Reviewerly/exHarmony huggingface dataset:
SDNet: A Novel Separable Structure for Single Image Deraining

Abstract Rain streaks degrade visual quality of images and severely affect the performance of computer vision system. Recently, deep convolutional neural networks (DCNNs) have shown the potential in single image deraining. However, their complex structures with large-scale of parameters lead to strong requirement of large storage, which still limits the applications of deraining systems. In this paper, we propose a separable deraining network (SDNet) which provides a more flexible and simply baseline of deraining network by using a cascaded framework to achieve automatic trade-off between model performance and computational resources. Besides, a novel module called group-atrous spatial pyramid (G-ASP) and a network separation strategy (NSS) are proposed to extract more discriminative multi-scale information with fewer parameters and adapts our model to different levels rain-streaks for reasonable allocation of computational resources automatically. The experimental results on public datasets demonstrate that our SDNet achieves promising performances with much fewer parameters than other methods.
Small Area Configurable Deep Neural Network Accelerator for IoT System

With the development of Internet of things (IoT) technology, deep learning algorithm has been widely used in IoT devices. Convolutional neural network (CNN) and recurrent neural network (RNN) play a significant role in image field and sequence data respectively. In order to enable the IoT terminal SoC with limited computing power and resources to support CNN and RNN algorithms, this paper proposes a small area configurable deep neural network accelerator with fixed-point accuracy. The main computing components of CNN and RNN are implemented by hardware. Each computing module completes the calculation of complex neural network through parameter configuration and combination. In order to verify the performance of the accelerator, a SoC verification system based on Cortex-M3 was constructed. The lenet-5 network and the Long Short-Term Memory (LSTM) network with 2 layers and 128 hidden units are implemented on this accelerator, and the execution time of the two networks on Intel i5 7500, Cortex-A53 and Cortex-A7 processors is compared. The comparison results show that the CNN and RNN computing power of the accelerator exceeds that of Cortex-A53 and Cortex-A7 at the main frequency of 50MHz.
An Improved Target Tracking Method Based on DIMP

To address tracking failures of the DIMP method in scenarios of motion blur, illumination variation, and deformation, etc., this paper proposes an improved DIMP method, which uses the IResNet network with higher accuracy and better learning convergence for visual object tracking task. Firstly, we extract the depth features from a set of labeled sample images using the IResNet network, and then use a convolution block (Cls Feat) to classify the features. Secondly, the features are input into the model predictor to generate the weights to update the sample model. Finally, based on the weights, the convolution layer selects the region with the highest confidence score in the test frame as the target region. Experimental results on the benchmark OTB-2015 illustrate that our method, running at 53 fps, improves the precision and success rate by 1.8% and 1.1%, respectively, compared with DIMP. Therefore, our method effectively improves the tracking accuracy and robustness.
Big Data Management Using Ontologies for CPQ Solutions

In recent years, due to a progressive complexity of handling and processing business data, proper big data management has become a challenge, especially for SMEs that have limited resources for investing in the requested business transformation process. As a solution, we suggest an ontology-based CPQ software approach, where we show how the implementation of semantic technologies and ontologies affects data integration processes. We also propose a method called "ontology-based data matching", which allows the semiautomatic generation of alignments used to formalize the coherence between ontologies. The proposed method will ensure consistency during integration, significantly improving the productivity of enterprises.
Development of a Modular Control System for an Industrial Dismantling Robot

This article discusses the development of a new control system and data exchange protocol for a robotic complex. The control object is a remotely controlled robot ROIN RTS R-300/04. The standard robot control system ROIN RTS R-300/04 does not have sufficient fault tolerance. To solve the tasks, a modular control system concept was proposed according to which the entire control system is divided into functional units that provide local control of any robot equipment. All units are connected to a common information-command network. Each unit receives a command in a specific format and in response transmits information about its status. Thus, complex control tasks are sort of spread between the units, and the main unit at any time receives information about the status of each of the robot subsystems, which greatly facilitates the diagnosis and troubleshooting. The developed control system was assembled and installed on a serial ROIN RTS R-300/04. The number of malfunctions due to malfunctions of the control system was minimized. The modernized robot has been successfully used by UMMC.
An Efficient Accelerated Learning Algorithm For Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks

