Reviewerly/exHarmony huggingface dataset:
Web-based application as a communication modeling for street children

Abstract Effective communication is one of the keys success factor in implementing an activity, a common problem that often occurs in social communities problem of street children is the ineffective communication process each community stands alone without any mutual coordination. The purpose of this study is to model the process of communication between social communities utilizing a web-based application, using the waterfall approach for application design. The expected results of this study are as a forum to facilitate communication between existing social communities. The existence of communication applications between street children communities can help to optimize the communication function so that the implementation of various activities that can help target the implementation of activities can run better and on target.
Automatic Estimation and Feature Word Analysis of Universities Using University Medium-term Plans

Japanese national universities are required to draw up medium-term plans based on logic models. However, universities continue to struggle due to not knowing how to describe the five viewpoints (purpose, input, activity, output and outcome) of the logic model. In this paper, machine learning was applied to the text of third-phase medium-term plans, categorized according to the logic model, and an investigation was made into its performance in identifying each university. In addition, the feature words of the universities were analyzed, revealing the issues facing each of them.
Taxonomic Indexes for Automatic Prostate Segmentation on 3D MRI Scans Using Superpixels and Probabilistic Atlas

In prostate analysis clinical routine, manual and semi-manual techniques are used to interpret MRI scans, which in addition to being subjective, are time-consuming and operator-dependent. Prostate segmentation is necessary for the diagnosis of a possible tumor, biopsy, staging, monitoring and treatment. Thus, an automatic segmentation can be crucial for fast and reliable diagnosis, but is challenging to accomplish due to the varying sizes, shapes and also unclear prostate boundaries that are blended with surrounding tissues. Thereupon, this work proposes an automatic method for prostate segmentation on 3D MRI scans based on superpixels, taxonomic indexes, probabilistic atlas and the extreme gradient boosting algorithm (XGBoost). In addition, the proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a dice similarity coefficient of 84.03%, volumetric similarity of 95.42%, recall of 88.11%, specificity of 90.82%, and an accuracy of 90.06%. Experimental results demonstrate the high potential of the proposed method comparable to those previously published.
Design and Implementation of Music Online Network Course Based on Cloud Computing

Based on the analysis of typical PaaS platform technology at home and abroad and the current status of the construction of music education online courses, an Azure-PaaS-based music education network course architecture is designed. The development environment for online courses is configured, created, tested, and deployed to the Azure platform The other steps have realized the construction of music education online courses using cloud computing technology, which has achieved the high scalability and openness of online courses, and effectively met the needs of learners to realize distributed online learning.
Binary Classification using Convolutional Neural Network for Attributes Identification of Pedestrian Image

Abstract Attribute Identification is proposed in this research to help the security system for identifying the attribute of the pedestrian image. Convolutional Neural Network is used as the binary classifier in the proposed research because it has the ability of feature learning; hence handmade feature is not required in this network. The network architecture consists of the Convolutional, Rectified Linear Unit, Pooling, and Fully Connected Layer. Three models are proposed for the attributes identification based on the number of weight layers, they are, ten, eight, and six. There are seven attributes are identified using seven binary classifiers. They are male, hat, long hair, backpack, carrying nothing, shorts, and sandals. The conducted experiments showed that our proposed model achieved higher accuracy for several attributes compare to the previous model. The highest accuracy achieved in this research is 86.7% for the male attribute, and the lowest is 26.7% for footwear sandals.
Challenges to exploration in Greenland’s High Arctic plains and plateaus

The most likely type of mineralisation in the extensive carbonate platform areas of North Greenland is epigenetic lead–zinc. Flat-lying and virtually undeformed Palaeozoic carbonate rocks cover the areas closest to the margin of the Inland Ice, forming rather featureless plains or plateaus separated by wide valleys. Quaternary glacial deposits are ubiquitous and tend to partially cover prospective ground and dilute or obscure the geochemical signature of underlying bedrock. This, and the somewhat anonymous petrophysical properties (especially conductivity) of lead and zinc minerals, to some degree limit the ability of regional geochemical and airborne geophysical surveys to detect mineralisation in these areas. Thorough fieldwork in prospective areas remains essential to localising targets for detailed geophysics and drilling. Remote sensing using hyperspectral image analysis, however, is a new technique that should have great potential as a regional exploration tool in these regions.
Clothing Display Method Based on VR Technology

