Reviewerly/exHarmony huggingface dataset:
Route Packing: Geospatially-Accurate Visualization of Route Networks

We present route packing, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes.The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions.Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it.We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique.Our findings highlight the visual parameters under which the technique yields optimal performance.
Analysis of Digital Media Art Teaching Innovation Based on Network and Cloud Computing

The art form of digital media art is popularized with the development of computer. Since the middle of twentieth century, computers have changed people’s way of thinking and habits at an amazing speed. Digital technology has a profound influence on the artistic expression and language form changes. At present, the development of various digital technologies has emerged in an endless stream, which has brought a bright future for the development of digital technology in China. The digital media art major is an applied subject, and the practical teaching occupies an important position in the whole teaching system. In recent years, new design concepts, new means of expression, new technology and so on are constantly emerging, and the requirements for design talents are becoming higher and higher. In the construction of practical teaching system, we should focus on the reform and construction of professional courses, formulate the planning ideas of curriculum construction, and revise the teaching plan, update the contents of practice teaching, and form a plan to improve teaching quality.
LibGuides: NHLI FUTURE LEARNING PATHWAYS: INFORMATION AND CALENDAR

FUTURE LEARNING PATHWAY DEVELOPMENT: PLACING STUDENTS AT THE CENTER AND INSPIRING HOPE FOR THE FUTURE.
Smart Embedded Systems with Decisional DNA Knowledge Representation

Embedded systems have been in use since the 1970s. For most of their history embedded systems were seen simply as small computers designed to accomplish one or a few dedicated functions; and they were usually working under limited resources i.e. limited computing power, limited memories, and limited energy sources. As such, embedded systems have not drawn much attention from researchers, especially from those in the artificial intelligence area. Thanks to the efforts of scientists over recent years, great progress has been made in both computer hardware and software, which enables us to have much more powerful computers in very small sizes and with many more functions. Consequently, new expectations and needs for embedded systems have increased considerably. Today, smart embedded systems are expected, which are supposed to have capability to learn from past task executions and evolve their performance based on learnt knowledge, and assist users to make good decisions more efficiently. Therefore, how to make embedded systems smart is becoming one of the researchers’ new challenges. In this chapter, we introduce the Experience-Oriented Smart Embedded Systems (EOSES) that is proposed as a new technological scheme providing embedded systems with capabilities for experiential knowledge capturing, storage, reuse, evolving, and sharing. In this scheme, knowledge is represented as the Set of Experience Knowledge Structure (SOEKS or shortly SOE) and organized as Decisional DNA. The scheme is mainly based on conceptual principles from embedded systems and knowledge management. The objective behind this research is to offer large-scale support for intelligent, autonomous, and coordinated knowledge management on various embedded systems. Several conceptual elements of this research have been implemented in testing prototypes, and the experimental results show that the EOSES scheme can not only provide active knowledge management to different embedded systems, it can also enable various systems to learn from their daily operations in many different fields to acquire valuable knowledge, assist decision making, reduce human workers’ workload, and improve the system’s performance. As a result, the EOSES has great potential for meeting today’s demands for embedded systems, and providing a universe knowledge management scheme for mass autonomous mechanisms.
THE INFLUENCE OF ROLE PLAYS AND LEARNING MOTIVATION TOWARDS THE FIRST YEAR ROOM DIVISION STUDENTS’ SPEAKING ABILITY AT TOURISM POLYTECHNIC OF PALEMBANG

Abstract This study is concerned withthe influence of Role plays and Learning Motivation towards the first year Room Division students’ Speaking Ability at Tourism Polytechnic of Palembang. In this study, 32 students of first year room division department were chosen as samples by means of two stages random sampling. This study applied factorial design. The data were collected by using questionnairres and speaking test. The data were analyzed by using Paired Sample T-test, Independent Sample T-test, and Two-Ways Anova. The result showed that first, there was significant influence of Role Play towards the first year room division students’ speaking ability. Second, there was significant influence of conventional teaching technique towards the first year room division students’ speaking ability. Third, there was significant influence of role play and conventional teaching technique towards the first year room division students’ speaking ability between students who have high and low learning motivation at Palembang Tourism Polytechnic. Fourth, there was interaction effect of role play and learning motivation towards the first year room division students’ speaking ability. It can be concluded that Role Play technique was quite effective to improve students’ speaking achievement. Keywords: Role Plays, Learning Motivation, Speaking Ability
LibGuides: Language and Literacy Education: Home

This guide is for the subject Language and Literacy Education. It contains resources available in the NCU Library.
A Study of Technology Integration in Physical Education