An efficient accelerated learning algorithm is proposed and discussed for tracking of spatially correlated signals in ad-hoc wireless sensor networks. The proposed algorithm is low-cost and computationally efficient. It models an unknown, spatially correlated signal using a number of its contour lines at equally spaced levels. In the proposed algorithm, each sensor is modeled as one neuron in a neural network. The accelerated learning's agent is implemented at the fusion center (FC). The algorithm is performed in two phases of spatial modeling and spatial tracking. In spatial modeling phase that accelerated learning is implemented, the algorithm discovers the model parameters. In spatial tracking phase, the model parameters are updated to track the varying, unknown spatial signal. Those sensors (neurons) that their observation are in a given margin of at least one of the contour levels, report their filtered observations to the FC. The FC updates the model parameters based on the reported observations and returns the model features to the sensor network for the next iteration step. The performance evaluation results show that the proposed accelerated learning is low cost and converges faster than single layer machine learning approach. The modeling performance, convergence speed and the cost of the proposed algorithm are compared with those of single layer machine learning algorithm. The algorithm is proposed for environmental monitoring.
An Image Watermarking Scheme Based on Voting Mechanism in Balanced Multiwavelet Domain

A digital image watermarking algorithm based on balanced Multiwavelet transform and voting mechanism is proposed in this paper. The algorithm embeds the binary watermark image bits which have been pre-processed into low-pass sub-band coefficients in multiwavelet transform domain. According to the virtually identical quality of the energy of four low-pass subbands, the binary watermark image bits are embedded into four low-pass sub-bands coefficients four times respectively. Due to the different characteristics of each low-pass coefficients block, the largest singular value of the selected blocks is adaptively operated by different quantization step for embedding watermark information. Finally, the voting mechanism is introduced when the watermark extracting. Experimental results show that the watermarking algorithm not only has good invisibility, but also has robustness against some common image processing such as JPEG compression, noise addition, filtering, etc.
Behavior Tree-based Object Event Modeling with Dynamic Change Control for Virtual Reality Fire Fighting Training Contents

This paper defines the behavioral expression structure of objects in the virtual reality environment to support various user experiences, and describes the current status of research on a modeling platform that supports simulation of changes. We define the behavior of any object based on the changing behavior tree, and propose a structure that allows each object to change its behavior according to user interaction with objects at runtimme. In the process of developing realistic fire drill system, we checked the utilization potential of the proposed technology based on various fire-causing situations, and propose future research issues to implement experience scenario diversification from a more macroscopic perspective.
SOLAR RESOURCE MAPPING IN THE VALE SÃO FRANCISCO DA BAHIA USING THE WRF-SOLAR MODEL

To assist the planning of the electrical system with reliable information on the temporal variability of solar irradiation and consequently efficient use of this source, the present work aims to estimate and map solar radiation in the micro-region of Barra, located in the Vale São Francisco of the Bahia, using the numerical model WRF-Solar.The period of the simulations was the months of January and July 2019, a period in which the days of perihelion (3th January) and aphelion (4th July) occur.The simulations were satisfactory through statistical analysis between simulated and observed data, with a normalized quadratic error of 0.08 and 0.13, a factor of two of 0.81 and 0.76, and a correlation coefficient of 0.90 and 0.76, respectively, for the months of January and July.Therefore, the computational tool has good forecasting capabilities, with great potential for operational, research, and technological development purposes.
Design and Implementation of a Low Cost Electrooculography (EOG) System

Electrooculogram (EOG) is the electrical activity caused by corneo-retinal standing potential due to the movements of the eyeballs. The technique of measurement and recording of EOG signal is called Electrooculography (EOG). Electrooculography is used for health monitoring, disease diagnosis and human-machine interface (HMI). In this paper we present the design and development of a low cost acquisition system detect the EOG due to the different eye ball movements. The system was consisted of an instrumentation amplifier, passive filters (frequency range of 0.1 Hz to 40 Hz) and Arduino UNO (for serial communication with computer). The system detected horizontal and vertical movements and Blinking action of the eyes. Analog to Digital Conversion (ADC) and interfacing with MATLAB and Simulink were done by using Arduino UNO. In this system a Simulink model was designed for digital filtration, analysis and displaying the EOG signal. The stored signal further exported to the workspace of MATLAB where different types of signal analyses were performed. Our objective was to developed a cost effective EOG acquisition device which will be used for making assistive applications for physically disable person in developing countries.
Cognitive Defeasible Reasoning: the Extent to Which Forms of Defeasible Reasoning Correspond with Human Reasoning

