Reviewerly/exHarmony huggingface dataset:
Classification of hand over face gestures using deep learning

Facial expressions and hand gestures are recognized as a part of human emotions especially in a feeling of fatigue signs. In computer vision research, a positioning of hand over face is one of the challenging problem caused by difficulty of the difference of skin color for hands and face. In this paper, we present a method for classifying six positions of the hand over face which is able to identify the signs of feeling fatigue for the visual display terminal (VDT) workers. We apply a deep learning method in order to compare with the methods used the face and skin colors detection, processing the edge detection and feature extraction algorithms. In addition, GoogleNet is used for training a data set made in the simulated VDT workers environment. The data set includes 1,440 images, the participants from several countries, Egypt, Japan, Bangladesh, Mongolia and Rwanda to cover a wide range of skin tones. The data set is categorized by six groups of the positions of hand over face. These groups consist of hands on a forehead, eyes, nose, mouth, right and left face. The experiments were performed using MATLAB to implement our proposed method. The system achieved average recognition ratio 99.3 % in all hand over face gestures.
Convolutional Neural Networks for Scene Image Recognition

Words are the most indispensable information in human life. It is important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level meaning information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, the research on text information recognition has made great achievements. However, in the face of dealing with text characters in natural scenes, there are certain limitations in the recognition of natural scene images. Because scene images have more interference and complexity than text, these factors make the identification of natural scene image texts facing many challenges. This paper focused on the recognition of natural scene image texts, and mainly studied a text recognition method based on deep learning in natural scene images. Firstly, text recognition is based on Kares using the Dense Convolutional Network (DenseNet) network model by using the existing standard test data set. Secondly, each character is classified using Softmax outputs to achieve the use of automatic learning. The characteristics of the context replace the manually defined features, which improve the recognition efficiency and accuracy. Lastly, the text recognition of the natural scene image is realized. And the method is suitable for problems encountered in medical images.
Optimizing Social Media Data Using Genetic Algorithm

Twitter-based research for sentiment analysis is popular for quite some time now. This is used to represent documents in a corpus usually. This increases the time of classification and also increases space complexity. It is hence very natural to say that non-redundant feature reduction of the input space for a classifier will improve the generalization property of a classifier. In this approach, the researchers have tried to do feature selection using Genetic Algorithm (GA) which will reduce the set of features into a smaller subset. The researchers have also tried to put forward an approach using Genetic Algorithm to reduce the modelling complexity and training time of classification algorithm for 10k Twitter data based on GST. They aim to improve the accuracy of the classification that the researchers have obtained in a preface work to this work and achieved an accuracy of 87% through this work. Hence the Genetic Algorithm will do the feature selection to reduce the complexity of the classifier and give us a better accuracy of the classification of the tweet.
Crime scene processing and analysis

Crime scene investigation is one of the most important and most challenging area in forensic investigation. It consists of two aspects: processing and analysis. Classification of crime scenes may offer a guide to the investigator on how to proceed with crime scene processing, may suggest types of evidence more likely to be found in the type of case, and may provide a basis for an investigation logic tree. Sometimes, crime scene safety is a high priority to protect a victim, suspect, witness, ourselves, or our partner from a dangerous condition. The steps in crime scene processing are briefly described: scene survey and evidence recognition, documentation, scene searches, evidence collection and preservation, preliminary scene analysis and reconstruction, and release of the scene. Scene survey and evidence recognition is the most dependent on the experience and training of the crime scene investigator. The classical methods for crime scene documentation include notes, sketches, and photography.
Exploring Rhetoric Theory in Persuasive Design: A Mobile Web Application for Obesity Prevention

This paper is a report of an exploratory study of persuasive design for changing health attitudes and behaviors. The researchers designed a mobile web application for Latino parents about childhood obesity prevention called Lifecast and created three rhetoric theory-driven versions of the application changing rhetorical appeals – rational, emotional and credibility characteristics in graphics and content. The researchers evaluated the appropriateness of the application and its versions observing users and conducting in-depth interviews. In the evaluation, they found that versions did not have notable differences; in general parents and their children were motivated to use all versions of the app and reported some changes in their attitudes. They concluded that an effective strategy of rhetorical appeals should be planned and evaluated iteratively. Future work should focus not only on the appeals of visual and textual elements, but also on the rhetoric of the design concept, format, and interactivity.
When Side Channel Becomes Good: Kernel Malware Attack Investigation

