Reviewerly/exHarmony huggingface dataset:
Effectiveness Evaluation of Public Security Face Recognition Systems Based on Improved Unascertained C-Means

In order to evaluate and improve the effectiveness of the public security face recognition systems reasonably and scientifically in real scenes, we create an effectiveness evaluation index system and the improved Unascertained C-means model. The data of indicators are obtained through measuring practical application systems of five public security bureaus. Then we carry out data statistics and pretreatment. The improved Unascertained C-means algorithm is realized by programming in MATLAB. After inputting the data into the model, we obtain the evaluation results. By comparing the real achievements of each system during the evaluation cycle, we verify the consistency between the evaluation results and realities. On the basis of the comparative analysis of the evaluation results, we identify the weaknesses of a system, and put forward some optimization strategies of the system effectiveness. In the paper, we synthetically apply various methods, such as qualitative analysis, quantitative analysis, theoretical analysis and experimental analysis, to study the uncertainty problem of the effectiveness evaluation of the public security face recognition systems. The evaluation process is objective and impartial, the results are valid and reasonable, and the strategies are targeted and operable. This paper is of certain theoretical significance to the research on effectiveness evaluation, and it is of certain practical significance to effectiveness improvement of the practical application systems.
Camera calibration and orientation for PCB jet printing inspection

Abstract To assess the quality of printed solder joints in the jet printing of printed circuit boards, a calibrated camera is used to reconstruct the solder volume via photogrammetry as a specific task of computer vision. This requires a procedure for calibration and orientation determination due to restrictions within the image acquisition process. We herein consider a novel application area called an ultra-close range normal case photogrammetry , where a camera acquires small objects at small operating distances and fields of depth. Because the camera cannot rotate or translate in the z-direction, we propose and evaluate four calibration procedures in terms of their capability in a normal case calibration within the ultra-close range. To set up an optimal calibration pipeline, a three-dimensional (3D) calibration field is used for single and multi-image calibrations. To enhance the accuracy and to simplify the assignment of 3D coordinates to the detected markers, we propose a geometry-based estimation of the lens model to undistort the image points. Close-range applications utilize spacer rings and extension tubes to enlarge the magnification and reduce the operating distance. We also examine the influence of these extensions on the intrinsics of the camera and the reconstruction result. Additionally, we demonstrate the dependence of the accuracy on the lens model in terms of radial and tangential distortions and the number of distortion coefficients regarding the reprojection error $$\epsilon _{repro}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ϵ</mml:mi> <mml:mrow> <mml:mi>repro</mml:mi> </mml:mrow> </mml:msub> </mml:math> . Finally, we provide recommendations for a lens configuration for ultra-close range normal case calibrations and measurements, based on the calibration and reconstruction results, which are evaluated by the 2D reprojection error $$\epsilon _{repro}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ϵ</mml:mi> <mml:mrow> <mml:mi>repro</mml:mi> </mml:mrow> </mml:msub> </mml:math> and the 3D reconstruction error $$\epsilon _{recon}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ϵ</mml:mi> <mml:mrow> <mml:mi>recon</mml:mi> </mml:mrow> </mml:msub> </mml:math> obtained from a second independent calibration field.
THE IMPACT OF COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) MODEL ON STUDENTS’ LEARNING ACTIVITY

Study Issues in this article is Learning Model: The Cooperative Integrated Reading and Composition (CIRC) learning model has not yet been implemented and Learning Active: Student learning activeness is still not optimal. Based on a theoretical study it can be concluded that the CIRC method influences learning activities. The CIRC method uses a syntax of seven steps: reading in pairs, writing concerned with grammar, pronouncing words aloud, word meanings, retelling, checking by partners, and writing tests. The method that has been run according to the syntax will have an effect on the activity of learning, namely the inductor: Activities, visuals, verbal, listening, writing, drawing, motoric, mental, and emotional.
Spectrum of Robots

A spectrum of various robots has been used for space and planetary surface exploration missions calling for a spectrum of robotic intelligence. Most have been directly teleoperated, automated, or modestly autonomous. In order for intelligent robots to be legitimate members of a future human robot society on a remote planet, they must possess higher degrees of artificial intelligence and cognition. To set the stage for exploring the potential that concepts of memetics can bring to the realization of such future robots, this chapter considers progress to date and the current state of robotic intelligence along the spectrum of actual planetary robots the ancient ancestors of the brand of future robots foreseen and characterized in later chapters.
SocialEdge: Enabling Trusted Data Processing Workflow in Smart Communities