Classical logic forms the basis of knowledge representation and reasoning in AI. In the real world, however, classical logic alone is insufficient to describe the reasoning behaviour of human beings. It lacks the flexibility so characteristically required of reasoning under uncertainty, reasoning under incomplete information and reasoning with new information, as humans must. In response, non-classical extensions to propositional logic have been formulated, to provide non-monotonicity. It has been shown in previous studies that human reasoning exhibits non-monotonicity. This work is the product of merging three independent studies, each one focusing on a different formalism for non-monotonic reasoning: KLM defeasible reasoning, AGM belief revision and KM belief update. We investigate, for each of the postulates propounded to characterise these logic forms, the extent to which they have correspondence with human reasoners. We do this via three respective experiments and present each of the postulates in concrete and abstract form. We discuss related work, our experiment design, testing and evaluation, and report on the results from our experiments. We find evidence to believe that 1 out of 5 KLM defeasible reasoning postulates, 3 out of 8 AGM belief revision postulates and 4 out of 8 KM belief update postulates conform in both the concrete and abstract case. For each experiment, we performed an additional investigation. In the experiments of KLM defeasible reasoning and AGM belief revision, we analyse the explanations given by participants to determine whether the postulates have a normative or descriptive relationship with human reasoning. We find evidence that suggests, overall, KLM defeasible reasoning has a normative relationship with human reasoning while AGM belief revision has a descriptive relationship with human reasoning. In the experiment of KM belief update, we discuss counter-examples to the KM postulates.
Image Super-Resolution via Generative Adversarial Network Using an Orthogonal Projection

In this paper, we propose a simple but powerful idea to improve super-resolution (SR) methods based on convolutional neural networks (CNNs). We consider a linear manifold, which is the set of all SR images whose downsampling results are the same as the input image, and apply the orthogonal projection onto this linear manifold in the output layers of the CNNs. The proposed method can guarantee the consistency between the SR image and the input image and reduce the mean squared error. The proposed method is especially effective for SR methods based on generative adversarial networks (GANs), composed of one generator and one discriminator, since the generator can learn high-frequency components while maintaining low-frequency ones. Experiments show the effectiveness of the proposed technique for a GAN-based SR method. Finally we introduce an idea of extension to noisy images.
Orthogonal Features Fusion Network for Anomaly Detection

Generative models have been successfully used for anomaly detection, which however need a large number of parameters and computation overheads, especially when training spatial and temporal networks in the same framework. In this paper, we introduce a novel network architecture, Orthogonal Features Fusion Network (OFF-Net), to solve the anomaly detection problem. We show that the convolutional feature maps used for generating future frames are orthogonal with each other, which can improve representation capacity of generative models and strengthen temporal connections between adjacent images. We lead a simple but effective module easily mounted on convolutional neural networks (CNNs) with negligible additional parameters added, which can replace the widely-used optical flow network a nd significantly improve th e performance for anomaly detection. Extensive experiment results demonstrate the effectiveness of OFF-Net that we outperform the state-of-the-art model 1.7% in terms of AUC. We save around 85M-space parameters compared with the prevailing prior arts using optical flow network without comprising the performance.
Permainan Crossword Puzzle untuk Meningkatkan Aktivitas dan Hasil Belajar PKn Materi Sumpah Pemuda pada Siswa Kelas III SDN 1 Sidowayah Polanharjo Klaten Semester I Tahun Pelajaran 2018/2019

The purpose of this study was to find out: (1) Activity of students in Civics learning material in the system of regency, city and provincial government with crossword puzzles in class III SD Negeri 1 Sidowayah Polanharjo Subdistrict, Klaten District 2018/2019 (3) Student learning outcomes in Civics learning material in the regency, city and provincial government system with crossword puzzles in class III SD Negeri 1 Sidowayah Polanharjo Subdistrict, Klaten District 2018/2019. This type of research is Classroom Action Research (CAR), with research procedures: planning, action, observation, and reflection. Data collection techniques use: observation, tests, interviews, and documentation. The results of the study concluded: (1) Civics learning with crossword puzzles (TTS) increases the active involvement of students during learning, and increases student learning enthusiasm. Students feel happy, and can learn to cooperate constructively and respect each other in group learning. The activities of teachers in Civics learning with crossword puzzles (TTS) do not dominate the learning process. The teacher has more role as: motivator, which is a lot of motivation for students; mediator namely mediating between students when the discussion takes place; and the facilitator is giving direction to students when learning takes place; (2) Civic learning with crossword puzzles (TTS) makes it easy for students to exchange ideas and understand subject matter, improve student learning outcomes, and student learning completeness. After being given learning with TTS games, student learning outcomes are quite high. At the end of the second cycle, students who scored very well (or scored 80 to 100) were 7 students (64%). Students who get a score of equal to or more than 70 (KKM) are 10 students (91%). Thus, the TTS game is very effective in improving learning outcomes and student learning completeness.
Decentrally-Consented-Server-Based Blockchain System for Universal Types of Data

