Reviewerly/exHarmony huggingface dataset:
Macro unit-based convolutional neural network for very light-weight deep learning

When implementing a deep neural network in an embedded system or SoC for mobile devices, its large parameter size can be a significant burden on the internal memory design. In this paper, we propose a new deep neural network that reduces computation and the number of model parameters but maintains reasonable performance. The configuration of the proposed network is as follows: First, we present a macro unit (MU) to reduce heavy computations and also to learn sufficient feature maps. Second, we employ asymmetric convolution of the well-known Inception network to further efficiently manipulate feature maps within the MU. Third, all the feature maps produced from MU(s) of each layer are concatenated and then the grouped feature map is distributed to all the MUs of the next layer for transferring richer information. Experimental results show that the proposed network achieves about 10% higher performance than DenseNet-BC in case of extremely small parameter size for CIFAR-100. The proposed network also has very few learning parameters and smaller floating point operations per second (FLOPS) than the other networks optimized for mobile devices such as MobileNet V2.
Classification of project management tool reviews using machine learning-based sentiment analysis

Managing the daily responsibilities in an organization is a great task for company administrators. Employing a dedicated project management tool is a great aid in all the phases of project management. With a variety of project management tools available in the market, it is a demanding task to look for a tool appropriate to the needs of the organization. It is an observation by scholars and suggests project management tool users tend to read first the reviews and comments of the product before deciding on the tool they are going to implement. This study facilitates sentiment analysis which can help identify effective and efficient project management tool. Microsoft Office Project is one of the most popular and most reviewed tools for more efficient project and portfolio management. The researchers manually extracted data from various websites containing comments and reviews from different users or reviewers. Machine learning-based-approach using RapidMiner was utilized to analyze the collected data from the web reviews. Sentiment analysis from MS Project reviews was applied using supervised learning K-nearest neighbor (KNN). The first level of classification involved classifying statements as "satisfied" and "dissatisfied." The second level of classification involved clustering sentiments as cost, experience, task, support and interface. Statements classified as satisfied is greater than dissatisfied. The best features to apply when classifying the PM tool reviews dataset are using stopwords, length, stemming and trigram.
SWISH

Mixed-reality haptic devices introduce a gateway to otherwise intangible virtual content, creating a life-like immersive experience. Congruent haptic sensation requires faithful integration of visual stimuli and perceived tactile sensation. Unfortunately, current commercial mixed-reality systems are unable to reproduce the physical sensation of fluid vessels, due to the shifting nature of fluid motion. To this end, we introduce SWISH, a novel type of ungrounded mixed-reality system, capable of affording the users a realistic haptic sensation of fluid behavior. We also present solutions to prominent challenges of rendering haptic fluid behavior, especially in coordinate translation and virtual adaptation to physical limitation. Our virtual-to-physical coupling uses Nvidia Flex's Unreal Engine integration, wirelessly controlling a motorized mechanical actuation system housed in a plastic "vessel''. In this paper we discuss the current state of SWISH and present results from our preliminary user study, followed by a description of our future planned phases.
A Relation Extraction System for Indian Languages

Relation Extraction is an important subtask of Information Extraction that involves extracting significant facts from natural language text.Extracting structured information from the plaintext is the ultimate goal of IE systems.The Indian language content on the internet is increasing day to day.Extracting relevant information from this huge unstructured data is a challenging task especially when the business firms are interested in ascertaining public view on their products and processes.The primary objective of relation extraction systems is to find those entities which can be targeted through social networking and digital marketing.Cannibalisation of the product is nowadays done using these Social Networks.Different methods are proposed and experimented for Relation extraction problems.In this paper, we propose a Relation Extraction system using Convolutional Neural Networks.Deep learning based methods have produced state of the art results in many domains.Training and testing are conducted using the shared corpus provided by 'ARNEKT-IECSIL 2018' competition organisers.The evaluation results show that the proposed system could outperform most of the reported methods in the competition.
Merging Realities in Space and Time

The rapid developments in online technology have provided young people with instant communication with each other and highly interactive and engaging visual game playing environments. The traditional ways of presenting museum and heritage assets no longer, therefore, hold their attention and provide them with an exciting and dynamic visitor experience. There is considerable interest in the use of augmented reality to allow visitors to explore worlds that are not immediately accessible to them and relating them to the real worlds around them. These are very effective in providing much needed contextual information, but appear rather static when compared with multi-player games environments where players interact with each other and robotic characters (non-player characters) in real time. By fusing these technologies, the authors postulate a new type of conceptually-led environment (cyber museum) that fuses real (physical), virtual worlds and cyber-social spaces into a single dynamic environment that provides a unique experience of exploring both worlds simultaneously.
Data Loss Prevention Using Document Semantic Signature