Security and privacy issues have been a concern and have become one of the main factors hindering the promotion and popularization of cloud computing. In recent years, cache side channel attack are presented by many researchers to crack cryptographic algorithms (e.g., AES, RSA), bypass ASLR and etc. Cache side channel had been considered as a hacking tool to conduct harmful activities on victim systems. However, from a defender’s perspective, cache side channel can also be employed to explore valuable information. Our paper employs cache side channel to obtain a deep insight on what kind of behaviors kernel malware may conduct. In specific, we propose a novel approach to conduct kernel malware attack investigation with Flush+Reload cache side channel. We have built a proof-of-concept prototype and designed some case studies to conduct extensive experiments. The evaluation results show that our system is capable of understanding what kind of behaviors kernel malware may conduct correctly.
Useful Signal Extraction from Noise by Method of Mirror Noise Images in Information Measuring Systems

An algorithm to implement a new method to extract useful signal from noise known as “Method of mirror noise images” is offered. The results of the research have shown an essential gain in signal to noise ratio in comparison with narrowband signal filtration. The algorithm is of special interest for information measuring systems operating by “turn on-measure-turn off” principle.
Evolvable Media Repositories: An Evolutionary System to Retrieve and Ever-Renovate Related Media Web Content

The paper tackles the question of evolvable media repositories, i.e., local pools of media files that are retrieved over the Internet and that are ever-renovated with new, related files in an evolutionary fashion. The herein proposed method encodes genotypic space by virtue of simple undirected graphs of natural language tokens that represent web queries without employing fitness functions or other evaluation/selection schemata. Once a first population is seeded, a series of modular crawlers query the particular World Wide Web repositories of interest for both media content and assorted meta-data. Then, a series of attached intelligent comprehenders analyse the retrieved content in order to eventually generate new genetic representations, and the cycle is repeated. Such a method is generic, scalable and modular, and can be made fit the purposes of a wide array of applications in all sorts of disparate contextual and functional scenarios. The paper features a formal description of the method, gives implementation guidelines, and presents example usages.
Configuring Graph Traversal Applications for GPUs: Analysis of Implementation Strategies and their Correlation with Graph Characteristics

Implementing a graph traversal (GT) algorithm for GPUs is a very challenging task. It is a core primitive for many graph analysis applications and its efficiency strongly impacts on the overall application performance. Different strategies have been proposed to implement the GT algorithm by exploiting the GPU characteristics. Nevertheless, the efficiency of each of them strongly depends on the graph characteristics. This paper presents an analysis of the most important features of the parallel GT algorithm, which include frontier queue management, load balancing, duplicate removing, and synchronization during graph traversal iterations. It shows different techniques to implement each of such features for GPUs and the comparison of their performance when applied on a very large and heterogeneous set of graphs. The results allow identifying, for each feature and among different implementation techniques of them, the best configuration to address the graph characteristics. The paper finally presents how such a configuration analysis and set allow traversing graphs with throughput up to 14,000 MTEPS on single GPU devices, with speedups ranging from 1.2x to 18.5x with regard to the best parallel applications for GT on GPUs at the state of the art.
Combination between DASH and SVM machine learning in the field of video delivery

With the development of network and streaming media technology, network video traffic is growing rapidly. In order to better control and manage network traffic and guarantee the quality of service of network video, it is necessary to classify network video services effectively. In traffic identification and classification, feature analysis and acquisition of better features are the key points to achieve efficient classification. Starting with the characteristics of packet size distribution, rate, IP alternation, byte number ratio between downstream and upstream, number of sub-stream fragments and average packet arrival time interval, this paper uses Support Vector Machine (SVM) to verify the classification effect of this feature, and achieves a high classification accuracy
On the Form and Content of a Forensic Handwriting Expert’s Opinion

The content of a forensic handwriting expert’s opinion has been holding attention of both research scientists and practicing experts for many decades now since it is not only an essential evidence in court often being crucial to deciding on a case but also a document bringing together procedural and scientific methodological elements. Forensic handwriting examination is much in demand for court, it is also profoundly and comprehensively developed, supported by detailed methodological recommendations on drafting and processing of an expert’s opinion. However, the analysis of experts’ opinions from state forensic institutions as well as from non-state brought to light several shortcomings which do not enhance their evidentiary status. This applies in particular to performing the forensic examination by non-state experts who tend to lack competence. At the same time the number of forensic handwriting examinations assigned by court to the non-state forensic institutions prevails significantly. The most common mistakes made by the handwriting experts when drafting opinions are reviewed in the article, some ways to resolve them are proposed. The need to provide methodological recommendations for forensic handwriting experts incorporating current requirements to the experts’ opinions is acknowledged.
Research and Improvement of Image Encryption Algorithms Based on Chaos

