Reviewerly/exHarmony huggingface dataset:
Cooperation among Special Needs Education School, Technical High School and Institute of Technology through Development of an Application

This paper proposes a new cooperative association between special needs schools, technical high schools, and institutes of technology through the development of an application. The authors organized various classes over a one-year period in upper secondary schools and universities to enable high school students and undergraduates to collaborate toward a shared goal. The analysis of reports written by participating students revealed the educational effectiveness of the program in learning pertaining to realistic problem solving. With an eye to the future, tasks such as effective schedule coordination among the three parties, the development of a travel budget for students, and better technical assistance from undergraduates to high school students need to be undertaken and the issues therein must be addressed.
Design and Development of Sentence Parser for Afan Oromo Language

Parser is an efficient and accurate enough to be useful in many natural language processing systems, most notably in machine translation [1]. Previously many sentence parsers are developed for foreign languages such as English, Arabic, etc. as well as for Amharic language from local languages of Ethiopia. However, to the best of the researcher’s knowledge concerned, there is no Afan Oromo sentence parser for simple and complex sentences. Thus, we proposed to develop a sentence parser for Afan Oromo language. Parsing Afan Oromo sentence is needed and a necessary mechanism for other natural language processing applications like machine translation, question answering, knowledge extraction and information retrieval, particularly for Afan Oromo language. Rule-based parser using a top-down chart parsing algorithm for Afan Oromo sentences presented in this paper. Context Free Grammar (CFG) is used to represent the grammar. 500 sentences were prepared for sample corpus and CFG rules are extracted manually from sample tagged corpus. We also developed simple algorithm of a lexicon generator to automatically generate the lexical rules. Python programming language and NLTK are used as an implementation tools for this study. From the total of sample dataset 70% is simple sentence type because of we considered four different types of simple sentences (declaratives, interrogatives, imperatives and exclamatory sentences) and the rest 30% is complex sentence type. The parser was trained on 400 sentences of training dataset with the accuracy of 98.25% and tested on 100 sentences of testing dataset with the accuracy of 91%. The experimental results on a parser is an encouraging result since it is the first work for simple and complex sentences of Afan Oromo language.
A Critical Review of Korean Cultural Policy on Religion

The purpose of this paper is to critically examine the Korean government’s cultural policy on religion. The Korean Constitution clearly declares the principle of the separation of religion and politics. Yet despite this, the Korean government has provided a range of policy benefits to organized religions. For this reason, the Korean state can be criticized for having corrupt relationship with religion. Nevertheless, there has been little public criticism of this so far, mainly because most of the financial supports for religion have been disguised as cultural policy programs. This paper closely examines 1) the Religious Affairs Office’s budget for subsidizing religious facilities and 2) the government subsidy programs for constructing religious sanctuary areas. It will also analyse why the subsidy for religion disguised as cultural policy has persisted for so long, despite its lack of legitimacy.
Evaluating CNNs for Military Target Recognition

Convolutional neural network (CNN) is an efficient algorithm in deep learning. Aiming at the field of military target recognition, this paper constructs a dataset for military target recognition in battlefield, which contains ten kinds of targets. The characteristics of the dataset are described and analyzed. Three classical CNN models (AlexNet, VGGNet and ResNet) and two learning strategies (dropout and data augmentation) are evaluated on the dataset. Under the same condition of the dataset and the same super-parameter setting, the effects of different models are presented and analyzed. The experimental results show that the mean average precisions of ResNet and VGGNet are better than AlexNet, and the accuracies of both ResNet and VGGNet are over 90% with only thousands of training images. At the same time, the dropout and data augmentation strategies have a strong effect for improving the performance.
Figure/ground modeling combined with the context matching for visual object tracking

The aim of this paper is to track objects in a sequence of surveillance video frames by providing accurate object contours. Video analysis is performed in four key phases: (i) modeling the object/background by means of the Gaussian Mixture Model and the Kalman Filter, (ii) extracting a rough contou r for the object through separating the moving objects from the still background, (iii) finding the corresponding points and the object location using the shape context matching algorithm, and eventually, (iv) extracting the exact shape contour. The suggested methodology is accurate enough to track objects in terms of scale change, translation, rotation, and partial occlusion and it can be used in real-time applications. Results on different video sequences establish the capability of this technique for a variety of applications such as traffic control cameras, speedometers, CCTV security cameras, photography and etc.
Measuring mobile performance in the Tor network with OnionPerf