Data protection and insider threat detection and prevention are significant steps that organizations should take to enhance their internal security. Data loss prevention (DLP) is an emerging mechanism that is currently being used by organizations to detect and block unauthorized data transfers. Existing DLP approaches, however, face several practical challenges that limit their effectiveness. In this chapter, by extracting and analyzing document content semantic, we present a new DLP approach that addresses many existing challenges. We introduce the notion of a document semantic signature as a summarized representation of the document semantic. We show that the semantic signature can be used to detect a data leak by experimenting on a public dataset, yielding very encouraging detection effectiveness results including on average a false positive rate (FPR) of 6.71% and on average a detection rate (DR) of 84.47%.
Constructing two vietnamese corpora and building a lexical database

Corpus-based research has formed the backbone of linguistic research in recent decades. Large text corpora are used for solving various kinds of linguistic problems, including those of quantitative linguistics, cognitive linguistics, and psycholinguistics. This paper reports the creation of two corpora of contemporary Vietnamese. It also describes the construction of these two equally sized Vietnamese corpora (a corpus from Vietnamese film subtitles, subtlex-viet, and a general corpus of varieties of online newspapers and stories, genlex-viet). We document the general steps of the construction and extraction of linguistic information from the language corpora and provide a road map for others who would like to create similar corpora. The resultant corpora are available in three versions: plain text, tokenized, and POS tagged. In the second half of the paper, the construction of a lexical database derived from the corpora is described. The database includes measures such as frequency of occurrence, dispersion, Mutual Information, Inverse Document Frequency, as well as vector space measures based on Latent Semantic Analysis and Hyperspace Analogue to Language. We conclude by reporting a comparison of the lexical predictors and a validation using psycholinguistic data from visual lexical decision experiments.
Digital Forensic Analysis using Android Application Cache Data

When we use an application on Android, cache data is generated. This can be an important clue to the investigation and needs analysis. However, the present analysis is performed without understanding the generation principle of various cache artifacts, such as purpose and structure, and the data obtained through this simple analysis is used only indirectly for investigation because of lack of information. Therefore, in this paper, we expect to improve the usability of cache by analyzing the type, meaning, and structure of the cache that is stored when the application is used and suggesting an analysis method according to each cache.
Towards a Hybrid System for the Identification of Arabic and Latin Scripts in Printed and Handwritten Natures

This paper deals with the problem of script identification. Our aim is to use a global approach as a first step towards a hybrid one in order to resolve the problem of differentiation between the Arabic and the Latin scripts in printed and handwritten natures. In our proposed system, features are extracted using three tools namely, Gabor filters, Gray-Level Co-occurrence Matrices (GLCMs) and wavelets. The highest correct identification rate is obtained with GLCMs (84.75%). This result could be assessed as satisfactory when considering the simplicity of the approach and the complexity of the treated problem.
A Novel Method for Designing S-Boxes Based on an Improved Logistic Map

In this paper, we study the method for the construction of S-boxes. By a fractional-order differential equation, an improved logistic map is obtained. And then, we propose a S-box with excellent performances by using the chaotic map.
Depth Learning Standard Deviation Loss Function

Deep learning is a branch of the field of machine learning that outp-erforms humans in image and natural language processing. This mainly benefits from powerful computer processing capability and massive datasets. In the training model, the loss function needs to be used to evaluate each training result The quality of the loss function directly affects the correctness and validity of the model. In this paper, four kinds of common loss functions in deep learning are studied and our own loss function is proposed; Then the MNIST dataset is adopted to classify and train them; Finally, in the course of training, the change and the classification correct rate of the loss function value and model parameters are observed. Firstly, the different loss functions are experimented and their performance and application fields are analyzed, and secondly, our loss function is defined. After 30,000 times of iterative training, the test set data is used to test the loss function proposed, the correct rate reaches 98.53%, and achieves better accuracy in fewer training iterations, saving training times and resources.
Common Mistakes in Delivering Cybersecurity Awareness

