 5, 14, and 38, respectively for the Taiwan DTV system. Under the 256QAM carrier modulation at -73dBm level, there is observable mosaic on the image for the European system. However, under the 256QAM carrier modulation at -68dBm level, there is serious mosaic on the image for the Taiwan system.