In typical commercial cloud and edge providers data collection and processing nodes might be owned by a single trusted entity. In contrast, in a voluntary infrastructure, ownership is distributed. In such a setup, while individuals contributing data might demand constraints on how their data is accessed and used, volunteers of compute resources might want assurance that their resources are used only for intended purposes. These significant differences motivate SocialEdge, an edge orchestration framework that 1) leverages online social network for bootstrapping and permissioned block chain for information assurance, 2) exploits recursive containerization for enabling secure data access, ensuring only intended workloads are run and enforcing fine-grained resource control, and 3) leverages components from Kubernetes to instantiate and manage an ephemeral cluster composed of volunteered resources. In this paper we describe the design and prototype implementation of SocialEdge and model the problem of orchestrating the on-demand data processing infrastructure under data access constraints as a variant of the multi-commodity flow problem. We propose and evaluate mixed integer programming and greedy heuristic approaches, and characterize recursive containerized environment experimentally using benchmarks.
Hybrid Reduction Dimension on Clustering Text of English Hadith Translation

Clustering results are strongly influenced by the selected technique and data dimensions. Large data dimensions become the main problem that must be considered. Therefore, a dimensional reduction is needed to select sub-feature that provides important information. One of the dimensions reduction methods is the hybrid method. The hybrid method combines the method of feature selection and feature extraction to select informative sub-feature. Furthermore, the simplest clustering technique is the k-means algorithm, which divides n data into k cluster based on the centroid. This study carried out clustering using the k-means algorithm after reducing dimensions on 892 English translation hadith documents. The clustering results are validated using the silhouette coefficient and Davies Bouldin index (DBI). Experimental results show that dimensional reduction can improve the cluster quality.
An Approach to Automatic Layout of Eclipse Papyrus Class Diagrams

The paper presents an algorithm for automatic layout of class diagrams. The presented algorithm efficiently and openly tries to lay out a diagram in the landscape format using a modified Sugiyama algorithm. The proposed algorithm is implemented as an Eclipse plug-in and works within Papyrus class diagram modeling projects.
A Third Order based Additional Regularization in Intrinsic Space of the Manifold

Second order graph Laplacian regularization has the limitation that the solution remains biased towards a constant which restricts its extrapolation capability. The lack of extrapolation results in poor generalization. An additional penalty factor is needed on the function to avoid its over-fitting on seen unlabeled training instances. The third order derivative based technique identifies the sharp variations in the function and accurately penalizes them to avoid overfitting. The resultant function leads to a more accurate and generic model that exploits the twist and curvature variations on the manifold. Extensive experiments on synthetic and real-world data set clearly shows that the additional regularization increases accuracy and generic nature of model.
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model

Deep Learning algorithms are often used as black box type learning and they are too complex to understand. The widespread usability of Deep Learning algorithms to solve various machine learning problems demands deep and transparent understanding of the internal representation as well as decision making. Moreover, the learning models, trained on sequential data, such as audio and video data, have intricate internal reasoning process due to their complex distribution of features. Thus, a visual simulator might be helpful to trace the internal decision making mechanisms in response to adversarial input data, and it would help to debug and design appropriate deep learning models. However, interpreting the internal reasoning of deep learning model is not well studied in the literature. In this work, we have developed a visual interactive web application, namely d-DeVIS, which helps to visualize the internal reasoning of the learning model which is trained on the audio data. The proposed system allows to perceive the behavior as well as to debug the model by interactively generating adversarial audio data point. The web application of d-DeVIS is available at ddevis.herokuapp.com.
Online Writer Identification using GMM Based Feature Representation and Writer-Specific Weights

This paper focuses on a method to ascertain the identity of an online handwritten document. The proposed methodology makes use of a set of descriptors that are derived from features obtained in a probabilistic sense. In this regard, we employ a GMM-based feature representation where in each point-based feature vector in the online trace is represented by a vector. Each element of the aforementioned vector quantify the membership to a particular Gaussian in the GMM. A differing aspect is in the proposal of a weighting scheme that measures the influence of each Gaussian of a writer in the probabilistic space. For deriving these weights, we rely on the information obtained from a histogram, by formulating a function of the sum-pooled posterior probabilities obtained across all the enrolled documents in the database. The identification is performed by an ensemble of SVMs where each SVM is modelled for a given writer. The experiments are performed on the publicly available IAM Online handwriting database and the results are competitive with respect to prior works in literature.
An Approach of Usability Testing for Web User Interface Through Interaction Flow Modeling Language (IFML) Models