Human error is the cause of over 95% of data breaches and the weakest aspect of cybersecurity in nearly all organizations. These errors guarantee that hackers can easily gain access to almost any network in the world and take complete control of systems, data, and more. This chapter outlines the top mistakes organizations make in security awareness and why most companies are failing to properly prepare their users for cyber-attacks. Each point is accompanied by actionable data derived from real-world training program successes and failures.
Wheelchair robot navigation in different weather conditions using deep learning and evolved neural controller

Purpose Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot navigation method in different weather conditions, with single camera vision to assist physically disabled people. Design/methodology/approach A road detection method, called dimensionality reduction deep belief neural network (DRDBNN), is proposed for drivable road detection. Due to the dimensionality reduction ability of the DRDBNN, it detects the drivable road area in a short time for controlling the robot in real-time. A feed-forward neural network is used to control the robot for the boundary following navigation using evolved neural controller (ENC). The robot detects road junction area and navigates throughout the road, except in road junction, using calibrated camera and ENC. In road junction, it takes turning decision using Google Maps data, thus reaching the final destination. Findings The developed method is tested on a wheelchair robot in real environments. Navigation in real environments indicates that the wheelchair robot moves safely from source to destination by following road boundary. The navigation performance in different weather conditions of the developed method has been demonstrated by the experiments. Originality/value The wheelchair robot can navigate in different weather conditions. The detection process is faster than that of the previous DBNN method. The proposed ENC uses only distance information from the detected road area and controls the robot for boundary following navigation. In addition, it uses Google Maps data for taking turning decision and navigation in road junctions.
Method and Model of Analysis of Possible Threats in User Authentication in Electronic Information Educational Environment of the University

The article presents the research results of the development of a method and mathematical model of subject authentication in the electronic information educational environment of universities (EIEEU). The problem was solved on the basis of processing updated data sets with software and hardware detection tools. It is proposed to separate the verification procedures for new threats and precedents that have already been entered into the knowledge base of protection and cybersecurity systems of EIEEU. A method for analyzing threats is described, which involves the use of information sets characterizing each of the threats by features typical of their implementation, and takes into account the information content of each of the feat user. It is shown that the use of the proposed method and mathematical model of the subject authentication in the EIEEU, allows allocating resources of information protection systems and cybersecurity of educational institutions effectively. A reduction in the number of erroneous and false reactions while identifying threats, as well as an increase of the probability of detection of new cyber threats in the EIEEU, are confirmed.
A Neighborhood Centroid Opposition-Based Grasshopper Optimization Algorithm

Grasshopper Optimization algorithm (GOA) is proposed in recent years as an intelligent optimization algorithm. GOA has the advantages of simple structure, less parameters, the characteristics of rapid convergence, but it's also need to further improve the population diversity and convergence precision. Therefore, for improve the development and exploration and exploitation ability of my algorithm, my paper proposed a Neighborhood Centroid Opposition-Based Grasshopper Optimization algorithm (NCOGOA). In NCOGOA, population is divided into multiple areas, direct interaction between the individual and to calculate reference point reverse point, for sufficient the group of search experience while maintaining the diversity of population. In this paper, NCOGOA is verified by 3 reference functions. Compared with five different classical algorithms, the experimental results find that GOA can get more accurate solutions than other group algorithms and with fast convergence and good stability.
Research on the Interaction Design of AR Picture Books via Usability Test

In children’s books, picture books account for seventy percent. With the development of information technology, picture book with Augmented Reality (AR) is an emerging application under the influences of digital technologies, which is a new kind of picture book with the auxiliary of games. The AR picture book bases on the children’s cognitive level and the operation of the mobile devices, satisfying their needs of exploring and learning new things. Under the literature review and competitive analysis, we exploratively put forward the prototypes of interactive AR picture books, especially for the kids aged from 5 to 8. Furthermore, we designed the usability test on one of our prototypes, Rocket Dream, to better understand the process of interaction design. According to the results, we testify the feasibility of AR picture books as well as figure out some issues in the interaction processes, such as the weak guidance in the interactive operation, incomplete essential information in the interface, as well as the fact that children’s preference of visible interaction and touch operations and they are more sensitive to large objects. Also, we propose potential solutions to these issues in the following parts, which could be useful for the future of AR picture books.
Opportunities for the Internet of Things in the Water, Sanitation and Hygiene Domain