The Tor network is the largest public deployed anonymity network using the Internet. While there has been a longitudinal study into the performance of the network in progress since 2009, it has only used vantage points in data centre networks. In this paper we propose modifications to the performance measurement tool, OnionPerf, to enable its use for measuring performance from a mobile end-user's perspective. We provide initial findings on simulated mobile networks, using two types of emulated links, using both the public Tor network and in a private test Tor network.
A new strategy based on approximate dynamic programming to maximize the net revenue of IaaS cloud providers with limited resources

With rapid development of cloud computing, more conventional Internet Data Centers get involved into this business model. Providers of small and medium sized data centers have relatively limited computing resources compared to global IaaS providers such as Amazon. The demand for computing at these service providers may be above the available capacity especially during peak shopping periods (e.g. mother’s day or singles’ day). However, it is undesirable to blindly expand capacity, which will not only incur the purchase cost of new machines but also the maintenance costs associated with resource over-provisioning. For these service providers, the most important thing is how to effectively manage resources during peak demand periods to maximize their net revenue. In this context, we focus on the joint admission control and virtual machine placement problem, and propose a dynamic optimization model considering that customers dynamically create and terminate virtual machines in cloud computing environment. In terms of problem complexity, the developed dynamic optimization model typically suffers from “curse of dimensionality” and is computationally intractable. So we resort to approximate dynamic programming framework and propose a new strategy to yield a tractable model for real-time decision-making. Extensive simulations are conducted to compare our proposed strategy with other strategies including existing methods of virtual machine placement. The simulation results demonstrate that our customized strategy achieves substantial revenue improvements and the improvement is more significant as resource provisioning is tighter. In addition, it can be scalable to solve large cases up to 1000 physical machines and yield a real-time decision in a reasonable time.
A Hybrid of Background Scatter and Foreground Contour Completeness for Salient Object Detection

In past decades, salient object detection has witnessed rapid progress. In this paper, we propose an efficient framework for salient object detection by considering background scatter and foreground contour completeness. First, the method utilizes boundary prior embedded with background scatter to yield the boundary contrast map (BCM). Then, a contour completeness map (CCM) is constructed using the completely closed shape of the object. Next, an adaptive fusion algorithm is designed to fuse both BCM and CCM. Finally, we develop a post-processing refinement for fused map to obtain the final saliency map. On three datasets, the proposed method significantly outperforms eights state-of-the-arts both quantitatively and qualitatively.
The Content and Workflow of Game UI Design

Objective: To research and study the content and workflow of game UI design and propose a practical guidance for the people related to the game design industry. Methods: Analyse the content of game UI design from the perspective of different human sensations. And study the general workflow of game UI design base on actual projects. Results: A brief theory and guidance for game UI design. Conclusion: The UI design of games is different from that of the other fields. Therefore, it is necessary to systematical study and analyse the content and workflow of game UI design.
Diagnostic Differentiation of Crimean Congo Hemorrhagic Fever, Influenza and Bacterial Meningitis by Classic and Fuzzy Mathematics

Background: A correct diagnosis of a disease among several diseases with the same clinical symptoms is very important and is a difficult task in medical science. Misdiagnosis of these diseases in the short term causes very high and serious damage to the health of patients and usually results in loss of golden time. Objectives: In this paper, our purpose is to achieve the best conclusion, which contributes to the diagnosis of the critical illness without losing the golden opportunity based on clinical data and using mathematical models, especially fuzzy mathematics. Methods: The data regarding patientâs signs and symptoms were collected in the hospitals. We attained the best choice of diseases among the considered options of diseases by using basic fuzzy rules, fuzzy control techniques, fuzzy mathematics and fuzzy systems. To write the basic fuzzy rules, the information that we used was adopted by experts in infectious diseases or data records of patients who reached a definite diagnosis of disease by various tests. Then, by using these rules, the system of mathematical equations was formed. By solving this system, coefficients of a linear equation were estimated witch its values according to the clinical signs of a patient indicates the probability that the patient will be infected with that disease. In this process, the number of patients studied is n not effective. But the more patients are studied, the more accurately the coefficients of the diagnosis equation are obtained. Results: The symptoms of some patients whose disease have been definitely diagnosed were used as inputs of the system of our equations and it was observed that the systemâs outputs approximately coincide the exact diagnosis of the disease, which indicates that the equations obtained for the diagnosis of diseases are acceptable. Conclusions: The findings of this study can help to correctly diagnose the disease without losing golden opportunities. We hope that using the results of this research, the error in the initial diagnosis of diseases is significantly reduced.
Hardened Materials Material Deformation Study Using Digital Image Correlation Technique for Nuclear Power Plant Applications

