Reviewerly/exHarmony huggingface dataset:
Advanced Augmented Reality TAPS Software for Visualizing 4BL Mechanisms with Touch to Print Technique

Many learning methods have changed the way students learn. One method that is achieving much attention is augmented reality (AR). AR is a technology that blends simulated and real environment during the learning, interaction, and visualization process. As such, an AR ATAPS with a new interaction technique (touch-to-print) was designed, tested, and evaluated. The aim was to provide an improved user interface i.e. without having to use markers so as the learner could focus more on the visualization process. The AR ATAPS is capable of recognizing the 4BL mechanisms (based on Grashof's law and user input data) (i.e., drag-link, crank-rocker, double-rocker, and parallelogram linkage) and have been used in this study as an adjunct to traditional problem-solving method. The touch to print interaction technique, which is the main contribution of this research, has been useful to engage the user in the problem with a new interactive and learning experience as compared to the previous method (i.e., the use of markers to interact with a virtual object). The interaction technique method uses seven functions that are recognizable by the ATAPS (rotational, link colour change A, B, C, D, pause and voice command) for the user to touch the symbols on the paper and the system to model and analyze accordingly in real-time 3D environment. This study explores how far AR technology has come to support students in their learning and interest in using this technology. The objective of this chapter was to determine the usefulness of touch to print interaction user interface for an AR application. A hands-on practical lab was conducted with first year engineering students at UNITEN. The evaluation and effectiveness of the AR ATAPS as an alternative to textbooks and current software learning packages was examined by means of a single-institutional evaluation study using mainly statistical quantitative techniques and ANOVA analysis. The prospective study (total sample size = 30) at University Tenaga Nasional (UNITEN) validated aspects of AR ATAPS interaction technique and provided feedback on the interface design and its problem-solving method. The results of the study showed that most of the participants never been experienced with AR applications before, but the ideas of implementing AR as a simulation tool for learning the kinesthetic and dynamic subjects is well accepted with a very beneficial feedback. Based on the findings, it was found that there is a positive changing in terms of the visualizing and imagining of the four-bar linkage mechanisms (4BL) which led to a good understanding of this subject. Further development of AR applications in the learning environment is being discussed.
Exploration And Practice Of Blended Teaching Based On Mobile Internet And Matlab : Taking The Course Of Mathematical Analysis As Example

Mathematical analysis is the most important course for students majoring in mathematics and engineering. This article discusses the blended teaching based on mobile internet and Matlab in the teaching of Mathematical Analysis course in local colleges and universities. The article first discusses the background of construction of the blended golden course. Then the importance of mathematical analysis courses and the necessity of developing blended teaching via mobile internet and Matlab are explained. Thirdly, we discuss how to use mobile internet and Matlab in the blended teaching for Mathematical Analysis course in local undergraduate colleges. Finally, we use the statistical u-test model to show that blended teaching based on mobile internet and Matlab has effectively improved the teaching effect and academic performance of students.
Ambiguity and Clarification

By the time web engines were developed, the number of queries prompted by users had grown exponentially. This fast growth shows the high demand of users from web search engines. This high demand made search engines responsible for the users' satisfaction during a search session. One way to improve a user's satisfaction is to visualize search engine result page (SERP). Recent studies for meeting this aim focused on a whole page thumbnail for assisting users to remember recently visited web pages. This chapter explores how a specific visual content of a page can allow users to distinguish between a useful and worthless page within results in SERP especially in an ambiguous search task.
Underwater Image Refinement Using Color Cast and Wavelet Transform

A fusion based image refinement system has been presented here to enhance the underwater images as they afflicted from inconsistency in lighting and a lack of contrast, blurriness and vilified color. The considered strategy is based on fusion principle which focusses on input images, white balance measurement from the degraded or noised underwater image and wavelet transform.The method practically aims to yield an image that overcomes the deficiencies of initial image or noised image which lacks clear visibility by employing several white balancing maps and then applying wavelet transform on the above mentioned techniques.Underwater image acquired has the characteristics as reduction in noise levels. The fusion of images involves combining of two or more pictures into one and the resulting reconstructed image is the better quality of image. By focusing on fusion based techniques certain aspects such as resolution of the initial image has also been compromised, but using this method defining the proper inputs and white balancing weights are utilized from the original degraded image which helps a lot in the image visibility. Enhanced results are being obtained by two input underwater images in a pre-pixel fashion. The overall results have been discussed and is found that the method provides superior quality of the underwater images which has high noised ratio and more color disruption in initial input underwater images.
Operating System Security and Access Control

