Reviewerly/exHarmony huggingface dataset:
Development of Real-Time Style Transfer for Video System

Re-drawing the image as a certain artistic style is considered to be a complicated task for computer machine. On the contrary, human can easily master the method to compose and describe the style between different images. In the past, many researchers studying on the deep neural networks had found an appropriate representation of the artistic style using perceptual loss and style reconstruction loss. In the previous works, Gatys et al. proposed an artificial system based on convolutional neural networks that creates artistic images of high perceptual quality. Whereas in terms of running speed, it was relatively time-consuming, thus it cannot apply to video style transfer. Recently, a feed-forward CNN approach has shown the potential of fast style transformation, which is an end-to-end system without hundreds of iteration while transferring. We combined the benefits of both approaches, optimized the feed-forward network and defined time loss function to make it possible to implement the style transfer on video in real time. In contrast to the past method, our method runs in real time with higher resolution while creating competitive visually pleasing and temporally consistent experimental results.
A preprocessing method combined with an ensemble framework for the multiclass imbalanced data classification

Skewed distributions appear in many real-world classification problems. Skewed distributions, underrepresented classes, and multiple overlapping regions in multiclass imbalanced datasets deteriorate the performance of existing classification algorithms and approaches. In this context, we combine a novel preprocessing procedure to tackle minority classes in multiclass imbalanced problems with an ensemble framework. The preprocessing method oversamples the minority classes based on normalized probability, and then an ensemble called a stacked generalization framework is used to train the model. The motive behind combining the ensemble framework and the preprocessing procedure is to enhance the overall classification performance of the classifier for multiclass imbalanced problems. Experimental results on 20 multiclass imbalanced datasets show that the proposed preprocessing method with the ensemble framework outperforms the representative approaches in 13 datasets for macro average arithmetic (MAvA) and mean F-measure (MFM) metrics. In the case of state-of-the-art techniques, the proposed approach steered 14 datasets for the MAvA metric and 15 datasets for the MFM metric to success.
Using Stories to Understand Technology Needs and Technology Reuse by Rural Communities

There is an increased sensitivity by people about how companies collect information about them, and how this information is packaged, used and sold. This perceived lack of control is highlighted by the helplessness of users of various platforms in managing or halting what data is collected from/about them. In a future where users have wrested control of their data and have the autonomy to decide what information is collected, how it is used and most importantly, how much it is worth, a new market emerges. This design fiction considers possible steps prescient companies would take to meet these demands, such as providing third-party subscription platforms offering personal data trade as a service. These services would provide a means for transparent transactions that preserve an owner's control over their data; allowing them to individually make decisions about what data they avail for sale, and the amount of compensation they would accept in trade.
Parallel Multi-Branch Convolution Block Net for Fast and Accurate Object Detection

In order to maintain the high speed advantage of single-stage object detector and improve its detection accuracy, in this paper, we propose a parallel multi-branch convolution block, called PMCB, which can efficiently extract multi-scale object information at a specific layer to form a discriminative feature layer and boost the detection performance with little computational burden. Based on the PMCB module, we build PMCB Net on the basis of the single shot multibox detector (SSD) network by replacing the conventional convolution with PMCB at a specific layer. The performance of the proposed algorithm is compared with that of other state-of-the-art methods on PASCAL VOC2007, MS COCO test datasets. The experimental results show that the proposed algorithm greatly improved detection accuracy performance while only adding a negligible computational burden, which is very important for practical engineering applications.
GPU Implementation of Adaptive Fourier Decomposition

Adaptive Fourier decomposition (AFD) provides an expansion of an analytic function into a sum of basic signals, called mono-components. These signals are assumed to have non-negative phase derivative which means that their instantaneous frequency is physically meaningful. Unlike the conventional Fourier series, the AFD is based on adaptive rational orthogonal system. There are several types of the AFD including core AFD, unwinding AFD and cyclic AFD, but all of them determine decomposition parameters in accordance with maximal selection principle (MSP). The most common implementation for the MSP is the exhaustive search method, which is time-consuming. Modern graphics processing units (GPUs) support both graphics and general purpose (GP) computing. In this paper we show that the exhaustive search method for the MSP can be easily optimized using low-cost general purpose GPU technologies. We implemented the core AFD algorithm using OpenCL programming toolkit and clBLAS subroutines. Experimental results show that, comparing to the conventional MATLAB implementation, the GPU-based AFD obtains reduced computation time while maintaining similar accuracy.
One day ahead prediction of PV power production: case study of Oued-Elkebrit's station (Algeria)

