Reviewerly/exHarmony huggingface dataset:
Augmented Reality as an Advanced Learning Tool for Pneumatic Control

This paper presents an advanced augmented reality smartphone application for learning pneumatic control. The application is developed with the aim to make easier for students to understand the basic principles of this field of engineering. It is developed for the Android platform and enhances the pneumatic control workbook with additional digital content. This digital content is in form of 3D animations, simulations and also, the application redirect the user to different web pages.
Develop Accessibility Design for Increase Disabilities Labor Participation in Manufactured in Indonesia with Design Thinking Approach

Indonesia is a large country with the population of disabilities among 21,84 million that still lack of participation in profession especially formal working in the manufacturing sector [1]. There is a high standard about labor requirements in much manufactory that is still stereotyped by physical conditions. Despite of the incapability of body functions, people with disabilities optimize it in another physical. The reality that founded is like a Blind person having a sense of touch and memorize is more persistent than a normal person, or a Deaf person using more visual perception and imagination in advance than a hearing person and another example. It represents their capable to work, but still difficult because on discrimination and facilities. To evolve disabilities on work labor this research is study about routes system that make it easier person with disabilities to walk around factory and more independently. To designing the routes system using qualitative method with design thinking approach and adopts the concept of “universal design” that introduced by Ronald Mace in terms of accessibility to disabilities person able to do an activity more assisted. Accessibility also can encourage the involvement of persons with disabilities to a good economic opportunity. which will be used in developing a standard of accessibility in a manufacturing work place that is friendly for disability.
Task Scheduling Approach to Save Energy of Heterogeneous Computing Systems

Today's computer systems are widely integrated with GPUs to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a METS (Minimizing Execution Time Slot Scheduling Scheme) to reduce energy by rationally allocating the tasks across the GPUs. It first collects the estimated performance information. Next, it uses above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed METS approach. The experimental results show that METS can save 8.43% energy compared with that of the existing approach. This shows that the proposed METS method is effective, reasonable and feasible.
Co-Occurrence Morphological Edge Detection

Morphological edge detection plays a significant role in image processing and computer vision. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects also. However, edge detection is challenged and difficult to find the discontinuities in surface orientation, changes in material properties and variations in scene illuminations. In this paper, an improved co-occurrence morphology edge detection algorithm is proposed. We exploit the co-occurrence filter (a combination of bilateral filter and gray-level co-occurrence matrix) to handle with noisy images, and two novel structural elements are used for edge detection. The proposed edge detection algorithm can reasonably consider noise reduction and preserve the detailed edge information. The experimental results demonstrate the superior performance with some existing methods and exhibit better anti-noise ability.
Design of the Reference Model of Smart Campus in Universities Based on IoTs Technology under the Background of Big Data

With the development of information technology and communication technology, information technology has been integrated into the education industry, which has had a profound impact on the reform and development of education. The education industry has also entered the era of big data, and smart education has received more and more attention from universities. The development of smart education must rely on the construction of a smart campus. Based on the analysis of the development stage of informationization in colleges and universities in China, this paper puts forward the definition of smart campus in universities, and uses the Internet of Things technology to establish a conceptual model of smart campus in universities. This paper proposes the spatial architecture of colleges and universities' smart campus, establishes a modular framework model, and designs a modular architecture model including communication modules, environment modules, data modules and service modules, and initially forms a smart campus reference model based on Internet of Things technology.
A kind of Recommendation Algorithm of Matrix Decomposition Based on Power Grid User Rating Characteristics

Recommender system can effectively solve the information overload problem in the era of big data. Recent research on recommender systems, specifically Collaborative Filtering, has focused on Matrix Factorization methods, which have been shown to have excellent performance in the recommendation. However, these methods do not pay attention to the influence of user's rating characteristics, which are especially important for the accuracy of prediction or recommendation. Therefore, in order to get better performance, this paper proposes a novel method based on matrix factorization. We consider that the user's rating score is composed of two parts, one part is called real score which is decided by the user's preferences, and another part is called bias score, which is decided by the user's rating characteristics. We analyze the user's historical behavior to find his rating characteristics by using the matrix factorization technique, and then use them to adjust the final prediction results. By comparing with the latest algorithms on the existing datasets, we verified that the proposed method can significantly improve the accuracy of recommender systems and achieves the best performance in terms of prediction accuracy criterion over the state-of-the-art methods.
Evaluation of machine learning applied to the realignment of hierarchies for image segmentation

