Reviewerly/exHarmony huggingface dataset:
A Specified Ubiquitous Learning Design for Seamless Learning

Seamless learning and ubiquitous learning are nested concepts and they may be confused notions without elaborating definitions and implications. In this chapter, seamless learning is handled deeply, and ubiquitous computing and ubiquitous learning concepts are explained. Seamless learning is explained through the definition stated by Kuh and implications carried out by numerous researchers who conducted many studies in the field. Each implication and study are analyzed and elaborated carefully in order to propose an efficient seamless learning management system. In addition to those analyses, after explaining ubiquitous computing and ubiquitous learning concepts, MOOCs are mentioned in order to give a well applied ubiquitous learning system. Finally depending on seamless learning fundamentals (stated by authors) an efficient seamless learning management system is proposed.
A Model-Driven Needs Based Augmented Reality: From Model to Program

The process of creating augmented reality experiences has developed greatly to break the lab boundaries and become available to every person with a smartphone. AR experiences for the most part focus on the object for which they are developed: the product to be advertised, the process to be taught, or the history to be shown for examples. Little focus is given to the person experiencing AR. It is similar to stepping into an enhanced world with information at the user’s fingertips that was dictated by the experience developer rather than what the user actually needs to have from this experience. This controversy originated the idea for this research. In this research we aim to explore the concept for Model Driven Development to create augmented reality based on human needs. Towards achieving this concept, we present a conceptual model, analyze its components and study the application of Model driven concepts to this application idea.
A new method for solving the height problem in deflectometry

Phase-measuring deflectometry (PMD) is an established measurement method for determining the topography of specular free-form surfaces. However, since each camera pixel only contains information about the location of the observed screen point, but not about the incident ray direction, the surface slope cannot be determined unambiguously without prior knowledge of the height. In order to overcome this difficulty (known as the ”height problem” of PMD), several cameras with overlapping fields of view are often used. This, however, requires a very large screen and additional installation space in order to ensure sufficiently diverse viewing directions within the overlap region. Other solutions, such as multiple measurements with shifted screen positions or the use of additional measuring equipment to determine the object position, also have considerable drawbacks due to the longer measurement time and the greater calibration effort. A new method is presented which avoids these disadvantages.
Optimization of Three - Frame Difference Method and Improvement of Pedestrian Detection Code Book

Abstract Pedestrian detection is one of the most important applications of computer vision. In this paper, aiming at the problem of poor anti-interference ability of traditional codebook algorithm in the complex background and the cavity-prone problem of traditional frame difference method, a pedestrian detection algorithm integrating the optimized frame difference method and improved codebook is proposed. In this algorithm, a double code word structure is designed. An update mechanism is added to the code words in the codebook background modeling process and detection process, and improved double code word book algorithm based on YUV color space is designed to realize the real-time background update function. After that, bilateral filtering is added to the traditional three-frame difference method to further remove the noise. At the same time, the extracted rectangular region is used for filling operation and morphological processing to obtain the corresponding foreground image. Finally, the foreground image of codebook model and the foreground image of frame difference method are calculated with “and”, “different or” and “or” of pixels to obtain the precise region of moving target. The experimental results show that this algorithm combines the advantages of the two methods and abandons the disadvantages of each method in the detection prospect. It can accurately detect moving targets and has good robustness. The effectiveness of this algorithm is also verified by experiments.
Extraction of phase profile at discrete spatial frequency bands using phase shifting interferometry

In this paper, we report an interferometric method to extract the phase information available in a desired band of spatial frequencies. The phase sample is placed in the path of a converging beam of light entering a Mach Zehnder interferometer so that the two Fourier Transform (FT) planes are located in the two interferometer arms. One of the FT planes is filtered by suitable masks so that only the frequencies that are blocked appears in the final interferogram. An imaging lens images the phase object on a CCD. Polarization Phase Shifting is incorporated so that the final frequency filtered image is reconstructed from the four phase shifted interferograms. The interferometer is made to operate in null fringe condition so that residual phase is eliminated. Simulated and experimental results are presented.
Precise microscopic profilometry using diffractive image correlation and theoretical model simulation