Clothing display is a kind of merchandise display. Good clothing display effect will attract customers’ interest. Traditional physical clothing display has problems such as high cost and easy damage, and once it is not handled properly, it is likely to leave a negative impact on customers. In view of the above problems, this paper proposes a research on clothing display methods based on VR technology. First create a 3D model of the clothing, then render the scene and map the stripes. In terms of model construction for clothing display, it is realized through MultiGen Creator. According to a survey of users, it is found that the scores of users in sensory experience, interactive experience, emotional experience, and cognitive experience all exceed 85 points, indicating that the method proposed in this article has significant effects.
Cultural and Creative Design Service of Faw Red Flag Car Based on Big Data Analysis Is Integrated with Automobile Industry

The mechanical speed of industrialization accelerating gave rise to the cultural creative design services and the fusion of traditional industries, since 2014, when prime minister li keqiang, promoting cultural creativity and design services and related industrial convergence development since this concept, the industry development of our country began to highlight the concept of culture creative services, the current this concept has gradually penetrated into all fields, most evident in the auto industry. This paper is to solve various problems in the fusion process and promote the deep integration of faw hongqi car cultural creative design service and automobile industry. This paper takes the development of faw hongqi sedan as a typical example, and analyzes the feasibility of integrating cultural creativity and design services with the automotive industry with the help of fusion algorithm. Finally, the main strategies to promote the deep integration of the two are obtained. The experimental results show that the integration of cultural and creative design services and automobile industry is conducive to promoting the transformation and upgrading of the automobile industry and realizing the sustainable development of the industry.
Improved Image Deblurring Using GANs

This paper proposes a new generative adversarial network that attains statistically significant improvements over some current image restoration techniques and traditional image restoration algorithms. Most of the existing image restoration techniques depend on prior information about the image. More flexibility can be obtained by using data-driven techniques to restore the image. Data-driven techniques like deep learning (DL) algorithms along with generative adversarial networks (GAN) have been showing great potential in the domain of computer vision (CV). However, it still has a scope of improvement in image denoising, deblurring, and super-resolution as the basic formulation of generative adversarial networks (GANs) cannot be directly used to generate realistic high-resolution images and some structures of the estimated images are usually not preserved well. Images from domains such as medicine and crime highly depend on the reality of the image and cannot entertain false positives and false negatives in their analysis. Such domains are still facing challenges to obtain true better quality images with the existing techniques. This paper also intends to solve some of these problems by giving better outputs with the proposed method. The improvised GAN proposed in this paper uses image quality parameter SSIM to improve restoration.
A Framework for Analyzing Structural Mechanisms Deployed to Support Traditional and Agile Methods

People rely on structures to make their worlds orderly. This chapter conceptually probes into the problem of the differences between organizational structures deployed in traditional and agile environments. The authors develop an argument that all common forms of organizational entities can be classified by involving a two-dimensional classification scheme. Specifically, they constructed a typology to examine the issues of formal vs. informal authority, and disciplinarity vs. cross-functionality in terms of their significance for traditional and agile software development workplaces. Some examples of concrete organizational forms—including traditional project team, independent test team, self-organizing agile team and developers' community of practice—are discussed. In sum, they argue that by employing this classification scheme, they can theorize the nature of the on-going structural shift observed in conjunction with deploying agile software development methods. They acknowledge that the structures have fundamentally changed, terming the move “democratization” in the software development workplace.
The Application of Tridiagonal Matrices in P-polynomial Table Algebras

In this paper, we calculate the characters of two classes of P-polynomial table algebras. To this end, we apply some linear algebra methods and tridiagonal matrices to obtain the characters of these table algebras.
Hypertext as a Way of Organizing Educational Space (Professional Communication in the Context of Russian as a Foreign Language)

Arguing that the modern educational space in connection with the change in the paradigm of information presentation and knowledge acquisition presupposes the presence of new approaches and new, more advanced and adequate tools, the author of the article suggests organizing propaedeutic training of foreign students of a natural science profile in the hypertext format. The author describes in General terms the model of hypertext educational environment and argues its advantages.
Implementation of Problem Based Learning Model Assisted Media Stop Motion in Learning IPS Elementary School

Abstract. The issue of social studies learning is an urgent problem and needs to be immediately sought for alternative problem solving to improve and improve the quality of social studies learning at SDN 2 Pangenjurutnegah. To overcome these learning problems, it is necessary to apply innovative learning models that can increase student motivation including teacher skills, student activities, and student learning outcomes that encourage active student involvement in learning. In addition, the learning process is expected to be systematic, comprehensive and integrated in the learning process towards maturity and success in life in society. This design in each cycle consists of planning (plan), implementation (act), observation (observ), and reflection (reflect). Data collection techniques used were observation, interviews and documentation studies. Based on the four cycles carried out, each indicator experienced a consistent increase. When the media stopmotion is displayed students show concern for the material, then the curiosity and the emergence of pleasure can be seen when students work on the task. This shows that the problem based learning model assisted by stopmotion can improve student learning skills. Keyword: PBL, Stop Motion, Motivation
High-quality depth up-sampling via a supervised classification guided MRF model