A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects can be located at different scales due to their size differences or to their distinct distances from the camera. In literature, many works have been developed to improve hierarchical image segmentation results. One possible solution is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of random forest and artificial neural network as regressors models to predict score values for regions belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation witch considering all user-defined segmentations that exist in the ground-truth. Experimental results are presented for two different hierarchical segmentation methods. Moreover, an analysis of the adoption of different combination of mid-level features to describe regions and different architectures from random forest and artificial neural network to train regressors models. Experimental results have point out that the use of new proposed score was able to improve final segmentation results.
증강현실 학습자료에서 가상캐릭터의 제시방법이 학습자의 의인화 지각, 인지부하의 변화와 주의집중에 미치는 효과

The purpose of this study was to examine the effects of presentation methods of the virtual character in the augmented reality learning environment. For this study, the dependent variables were persona effect by the virtual character, cognitive load, and attention level in the augmented reality-base...
STATISTICAL EVALUATION OF THE ENERGY, ECONOMIC AND SOCIAL POTENTIAL FROM RENEWABLE SOURCES IN ROMANIA

Worldwide, reducing energy consumption and increasing energy efficiency are the main objectives of an integrated approach to economic, energy and climate policy.The energy sector has vital importance for the economic and social development and improves the quality of life.Ensuring sufficient energy supply and broad access to energy services, particularly in the green from renewable sources is a basic requirement of sustainable development.The use of renewable energy has many potential benefits, including a reduction in greenhouse gas emissions, a diversification of energy supply and a reduction in dependency on fossil fuels markets.The development of renewable energy sources can also have the potential to stimulate employment by creating jobs in the new "green" technologies sector.In this context, the study aims to achieve a statistical evaluation on the energy potential from renewable sources in Romania.It also will carry out an assessment of the renewable energy sources available at national level and their potential benefits.Based on the results obtained, we emphasize that economic growth can be achieved by adopting a common vision based on efficient and nonpolluting alternatives for the energy production.
Parameter calibration of a vision sensor with a geometric similarity constraint

The precise parameters of vision sensors determine how accurately the device obtains the result. A novel effective approach based on geometry similar constraint has been derived. The proposed method decouples the lens distortion coefficients and linear parameters of the camera successfully. Meanwhile, the proposed method is also able to estimate the camera's parameters with a small number of collinear points. All the distortion coefficients are solved through a geometry similar constraint model. Then corrected images are used to estimate linear parameters once the distortion coefficients are acquired. Both computer simulation and real experiment are carried out to validate the performance of the proposed method. The number of collinear points firstly has discussed in train data, and results showed the proposed method works better when compared to the currently widely used methods. The proposed method is also compared with the current planar target-based methods, which are commonly adopted in practical experiments. The results indicate that the proposed method is more reliable and effective.
Finding Images by Dialoguing with Image

Image retrieval in complicated scene is a challenging task that requires the comprehensive understanding of an image. In this paper, we propose a scene graph based image retrieval framework that combines the scene graph generation with image retrieval and fine tuning the searching results via a dialogue mechanism. Specifically, we proposed an image retrieval oriented scene graph generation model that takes an image and a text describing the image as inputs. The additional text input is used to control the generated scene graph. It provides information for a newly introduced attributes head to better predict the attributes and helps constructing an adjacency matrix at the same time. Graph Convolutional Network is further used to gather information among nodes for precise relation estimation. Moreover, modification on the scene graph can be done by changing the text. Our proposed approach achieves the state-of-the-art performances in both scene graph based image retrieval and scene graph generation in the Visual Genome dataset.
Soft-set based Double-Deep-Q Scheduler for Optimal Task Scheduling under Long-term Uncertainty in the Cloud

Tasks scheduling in the cloud is one of the preliminary requirements to achieve optimized performance by the efficient allocation of tasks to resources. The cloud platform is vulnerable to the uncertainty in the task and resource parameters; hence, there is a need to develop self-learning models which handle uncertainty before taking scheduling decisions. In this paper, to handle the task uncertainty soft-set of tasks is created, and to handle the resource uncertainty soft-set based clustering mechanism is used. After handling the uncertainty, Double-Deep-Q learning (DDQ) scheduler is employed to draw optimal policies to efficiently map the soft-set of tasks to soft clusters of the resources. The expected value analysis method is used to analyze the performance of the DDQ scheduler in the finite and infinite cloud scenario. The performance of the DDQ scheduler compared to other well-known schedulers like PWL, PCS, PGA, and TPO is found to be good towards parameters such as execution time, accuracy, resource utilization ratio, and learning rate.
Distributed Hierarchical Fault Diagnosis Based on Sparse Auto-Encoder and Random Forest