Abstract This paper analyses the water, sanitation and hygiene (WASH) context in South Africa and presents views regarding the possible use of the Internet of Things (IoT) for WASH in South Africa. The views were informed by an analysis of literature related to the WASH domain nationally and internationally. Example case studies of the use of IoT in the WASH sector in developing countries, and where possible Africa, provided further background. As a triangulation exercise, a survey amongst various stakeholders in the WASH domain was conducted. To contextualise the WASH sector in South Africa, value chains were derived. To identity the opportunities for IoT, the findings of the study were combined with insights acquired on the IoT domain in general, both from literature and our experience and learnings.
Research and Implement of Course Early Warning System Based on Teaching Behaviour Data

Digital early warning research based on the course performance of students, is constantly advancing. However, there are a few scholars combing one of specific course, taking the early predication by digging and analysing the data of teaching behaviour and provide real-time intervention. This research, making Java program design as entry point design an overall framework for early warning system based on the data of teaching behaviour, and combines the certain algorithm to implementing the early course predication model with relatively higher accuracy by gathering and analysing students' past learning data as well as real-time teaching behaviour's data. Practice indicates that, according to the grade and GPA (grade point average) of pre-course as well as study behaviour during week 4 and week 6, there can be predicted course pass rate of students accurately, especially from week5, which has high accuracy of predicting. Moreover, the model is able to provide early intervention and automatically sending learning materials for some students who get unsatisfied performance basing on the predicating data, to improve the pass rate for students.
Effects of curved computer displays on visual performance, visual fatigue, and emotional image quality

Abstract This study investigated the effects of the curvature of curved computer displays on the visual performance, fatigue, and emotional image quality experienced by users during the performance of visual tasks. The characteristics of curved displays were compared and analyzed for different curvatures to investigate whether the curved displays showed improvements in terms of convenience of use when compared with flat displays. The visual performance results for the evaluated displays can be classified into three groups according to their response times, as (2000R = 2500R) &gt; (3000R) &gt; (3800R = 4500R = Flat). The visual fatigue results for average pupil size during a search task was 3.468 mm for flat and 3.355 mm for 2000R. This means that a 2000R curved display was found to be easier to use and thus generated less visual fatigue than a flat display when performing the same visual search task. Further, the results indicated that visual performance improved and visual fatigue decreased significantly as curvature increased. In this study, a curvature limit of 1700R was derived, when considering the image distortion of a 34‐in 21:9 curved display, and there was found to be no difference in the emotional image quality at curvatures greater than 2000R.
Improving question retrieval in community question answering service using dependency relations and question classification

To build an effective community question answering (cQA) service, determining ways to obtain questions similar to an input query question is a significant research issue. The major challenges for question retrieval in cQA are related to solving the lexical gap problem and estimating the relevance between questions. In this study, we first solve the lexical gap problem using a translation‐based language model (TRLM). Thereafter, we determine features and methods that are competent for estimating the relevance between two questions. For this purpose, we explore ways to use the results of a dependency parser and question classification for category information. Head‐dependent pairs are first extracted as bigram features, called dependency bigrams, from the analysis results of the dependency parser. The probability of each category is estimated using the softmax approach based on the scores of the classification results. Subsequently, we propose two retrieval models—the dependency‐based model (DM) and category‐based model (CM)—and they are applied to the previous model, TRLM. The experimental results demonstrate that the proposed methods significantly improve the performance of question retrieval in cQA services.
Enhanced pulse amplitude estimation for electronic warfare systems

In electronic warfare systems, the pulse amplitude (PA) of the received signals, which are emitted by enemies, has been used as one of the essential quantities for separating the mixed source signals. The PA is affected by time of arrival (TOA) and time of departure (TOD) estimation performance. Among methods of estimating TOA and TOD, there is a method of processing a signal in sample units in the time domain. This method has a disadvantage that it is completely affected by noise. This disadvantage can be overcome by using denoising techniques as in previous studies. However, conventional studies applying denoising are limited to the subject of signal detection, and there is no discussion on the problem of PA estimation due to the TOA and TOD estimation errors that occur after denoising. Therefore, we propose a power estimation-based PA estimation method using shrinked PW to be robust for TOA and TOD errors in sample units after wavelet packet denoising with high-order statistics. Simulation results show that the proposed PA estimation method has a smaller error at the same SNR when an unmodulated signal is received. Moreover, the proposed method can provide good performance even when a frequency modulated signal is received.