Usability of the web User Interface (UI) is totally dependent on the user’s interaction with system. Usability is to check how efficiently and easily user utilizes the system. To design a clear, well-managed UI that depicts all the elements of web to end user is a very complex task. As the creation of the UI being a complex and cost-effective task, most of the organizations squander most of their budget on it. In this research, we have proposed a usability testing model for the web UI. This model creates a checklist for the usability testing of web UI, then transforms this checklist into IFML constructs. Using this checklist, we have generated test cases to validate the web UI. This checklist can be used to design a new front-end or to test an existing UI. We have applied this checklist on MobileWeb case study. The proposed approach is validated using a UML Domain Model Diagram and IFML models. Finally, the comparison of general checklist items of MobileWeb is performed with IFML constructs.
GRAY WATCH: An Extended Design Process

The process of developing a software product must have mechanisms to verify the quality of the product and the process under development. Talking about the quality of the resulting software requires taking into account a series of parameters that allow defining the minimum levels that a software product must meet to be categorized of quality. Thus, quality concerns corresponds to properties that characterize an architectural solution. The GRAY WATCH (GW) method is framed in the development of business software under the component reuse paradigm. This method lacks mechanisms that allow obtaining a quality architecture and needs to be improved. In order to resolve this deficiency, this article proposes to define and incorporate the activities and products that allow to expand the design process of GW. The aim of the proposals is to consider the ISO/IEC 25010 product quality standard, which defines criteria to stipulate the quality requirements for a software product, their measurement criteria and evaluation. It take into consideration a quality model composed of characteristics and sub-characteristics. The extension is supported by the Domain Engineering process, based in Software Quality “InDoCaS” and the “InDoCaSE” process as the methodologies for the definition of all products and activities in the Design process.
Concept Drift Detection in Email Dataset through Intention Based Segmentation

In a dynamic environment, detection of changes in concept is very important for prediction and decision-based applications. Concept drift detection helps decision makers to perform smarter maintenance and operations at an appropriate time. In the context of Emails, concept drift is defined as how the concepts in Emails are changing with time. In this paper, a novel method is presented based on intention based segmentation to calculate concept drift in Email dataset. In intention based segmentation instead of comparing the contents of email as whole, it is divided into segments focusing on same intention and compared. Division of sentences is done on the basis of voices and tenses with the help of POS Tagging. After that, Hierarchical Clustering is used for clustering of segments and comparison is done using Vector Space Model. Importance of concept drift detection in Email domain is to find the emails which will be of less interest to user. It also helps to find the current interests of user by analyzing the changes in concepts over time.
Transfer Metric Learning for Unseen Domains

We propose a transfer metric learning method to infer domain-specific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains. When training and test distributions are different, the standard metric learning cannot infer appropriate data embeddings. The proposed method can infer appropriate data embeddings for the unseen domains by using latent domain vectors, which are latent representations of domains and control the property of data embeddings for each domain. This latent domain vector is inferred by using a neural network that takes the set of feature vectors in the domain as an input. The neural network is trained without the unseen domains. The proposed method can instantly infer data embeddings for the unseen domains without (re)-training once the sets of feature vectors in the domains are given. To accumulate knowledge in advance, the proposed method uses labeled and unlabeled data in multiple source domains. Labeled data, i.e., data with label information such as class labels or pair (similar/dissimilar) constraints, are used for learning data embeddings in such a way that similar data points are close and dissimilar data points are separated in the embedding space. Although unlabeled data do not have labels, they have geometric information that characterizes domains. The proposed method incorporates this information in a natural way on the basis of a probabilistic framework. The conditional distributions of the latent domain vectors, the embedded data, and the observed data are parametrized by neural networks and are optimized by maximizing the variational lower bound. The effectiveness of the proposed method was demonstrated through experiments using three clustering tasks.
Detecting Mood of Aesthetically Pleasing Videos Using Deep Learning

Human interaction and carrier of feelings among humans are accomplished mainly through five senses, such as touch, smell, taste, audio, and Visual. Considering Visual sense, images and videos are important gradients in day-to-day life. It can elevate/depress mood of a person. Video Aesthetics improves user satisfaction. In this study of mood detection, we are demonstrating an approach which is automatically assessed the aesthetics of videos, with emphasis on detecting the mood of videos. With the rising popularity of DSLR camera, Mobile dual camera, the amount of Visual data available on the internet is expanding exponentially. Some of the videos and pictures are aesthetically pleasing and beautiful to human eyes. But there are some videos as well as photos which are uninteresting and of low-quality. This paper demonstrates a simple but powerful method to classify videos into pleasing and non-pleasing categories. Further, our aesthetic quality assessment will find the mood of aesthetically pleasing video (i.e. video representing a happy video or sad or fear, etc), that reflect on a person's mood.
Amón_RA

