Reviewerly/exHarmony huggingface dataset:
Autonomic Computing Towards Resource Management in Embedded Mobile Robots

Small and lightweight robots are becoming a reality due to the increasing pervasiveness of embedded computers. However, much of the research in mobile robotics is focused on increasing the robots' autonomy, disregarding the computational limitations of embedded computers. The area of autonomic computing defines self-managing systems, also known as autonomic systems, which manage computational resources (e.g. CPU load) and reduce the effect of processing bottlenecks, as well as alleviating energy consumption in embedded mobile robots. In this paper, we propose the use of autonomic systems with control theory for resource management in embedded mobile robots. Our expected contribution is to use autonomic computing to develop self-contained embedded robots, capable of operating with few embedded computers and without the aid of external servers. We implement an autonomic system which successfully controls the latency and CPU load of a dense optical flow application, running on a Raspberry Pi 2 embedded computer, which validates the proposed idea.
Research and Design of Electronic Dictionary for Key Data Index of Chinese Patent Medicine

The electronic dictionary of key data index of Chinese patent medicines is a combination of Chinese patent medicines and computer technology. Its key point is to efficiently and quickly realize the function of searching massive information of Chinese patent medicines. Solr is an open source, high performance, scalable and easy to integrate with web applications. The electronic dictionary of key data index of Chinese patent medicine is a full-text retrieval system based on Solr technology, which can query the corresponding Chinese patent medicine information, prescription information and some rational drug use knowledge base information quickly and efficiently by keywords. In addition, the perfect combination of B/S and C/S form interface makes the system have both B/S characteristics and high experience effect of C/S. The aim is to construct a knowledge base system of Chinese patent medicine with efficient and fast retrieval function, and provide technical support for the development of Chinese medicine.
Reinforcement Learning Based on Multi-subnet Clusters

The main task of reinforcement learning is to enable the subject to obtain the most reward from the environment. Reinforcement learning has been proposed and achieved certain results. However, many reinforcement learning methods still have inefficiencies that result in inability to meet the demand in some applications. Aiming at the above problems, this paper proposed a reinforcement learning algorithm based on multi-subnet cluster (MSC-RL). The proposed network consists of multiple subnet clusters and the primary storage network. Each subnet cluster is composed of multiple subnets and one sub-storage network. In the subnet cluster, multiple subnets are used to explore the solution space simultaneously and saves the searched information to the sub-storage network. At regular intervals, the subnet cluster saves the searched information to the primary storage network. In traditional reinforcement learning, there is not enough interaction between the independent subnets. Insufficient information interaction between the independent subnets can cause the algorithm to fall into local optimum. MSC-RL can exchange information searched by each subnet through the sub-storage network to realize information interaction within the subnet cluster. Each cluster uses the primary storage network for information interaction. The method enhances the information interaction between subnets and improves the ability of the algorithm to optimize. This paper uses the Atari game to verify the performance of the proposed method and compared it with some mainstream reinforcement learning methods. The experimental results show that the proposed algorithm is superior to some mainstream reinforcement learning methods in the performance of the Atari game.
A Real-Time Lossless Data Compression Method for Intelligent Train Monitoring System

The real-time monitoring system during train operation generates a large amount of data, which will be transmitted to the intelligent control system by cable. Massive data transmission will bring tremendous challenges to train communication network. In addition, a large amount of data will also take a lot of time to verify and preprocess. In this paper, an improved real-time lossless data compression method is proposed according to the characteristics of data in freight train. This method can realize lossless data compression and is suitable for real-time transmission system. Different from traditional DSP implementation, this paper uses FPGA to implement data compression module in the form of hardware circuit.
Kinematics Analysis of Rowing Machine Based on Ergonomics

Based on ergonomics, this paper analyzes the structure of the rowing machine and calculates the size range of each part of the rowing machine relative to the human limb structure. Then the position of the saddle, ankle, and wrist is established by the kinematic analysis of mechanism. Then analytical analysis is conducted to determine the optimal length of each component.
Deep Learning Framework to Predict and Diagnose the Cardiac Diseases by Image Segmentation