This paper investigates the results of tensile tests to estimate the mechanical properties of the hardened work material. The proposed Digital Image CorrelationDigital Image Correlation (DIC) (DIC) technique has been applied to estimate the mechanical properties of hardened bearing steels (AISI 52100, AISI 4140) that is used in the slewing ring for special applications in nuclear reactors, cranes, excavators, wind turbines, etc. Through this non-contact technique estimation of the mechanical properties in both elastic and plastic regions with significant strains becomes comparatively easy with tensile testing. Tensile tests have been conducted for each specimen of hardness (HRC) 50 and 60. Based on the results, Engineering sample Stress–Strain curvesStress–strain curve are plotted for hardened steel specimens. Then these results are used for the design and the development of large diameter slewing bearing in the fuel handling unit for fast breeder reactor because through DICDigital Image Correlation (DIC) technique accuracy of strain rate value gets increased in comparison to UTM results. The scope of the research is to study the contact stress, load versus deformation curves, temperature distribution, and any FEM analysis for higher accuracy.
Towards Information Transparency: Current Posture and Advocacy for Open Data Systems for Efficiency, Equity and Justice – The Nigerian Oil and Gas Experience

Abstract Transparency in information is one of the most important undertaking that the government in collaboration with oil and gas stakeholders can offer to the public. Open, and therefore, accessible, nonproprietary data in the current information age is a tool for improved governance, transparency and innovation. The oil and gas industry is a huge data generator through its daily operations, it is a leader and in fact, a home to big data. From existing development assets, to supply chains, customer relations etc, data provides a competitive advantage for organizational growth and management. Beyond the strategic opportunities that big data provides within the industry, is the enormous capabilities that big data through open data initiatives can have on company-community relations. In this paper, we examine the various acts and legislations governing oil and gas industry operations and their position on data transparency. Our examination sheds light on current positions of the tripartite of law, policy and practice and the demonstrated gaps between this tripartite. In addition to the legal analysis, we also examine the issue of open data through the petroleum engineering prism. The benefits and significant impact a movement in open data will deliver to the industry are also examined which include but are not limited to: addressing public concerns and increasing community workforce participation and protection of oil and non-oil natural resources. Finally, because discussion on open data is incomplete without the necessary data literacy skills needed to utilize, analyze and draw meaningful conclusions from data, we examine how best we can increase these skills within the community to bridge the information divide. As we enter 20 years of steady democracy and political process in Nigeria, open data will certainly disband doubts and fake news in our energy conversations as it will provide a level playing field for all parties. Public discourse will be more focused on facts and lead to improved and sound decision making.
Skin Cancer Monitoring Project

Skin Cancer (SC) is a major health problem in Australia. It is a syndrome which infects the Cells of our skin that is affected principally to Ultraviolet Radiation (UVR) rays. The SC that happens in Australia has three foremost arrangements which are termed permitting to the cells that are affected: Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma are also renowned as the poisonous form of SC. Normally, BCC and SCC are also known as non-melanoma SC.
Planning TS Trajectory Using MLAT in $\mathrm{o}(\mathrm{n}\log \mathrm{n})$

The Multi-Level Adaptive Technique (MLAT) is a technique used for solving problems approximately and iteratively at levels with various resolutions, and then injecting the corresponding solution at each level into a problem with close resolution for the next iteration. MLAT was originally applied solving successfully partial differential equations using the so called Multi Grid method, using a set of grids with gradually varying mesh sizes. Each grid was treated separately using the so-called relaxation, and then traversing (injection from fine to coarse grid, and interpolation from coarse to fine grid) the data between the grids at two close levels. The Traveling Salesman (TS) problem - popular in Motion Planning, solved by MLAT using Graph Theory. The vertices are specially partitioned into groups, which are moved into more general groups, and again into higher level groups. This grouping is repeated until the number of elements at the highest level collected, does not have too many elements in order to enable their easy and fast manipulation. The TS problem, namely, searching for the shortest path traversing all the vertices in the graph, is approximated by MLAT by partitioning the graph into small subgraphs by selecting the odd vertices in the original graph; each subgraph is similarly divided again into a smaller subgraph. This procedure is repeated until a subgraph is obtained, which is small enough. The TS problem is solved using the coarsest subgraph obtained. This solution is injected into the finer subgraph to improve the approximate solution by relaxation, on the current subgraph. The injection from a coarse graph to a fine graph is followed by relaxation, which is repeated on all the pairs of the graph and its subgraph. Then the opposite direction is applied injection: bottom up, from a finer graph to a coarser graph. Relaxation is an iterative process in which a graph's vertices are traversed, improving the solution. In order to increase the chances of a more accurate solution, the algorithm's direction is determined in a nondeterministic way using so-called Simulated Annealing. The MLAT implementation may enable a Multi-Processing leading to Parallel-Processing, which is an additional advantage.
Natural Computing and Optimization