This work presents a novel microscopic profilometry using diffraction image correlation, which is appropriate for in-situ automated optical inspection (AOI). In contrast to the traditional confocal microscopy, the developed technique replaces the detector pinhole by observing and matching the diffraction patterns. Thus accurate surface depth detection can be achieved by eliminating time-consuming vertical scanning operation. The development is the first attempt in optics to use image correlation between the pre-calibrated database of diffraction images and the measured one of the detecting object surface. The feasibility of the method has been theoretically verified by scalar diffraction theory, then verified by experimental testing. Also, a depth response curve with the physical meaning of similarity is introduced and interpreted. Meanwhile, multi-point lateral scanning in one field of view (FOV) is achieved by quickly switching micromirrors on the digital micromirror device (DMD), thus the quasi full-field 3-D reconstruction can be acquired by combining tens of captured images. Verified experimentally, a 3-D reconstruction with sub-micrometer vertical resolution can be realized, with a tunable lateral resolution.
Simple generative model for assessing feature selection based on relevance, redundancy, and redundancy

An experimental procedure is proposed for measuring the performance of feature selection algorithms in a way that is not directly tied either to particular machine learning algorithms or to particular applications. The main interest is in situations for which there are a large number of features to be sifted through. The approach is based on simulated training sets with adjustable parameters that characterize the &#114;elevance" of individual features as well as the collective &#114;edundancy" of sets of features. In some cases, these training sets can be virtualized; that is, having specified their properties, one does not actually have to explicitly generate them. As a specific illustration, the method is used to compare variants of the minimum redundancy maximum relevance (mRMR) algorithm, and to characterize the performance of these variants in different regimes.
Conflict Resolutions for Trajectory Co-ordination of Multiple Mobile Robots

Prioritized planning approaches are commonly used for finding coordinated trajectories for multiple robots so as to prevent collision among multiple robots. Classical methods for the same fail to solve the problem instance when there are conflict situations. In this paper, we analyze such conflict situations and offer heuristic solutions for the multiple robots to reach their destination or goal positions. The contributions in this paper are: (1) Identification of conflict situations where classical prioritized path planning methods failed to provide deterministic solution. (2) Propose heuristic solutions for overcoming the conflict situations.
Depth Mapping Method Based on Stereo Pairs

The paper proposes a new method for solving the problem of constructing a depth map based on a stereo pair of images. The result of the depth information recovery can be used to capture the reference points of objects in the film industry when creating special effects, as well as in computer vision systems used on vehicles to warn the driver about a possible collision. Proposed method consists in using the theory of active perception at the stage of segmentation and image matching. To implement the proposed method, a software product in the C# language was developed. The developed algorithm was tested on various sets of input data. The results obtained during the experiment indicate the correct operation of the proposed method in solving the problem of constructing a depth map. The accuracy of depth mapping using the described method turned out to be comparable with the accuracy of the methods considered in the review. This suggests that this method is competitive and usable in practice.
Content-adaptive frame level rate control for video encoding using a perceptual video quality measure

Reaching an optimal trade-off between maximal perceptual quality of reconstructed video and minimal bitrate of the compressed video stream under a maximum bitrate constraint, for a wide variety of content, is a significant challenge and one that has major cost and user-experience implications for video content providers and consumers alike. This challenge is often addressed with content adaptive encoding, and generally strives to reach the optimal bit-rate per content at clip or scene level. Our solution presented herein, goes a step further, and performs encoder adaptation at the frame level. In this paper we describe our closed loop, optimized video encoder which performs encoding to the lowest bitrate which still preserves the perceptual quality of an encode to the target bitrate. The optimization is performed on a frame-by-frame basis, guaranteeing the visual quality of the video, in a manner that minimizes additional encoder complexity, thus making the solution applicable for live or real-time encoding. We also describe our subjectively tuned, low complexity, perceptual video quality metric which is the engine driving this solution.
Current Prediction Model of GPU Oriented to General Purpose Computing

In order to reduce the complexity of energy consumption analysis in GPU general computing and solve the dependence of energy consumption analysis on hardware tools, a GPU general-purpose computing current prediction model was proposed based on the analysis of different GPU architectures and CUDA program execution rules. Firstly, the GPU program was decomposed and the features of the source program were extracted. Then, the relationship model between program features and running current consumption was figured out by using multiple linear regression and BP neural network. Finally, two prediction models were trained by typical sample data. The experimental results showed that the single-program prediction error was less than 10% and the average prediction error was less than 6%. The proposed two GPU current prediction models had good prediction accuracy and good universal property for different GPU architectures. The current prediction model laid the foundation for further analysis of GPU general-purpose computing energy complexity.
Style Transfer with Adaptation to the Central Objects of the Scene