Abstract Due to user privacy protection and other demands, image encryption is always a hot spot in the research field. In view of these problems, this article proposes a chaotic image encryption algorithm, which is named permutation-diffusion algorithm. Specifically, the image of the pixels is permuted, so that the pixel position changes, and then spreads, to change the pixel value. It can be observed from the test results that the algorithm is better in terms of efficiency and safety, compared with the state of the art.
Data-Driven Maps of Art History

Many approaches to explain conceptions of developments in the arts to both peers and a more general audience included diagrams representing networks of related entities such as genealogies of important historical actors or styles. While such visualizations were traditionally created by hand, the recent emergence of extensive digital repositories of art history information enable new means of presentation. This work seeks to explore the potential of openly available data sources to create bottom-up, data driven versions of such network maps of art history. It highlights commonalities and differences between views derived from institutional and crowd-sourced data repositories and compares them with identified historical examples. The results suggest that the available data can be used to create largescale views on specific developments in art history, potentially serving as aid for navigating vast online collections of digitized artworks but also as means of reflection on the origin of the data sources themselves.
Refactoring Code Clone Detection

Refactoring duplicate code is an important issue and is one of the most important smells in software maintenance. There is an important relationship between clones and code quality. Most programmers use clones because they are cheaper and faster than typing the program code. A cloning code is created by copying and pasting the existing code fragments of the source code with or without slight modifications. A major part (5% to 10%) of the source code for large computer programs consists of copy codes. Since cloning is believed to reduce the possibility of software maintenance, many techniques and cloning detection tools have been recommended for this purpose. The basic goal of clone detection is to identify the clone code and replace it with a single call to the function, where the function simulates the behavior of one instance of the clone group. This research provides an overview of the refactoring IDE. The aspects of cloning and detection of cloning are explained. In the copy detection algorithm, the source code is created in XML format.
A Computer-Aided-Grading System of Breast Carcinoma: Pleomorphism, and Mitotic Count

Breast cancer has become the third leading cause of death for women in Taiwan. For clinical pathologists, the grading criteria: Nottingham Modification of the Bloom-Richardson (NBR) System based on histological pathology is a gold standard to assess the lesion severity of the invasive ductal carcinoma. The grading indices for the disease based on NBR include tubular formation, pleomorphism, and mitotic count. Because the manual grading is measured depending on qualitative analysis, it usually causes a big workload due to its various variability. The major goal of this work is to extend our previous work and propose a computer-aided-diagnosis system to assess quantitatively the severity of the breast carcinoma. To this end, it first analyzes the H&E stained slide images of the breast specimen using a series of image processing operations to extract feature parameters related to morphometry of mammary tissue, and hyperplasia degrees of nucleus, and mitotic count of nuclei based on histology and cytology, and choosing important features with feature selection, and identify the scores using support vector machine finally. Experimental results reveal that the proposed system not only can obtain satisfactory performance, but also provide histological grade and prognosis information for clinical pathologists to improve the efficiency of diagnosis.
The Quantification of Human Facial Expression Using Trapezoidal Fuzzy Membership Function

Fuzzy Inference System is an interesting approach. Major benefit of the FIS is, it permits the natural narration in linguistic terms of tribulations that can be resolved rather than in requisites of associations between accurate arithmetical points. This helps, handling with the complicated systems in easy way, is the major motive why fuzzy system is broadly incorporated in practice. In the present research paper, an effective approach is proposed that quantifies the human facial expression using Mamdani implication based fuzzy logic system. The recent principle engages in retrieving arithmetical values from person’s face and feed them to a fuzzy classifier. Fuzzification and Defuzzification process issues trapezoidal fuzzy membership function for input as well as output. The diverse characteristic of this method is its effortlessness and maximum correctness. Experimental outcome on Image dataset depicts excellent accomplishment of the proposed methodology. In this paper, a legitimate procedure proposed for quantification of human facial expression from the features of the face by means of Mamdani type fuzzy inference system, which is proficient to set up a convenient membership association involving the various dimensions of the happy expression. Values representing features of the face are fed to a Mamdani-type fuzzy classifier. This system recognizes three levels of same happy expression namely Normal, Bit Smiley and Loud Laugh. The total output expressions for this proposed scheme is three. Another discrete element of the proposed methodology is the membership method model of expression outcome which stands on various surveys and readings of psychology.
Ambient Intelligence Model for Monitoring, Alerting and Adaptively Recommending Patient’s Health-Care Agenda Based on User Profile

