Reviewerly/exHarmony huggingface dataset:
Application of Multi-target Tracking Technology for Packing Box

Product information is usually printed on the packaging of products in the form of graphics, images or text during the packing process. But the information printed on the packing boxes is difficult to change and cannot provide multiple kinds of data presentation for consumer. With the development of augmented reality technology, we have a new solution for this problem. Based on the analysis of augmented-reality technology and the characteristic of packing box, we adopt a technique that can identify and track multiple target faces at the same time. Thus, the augmented reality based on multi-target tracking technology can be applied to the expression of product information printed on the packing box. The new approach can be used to identify and track six target faces in real time to determine the packing box’s coordinate and rotation angle, then calculate the spatial relationship between virtual information and real packing box. The approach proposed in this paper can provide multiple kinds of presentation for information, which ultimately improve the user experience.
Knowledge Management, Arts and Humanities: Setting the Scene

This short introductory chapter aims to familiarise readers with the main theme of the book and its structure. It argues the need for expanding the realm of knowledge management and suggests the benefits of interdisciplinary collaborations between knowledge management and arts and humanities. Then it briefly presents individual chapters of the book and explains how they contribute novel theoretical approaches and empirical evidence of the nature and value that arts and humanities bring to knowledge management and vice versa.
Training Division Innovative Thinking in the Higher Vocational and Technical Teachers College

This article concerns the significance of fostering innovative talents. It presents the status quo and the exiting problems of the pedagogical methodology to cultivate innovative talents in the current vocational and technical schools; and elaborates the new explorations and practices that some of the vocational and technical schools have carried out in terms of innovative-talent training model.
ORGANIZATION OF QUEST FOR FAMILIARIZATION OF STUDENTS WITH INNOVATIVE EQUIPMENT

The article presents the results of the project work of the team of authors to create a quest for technology/informatics. During the quest students, engaging in interaction and playing, get acquainted with innovative equipment. This is a consecutive (linear) quest: after completing one task, the students receive the next one, and so on until they have completed all the tasks. Doing the tasks students get acquainted with the 3D printer, laser engraving machine, robot, QR coding technology and other technologies and equipment. The principles and features of the development of quests are described in the article. Methods of organizing the quest and the developed programs and resources will be interesting and useful to teachers of informatics and technology, methodologists, specialists of additional education.
The Syntax and Prosody of Discourse Markers in Korean

This paper provides an experimental approach following Burton and Matthewson (2015)’s storyboard method, to elicit natural data concerning the...
Quantifying High-Dimensional Entanglement with only Two Measurement Settings

We develop and experimentally demonstrate a method for certifying high-dimensional entanglement that uses carefully constructed measurements in two bases. We are able to certify a record 9-dimensional entangled state in an assumption-free manner.
Recurrent Neural Network for Human Action Recognition using Star Skeletonization

Human Action Recognition has been an active research topic since early 1980s due to its promising applications in many domains like video indexing, surveillance, gesture recognition, video retrieval and human-computer interactions where the actions in the form of videos or sensor datas are recognized. The extraction of relevant features from the video streams is the most challenging part. With the emergence of advanced artificial intelligence techniques, deep learning methods are adopted to achieve the goal. The proposed system presents a Recurrent Neural Network (RNN) methodology for Human Action Recognition using star skeleton as a representative descriptor of human posture. Star skeleton is the process of jointing the gross contour extremes of a body to its centroid. To use star skeleton as feature for action recognition, the feature is defined as a five-dimensional vector in star fashion because the head and four limbs are usually local extremes of human body. In our project, we assumed an action is composed of a series of star skeletons overtime. Therefore, images expressing human action which are time-sequential are transformed into a feature vector sequence. Then the feature vector sequence must be transformed into symbol sequence so that RNN can model the action. RNN is used because the features extracted are time dependent
Study of Flexibility

Security is the most critical part of today’s information systems. Modern applied cryptography in communication networks requires secure kernels that also manifest into low cost and high performance realizations.
An Epistemological Model for a Data Analysis Process in Support of Verification and Validation