Initially, protection meant limiting memory addresses accessible to processes, in conjunction with early virtual memory address translation, and access control lists were developed to enable resource sharing. These remain protection fundamentals. Learning about such protection in operating systems provides a solid basis for understanding computer security. Aside from Unix, we base our discussion in large part on Multics; its segmented virtual addressing, access control, and protection rings heavily influenced later systems. Providing security-related details of all major operating systems is not our goal—rather, considering features of a few specific, real systems allows a coherent coverage highlighting principles and exposing core issues important in any system design. Unix of course has many flavors and cousins including Linux, making it a good choice.
A Low Cost Pupillometry Approach

The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-cost web camera in real time. The proposed approach detects the face and the eyes area at first stage. Subsequently, optimal iris and sclera location and radius, modeled as ellipses, are found using efficient spatial filtering. As a final step, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental results show both the efficiency and the effectiveness of our approach.
An efficient face recognition attack method based on generative adversarial networks and cosine metrics

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks.
A Framework for Self-Tuning Algorithms

All nature-inspired algorithms have algorithm-dependent parameters. Tuning such parameters can be a challenging task. This chapter introduces the objective of parameter tuning and highlights the challenges in this area.
Exploring the Use of Cloud Computing Systems in Tertiary Education

Research on cloud computing systems has largely focused on the technology and security issues at the expense of understanding users' experiences with the technology. Through a phenomenological approach, this study explored the lived experiences of faculty members of tertiary institutions with respect to their use of cloud computing systems. We carried out an in-depth phenomenological interview among faculty members of selected tertiary institutions in Ghana. We explored their lived experiences on the use of cloud computing systems for academic activities. The results point to tensions between faculty members' identity and information privacy on one hand, and the requirement to effectively execute their academic and professional responsibilities. We conclude that while cloud computing systems enhance the reach and richness of academic information dissemination and academic interactions, faculty members must establish personal and professional boundaries in order to derive meaningful benefits from the use of such systems
Research on the Application of Classification Model Algorithm in English Grammar Error Correction

As the main international common language, the popularity of English is showing a growing trend, which makes the research of English grammatical error correction receive more and more attention. In order to explore the application effect of classification model algorithm in English grammar error correction, this study focuses on the classification model algorithm based on GRU neural network, and analyzes left and right GRU operations and attention mechanism operations. The results show that the classification model algorithm based on the GRU neural network has a high accuracy and recall rate, and can efficiently implement error correction for different types of English grammar.
When Function Signature Recovery Meets Compiler Optimization

Control-Flow Integrity (CFI) [] is a promising technique in defending against control-flow hijacking attacks [, , , ] by enforcing that runtime control flows follow valid paths in the program’s Control-Flow Graph (CFG).
Matching Sensor Ontologies with Neural Network

With the widespread adoption of sensors, many research efforts in recent years have focused on the wireless sensor networks. Since a wireless sensor network consists of a large number of sensors, its trusted communication become a hot research topic. Sensor ontology matching technology is able to solve the sensor information heterogeneity problem, which ensures the communication quality among different wireless sensor networks. In the matching process, different classes of similarity measures have different contributions in matching sensor ontologies. How to determine the optimal weights to aggregate multiple similarity measures to obtain high quality ontology alignment becomes a challenge in sensor ontology matching domain. To face this challenge, this work proposes a neural network-based sensor ontology matching technique. In particular, a single layer perceptron is used to aggregate multiple similarity measures and the neural model is trained with different training examples to obtain higher ontology matching accuracy. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matchers under different domain knowledge such as bibliographic and real sensor ontologies.
A Scene Text Detection Algorithm with Multiple Feature Fusion Based on Multiple Kernel Support Vector Machine