Technologies have been vastly developed in all sectors of the society. Especially in medical field, its growth has been at rapid phase. The newly invented medical equipment has currently been playing massive role in saving the lives of many patients if given the proper and timely treatment to them. Among many modern medical equipment, MRI scanning deserves special mention. Using this technology, we can have the detailed images of organs inside the body as well as categorize and identify the stage of disease. Moreover, with the help of MRI, myocardial disease can be categorized and assessed with several conditions. In particular, we can save patient from a critical situation. However, it is difficult to have an accurate prediction of the cardiac disease. Furthermore, the current medical procedures require more time and medical care to accurately diagnose cardiovascular diseases. Under these circumstances, deep learning method can be useful to have a segment clear and accurate cine image in very less time. A deep learning (DL) technique has been proposed to assist the atomization of the cardiac segmentation in cardiac MRI. We have adopted three types of strategies, according to which we firstly optimize the Jaccard distance to accept the adjective function and then implement the residual learning techniques to integrate it into the code. Finally, a fully convolutional neural network (FCNN) was trained to introduce a batch normalization (BN) layer. However, our standard results show for myocardial segmentation that time taken for volume of 128 × 128 × 13 pixels is less than 23 s which is found when the process is done by using 3.1 GHz Intel Core i9 to be volume.
Visual Tracking by Gated PixelCNN Model

This paper proposed a novel visual tracking method based on the Gated PixelCNN and particle filtration. First, unlike the convolutional layers in traditional trackers, Gated PixelCNN is a generative model of images with a tractable likelihood. When trained, it is not limited to the existing classified datasets and video datasets. Second, similar to RNN, its shared convolutional layers extract the generic image features by calculating the pixel relationship maps of the pixels between the first pixel and the N th pixel, instead of just adjacent pixels. Through comparative experiments, We prove that the convolutional layers of Gated PixelCNN are not inferior to convolutional layers in traditional trackers. In this paper, we employ this new kind of convolutional layers in tracking. In addition, Particle Filtration is introduced in our algorithm to increase the tracker's resilience to occlusions. In the experiments, we compared our tracking method with other well-known trackers on 12 video sequences to evaluate the performance. The experimental results showed that the proposed algorithm demonstrated better overall performance than other state-of-the-art methods.
Implementation of 3D Drawing to Interactive Display System

Our laboratory has been developing an entertainment system called the “light crayon”. This system can draw on a screen with light pointer. In this paper, we propose a new system that can draw on 3D(three-dimensional) space with Mixed Reality technology.
Proportional Analysis of Feature Extraction for Tamil Handwritten Characters Using Centroid Based Method

A system for recognizing offline handwritten Tamil characters, using Feed Forward neural network, the primary objective of OHR is enhancement of a software for processing the images with different scripts and writing styles with high accuracy. The quality of the image is enhanced using diverse pre-processing operations; the feature of the character image is extracted using Centroid Based Distance Measure. In this paper a general description of the Algorithm is presented and classified using the Neural Network classifier. The proposed method is used for testing of handwritten Tamil character and has made a comparative analysis of different Distance Measure like Euclidean distance, Manhattan distance and Chessboard distance.
Selective and Coverage Multi-head Attention for Abstractive Summarization

Abstract Although the Transformer model has outperformed traditional sequence-to-sequence model in a variety of natural language processing (NLP) tasks, it still suffers from semantic irrelevance and repetition for abstractive text summarization. The main reason is that the long text to be summarized is usually composed of multi-sentences and has much redundant information. To tackle this problem, we propose a selective and coverage multi-head attention framework based on the original Transformer. It contains a Convolutional Neural Network (CNN) selective gate, which combines n-gram features with whole semantic representation to obtain core information from the long input sentence. Besides, we use a coverage mechanism in the multi-head attention to keep track of the words which have been summarized. The evaluations on Chinese and English text summarization datasets both demonstrate that the proposed selective and coverage multi-head attention model outperforms the baseline models by 4.6 and 0.3 ROUGE-2 points respectively. And the analysis shows that the proposed model generates the summary with higher quality and less repetition.
Rotational Cryptographic Technique (RCT)