이 논문는 테크놀로지융합 체육교육 활용방안에 관해 논하기에 목적이 있다. 지난 10년 동안 테크놀로지는 눈부신 발전을 이루어 왔다. 약 10년 전 만해도 생소했던 스마트폰과 급속도로 성장한 무선인터넷은 현대인들의 삶에 커다란 변화를 가져왔으며 교육 패러다임의 변화도 불러일으켰다. 교육학자들은 교육환경 안에서의 테크놀로지 활용은 일방향적이고 지식전달 형태였던 기존의 교육방식을 탈피해 향상된 학생-학생, 교수-학생 간의 상호활동을 통해 좀 더 능동적 학습을 촉진시킬 수 있다고 밝혔다. 체육 분야에서의 일부 선행연구들은 테크놀로지 융합 체육수업은 학생들의 동기, 체육지식, 신체활동량에 긍정적인 영향을 준다고 하였다. 이러한 사회적 변화에 맞추어 대한민국 정부는 교사들의 테크놀로지 융합교육을 격려하였지만 여전히 테크놀로지는 체육수업에서 자주 활용되지 않고 있는 상황이다. 따라서 이 논문에서는 체육교사들의 테크놀로지 활용을 격려하기 위해 체육수업에서 활용될 수 있는 다양한 테크놀로지를 소개하고 교육학적으로 적절한 활용방안에 대해 논하였다.The purpose of this article is to discuss a variety of educational technologies and their possible application strategies into physical education(PE) classes. For the last few decades, there has been a rapid growth in technology. The rapid development of smart phone and wireless network has changed not only many aspects of human life but also the ways to teach and learn in the field of education. Educational scholars have suggested that use of technology in education can move away from traditional education system (passive learning style) and facilitate student-student and teacher-students interaction which in turn results in active learning style. Researchers in the field of PE have found that technology use in PE can increase motivation, knowledge about physical activity and sports, and physical activity levels. In respond to this, Korean government have encouraged teachers to implement technology into their teaching and learning, but there has been a lack of technology use by PE teachers. Therefore, this article discussed a variety of educational technologies and their possible use in PE classes to encourage PE teachers to implement them to use in their PE classes.
RBM Image Generation Using the D-Wave 2000Q

Currently implementing machine learning using the D-Wave system for image processing is challenged by the limited number of qubits available. A commonly used neural network method on the D-Wave is the Restricted Boltzmann Machine (RBM) as it uses an energy-based model that is consistent with the D-Wave, whereby the D-Wave acts as a physical Boltzmann machine. However, there has been limited practical application of RBMs for use in image generation due to the limited number of qubits and due to the binary output returned from the D-Wave. Deep autoencoders are trained to take image inputs, compress the input using a bottleneck in the network, and take the compressed data back to the original input size. We show how to use a stacked autoencoder to compress MNIST digits from a size of 28 x 28 to a size of 7 x 7 and to translate from the binary representation back to a grayscale representation consistent with the original 28 x 28 MNIST digits. We first train the stacked autoencoder to compress and translate MNIST digits into 7 x 7 binary digits. We train our RBM on the D-Wave using the compressed, normalized weights from the encoder. We then sample from the D-Wave and show how these samples when decoded with our stacked autoencoder recover MNIST digits to a size of 28 x 28.
A hybrid generative-discriminative model for abnormal event detection in surveillance video scenes

Detecting anomalous events in densely pedestrian traffic video scenes remains challenging task, due to object's tracking difficulties and noise in the scene. In this paper, a Novel Hybrid Generative-Discriminative framework is proposed for detecting and localising the anomalous events of illegal vehicles present in the scene. This paper introduces a novelty in the application of Hybrid usage of latent Dirichlet allocation (LDA) and support vector machines (SVMs) over dynamic texture at sub-region level. The proposed HLDA-SVM model consists mainly of three steps: first local binary patterns from twelve orthogonal planes (LBP-TwP) technique is applied in each spatio-temporal video patch to extract dynamic texture; then LDA technique is applied to the extracted dynamic textures for finding the latent topic distribution and finally, training is done on the distribution of topic vector for each video sequence using multi way SVM classifier. The proposed HLDA-SVM model is validated on UCSD dataset data set and is compared with mixture of dynamic texture and motion context technique. Experimental results show that the HLDA-SVM approach performs well in par with current algorithms for anomaly detection.
RESEARCH ON 3D BUILDING VISUALIZATION BASED ON TEXTURE SIMPLIFICATION AND FRACTAL COMPRESSION