In this paper, a Supervised Classification assisted Markov Random Field (SC-MRF) model is proposed for generating high-quality up-sampled depth maps. The proposed model aims to reduce depth bleeding and depth confusion artifacts that can be produced at boundary regions of the up-sampled depth maps. In the proposed model, segmentation of low-resolution (LR) depth map is first used to supervise the classification of corresponding high-resolution (HR) color image. With this supervised classification, not only can the depth edges be retained, but redundant textures in the HR color image can be omitted. The classification result is then introduced into the design of a MRF energy function, and the final up-sampled depth map is obtained by optimizing this energy function with the gradient descent algorithm. For simplicity, classical K-means clustering is adopted to segment the LR depth map into several classes, and a feature-based K-nearest neighbour (K-NN) method is utilized for the supervised classification. With the proposed SC-MRF model, interaction between depths of different classes will be strongly suppressed, meaning depth edges are well preserved. Comparisons with the state-of-the-art demonstrate the strong performance of the proposed method both visually and by quantitative evaluation.
Accuracy Enhancement in Face-pose Estimation Network Using Incrementally Updated Face-shape Parameters

In this paper, we pursue the refinement of a face-pose estimation method using incrementally updated face-shape parameters. Network-based algorithms generally rely on an on-line training process that uses a large dataset, and a trained network usually works in a one-shot manner, i.e., each input image is processed one by one with a static network. On the other hand, we expect a great advantage from having sequential observations, rather than just single-image observations, in many practical applications. In such cases, the dynamic use of multiple observations can contribute to improving system performance. In our previous study, therefore, we introduced an incremental personalization mechanism using sequential observations to a network-based face-pose estimation method, where the averaged parameters in iterative face-shape estimations are used for face-pose estimation. The head pose estimation accuracy of our method was about 2 degrees. In this paper, we conduct an experiment to examine the error distribution of face-shape estimation and discuss an effective incremental personalization mechanism to update the face-shape parameters based on the error distribution.
Petrographic Study and Geochemical Analysis of Basement Rocks in Federal University Gusau and Its Environs, Zamfara State, Northwestern Nigeria

Petrographic and geochemical analysis of basement rocks in Federal University Gusau and its environs, Zamfara State, Northwestern Nigeria were carried out in order to unveil the major rock forming minerals as well as the textural relationships composition within the rock. The identified rock unit is granite and they are mainly whitishgrey in colour, with the majority of them having porphyritic-coarse-grained texture and minor with fine-grained texture. The light coloured crystals in hand specimens are the felsic minerals mainly quartz, and feldspars while the grey-dark crystals are the mafic minerals like the biotite-mica. The result of the thin section analysis depicts that the minerals present within the rock samples in the area: quartz (40%), orthoclase (40%) and biotite (20%). These identified minerals are classified as the major rock forming silicate minerals. The geochemical analysis result shows that the rocks are characterized by high percentage of quartz (SiO2) in the range of 77.4-98.2 wt% with an average of 84.87 wt%; subordinate amounts of hematite (Fe2O3) with an average concentration of 4.08 wt% as well as low concentration of K2O, CaO, TiO2, V2O5, MnO, NiO, CuO, ZnO, RuO2, In2O3 and other minor constituent oxides. The variation diagram of SiO2 and other oxides within the study area reveals a very strong negative correlation of SiO2 against CaO, TiO2, V2O5, MnO, Fe2O3, CUO, PbO, and Eu2O3 respectively, with the average correlation value of -0.91. The geochemical analysis results also reveal minor alteration of modal composition amongst the samples analysed which suggest the similarities in composition of all the samples within the study area.
IMS: An Identity-Based Many-to-Many Subscription Scheme With Efficient Key Management for Wireless Broadcast Systems