For the diagnosis of large-scale local devices, the traditional centralized fault diagnosis systems are becoming incompetent to meet the requirement of real-time monitoring. This paper proposes the Distributed hierarchical Fault Diagnosis System (DFDS). Specifically, DFDS implements fault monitoring by an improved Sparse Auto-Encoder (SAE) on the monitor layer, classifies faults and identifies unknown faults by an improved random forest on the classification layer, learns new knowledge and updates the system on the decision layer. We apply DFDS in the laboratory data of Case Western Reserve University to verify the efficiency of the proposed system. The experimental results show that our method can accurately detect the fault and accurately identify the fault type.
The Augmented Reality Application for Teaching and Learning Basic PC Maintenance

Augmented reality is hidden content, most commonly hidden behind marker images, that can be included in printed and film media, as long as the marker is displayed for a suitable length of time, in a steady position for an application to identify and analyze it. Depending on the on the content, the marker may have to remain visible. Using a mobile application, a mobile phone’s camera identifies and interprets a marker, often a black and white barcode image. The software analyses the marker and creates a virtual image overlay on the mobile phone’s screen, tied to the position of the camera. This means the application works with the camera to interpret the angles and distance the mobile phone is away from the marker. The use of augmented reality for teaching and learning: Basic PC maintenance will be the best way to attract and deliver knowledge. The scope of this project will involve staff and students of Universiti Selangor. The cost of print could also be reduced with the use of augmented reality technology for learning and teaching process.
California Launches Nation’s First Earthquake Early Warning System

The country’s first publicly available, statewide warning system could give California residents seconds to duck and cover before a quake.
THE USE OF ANIMATION VIDEO TO IMPROVE STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXT TO THE SECOND GRADE STUDENTS OF SMPN 1 LAHAT, SOUTH SUMATERA

The objectives of this research were to find out whether there was an improvement on student’s reading comprehension after being taught by using animation video. This study was conducted through quantitative approaches, with a total of 30 eighth-grade students at SMPN 1 Lahat, South Sumatera. The data were collected by using a reading test. Data were analyzed by using Paired Sample t-test. The result shows that there is a significant improvement in the students’ reading comprehension of pretest and posttest in which reference received the highest improvement and inference received the least. It could be seen from the mean score of pretest was 69.27 and the mean score of posttest was 80.42. It also could be seen from the result of Tvalue compared to Ttable in whichTvalue>Ttable (12,850 > 2.045). It can be concluded that animation video is effective to improve students’ reading comprehension (p=0.000<0.05).Keywords: reading, reading comprehension, animation video.
Computer Aided classification of breast Lesions in Digital Mammograms

Objective: Mammography technique is mostly used for detecting the presence of abnormal breast lesions among women. Differentiating these abnormalities is a most difficult task faced by the radiologists. By using this proposed technique the rate of unnecessary biopsies can be limited. This paper deals with an effective way of detecting the breast lesions using curvelet transform. This proposed paper follows a stepwise procedure such as (a) Preprocessing (b) Region of Interest Segmentation. (c) Applying Curvelet Transform (d) Feature Extraction & finally (e) Classification of features using different kernels of SVM. It is inferred from the observed results that the SVM(Linear) classifier showed a good accuracy rate of 80%.
Generating DDI 4 in the Language of your Choice

Model Driven Architecture for Metadata Standards in Practice Half-day tutorial, target group: software developers This tutorial provides the knowledge and skills on how a syntax representation of the DDI 4 UML model can be generated in any language. The focus is on the DDI 4 Core model, which is in many important respects independent from the social science domain and therefore suitable for cross-domain purposes (the public review of DDI 4 Core is planned for the beginning of 2020) The first part of the tutorial gives an overview of the model-driven approach, the model itself, used UML modeling techniques, and the generation of representations in XML Schema (for XML instances) and in OWL (for RDF instances). It describes how the UML model can be used as basis to generate a syntax representation in a chosen language. The second part provides time for exercises. Eclipse modeling tools are used for this purpose. Participants are encouraged to choose a language in which they wish to use DDI 4. You should bring your own laptop and be ready to install the required software (a detailed list will be provided) prior to the tutorial. Agenda Part I Overview Model-driven approach DDI 4 Core model – UML subset and model structure Generation of XML Schema for XML instances Generation of OWL Vocabulary for RDF instances Approach for the generation of any syntax representations Part II Eclipse environment Exercises
Efficient Approximation of Gaussian Function for Signal and Image Processing Applications