Abstract. There are some problems in the visualization of 3D model of buildings at present, such as redundant texture data and large memory occupation of loading texture data, which pose challenges to the smooth loading of 3D building visualization. In this paper, we propose a method which using fractal quadtrees to simplify texture data and compress management. Firstly, this method uses the fractal self-similarity and the feature that the same texture has the same fractal dimension to screen all the textures, when the fractal dimension of multiple textures is within the threshold range. Radon transform will be performed on these textures to calculate their standard deviation and further simplify the texture. Secondly, we use fractal texture compression to create a multi-resolution texture data structure with quadtree structure, so that the building 3D model can achieve dynamic visualization of different scales. Finally, the parallel texture mapping of all faces of the 3D model of the building is implemented according to the texture calling rules. Experimental results demonstrate that the texture is managed by the method proposed in this paper, which shortens the loading time of texture and reduces the memory occupation by about 48.92%. Therefore, the method of texture simplification and compression proposed in this paper has a significant effect in the process of building 3D model visualization.
Auralization as a tool of historic reconstruction of Vadstena Abbey church

Throughout human history, there have been locations used for performance of speech and music. Many of these locations are considered part of our common cultural heritage, and more and more of them can be visited virtually as technology advances to produce better and better reconstructions. However, the technological capabilities are not the only limitations when it comes to historical reconstruction, and one of the most important research questions is often to determine the actual historical configuration of a space. This is the case for a project regarding Vadstena Abbey church in Sweden. As a step in producing the virtual reconstruction, a number of different configurations for one of the major performance locations will be examined using acoustic simulations and auralizations. As the acoustic properties of a space affects the performances within it, there should be a correspondence between the musical and oral traditions of this location in particular and its acoustics. Historical records of the performance traditions within the space, as well as records of the acoustic experiences of visitors, will be used as an evaluation tool to distinguish between the models. (Less)
An Improved Compressed Sensing Method for High Resolution Image Denoising

As image resolution becomes higher and higher, high resolution image processing has become an urgent technological problem. To further improve the quality of reconstructed high resolution image, an improved compressed sensing (CS) iterative thresholding algorithm is proposed. First, an optimized wavelet threshold is proposed to select the image coefficients in each iteration; Then, an improved total variation (TV) method is proposed to adjust the reconstructed image for further improving the reconstructed image quality. The experimental results show that the algorithm proposed can recover a high quality image while preserving more detailed information. When compression sampling ratio is lower, the proposed algorithm still can obtain correspondingly higher peak signal to noise ratio (PSNR).
Sistem Pendukung Keputusan dalam Skema Pengusulan Awal Jabatan Fungsional Dosen Menggunakan Sistem Inferensi Fuzzy Tipe Mamdani

This study aims to build a WEB-based decision support system to simplify the process of assessing credit numbers for initial proposals for functional lecturers. With this system the lecturer will be made easier in terms of completing the required files and can quickly and accurately calculate the amount of cum that is obtained. The resulting system uses the Mamdani type Fuzzy Inference System (FIS) to analyze the variables used in assessing lecturers' functional position credit numbers. Furthermore, the system development stage uses the waterfall model. System design analysis includes outputs, inputs, file structures, programs, workflows, hardware and software needed. Then this system will certainly greatly reduce the use of paper because it is based on electronics. So later, with this system, it is expected that the credit score submission process does not take a long time and can increase the number of lecturers who have functional positions in STIKES Dharma Landbouw.&#x0D; Keywords: Fuzzy Logic, FIS, Decision Support System, Mamdan
FairCoop: The global cooperative and its collaborative cryptocurrency

The case is about today's crypto-activists' efforts to organise a cooperative global economic system that is an alternative to the State and to market capitalism. Through the vantage point of FairCoop, the reader gets an insight on the necessary elements needed to consider when organizing an economic system for the commons: a community that produces, a market in which products can be exchanged, and an infrastructure that enables the exchange of products. Readers are then presented with some of the challenges the initiative faces as it needs to interact with the global capitalist economy around it. The case ends at the moment the cooperative needs to decide on how to relate its currency to other currencies in the crypto-market.
Comparisons between the Local Polynomial Kernel estimators and the Cubic Smoothing Splines