Style transfer is a problem of rendering an image with some content in the style of another image, for example a family photo in the style of a painting of some famous artist. The drawback of classical style transfer algorithm is that it imposes style uniformly on all parts of the content image, which perturbs central objects on the content image (such as face and body in case of a picture with a person), and makes them unrecognizable. This work proposes a novel style transfer algorithm which automatically detects central objects on the content image, generates spatial importance mask and imposes style non-uniformly: central objects are stylized less to preserve their recognizability and other parts of the image are stylized as usual to preserve the style. Three methods of automatic central object detection are proposed and evaluated qualitatively and via a user evaluation study. Both comparisons demonstrate higher quality of stylization compared to the classical style transfer method.
Requirement Analysis of PACS and RIS Hospital Management Information System on Radiology Installation Based on Kano Method

Abstract Hospital management information systems are very helpful in carrying out services to customers. In this study, problems were found regarding the process of presenting the results of radiological image data that have not been facilitated throughout the patient’s examination room. The slow results of radiological image data, especially for immediate results (CITO) in emergency department services have not been facilitated due to having to wait for the results printed. The results of reading radiological images (expertise) are waiting to be printed first. To solve this problem, a research was conducted focusing on the development of the Radiology Information System (RIS) information system model and Picture Archiving and Communication System (PACS). The development of this system is analysed using the KANO method to classify the expected system menu needs attributes. The test results get the percentage of better value higher than the worse that is 9.81 which means that it can be known attributes that greatly influence the increase in customer satisfaction if these attributes are not fulfilled, the disappointment level is very high, but the implementation stage is not optimal so that future research can present results better research.
Research on Evaluation Method of LDA Topic Model in Mail Classification

Abstract The LDA topic model is a document generation model, which is an unsupervised machine learning technique that can be used in keyword extraction, topic classification, and so on. The main purpose of this paper is to effectively evaluate the number of optimal topics of the LDA topic model in the message subject classification and extract the importance of the topic, so that the LDA topic model can be highly readable after subject classification. Therefore, this paper proposes a keyword matching and subjective statistical value word comparison (KM-SSVW) for the subject classification of emails, the keyword matching technique in this method uses the TF-IDF technique. This method can accurately evaluate the extracted keywords and optimize the number of topics. The data in this article is mainly from the mail gate, a total of 7,000 messages that Hillary communicates with others. The empirical results show that the proposed method of keyword matching and subjective statistical value word comparison has a good effect on subject quantity optimization and subject word readable evaluation. However, there are still some limitations in this paper. In the experiment, new methods are not validated for other types of data sets, such as microblog short text, XML documents, and WeChat public platform articles.
Energy Efficient Smartphone-Based Users Activity Classification

Nowadays most people carry a smartphone with built-in sensors (e.g., accelerometers, gyroscopes) capable of providing useful data for Human Activity Recognition (HAR). Machine learning classification methods have been intensively researched and developed for HAR systems, each with different accuracy and performance levels. However, acquiring sensor data and executing machine learning classifiers require computational power and consume energy. As such, a number of factors, such as inadequate preprocessing, can have a negative impact on the overall HAR performance, even on high-end handheld devices. While high accuracy can be extremely important in some applications, the device’s battery life can be highly critical to the end-user. This paper is focused on the k-nearest neighbors’ algorithm (kNN), one of the most used algorithms in HAR systems, and research and develop energy-efficient implementations for mobile devices. We focus on a kNN implementation based on Locality-Sensitive Hashing (LSH) with a significant positive impact on the device’s battery life, fully integrated into a mobile HAR Android application able to classify human activities in real-time. The proposed kNN implementation was able to achieve execution time reductions of 50% over other versions of kNN with average accuracy of 96.55% when considering 8 human activities.
Beyond Labels: Permissiveness for Dynamic Information Flow Enforcement