The verification and validation (V&V) of the data analysis process is critical for establishing the objective correctness of an analytic workflow. Yet, problems, mechanisms, and shortfalls for verifying and validating data analysis processes have not been investigated, understood, or well defined by the data analysis community. The processes of verification and validation evaluate the correctness of a logical mechanism, either computational or cognitive. Verification establishes whether the object of the evaluation performs as it was designed to perform. (“Does it do the thing right?”) Validation establishes whether the object of the evaluation performs accurately with respect to the real world. (“Does it do the right thing?”) Computational mechanisms producing numerical or statistical results are used by human analysts to gain an understanding about the real world from which the data came. The results of the computational mechanisms motivate cognitive associations that further drive the data analysis process. The combination of computational and cognitive analytical methods into a workflow defines the data analysis process. People do not typically consider the V&V of the data analysis process. The V&V of the cognitive assumptions, reasons, and/or mechanisms that connect analytical elements must also be considered and evaluated for correctness. Data Analysis Process Verification and Validation (DAP-V&V) defines a framework and processes that may be applied to identify, structure, and associate logical elements. DAP-V&V is a way of establishing correctness of individual steps along an analytical workflow and ensuring integrity of conceptual associations that are composed into an aggregate analysis.
Rehabilitation recognition skeleton data depth learning based on RNN

With the extensive application of deep learning in the field of human rehabilitation, skeleton based rehabilitation recognition is becoming more and more concerned with large-scale bone data sets. The key factor of this task is the two intra frame representations of the combined co-and the inter-frame. In this paper, an inter frame representation method based on RNN is proposed. Pointtion of each joint is joint-coded they are assembled into semantic both spatial and temporal domains.we introduce a global spatial aggregation which is able to learn superior joint co features over local aggregation.
Co-exploring a Search Space in a Group Recommender System

This paper presents a group recommendation system that focuses on helping users to search for an item that is agreed upon by all users in a group. We consider the case with a group of two users as a starting point and regard the search process for the recommended item as a negotiation process between the two users. More specifically, after the user's preferences are inputted in the proposed system, the system maps possible items on a two dimensional-plane according to the item's utility values for the two users. The users are expected to negotiate their preferences to reach an agreement. To examine the characteristics of the proposed system, simulation experiments are conducted with four different user models, which are created based on conflict resolution behaviors as described in human psychology literature. These four user types are represented by different parameter values to control their behaviors. The paper presents the simulation experiments and their results.
Mobile Gaming Apps on Duterte’s War on Drugs

Asian Politics & PolicyVolume 11, Issue 2 p. 316-319 Media Review Mobile Gaming Apps on Duterte’s War on Drugs Karlo Mikhail I. Mongaya, Karlo Mikhail I. Mongaya University of the Philippines DilimanSearch for more papers by this author Karlo Mikhail I. Mongaya, Karlo Mikhail I. Mongaya University of the Philippines DilimanSearch for more papers by this author First published: 27 March 2019 https://doi.org/10.1111/aspp.12448Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume11, Issue2April 2019Pages 316-319 RelatedInformation
Intelligent Mobile Dictionary and Thesaurus

This project is a mobile dictionary application which takes advantage of camera with capability of Optical Character Recognition (OCR) to scan and extract words from the captured images then search offline dictionary database. The motivation that drives us toward this implementation is to contribute to any mobile users that prefer a dictionary application with OCR capabilities that is easy to use. The objectives of this project are to offer a mobile dictionary that provides OCR assistance to scan words from captured image and to allow users to search terms for synonym, nearest and antonym, and to connect with any dictionary databases. The results of this application are promising and show the potential for further model improvement. The target language for this application is English.
Cyclotomic cosets and primitive idempotents in semisimple ring

Let p1,p2,…,pr,q be distinct primes. Let m=∏i=1rpiαi, where at least two of αi’ s are nonzero positive integers. In this article, it is shown that with the help of λ-mapping it is easy to count the number of q-cyclotomic cosets modulo m. The explicit expressions of primitive idempotents are obtained in the semisimple ring Rm over Fq taking q as a primitive root (or a quadratic residue) modulo pi under the condition that gcd(Opi(q),Opj(q))=2(orgcd(Opi(q),Opj(q))=1), where Opi(q) denote the order of q modulo pi.
An adaptive soft threshold image denoising method based on quantum bit gate theory