The rapid increase of data makes the third party cloud storage more and more popular. However, decentralization, immutability, and integrity characteristics of Blockchain systems are making Blockchain systems another potential platform for data storage. Using the Blockchain system for data storage will eliminate the dependency on any central authority thus increasing the data security, privacy, data retention probability, and eliminate the single point of failure. Most of the Blockchain consensus mechanism have been developed over the decade are about storing the transactional data only. In this paper, we present a novel idea of the Blockchain consensus system for universal types of data. Our proposed consensus mechanism is designed in such a way that, most of the devices or entities can participate in the mechanism by doing low computational validation work. To add an extra layer of security we introduce a two-step validation process in this consensus mechanism. The probabilistic fair rotation of the block creator in our consensus mechanism will significantly reduce the probability of centralization which is a common issue in most of operating Blockchain consensus mechanism based on one user/group winning strategy. Moreover, we analyze our consensus mechanism using game theory and queuing theory.
Automated Generation of Test Questions and Solutions

While the automatic generation of test problems has been considered in the past, the importance of also generating solutions is largely unconsidered. Additionally, current approaches to automatically generating test problems are normally limited to multiple-choice questions (MCQs) and often only consider language-based problems. The generation of problems and solutions that are based on graphical processes is explored in an attempt to address these limitations. Rapidly generating questions and their solutions helps educators set better questions by allowing more iterations than manual processes, and allows students to be provided with worked examples. These and other considerations are illustrated by considering a number of examples, and suitable software tools are suggested.
The Research of Single Image Super-Resolution Based on the Generative Adversary Networks

Abstract In order to enhance the visual effects of the reconstructed images in the single image super-resolution, and solve the problem of instability of the training phase of super-resolution with generative adversary network (SRGAN), a new super-resolution model is built with more depth and width, and the corresponding super-resolution algorithm is proposed. The network structure of the original SRGAN is modified, so that deeper and wider convolutional networks can be used with high efficiency. Furthermore, the loss function is employed to balance the training of the generative network and the discriminator network, and solve the instability problem in the training phase. The training images for the network are obtained from the database of ImageNet on the web as other researchers. Experimental results show that images reconstructed by the proposed algorithm has better visual effects than those by the original SRGAN. In addition, the objective measures of PSNR and SSIM of the reconstructed images have also been improved.
Significant of Viral Marketing Dimensions (Word of Touch) on Buying Fast Food Restaurants Products: Case of Jordan

This study aims to investigate the significance of viral marketing dimensions on customer’s intention behavior to buy the fast food restaurant’s product in Jordan. This study uses the quantitative approach to collect the data from the respondents which are the fast food restaurant customers in Jordan specifically at the University Of Jordan Street in Amman, the capital of Jordan. The researcher in this study distributed one hundred and fifty questionnaires for three main fast food restaurants in this location which are (KFC, MacDonald and Burger king) with fifty questionnaires for each one of them. Question of this study adapted from previous studies and modified to be suitable for the aims of this study to collect the primary data. The findings of this study show that the viral marketing dimensions have highly significance on customer’s intention to buy the fast food restaurant products. Also the findings of this study indicate that the three viral marketing dimensions have a high significance on customer’s intention to buy the fast food restaurant’s products. However, brand association has great significance on customer’s intention following with brand confidence and brand consciousness. In addition, the results of this study show that the Jordanian customers have high intention to buy the fast food restaurants’ products when they watch it in social network applications like Facebook, Twitter and other social network applications. Finally, this study suggests some recommendations for academic researchers and practitioners.
Forensic and Anomaly Detection Using Generalized Audit Software

Detection of fraud is the responsibility of the auditor. In the industrial revolution 4.0, fraud no longer involves manual or physical activities, but auditors must improve their skills by understanding fraud in the form of data anomalies. In conjunction with the audit process, this company information system will be the source of data in the audit process. Technology-based company operations make operational data no longer be paper, but are stored in electronic form. Auditors need to understand the importance of understanding and using technology in this audit process because data is available in electronic form (database), so manual procedures for obtaining and analyzing data are no longer effective for use. Computer Assisted Audit Tools and Techniques (CAATs) is a term intended for the use of technology in assisting auditors in carrying out the audit process. Our research is qualitative descriptive, we used primary data from observation, and inquiry or interview. Our interviewees are auditor in accounting firm who already used computer assisted audit techniques in daily work for data extraction and analysis. Our research result shows that auditors can implement CAATs in GAS (Generalized Audit Software) which is software created specifically for audit purposes. GAS has features that can easily test management assertions. GAS able to help auditor in more effective and efficient data extraction and analysis in order to detect symptom of fraud such anomaly in data, but even GAS has a lot of advantage and benefit, still GAS have weakness and disadvantages