Currently, healthcare is a crucial issue for the entire population, especially for individuals who suffer from a chronic disease such as hypertension or diabetes. However, this care is carried out in medical centers, limiting the scope of health professionals. In fact, some monitoring, early warning processes, and health supporting that are not presently performed, could be carried out at the patient’s location. The aim of this paper is to integrate WSN, ambient intelligence, multi-agent systems, and ontologies, in order to develop an ambient intelligence model that provides alerts, personalized recommendations, and adaptive health-care agendas. Personalized agendas based on chronic patient profiles offer appropriate physical activity, personalized food diet, and specific activities in order to control stress levels. For the validation of the proposed model, a prototype was constructed and applied to a case study considering several chronic patients. The results demonstrate the effectiveness of the proposed health-care ambient intelligence multi-agent model.
Skill emergence and transfer in multi-agent environments

A central problem in training artificial agents to perform complex skills is specifying appropriate cost functions whose optimization will lead to the desired behavior. Specifying detailed cost functions is laborious and often inefficient. The training of agents in competitive and cooperative multi-agent environments provides an avenue to circumvent these limitations: By competition and cooperation agents provide to each other a natural curriculum that can lead to the emergence of complicated skills, even if the rewards of the multi-agent game are simple [1].
Learning Context-Sensitive Norms under Uncertainty

Norms and conventions play a central role in maintaining social order in multi-agent societies [2, 5]. I study the problem of how these norms and conventions can be learned from observation of heterogeneous sources, under conditions of uncertainty. This is necessary as it is not enough to simply hard code a set of norms into a new agent prior to entering society because norms can evolve over time as agents enter and leave the society [9].
Case Studies of Applications to Encourage Students in Cyber-Physical Environment

People became to have convenient lives in ICT society using the Internet. However, convenience is not the purpose to live. It is only the method to get something or know something quickly. Especially for learning, students have to overcome many obstacles actively to know and get knowledge. How we make an attractive point to be interested in the object? For active learning, we have researched applications to make some attractive points for students using psychological methods. Also, we had to arrange much information to make a point in cyber-physical environments. This paper explains about 2 kinds of applications. One is an application for inside studying using a Player versus Player (PvP) game. Another is for outdoor studying using Bluetooth Low Energy (BLE) beacon at the national park in Nikko. We developed applications of e-learning not for convenience but to feel beyond the smartphone screen. According to our experiments, such application makes students active to know about the subject.
Scaling of Depth Map for Stereo Vision Tasks

The article proposes an algorithm for reducing the resolution of reference depth maps (Ground Truth). The evaluating method is presented as well as a comparison of error values resulting from the reference depth map size reduction to various scaling algorithms. The aim of the work is to evaluate the accuracy of scaling depth maps by the proposed algorithm. The need for scaling arises in the tasks of reducing existing different test data sets to uniform parameters of a stereo system, one of which is sensor resolution. A generic data set with the same parameters is required for designing more accurate hybrid stereo vision algorithms with elements of artificial intelligence.
Developing Design Frameworks and Applications for Future Technologies Through Video Game Representations

Video games offer conceptual examples of future technologies and provide interactive contexts in which these technologies can be designed, tested, and evaluated. Human-Computer Interaction (HCI) has been an integral part of video games since their inception, as it offers interface designers and technology developers a platform by which they can build scenarios and interactive prototypes to engage players. Real-world and game-world technologies draw from each other and lend themselves to evaluation through the lens of human-computer interaction paradigms. This paper seeks to provide further evidence supporting the relevance of HCI principles for evaluating general video game and in-game world technologies, and to establish a set of frameworks through which technologists can examine future technology concepts in video games, often in the form of science fiction artifacts and systems.
Impedances of Memorable Passphrase Design on Augmented Cognition

The quest for optimum computer authentication continues to stimulate resolution toward memorable password design. As human cognitive load processes the transfer of information from working memory to long-term memory, intrinsic and extraneous complexities present impedances to cognitive load. This study applies human cognition to the use of passphrases characterized as assembled words that are secure and easy to recall [23]. "Passwords and passphrases, when married with psychology and psycholinguistics, yield an authentication scheme that is revocable, memorable, and secure" [21, p. 2]. Subsequently, this study examines augmenting cognitive load of passphrase design to enhance memorability and effectuate authentication performance.