Because the images are always contaminated by different kinds of noise in the courses of image acquisition, transmission and storage process, the image denoising is a very important step of image restoration. The key of denoising algorithm is making recovery image reserve as much as possible edge details when eliminating noise. Because noise and image details both are part of the high frequency components of image, to some extent, these two sides are contradictory. If the selection of the criterion and treatment for noise and marginal are inappropriate , denoising will make image details ( especially the marginal) become more vague, which must reduce the quality of the image and increase greatly the complexity of subsequent image processing. Since the quantum process and imaging process have the similar characteristics in the probability and statistics fields, a kind of soft threshold denoising algorithm is proposed based on the concept of quantum computation such as the quantum bit, superposition and collapse, etc. This filter algorithm can generate an adaptive template according to the characteristic of the edge of local image. Due to the algorithm is sensitive to the shape of edge, the balance is obtained between the noise suppression and the edge preserving.
Macro and Micro Level Classification of Social Media Private Data

In the modern world, social media private data evolves as a great asset to business and governments. While social media private data is a boon to the business, it is also causing concern to privacy regulators. We classify the social media data as the business and governments require. The social media private data is classified into two layers: macro level and micro level. The macro level classification is Static Private Data and Dynamic Private Data. The micro level classification includes four types: Personal Identity Data (Static), Relational Identity Data (Static), Personal Identity Data (Dynamic), and Relational Identity Data (Dynamic). Two software metrics “complexity” and “relevancy” are considered. Based on the macro and micro level classification, we measure the complexity and relevancy of social media private data from the perspectives of business and police communities. By conducting extensive experimental research, we study the relationship between different types of social media private data and different communities by the means of the two-metrics relevancy and complexity and justify the necessity of macro and micro level classification. The outcome of the experimental survey is interesting. Police officers are more interested in static private data than dynamic private data. Business managers are more interested in dynamic private data than static private data. While the police are interested in static private data, the business communities are interested in dynamic private data.
Document Performance Prediction for Automatic Text Classification

Query performance prediction (QPP) is a fundamental task in information retrieval, which concerns predicting the effectiveness of a ranking model for a given query in the absence of relevance information. Despite being an active research area, this task has not yet been explored in the context of automatic text classification. In this paper, we study the task of predicting the effectiveness of a classifier for a given document, which we refer to as document performance prediction (DPP). Our experiments on several text classification datasets for both categorization and sentiment analysis attest the effectiveness and complementarity of several DPP inspired by related QPP approaches. Finally, we also explore the usefulness of DPP for improving the classification itself, by using them as additional features in a classification ensemble.
A bivariate mixture of negative binomial distributions and its applications

We construct a new bivariate mixture of negative binomial distributions which represents over-dispersed data more efficiently. This is an extension of a univariate mixture of beta and negative binomial distributions. Characteristics of this joint distribution are studied including conditional distributions. Some properties of the correlation coefficient are explored. We demonstrate the applicability of our proposed model by fitting to three real data sets with correlated count data. A comparison is made with some previously used models to show the effectiveness of the new model.
Effects of Image Resolution on Automatic Face Detection

This study presents comparison of four well-known face detection algorithms. Face detection algorithms investigated in this study are, (i) Viola Jones (VJ) face detection algorithm, (ii) Normalized pixel difference (NPD), (iii) Histogram of Oriented Gradients (HOG), and (iv) Skin Color Based (SCB) face detection algorithm. Simulation results reveal that on the LFW database, and for image resolution of 40×40 pixels and below, only Skin Color-Based face detection algorithm was able to locate faces with a mean accuracy of 56% at the cost of highest computation complexity. Whereas, for image resolution of 60×60 pixels and above, the Viola Jones algorithm has the highest detection accuracy of 61% with average execution time of 0.605 seconds. On real life images that contained up to 64 faces in the input image, the Viola Jones algorithm surpassed the companion algorithms followed by the NPD and the HOG. The SCB algorithm is found to be least effective on real life images.
Dense Measurements for Quantum Computations

Quantum measurement is a fundamental cornerstone of experimental quantum computations. We define a novel measurement for quantum computations called dense quantum measurement. The dense measurement aims at fixing the main drawbacks of standard quantum measurements.
Learning of Nonnegative Matrix Factorization Models for Inconsistent Resolution Dataset Analysis