This chapter introduces the area of natural computing. The field encompasses a multitude of classes ranging from evolutionary algorithms to firefly techniques. The present chapter focuses on a selected set of established techniques: evolutionary algorithms, swarm-based methods, and neuroevolution. The first two classes are mainly used for parameter optimization (with the exception of genetic programming, a specific type of evolutionary algorithms) whereas the third class is applied for learning the structures of controllers. As such, the methods selected illustrate the main concepts of natural computing and serve to show the broadness of the application areas. The last part of the chapter is devoted to multi-objective optimization–an important task in practice which is often solved with natural computing.
An Effective Solution for Application Orchestration

Application orchestration technology has always been one of the research directions of Devops. The traditional application orchestration technology has some problems such as slow deployment and scaling speed. Therefore, this paper proposes a Docker-based hybrid cloud application orchestration scheme addresses these issues with traditional application orchestration techniques. The goal is to solve the problem of existing application orchestration techniques from the root by borrowing Docker’s quick-start feature and the universal characteristics of hybrid cloud technology. Firstly, this paper proposes a four-layer architecture including the base layer, core layer, gateway layer and interaction layer, and designs the application orchestration module, dynamic scaling module and system monitoring module in the core layer. Secondly, the implementation scheme is tested, and the experiment shows that the scheme shows good deployment speed, speed of expansion and high reliability.
Custom Silicon and Sensors Developed for a 2nd Generation Mixed Reality User Interface

Microsoft Hololens 2, like its predecessor, is an untethered holographic mixed reality (MR) headset that transforms the way we communicate, create, and explore. Hololens 2 advances MR ergonomics, intuitive interactions, and immersion. We describe the custom sensors and compute silicon developed to give hands-free user control of the headset and applications. With 3D Time of Flight (TOF) depth sensing, eye tracking and spatial array microphones, working with low power compute blocks aggregated in a custom ASIC, the hardware enables a comfortable, low latency user interface that sets the user free to focus on their work. We conclude with a look at how these building blocks can enable further innovation in the Intelligent Edge.
Select and Focus: Action Recognition with Spatial-Temporal Attention

With the rapid development of neural networks, human action recognition has been achieved great improvement by using convolutional neural networks (CNN) or recurrent neural networks (RNN). In this paper, we propose a model based on weighted spatial-temporal attention for action recognition. This model selects the key parts in each video frame and important frames in each video sequence. Then the model focuses on analyzing these key parts and frames. Therefore, the most important tasks of our model is to find out the key parts spatially and the important frames temporally for recognizing the action. Our model is trained and tested on three datasets including UCF-11, UCF-101, and HMDB51. The experiments demonstrate that our model can achieve a satisfactory result for human action recognition.
A SaaS Implementation of a New Generic Crypto-Classifier Service for Secure Energy Efficiency in Smart Cities

More and more, accessing data remotely in a secure manner appears like the backbone for providing new advanced and customized functionalities. The proliferation of social media and domotics urges a new kind of ubiquity where different kinds of features are deployed everywhere atop of the everyday life small devices and even embedded in our surroundings. While we measure the opportunities offered by such advancements in improving people lifestyles and obtaining immediate and useful services, it is necessary to consider the real impact on our privacy. Recently, a newly adopted European standard, the General Data Protection Regulation aims at codifying the rules of personal data use. We strongly believe in the necessity for a Smart City of adopting such privacy regulation while benefiting at the same time from new services, in a generic and easy way. In that sense, we build an approach that is at the frontier of the cryptoscience domain for a better privacy and the Micro Service-Oriented Architectures for an increased use by tiers. Indeed, our service is accessible in a Software-as-a-Service (SaaS) manner and uses encryption to ensure data privacy. The particularity of our system lies in the fact that the server performs a classification algorithm without any information about the sensitive data and without the capability to decrypt it. The underlying cryptographic technology used is homomorphic encryption, allowing to perform calculations directly on encrypted data. Our service is generic by design and could be applied over several metrics. We adopt an energy efficiency use case and propose a service for buildings energy diagnosis and classification towards renovations and/or reductions of the electric consumption. We showcase our prototype of crypto-classification service by involving different actors of a Smart City community. Finally, we assess our proposal thanks to a set of real data collected from an Irish residential district. Our SaaS crypto-classifier achieves acceptable performances in terms of security, execution times and memory requirements .