Since the text information in the image includes rich semantic meaning, the detection of scene text has very important meaning. The traditional use of single or certain feature methods to detect text is not ideal in complex scenes, and the use of deep learning methods requires a lot of calculations and a large number of training samples. In the case of a small sample field, the detection effect is not satisfactory. Satisfactory. This paper proposes a method based on the fusion of multiple features such as character geometric features and high-level features, and uses multi-core SVM to detect the detection area. By using multiple feature fusion, the text area can be detected more effectively in complex scenes. Multi-core functions can avoid the limitations of single-core functions, thereby improving its performance. Experimental results show that this method can effectively detect scene text.
Introduction

Through its flexibility, human body experience can integrate technical artifacts. This includes not only passive tools but also advanced robotic devices and is, especially, promising for situations with tight human-robot interaction. As the interaction of humans and robots is recently getting closer, e.g., in collaborative working scenarios or healthcare applications, an improved understanding of how devices are embodied by their users is of immense societal potential. To explore how human body experience and human-robot interaction relate to each other, fundamental aspects of body experience and representations need to be understood and this knowledge has to be made accessible to engineering design. This chapter gives a detailed motivation of why the consideration of human body experience is an important aspect of the evaluation of human-robot interaction and human-machine interfaces. A concise description of the objectives of this monograph outlines the need for an interdisciplinary approach and explains the focus on human-in-the-loop experiments and haptic interfaces. Finally, the structure of the monograph and the content of the individual parts and chapters are presented.
DYNAMIC STOP LIST FOR THE GUJARATI LANGUAGE USING RULE BASED APPROACH

Text classification, document cluster, and similar endeavors area unit important analysis spaces underpinning a large vary of problems and applications within the area of internet intelligence. A typical approach, once cluster or otherwise classifying documents algorithmically, is to represent an online document as a vector of numbers derived from the frequencies of words therein document, wherever the words area unit taken from the complete assortment of documents into consideration. A tried approach is to use an inventory of “stop” words (also known as a ‘stop list’), that establish frequent words like ‘and’ and ‘or’ which might be unlikely to Words within the stop list area unit thus not enclosed within the vector that represents a document. Current stop lists area unit arguably noncurrent within the lightweight of fluctuations in word usage, and conjointly arguably innocent of sure candidate generic stop lists, thus delivery into question that we tend to explore this by developing new stop lists in an exceedingly rule based method.
Incorporating Other Models and Technology Into the CCSMM

One thing about the nature of computer science in general and cybersecurity in particular is that they are both fields that are constantly changing. Whether it is because of a new version of an operating system being released, new technology that has been introduced, or a disclosure of a newly discovered vulnerability, the field is continually changing. Some changes will not have any impact on the CCSMM. Others may necessitate a change in some aspect at one or more levels. The model itself is extremely flexible and frequently does not specify the precise items that need to be covered but rather the more abstract concept that must be considered. This is true for not just changes in technology but also the introduction of new government guidance or regulations as well as the creation of other maturity models that are focused on some other aspect of cybersecurity. This chapter explores incorporating other models and technology into the CCSMM.
Detection Protocol of Possible Crime Scenes Using Internet of Things (IoT)

Forensics is a science that deals with using scientific principles in order to aid an investigation of a civil or criminal crime. It is a system of procedures that allow an investigator to use as much resources as possible in order to come up with a conclusion for an investigation. Since forensics is a very general term that encompasses an investigation process using scientific knowledge, one can separate a system of investigation based on how it is conducted. This chapter introduces of internet of things (IoT) forensics, IoT application in forensics field. Art-of-states for IoT forensics are provided. The issues for IoT forensics are identified. Also, we have introduced the proposed data classification in Iot forensics protocol. At the end of this chapter, we point out a brief summary and conclusion.
Grouping of Questions From a Question Bank Using Partition-Based Clustering