A Security scheme based on Block Cipher is being designed where the Encoding and Decoding be prepared using Modular Adding, Rotational Shift operation and binary XNOR operation. The original message is stream of bits, & be presumed to be isolated into an amount of blocks having s bits in each of the them, where s be power of 3 (e.g. 3, 9, 27, 81, 243). An amount of 0’s be added at the MSB location of the stream for constructing whole numeral of bits odd in every block. For getting the cipher text the entire operation is done in two phases. In the first phase modular adding is done on neighboring blocks taking modulus of adding be 2s. The amount restores the subsequent block, 1st block remnants unmoved. At the time of summing up, carry turned beginning the MSB be rejected. In the next phase left and right bit rotation are applied on the output stream and logical XNOR was executed on the result to get the cipher text. The technique be employed on blocks with changeable dimensions from 3 to 3n. For decryption, the order of XNOR operation & rotational shift will be reversed and modular decryption will be adopted.
The Influence of Time-delay for the Bifurcation of the Fractional Neuron Network

Bifurcation is a special phenomenon of a nonlinear system, which will cause the system to lose its original structural stability. Most of the existing researches are aimed at the neuron network systems with integer order, but few research on fractional order neuron system. Especially, it is more difficult to study the case of self-leakage delay at the same time. In this paper, the effect of delay on the bifurcation point of fractional neural network is studied, not only considering the delay between neurons, but also considering the leakage delay of each neuron's self-feedback. Considering the case of self-feedback information with both time delay and non-time delay, the complexity and difficulty of problem analysis are increased. And by changing the order of the system, the influence of the order of the system on the bifurcation point is analyzed. This paper uses fractional Laplace transform and variable substitution method to analyze the characteristic equation and determine the critical value of bifurcation. The results show that the proposed method can effectively calculate the bifurcation point in the system. When the time delay does not exceed this point, the system is stable, and vice versa. The effects of time delay and fractional order on the dynamical behavior of neuron network were investigated. Different system order and number of system neurons with leakage time delay have different effects on bifurcation. The stability and bifurcation of the neuron system are influenced by the interaction of system order, delay size and neuron parameters.
Safe Reinforcement Learning in Continuous State Spaces

In using reinforcement learning [1], [2], there is a problem that states are often provided as continuous rather than discrete values. In this study, we applied continuous state space to reinforcement learning based on the success probability and return (EQ) [3] of conventional research. In addition, we demonstrated that EQ learns more safely than a conventional method in reinforcement learning problem with a continuous state space.
Multi-rate Hierarchical Fusion Estimation of Nearest Neighbor Clusters in Sensor Networks Based on Target Tracking

In order to overcome the shortcomings of low precision and high speed target tracking in wireless sensor networks, multi-rate hierarchical fusion estimation method based on target tracking is proposed. Sensors are divided into several clusters, and only the nearest cluster is used for target tracking. Each sensor detects a local estimate of the data, and then uploads it to the cluster head node for fusion to get the accurate information of the target. In order to improve positioning accuracy, strong tracking filtering (STF) estimator is used to identify time-varying structural parameters. By orthogonalizing the residual after filtering updating, the fading factor is obtained to correct the filter covariance matrix in real time, which guarantees the tracking ability of the filter to the change of structural parameters. The cluster head node uses the matrix weighted data fusion algorithm to obtain the final fusion results. The simulation results verify the effectiveness of the method.
Distributed Multi-issue Multi-lateral Negotiation Using a Divide and Rule Approach

In this paper, we consider the problem of multi-issue multi-lateral negotiation. We assume that each agent may be interested only in a subset of issues at stake. They nevertheless have to make a collective choice that addresses all issues. We propose a heuristics-based negotiation model where the agents’reasoning mechanisms may be very complex as a result of multiple issues being negotiated. Given this complexity, we propose a distributed negotiation mechanism drawn on divide and rule. The proposed protocol consists of two iterative steps: the partitioning of the agents into groups and the negotiation step where the agents in each group interact without a central controller (no mediator). Our negotiation protocol converges and leads to efficient outcomes, as demonstrated by our empirical results.
A note on consistency of Bayesian high‐dimensional variable selection under a default prior

Zellner's ‐prior is one of the most popular choices for model selection in Bayesian linear regression. Despite its popularity, the asymptotic theory for high‐dimensional variable selection is not yet fully developed. In this paper, we investigate the asymptotic behaviour of Bayesian model selection under the ‐prior as the model dimension grows with the sample size. We find a simple and intuitive condition under which the posterior model distribution tends to be concentrated on the true model as the sample size increases even if the number of predictors grows much faster than the sample size does. Simulation study results indicate that satisfaction of this condition is essential for the success of Bayesian high‐dimensional variable selection under the ‐prior.
Students’ worksheet on circular cylinder topic using GeoGebra software through Discovery Learning model