The following topics are dealt with: photovoltaic power systems; power generation control; maximum power point trackers; power grids; solar cell arrays; machine control; control system synthesis; wind power plants; wind turbines; three-term control.
Classification of Schoolchildren on Professional Trajectories using Experience of Successful Specialists

In the paper, we propose a new approach to vocational guidance of school children based on classification of pupil wishes between given professional trajectories, which are presented by profiles of successful professionals. Both wishes and profiles are replies in free text form on a questionnaire proposed by skilled psychologists. Such an approach avoids the well-known deficiencies of traditional methods including binary questioning, talks about concrete professions, and interviews with school psychologists. We use the simple terms selection for preprocessing and the traditional method of voting for classification. The mentioned procedures are discussed and the proposed approach is preliminary checked on invited specialists. This joint Russian-Irish research has been carried out with Moscow school children (2 schools) and Moscow specialists (2 trajectories). The results of presented pilot study look very promising. It is the basis for current applied research in Moscow and the future activities in Dublin.
A SAR Image Enhancement Algorithm using the Discrete Wavelet Transform

Synthetic aperture radar (SAR) images are badly affected by the existence of the speckle noise. In this research, we present an algorithm for despeckling the SAR images. This algorithm makes efficient use of the discrete wavelet transform (DWT) and the directional smoothing filter (DSF). The DWT has been widely used in image denoising applications, while the DSF is well known for enhancing the SAR images in the presence of speckle noise. The efficiency of the combined algorithm has been demonstrated for both real and simulated speckled SAR images at different noise variances, and the obtained results have been compared to the results acquired while applying only DWT, DSF and other traditional methods. Moreover, the quality investigation has been performed to emphasize the algorithm efficiency.
Ant Colony Optimization Algorithm to Solve Electrical Cable Routing

Ant colony algorithms have been applied to solve wide range of difficult combinatorial optimization problems like routing problems, assigning problems, scheduling problems and revealed remarkable solutions. In this paper we are presenting a novel approach of ant colony optimization algorithm to solve the electrical cable routing problem. We have studied the objectives to optimize the cable routing and modified the ant colony system algorithm to get better solutions. This research focused on two objectives, route optimization and the obstacle avoidance. The results of this modified algorithm show an immense improvement in optimizing the electrical circuit designing in a building.
Decision Tree Approach for Fault Type Identification of Transmission Line

Abstract In order to solve the limitation of artificial identification of fault causes and insufficient application of artificial intelligence due to fewer data samples in transmission line faults, a decision tree-based model and method for fault cause identification of transmission lines are studied. Expert support and manual intervention are introduced to verify and confirm the diagnosis conclusions on the basis of automatic diagnosis. It is recognized that the automation of line fault diagnosis and the visualization of the whole process of fault diagnosis can be realized, thus reducing the workload of operation and maintenance staff. Based on the defect data of power company operation and maintenance, the validity and validity of the method are analyzed and verified, which can provide theoretical basis for equipment operation and maintenance decision.
Nonlinear Adaptive Control of an Autonomous Ground Vehicle in Obstacle Rich Environment: Some Experimental Results

Non-linear adaptive control of an Autonomous Ground Vehicle (AGV) is considered in this work. The adaptive control strategy has two main objectives: a) trajectory tracking in presence of actuator nonlinearity, b) navigation of the AGV in an obstacle rich environment. However, the proposed controller uses a conventional feedback controller in conjunction with an adaptive inverse dynamics strategy for the actuators. Two different models have been exploited namely Auto-Regressive Exogenous Model (ARX) and Non-Linear Auto-Regressive Exogenous Model (NARX) to identify the AGV's actuator inverse dynamics. The simulation results shows efficacy of the proposed indirect adaptive controllers in comparison to Inverse Dynamic Conventional Feedback Controller (IDCFB). Also, the experimental results in particular, show superiority of the proposed controller in terms of trajectory tracking while navigating an AGV in an obstacle rich environment.
Capacity of EM Side Channel Created by Instruction Executions in a Processor