Subscription-based services, where broadcasting companies make profits by providing subscription programs to users, have become a popular application of wireless broadcast systems. Driving by business interests and data security, companies usually encrypt content of programs with session keys, then only authorized users can access the contents of their subscribed programs, respectively. It is challenging to flexibly distribute and update the session key, we are therefore motivated to propose an identity-based many-to-many subscription scheme (IMS) with efficient key management. The IMS scheme possesses three novel advantages: First, it provides a whole set of subscription flow, from the selection of programs of interest to the generation and updating of keys. Second, it supports program updating and all subscription behaviors. Third, it employs a signature and authentication mechanism to stipulate that only authorized users can access the programs’ ciphertext. The theoretical analysis demonstrates that the proposed scheme is secure against chosen-ciphertext attacks (CCA) under the <inline-formula><tex-math notation="LaTeX">$ k$</tex-math></inline-formula> -bilinear Diffie-Hellman exponent (BDHE) problem, and has a decent performance in storage and computational overhead. The experimental analysis shows the total time cost of the proposed scheme is reduced more than 75.02 percent compared against the popular peer schemes under the condition of 2500 users.
Improved Multi-verse Optimizer for Numerical Optimization

In this paper an improved version of multi-verse optimizer proposed and tested on numerical optimization problems. Multi-verse optimizer (MVO) is novel optimization approach inspired from the concepts of cosmology, which are named as white hole, black hole, and wormhole. To carry out exploitation, exploration and local search mathematical modeling of these concepts has been performed. Improvisation in MVO has been made by introducing concept of dynamic variation in population size (universe). Improved multi-verse optimizer (IMVO) was tested on 13 benchmark functions having distinct complexity. Finally statistical comparisons of results were made with other methods, which show that IMVO performs better than other algorithms given in the literature.
Mass smart detection for breast cancer diagnosis in mammographic images

Background: Breast cancer is one of the most common types of cancer among women.Mammography is the most standard method for diagnosing breast cancer, which can be minimize the human error using computer systems of human error.Methods: In this paper, using the image processing techniques, the mass was detected and identified in the photograph and then intelligent system outlined its margin After removing the image noise, using the fuzzy inference system, the fuzzy edge improvement has been applied and then using the coordinate logic filter, the mass areas have been detected and shown in the image.Results: The proposed smart system have p <0.001 for the correct diagnosis compared to the human diagnostic methods. Conclusion:The smart system results have been tested on 322 MIAS database images.In this database, 120 cases have benign and malignant tumors and 202 are healthy.The smart system was able to detect 115 cancer cases (true positive) and 190 healthy people (true negative) correctly.The number of false positive and false negative are 12 and 5, respectively.Therefore, the accuracy of the smart system for the database is 95%, and the sensitivity and specificity are 96% and 94%, respectively.
What we learn when designing with marginalised children

Designing with marginalised children often produces detailed insights about their lives and communities. Whilst it is possible to extract methodological and artefact-centred knowledge from existing design cases, it can be difficult to utilise and build on some of the more complex and multifaceted issues that these generate, for instance, how researcher decisions inform design outcomes. In this workshop, we invite researchers to reflect on the insights design case studies with marginalized children offer to the larger Children-Computer Interaction (CCI) community. Our goals are to reflect on what kinds of insights are generated; what we as design researchers and practitioners would have wanted to know prior to undertaking such work, and; to identify ways of communicating these insights.
Housing Drainage Network Evaluation in Education Teacher Road Merauke

Abstract Increasing population, residential and land development of the narrow lead to flooding or waterlogging during the rainy season. Based on the author’s observation that this occurs as a result of population growth and settlement. Areas that were once as a rain water reservoir and the water reservoir tidal inland transformed into residential areas and development centers of economic activity, and social services. The residential area of Merauke teacher education road is low-lying areas and densely populated, thus increasing the flow of water entering the waste stream. Based on the calculation of discharge, obtained Qk = 79,586,978.48 m3 / sec. The magnitude of the flood Q Qb = 24,488,511, 58 m3 / sec obtained high discharge may cause flooding in the residential area of teacher education path Merauke. So it is necessary to increase the dimensions of the existing channels in order to cope with flooding during the rainy season.
Service-Oriented Computing Applications (SOCA) Development Methodologies

Software development methodologies (SDMs) have had an accepted evolution (i.e., the replacement of SDMs of one era to the next) through the pre-methodology and early-methodology eras to the methodology era. But in the last 20 years, the transition of the methodology era (rigor-oriented) to the post-methodology era (agile-oriented) has led a debate on benefits and drawbacks of rigor vs. agile orientation. Regarding the general software-engineering evolution, the service-oriented software engineering (SOSE) that studies service-oriented computing (SOC) development approaches, which are widely used to develop software-oriented computing applications (SOCA), has emerged. SOSE developers then face the problem of selecting and adapting a SOCA SDM. This chapter compares 11 SOCA SDM on agility-rigor balance by a framework of Boehm and Turner addressing the rigor-agility conflicts by defining three factors and their methodological characteristics. Each characteristic is evaluated for each SDM with a novel agility-rigor 45-point scale. Results suggest three of such SDMs are agility-rigor balanced.