The term Nonparametric regression comes to signify the absence of parameters in the regression model. The scope of nonparametric regression is very broad nowadays, ranging from "smoothing" the relationship between two variables in a scatter plot to multiple-regression analysis. Extraordinary, only several years ago, methods of nonparametric regression analysis have been used widely because of advances in statistical techniques and related computing facilities, and are now a serious alternative to many traditional parametric regression modeling. This paper aims to provide using two methods of the nonparametric regression, namely; the Local Polynomial Kernel (LPK) estimator and Cubic Smoothing Splines (CSS) estimator with emphasis on the role of selecting the smoothing parameter (bandwidth) in each estimator. We have investigated the performance of these two smoothing estimators (LLK and CSS) under two different settings, fixed or random designs. This was achieved through conducting many simulation studies using four different example regression functions; two different distributions of errors term either normal or mixture, and different sample sizes. Two study the estimation of the derivative functions using those two smoothing estimators (Local Quadratic/Cubic Kernel and Cubic Smoothing Splines) in the process of smoothing the 1st / 2nd derivative functions respectively also been considered through conducting several simulation studies as well. Applications on real datasets also been considered. Lastly, the advantages and disadvantages of these two nonparametric regression estimators have been discussed.
MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON QUANTUM ENTANGLEMENT

Abstract. Aiming at the problems of low accuracy and slow speed in the current remote sensing image classification algorithm,In order to improve remote sensing image classification, a quantum entanglement algorithm is proposed.The model transforms the classification process of remote sensing image into a random self-organization process of quantum particles in the state configuration space. The state configuration formed by entanglement of quantum particles evolves with time and finally converges to an average probability distribution.Taking Kunming city of Yunnan province as the research area, this paper compares the classification method in this paper with the traditional remote sensing classification method by using the 02C image data of yuanyuan1.Compared with other classification methods, the classification accuracy of this paper meets the requirements.
An Inclusive Prehistory Game by the Blind and Visually Impaired . Creating an Inclusive App Game on Prehistoric Archaeology with the BSVN e.V. for the Permanent Exhibition of the Neanderthal Museum

Can an app game make a museum exhibition on prehistoric archaeology more accessible for guests with visual impairments? This is the research question of the NMsee project, a cooperative undertaking of BSV Nordrhein e.V. and the Neanderthal Museum in North Rhine Westphalia, Germany. Archaeological museum exhibitions focus largely on visual information transfer and work with non-inclusive underlying concepts of learning and knowledge, which lead to seemingly unbreachable difficulties for blind and visually impaired museum visitors. The approach of the NMsee project is an inclusive one: by creating an app game in the museum which communicates archaeological information in a non-visual way and which can be played independent of one’s visual abilities, the project searches for inclusive ways of museum experiences. The game is created through a participatory and iterative process in order to stay in close contact with the needs and wishes of its target group. Starting in January 2019, the first prototypes and testing sessions will be launched in autumn 2019. This chapter presents the project idea, the problems leading to this research and the future goals of this undertaking.
Cultural Influence and the Effective Use of Security Awareness in Congolese Organizations

In today's global economy, there is a growing need to apply technological advancements as well as training and awareness materials from western countries on information security programs in developing nations. To understand the underlying drivers of employees' effective use behaviors in relation to security awareness programs in organizations, this study examines the extrinsic and intrinsic motivation factors that influence employees located in developing nations. The results indicate that influencing employees' attitudes toward security is a better predictor of employees' effective use of security awareness programs than their intention to comply.Cultural effects has also proven to have an influence on employees' effective use of security awareness programs.
Object Recognition and Content Abstraction of Surveillance Video

Video content abstraction is a useful tool for the object and event acquisition in long-term video. To solve this problem, the video processing is divided into three phases including foreground extraction, key frame selection, and object recognition module design. Firstly, foreground pixels are extracted from the original video. Then the semantic category information of foreground objects are also extracted. Finally, concerned video content are automatically synthesized into the produced video. Experiments show the practical effect of video abstraction in different public datasets.
Communication Management in Multiclient System Using SMS Gateway

This chapter is part of a wide work developed in the context of smart cities and Internet of Things (IoT). We proposed a new approach in communication with IoT devices via SMS messages. This can be beneficial during changing configuration of IoT devices or during sending data from IoT devices. Of course, using SMS has several disadvantages: security and effective payload. As the security is an ongoing topic in this field, with our solution we manage to speed up key exchange process up to 62% compared to other solutions. We add the possibility of quick exchange of API key. API key is a key part in the process of creating key for symmetric encryption in communication between IoT device and central server. With limitations of SMS length, we design the use of compression methods which can send 304 bytes in 140 bytes of SMS body. Depending on the final usage of IoT device there is a possibility to turn on/off the option for security and/or compression. These new functions for communication have only 4.3% time overhead in the process of sending data and that is negligible in devices which have to communicate usually in the case of special events or only a few times a day.