The main purpose of Amón_RA (Amón Augmented Reality in Spanish) project is "to develop augmented reality technology for the enhancement and dissemination of the historic urban landscape of Barrio Amón". To achieve this goal, the research project seeks the determination of contents for patrimonial heritage (urban, architectural, social and cultural analysis) of Barrio Amón with technological developments linked to the creation of a mobile application of Augmented Reality (AR). Amón_RA is a research project in development whose objective is to generate a mobile application of augmented reality in areas little explored within the Costa Rican context such as the enhancement of the city and heritage resources. Barrio Amón, as a study area, is a reference site of the historical-social development of the city of San José, of great patrimonial interest.
A Comparative Study of an ODA Country Program Evaluation Using the Meta-Evaluation Model

Recently, the discourse of the international community on ODA evaluation has been focused more on country program evaluations rather than on project and program evaluations for development effectiveness. In this context, this study aimed to suggest policy implications by comparing and analyzing the current status of Korea to that of other donor countries. Based on a meta-evaluation study aiming to improve the evaluation system itself, we set up a comparative analysis framework, the “evaluation environment-evaluation input-evaluation process-evaluation result and utilization.” We also defined details of evaluation criteria in order to compare the Korean case to that of Japan, the UK, Germany, and the Asia Development Bank. The analysis found that the evaluation system in Korea is weaker than that of other donors, especially in terms of evaluation results and utilization. Based on the results of the study, we propose policy alternatives for each of the four meta-evaluation model components and then strongly emphasize the establishment of an evaluation information management system and the official procedures for sharing results with stakeholders in the partner country.
Development of a Web-based Microlecture Mobile Learning System

The presence of multimedia and distance education is expected to have a great impact on the improvement of students' knowledge. Microlecture mobile learning (mm-learning) is a multimedia learning technique that can be accessed via mobile devices such as laptops, smartphones, and tablet computers. Developing mm-learning system that can be accessed anywhere and anytime is aimed to increase the knowledge, add new insights, and provide a different learning experience to the students without being limited by time and place. Mm-learning development design is based on the concepts of microlecture and mobile learning. Dick & Carey instructional design model with ADDIE stages (Analysis, Design, Development, Implementation and Evaluation) is adopted in development a system prototype. The prototype is then evaluated in an computing polytechnic by way of questionnaires and exam scores. The overall students' perception scores from using the prototype is 4.28, whereas that from the traditional learning is 3.29 (out of 5). Further, the overall results of students' test scores significantly differ with a value of sig. equals 0,000.
Evaluating Leadership Fuzzy Comprehensive of College Students Based on Triangular Fuzzy Number

With the rapid development of the global economy and the increasingly fierce employment market, modern society has higher requirements for managers' leadership and comprehensive quality. College students, as the main force of enterprise leaders, plays a decisive role in the future development of enterprises. The leadership evaluation of college students refers to the process of comprehensively using scientific and reasonable evaluation methods, study the actual situation of college students' leadership, make objective and effective evaluation results, and ultimately promote students to improve their own leadership ability and the quality of comprehensive quality of higher education. This paper mainly focuses on the central proposition of leadership evaluation of college students. The main research work is as follows: (1) construction of leadership evaluation index system for college students. (2) This paper completes the construction of the fuzzy comprehensive evaluation model of college students' leadership based on triangular fuzzy number analytic hierarchy process, and the case study of the leadership evaluation of college students.
The Impact of Blockchain on the Auditor's Audit Approach

The current standard in accounting practice is the double-entry approach. Basis of the double-entry approach is that every financial event brings two equal and offsetting entries. Since these financial events are not automatically confirmed by both parties, the accounting quality can be improved. The blockchain mechanism possibly offers a different take on accounting. Based on an experimentation approach, data was collected to compare the double-entry method with the blockchain-based triple-entry method. The results show that the main difference concerns determining the completeness of the financial statement items. In the situation of double-entry accounting, segregation of duties is applied to do so. In the blockchain situation, the underlying mechanism of the blockchain already ensures this.