Flow-sensitive labels used by dynamic enforcement mechanisms might themselves encode sensitive information, which can leak. Metalabels, employed to represent the sensitivity of labels, exhibit the same problem. This paper derives a new family of enforcers-k-Enf, for 2 ≤ k ≤ ∞-that uses label chains, where each label defines the sensitivity of its predecessor. These enforcers satisfy Block-safe Noninterference (BNI), which proscribes leaks from observing variables, label chains, and blocked executions. Theorems in this paper characterize where longer label chains can improve the permissiveness of dynamic enforcement mechanisms that satisfy BNI. These theorems depend on semantic attributes-k-precise, k-varying, and k-dependent-of such mechanisms, as well as on initialization, threat model, and lattice size.
Fusion Image Style Transfer Network

Abstract Generating art automatically and quickly for machines is always a difficult task. The existing image style migration algorithms can realize the rapid generation of art images. However, these algorithms can only generate artistic images by migrating existing content maps, and can not modify the content of images artificially. This paper proposes an image style migration approach which can stylize artificially modified fusion image. Particularly, this strategy can correct the modification of the photographic images and enhance the style shifting details. Aiming at eliminating traces of modification, we adopt a autoencoder network to increase fusion image coordination. This network increases detail representation of fusion image. In order to enhance visual effects of style migration, we improve a neural style migration network to generate fusion image stylized graph. We show that this approach make the fused image more coordinated, resulting in a richer detail of the style image.
Sublinear Computational Time Modeling by Momentum-Space Renormalization Group Theory in Statistical Machine Learning Procedures

We review sublinear computational time modeling using momentum-space renormalization group approaches in the statistical machine learning algorithms. The modeling scheme has been proposed and the basic frameworks have been briefly explained in a short note (Tanaka et al. in J. Phys. Soc. Jpn, 87(8), Article ID: 085001, 2018). We present their detailed formulations and some numerical experimental results of sublinear computational time modeling based on the momentum-space renormalization scheme.
Using Agile Approaches to Drive Software Process Improvement Initiatives

Software Process Improvement (SPI) initiatives intend to improve the quality of developed products by guiding development teams and aiming at increased efficiency and effectiveness on software development. However, some obstacles are perceived in companies that implement SPI, such as long periods of implementation, high cost, bureaucracy, lack of flexibility and misalignment with user’s needs. A proper SPI approach must guide new processes definition, mobilize proper resources and foster motivation for people to work on it. This paper presents an industry experience with a SPI approach based on Scrum to create new processes deliveries in short cycles. The implementation strategy was based on action research. Results show that the Scrum framework was helpful to create fast outcomes, Design Thinking workshops succeeded in generating initial versions of the product backlog, and User Experience aided defining the navigability of the defined processes.
Kernel Analysis Based on Dirichlet Processes Mixture Models

Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels. However, the number of components in a GMM is fixed. Thus, this model suffers from overfitting or underfitting. In this paper, we try to combine the spectral domain of kernels with nonparametric Bayesian models. Dirichlet processes mixture models are used to resolve this problem by changing the number of components according to the data size. Multiple experiments have been conducted on this model and it shows competitive performance.
Energy based image steganography using dynamic programming

Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery, while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represent the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stegoimage, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of image and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
Gravitational Search Algorithm for Non-linear System Identification Using ANFIS-Hammerstein Approach

The nature of real problems in engineering tends to non-linearity, so that, their identification must be solved using similar approaches what represents a high challenge. The Hammerstein models are widely used in non-linear systems identification, these models combine a static non-linear block connected in cascade to a linear dynamic system. The Adaptive-network-based fuzzy inference system (ANFIS) is a non-linear adaptive architecture which has certain similitude with the Hammerstein models, is formed by a non-linear block followed for a linear system. Under such a scheme, in this chapter, the non-linear system identification considering Hammerstein models is carried out. Taking advantage of the similitude of ANFIS with the Hammerstein models structure, the ANFIS model is adapted until the system is modeled. To modify the parameters of the model an optimization technique known as Gravitational Search Algorithm (GSA) is used. This optimization algorithm based on gravity laws is able to perform good solutions in complex problems regarding similar approaches. To verify the accuracy of the presented scheme, similar techniques have been used to validate the system identification over several experimental models.