Due to the need to protect personal information and the impracticality of exhaustive data collection, there is increasing need to deal with datasets with various levels of granularity, such as user-individual data and user-group data. In this study, we propose a new method for jointly analyzing multiple datasets with different granularity. The proposed method is a probabilistic model based on nonnegative matrix factorization, which is derived by introducing latent variables that indicate the high-resolution data underlying the low-resolution data. Experiments on purchase logs show that the proposed method has a better performance than the existing methods. Furthermore, by deriving an extension of the proposed method, we show that the proposed method is a new fundamental approach for analyzing datasets with different granularity.
Manual Optimizations for Efficient Designs

Requirement of very high performance (runtime) coupled with limited energy budgets naturally lead to the inclusion of cryptographic accelerators in today’s heterogeneous System-on-Chips (SoCs). Efficient accelerator design, however, needs a deep understanding of the cipher structure as well as hardware optimization principles. In this chapter, we discuss two such custom optimizations, which have been applied to the symmetric-key cryptographic primitives and since, have been integrated into the CRYKET toolflow.
Deep Learning and Vector Space Model

In recent years, a novel paradigm appeared related to application of neural networks to any tasks related to artificial intelligence [59], in particular, in natural language processing [39]. It became extremely popular in NLP area after works of Mikolov et al. starting in 2013 [74, 75]. The main idea of this paradigm is to apply neural networks for automatic learning of relevant features with various levels of generalization in vector space model. Sometimes this model of representation of objects is called continuous vector space model. In general, this paradigm is called “deep learning.”
An E-negotiation Agent for an E-tourism Platform

The article presents an electronic negotiation agent, integrated within a multiagent system for an electronic tourism platform. The e-negotiation process is based on a winner-winner approach, using a bargaining protocol. However, with the proliferation of services, the task of searching for relevant services becomes more and more difficult. Thus, the authors also propose a search agent to find tourism services corresponding to the client request and profile. The discovery process uses a quantitative similarity measure.
Long-Term User Experience and Persuasion on 3DFysio, A Mobile Rehabilitation Application

This paper presents a field study of a persuasive mobile application, 3DFysio, to support patients’ motivation in rheumatoid arthritis rehabilitation. The study was conducted with 10 patients over the period of 9 months, to study the user experience of the application and its persuasive features. The research data was collected with interviews and questionnaires in several phases of the trial. The findings show that the patients perceived the application as a motivational tool to support their rehabilitation process. The main persuasive and motivational aspects for patients were the interactive rehabilitation program, accurate and easily available physiotherapy exercises presented by 3D avatar, and the connection to the personal physiotherapist. 3DFysio seemed to motivate through a combination of persuasive elements on the application and real life. It also supported the establishment of an exercise routine. This paper provides new knowledge of persuasive design to support long-term rehabilitation process by means of mobile applications.
A novel method for SIFT features matching based on feature dimension matching degree

We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature descriptor vector, thus obtaining the number of dimensions matching between feature points and feature dimension matching degree. Then we use the feature dimension matching degree to obtain the suspect matching feature points. Finally we use the Euclidean distance to eliminate the mismatching feature points to obtain accurate matching feature point pairs. The method is essentially a high-dimensional feature vector matching method based on local feature vector element matching. Experimental results show that the new algorithm can guarantee the number of matching SIFT feature points and their matching accuracy and that its running time is similar to that of HKMT, RKDT and LSH algorithms
Novel Robust Digital Watermarking in Mid-Rank Co-efficient Based on DWT and RT Transform

Recent advancement in multimedia is facilitating humanity in data manipulation, transportation, and transmission. All at once, this improvement in multimedia data has exposed sensitive data to various threats, like threats of piracy and copyright materials. The most important application of digital image watermarking scheme are ownership protection of copyrighted materials and authorization of multimedia data. Digital watermark is an imperceptible mark embedded in digital images for various objectives including image captioning, authentication, authorization, copyright protection and proof of ownership of multimedia contents. Watermarking is a procedure of embedding secret information such as logo, number, text and image in multimedia data. The proposed watermarking scheme is based on Discrete Wavelet (DWT) and Ranklets Transform (RT) domain in which the mid-rank coefficients in low frequency sub-band (LL3) of DWT are selected for random number watermark embedding. Experimental results shows that no visible distortion among the cover and watermarked image, representing high level of imperceptibility. The scheme has been tested against various set of malicious attacks, proving that the proposed scheme computationally surpasses other state of the arts schemes.