This paper proposes a methodology to estimate leakage capacity of electromagnetic (EM) side channels created by execution of instruction sequences (e.g. a function, a procedure, or a program) in a processor. We propose to use Markov Source model to include the dependencies that exist in instruction sequence since each program code is written systematically to serve a specific task. The channel input sources are considered as the emitted EM signals while executing an instruction. We derive a mathematical relationship between the emanated instruction power (IP) and total emanated signal power while running a microbenchmark to obtain the channel input powers. The results demonstrate that leakages could be severe enough for a dedicated attacker to obtain some prominent information.
Disaster risk management strategy in the environment and disaster mitigation-based school (SWALIBA)

Abstract According to National Agency for Disaster Management data, more than 46, 000 schools in Indonesia had been affected by the disaster in the past 15 years and around 250, 000 schools in Indonesia are located in the disaster management area. Even though, school is a vital infrastructure for the development of a country’s civilization. In the school, the process of character forming for the next generation of the nation both socially and intellectually takes place. After the 9.1 SR earthquakes and tsunami in Aceh on December 26, 2004 the disaster risk management movements which were begun from school were more intensively conducted. Among the disaster risk management movements at the school level, there is a concept and formulation of the environment and disaster mitigation-based school (SWALIBA) by the Indonesian Geographer Association (IGI) and the Indonesian Geography Student Association (IMAHAGI). Many of the schools at the primary and secondary education level began to adopt that concept. One of the schools which adopted the SWALIBA concept is junior high school 41 of Semarang City and has been applying it for 5 years. This research was conducted to study the applied risk management strategies. The result showed the gap between the applied strategy and the perception which formed the preparedness characteristic of the students as the implementation object.
Improving Kannada OCR Using a Stroke-Based Approach

This paper presents a method of utilizing generated strokes as additional information to perform the task of Character Recognition. We propose a model which generates a sequence of strokes given the character, which when provided along with the complete image, improves the recognition. From our experiments, we observe that this approach makes the model better equipped for a noisy environment, and tolerant to cracks or gaps in the character.
Pressure Welding Processes of Tubular Parts and Pipe Segments

Abstract The joining of tubular (hollow, sectioned) parts to each other or for rods, sheet-like pieces and occasionally for pipe segments, is often achieved by using technology that uses a pressure welding process. These processes use mostly electrical, thermochemical or mechanical energy, and the implementation is butt, stud or projection welding. Most the pressure welding processes are suitable for the joining of different quality materials. This article summarizes the possible process variations.
A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

Abstract Text-to-speech (TTS) synthesis systems have been widely used in general-purpose applications based on the generation of speech. Nonetheless, there are some domains, such as storytelling or voice output aid devices, which may also require singing. To enable a corpus-based TTS system to sing, a supplementary singing database should be recorded. This solution, however, might be too costly for eventual singing needs, or even unfeasible if the original speaker is unavailable or unable to sing properly. This work introduces a unit selection-based text-to-speech-and-singing (US-TTS&amp;S) synthesis framework, which integrates speech-to-singing (STS) conversion to enable the generation of both speech and singing from an input text and a score, respectively, using the same neutral speech corpus. The viability of the proposal is evaluated considering three vocal ranges and two tempos on a proof-of-concept implementation using a 2.6-h Spanish neutral speech corpus. The experiments show that challenging STS transformation factors are required to sing beyond the corpus vocal range and/or with notes longer than 150 ms. While score-driven US configurations allow the reduction of pitch-scale factors, time-scale factors are not reduced due to the short length of the spoken vowels. Moreover, in the MUSHRA test, text-driven and score-driven US configurations obtain similar naturalness rates of around 40 for all the analysed scenarios. Although these naturalness scores are far from those of vocaloid, the singing scores of around 60 which were obtained validate that the framework could reasonably address eventual singing needs.
Visualization of Multi-Storey Building Visitor Guide Animation By Optimizing Macromedia Flash MX of Action Script Functions and 3D Max Studio (Study: Engineering Faculty Building, Muhammadiyah University of Yogyakarta)

Abstract Yogyakarta Muhammadiyah University (UMY) has 7 faculties, divided into 5 zones, namely the north zone, the west zone, the south zone, the east zone, and the middle zone. The Faculty of Engineering (FT-UMY) occupies the north zone. The problem is how one knows the location, position, and function of the room in the building, because every educational institution has many buildings, the shape of the building the same/similar, each building is occupied by many faculties, and has a large number of rooms. The concept of Visualization or Visual Communication Design was used to solve the problem, Visualization or Visual Communication Design is the study of communication concepts and creative expressions, techniques and media to convey messages and ideas visually, such as pictures, audio, video by processing graphic design elements . The Software/Tools used are Macromedia Flash MX and 3D Max Studio by utilizing and creating animation and visualization data base information systems with simple action script commands, generating visitor guide software has input data, namely: 1 faculty button and has 4 search buttons, i.e., find the lecturer button, find a room, search for majors, and find employee button. Has 4 output data consisting of: View (side view, rear view, and front view), path, floor plan, and building.
Long-Term Usage of a Consortial PDA Collection: If They Choose It, Will We Use It?*