Gaussian function has been frequently used in image and signal processing, especially on image denoising and edge detection. In this paper, we present an approximation model of the Gaussian function using the powers of a half-period of a sine wave. In the previous research, there is only exponential formulation of the Gaussian kernel with two parameters mean µ and standard deviation σ. The proposed model is based on harmonic decomposition of powers of sine with a preliminary accuracy better than 10−7 with two parameters n and f 0 . Moreover, the complexity of implementation of the proposed Gaussian model is reduced with more efficiency. On the other hand, we introduce a novel formulation of the 2D Gaussian kernel based only on sine wave for image processing applications. The performance and robustness of the suggested Euler Harmonic Gaussian model are illustrated on image filtering and edge detection applications compared to the existing algorithms.
Prediksi Data Curah Hujan Dengan Menggunakan Statistika Non Parametrik

Rainfall data analysis is the first stage of a water resource planning. One of  rainfall data analysis method is using rain frequency analysis. In this research, rainfall frequency analysis is used to prediction the probability of occurrence from hydrological event. The maximum monthly rainfall frequency distribution is affects to rainfall during high repeat periods. Rainfall is the amount of water that falls on a flat surface during certain repetitive periods. Secondary data is got from Temindung Station of  Samarinda City on 2007 to 2016. The type of distribution are used Normal, Gumbel, Log Pearson Type III, and Log Normal. Compatibility test of Non Parametric Statistics using Chi Square method. The results showed if the estimated rainfall at the highest repeating period of 2, 5, 10, 25, 50, and 100 years is Log Normal distribution. The distribution that requirement of qualify criteria is Log Normal and Gumbel distribution. The distribution that fit from Chi Square test is Gumbel distribution is 3,5177 and Log Normal distribution is 6,8945. From Kolmogorov Smirnov test the value of  Gumbel distribution is 0, and Log Normal distribution is 0,0805. Rainfall patterns for Normal distribution, Gumbel distribution, Pearson Log distribution Type III and Log Normal distribution are horizontal patterns.
White Superiority Deconstructed: Aborigine Values in Wongar’s The Track to Bralgu

This research aims to rewrite the paradigm of a superior invader and the inferior colonized using Derridian deconstruction in The Track to Bralgu, a novel by Streten Bozig Wongar. It examines the depictions of the Aborigine to identify the superiority paradigm that is represented on the characters’ description and reveals how the depiction of the characters in the story brings the idea of the superiority of the colonized toward the colonizer by deconstructing the White’s paradigm. A descriptive qualitative method is a method used and a postcolonial approach is applied to conduct the research. The results of the study show that (1) the qualities of the Aborigine people which show themselves not being inferior are tracking and living in the bush, taking care of human being, transforming from human form into an animal form, and honoring the corpse; (2) the qualities of the Aborigine which deconstruct White’s constructed paradigm are skillfulness, humanity, and faith; and (3) the Aborigine people have no intentions to be better than the White people, they just live the way they believe. By living the life the way they believe, it is proven that the Aborigine people do gain the paradigm of superiority in the period of colonization. Penelitian ini bertujuan untuk mengkaji ulang paham superioritas penjajah dan inferioritas masyarakat terjajah dalam The Track to Bralgu menggunakan konsep dekonstruksi dari Derrida. Penelitian ini menilik penggambaran karakter Aborigin untuk mengidentifikasi paradigma superioritas penjajah dalam penggambaran karakter dan menunjukkan bagaimana penggambaran karakter tersebut menuntun paradigma superiotas dari masyarakat terjajah dengan mendekonstruksi paradigma kulit putih. Metode deskriptif kualitatif adalah metode penelitian yang digunakan dan pendekatan poskolonial diterapkan untuk melakukan penelitian ini. Hasil penelitian ini menunjukkan bahwa (1) karakteristik orang Aborigin yang tidak merepresentasikan inferioritas mereka adalah melacak dan hidup di alam liar, peduli terhadap sesama manusia, bertransformasi dari wujud manusia menjadi hewan, dan menghormati jenazah; (2) karakteristik orang Aborigin yang mendekonstruksi paradigma penjajah ialah kepiawaian, kemanusiawian, dan kepercayaan; dan (3) orang Aborigin tidak memiliki intensi untuk menjadi lebih baik daripada penjajah, mereka hanya hidup dengan cara yang mereka yakini dan mereka menjadi superior dengan hidup dalam kebudayaan tersebut pada masa kolonial.