During automatic test paper generation, it is necessary to detect percentage of similarity among questions and thereby avoid repetition of questions. In order to detect repeated questions, the authors have designed and implemented a similarity matrix-based grouping algorithm. Grouping algorithms are widely used in multidisciplinary fields such as data mining, image analysis, and bioinformatics. This chapter proposes the use of grouping strategy-based partition algorithm for clustering the questions in a question bank. It includes a new approach for computing the question similarity matrix and use of the matrix in clustering the questions. The grouping algorithm extracts n module-wise questions, compute n × n similarity matrix by performing n × (n-1)/2 pair-wise question vector comparisons, and uses the matrix in formulating question clusters. Grouping algorithm has been found efficient in reducing the best-case time complexity, O (n× (n-1)/2 log n) of hierarchical approach to O (n × (n-1)/2).
Generalized Deep Implicit Surface Network for Image-based Three-dimensional Object Reconstruction

Three-dimensional(3D) Object Reconstruction is an activate field in Computer Vision and Computer Graphics. Deep Implicit Surface Network(DISN) utilizes Signed Distance Function(SDF) to generate a high-quality 3D mesh model from a 2D image. However this method can only handle the image with a clear background, since it was trained with rendered images from ShapeNet dataset. To solve this problem, we propose a mask generating method based on saliency map to make DISN more generalized. By performing saliency detection algorithm and morphological operations on image, the object to be reconstruct can be obtained and background would be removed. In order to enhance the representing ability of local features, we propose a channel-weighted local feature extraction algorithm. Using modified sigmoid function, discriminative channels in feature map would have larger weights than others. We evaluate our methods on Pix 3D and Pascal 3D+ datasets. Experimental results show that our methods perform better than other works.
Missing Value Imputation Using ANN Optimized by Genetic Algorithm

Missing value arises in almost all serious statistical analyses and creates numerous problems in processing data in databases. In real world applications, information may be missing due to instrumental errors, optional fields and non-response to some questions in surveys, data entry errors, etc. Most of the data mining techniques need analysis of complete data without any missing information and this induces researchers to develop efficient methods to handle them. It is one of the most important areas where research is being carried out for a long time in various domains. The objective of this article is to handle missing data, using an evolutionary (genetic) algorithm including some relatively simple methodologies that can often yield reasonable results. The proposed method uses genetic algorithm and multi-layer perceptron (MLP) for accurately predicting missing data with higher accuracy.
Advances in Information, Security, Privacy and Ethics

The future of education lays in the hand of Cloud Computing given the benefits of learning delivery, costs reduction, and innovation. At the same time, the threat of cyber-attacks and security breaches are also mounting for education based organizations and are a prime target given the amount and type of personal information available. This manuscript discusses the cloud security, privacy, and ethical mechanisms required from a teacher, student, and administrator perspective.
Privacy Preserving in Big Data: Summary of Classification techniques

A lot of data that AI systems and Machine learning algorithm use are sensitive data. As an example we can list Medical records, Genetic Data, and Search logs data which are among the top list in the sensitive data parsed to an AI system. Training a machine learning model on the these datasets leaks sensitive users' information. Differential Privacy is a gold standard defined to protect individuals privacy. In this paper, we provide a summary of works done on differentially private classification tasks. This includes Differentially Private Decision Tree, SVM and Naive Bayes classifier.
University Students' Perceptions of Personal Mobile Devices in the Classroom and Policies

This chapter discussed the results of a study that explored students' perceptions of personal mobile devices in the classroom and suggestions for policies. Thirty-four students enrolled in two undergraduate courses taught at a Brazilian higher education institution took part in the study. Data collection consisted of a survey and focus group interview. Quantitative data suggested an overall tendency to rare use of the devices for content and non-content activities. Qualitative results, however, showed that students may have used more often their devices in class. The results discussed several policies recommended by the students ranging from allowing the devices for content and emergency to not using social media for off- task activities. The study suggested that inappropriate use of mobile technology in the classroom may be minimized if students participate in the development of policies, and instructors integrate the devices in class to promote engagement and interest among students. Recommendations for practice and future research are discussed.