This paper examines the materials acquired during a consortial PDA pilot; which of the books are still being used, by which institutions, whether the size of a participating institution or the subject of a book have impact on the continued use of the books, and if there is a relationship between the schools that triggered a PDA purchase and the schools that have gone on to use a given title. Findings suggest that models involving some library mediation may be more effective in meeting the various needs of participating institutions.
Development of One Hand Drive Electric Assist Wheelchair System: Reproduction of pseudo inertia deceleration running by motors

The purpose of this research is to develop the electric assist wheelchair system for hemiplegic users. The proposed system is able to move in any direction by operating only one hand rim on one side. The hand rim can detect the user's operational intention by measuring contacting force and status of grasp and its style. The proposed system enables straight ahead, go back, gradual turn, pivot turn and stop based on the force being applied to the hand rim, whether the hand rim is grasped or not, and rotational speed of the hand rim. After the user operates the hand rim and releases it, the moving mode shifts to the deceleration mode. In the deceleration mode, the pseudo inertia deceleration is reproduced by motors. In this paper, we focus on the proposal of the system and the inertia deceleration by controlling motors, and verify how much it can reproduce in the outdoor environment.
Improving Deep Learning by Regularized Scale-Free MSE of Representations

Nowadays, the research for learning representations with deep neural networks (DNNs) is attracting more and more attentions. In general, most of studies focus on designing principles for learning representations when the learning model is stochastic, which is not widely used for all kinds of machine learning tasks. In this paper, we try to seek a principle to learn efficient representations for deterministic DNNs. Considering that the last DNN layer is often a linear mapping, we utilize the generalized linear model (GLM) theory to design a principle for learning representations. First, via choosing proper link functions, we build a connection between representation learning and the optimization of the last linear layer. Next, we propose a representation learning principle, scale-free mean square error (scale-free MSE), by leveraging the MSE analysis in the GLM. Here, we find that representations with a smaller scale-free MSE can lead to a better estimation of the parameters. Then, we further propose an algorithm to learn representations with a small scale-free MSE. Here, a regularization term is employed to make the scale-free MSE get small. Finally, we test our algorithm with typical DNN models in both supervised and unsupervised learning tasks. The experiment results show that our proposed algorithm is suitable to learn efficient representations over various of experiment settings.
Value management in Blockchain technology related projects

Abstract Because of the development of Bitcoin in recent years, the foundational technology behind it, the Blockchain technology, also begins to attract attention from international banks and companies, and entrepreneurs have developed Blockchain technology related projects not only in financial industries but also other industries. In this dynamic information economic environment nowadays, high-tech economic projects based on Blockchain are very important, and when it comes to the value management more factors have to consider. The research of value management is a long-term subject that brings benefits to projects and the analysis with Blockchain projects is very helpful for start-ups, entrepreneurs as well as stakeholders. The purpose of this study is to analyze the relationship between Blockchain technology projects and value management, and find better ways of value management in Blockchain projects.
ITS4S4: A Video Quality Study of Camera Pans

This technical memorandum provides technical details for the video quality subjective experiment its4s4. This experiment includes 196 videos, each 4 seconds in duration, depicting camera pans at different speeds. Other impairments are avoided where possible. This experiment implements an unrepeated scene design, so each 4 second video contains unique content. The its4s4 dataset provides training data for no-reference video quality metrics that predict quality impairments associated with camera pan speed. The dataset is freely available for research and development purposes.
46 Deep MR to CT synthesis using paired data in the pelvic area

Introduction. The establishment of a MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic-CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) to generate a fast sCT in order to simplify the clinical workflow and reduce uncertainties. We used a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD [ [1] Wang, T.-C. et al. High-resolution image synthesis and semantic manipulation with conditional gans. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 8798–8807 (2018). Google Scholar ] which addresses the two main issues of a GAN: the difficulty of generating high-resolution images and the lack of details in the previous high-resolution results. In this work, we trained this cGAN with paired MR-CT images in the pelvic area in order to have a fast, simple and accurate sCT generation.