Abstract Students’ worksheet is a sheet that contains material, descriptions, work steps, and exercises to help students comprehend and discover the concept of learning materials. But in fact, students’ worksheets do not work well in schools yet. Students’ worksheets only contain materials and exercises, not including the components of good students’ worksheets. Therefore, it is necessary to develop students’ worksheets which could help students to find their own concepts. This study aims to develop valid and practical students’ worksheets based on discovery learning model employing GeoGebra in the topic of circular cylinder. This study was held at Year 9 on junior high school. The study is developmental research with ADDIE model, which consists of 5 steps: analysis, design, development, implementation, and evaluation. The research used validity and practicality instruments to assess students’ worksheet. The validity instrument includes the assessment sheets for student worksheet and practical instrument includes student and teacher response questionnaires. The results showed that the students’ worksheet fulfilled criteria (1) valid within the average score 4.5 of 5 for students’ worksheets; and (2) practical with student response questionnaire average score of 88% and teacher response questionnaire average score 4.5 of 5.
Restoration of noisy radiographic images applied in Non Destructive Testing (NDT)

Restoring degraded images is a problem that is part of the digital image processing domain. When acquiring images, phenomena such as noise, blur and bad quality are always present. Our goal is to reduce noise in the case of radiographic images applied in Non Destructive Testing (NDT) to get closer to a more authentic image. In our simulation we considered a Gaussian noise and a real noise, we use methods based on minimization algorithms without constraints such as fixed step gradient, optimization algorithms with constraints such as the projected gradient algorithm and an algorithm that applies a regularization (Total Variation) based Rudin-Osher-Fatemi model (ROF) using the Chambolle projection to improve the quality of the results. At the end a comparative study of algorithms used.
A Reversible Data Hiding Scheme Using Difference Values Based on Similarity between Adjacent Pixels

This paper proposes a reversible data hiding scheme for medical images using Histogram Shifting. We refine the process of deriving the histogram to achieve a better hiding performance. In concrete terms, the proposed scheme obtains the histogram with a larger deviation than the conventional scheme by dividing the image into multiple blocks and calculating the difference values between adjacent pixels on a block-by-block basis, and then applies the shifting process to the histogram. Our scheme can suppress the degradation of the embedded image quality and enlarge the hiding capacity compared to the conventional scheme. The efficiency of the proposed scheme has been confirmed through our experimental results.
Dynamic deformation analysis using diffraction phase microscopy and space-frequency analysis

In this paper, graphics processing unit (GPU) assisted windowed Fourier transform algorithm is used to process interferograms obtained in diffraction phase microscopy for noninvasive measurement of dynamic deformation undergone by a test specimen under variable load. The practical viability of our approach is shown by measuring the dynamic out of plane displacement of a glass slide sample coated with Aluminum thin film.
A counterexample to the central limit theorem for pairwise independent random variables having a common absolutely continuous arbitrary margin

The Central Limit Theorem (CLT) is one of the most fundamental results in Statistics. It states that the standardized sample mean of a sequence of n mutually independent and identically distributed random variables with finite first and second moments converges in distribution to a standard Gaussian as n goes to infinity. In particular, pairwise independence of the sequence is generally not sufficient for the theorem to hold. In this paper, we review the literature on sequences of random variables that fail to satisfy the conclusion of the CLT. Additionally, we construct explicitly a sequence of pairwise independent random variables having a common but arbitrary marginal distribution $\mathcal{L}$, and for which the CLT is not verified. We study the extent of this 'failure' of the CLT by obtaining, in closed form, the asymptotic distribution of the sample mean of our sequence. It is asymmetric with a tail that is always heavier than that of a Gaussian. It is remarkable that this asymptotic distribution is parametric, its sole parameter being related to the median of $\mathcal{L}$.
Development of Southern Research Recommendation System

The research was to identify needs on design and development of a research recommendation system for southern province groups in Thailand. The system employs semantic search function, research system structure and scope of knowledge during January 2008 to December 2017. 3,873 entries had been gathered from ThaiLIS, TNRR, and Southern universities research database by employing textual analysis and knowledge organization focusing on Classification Approach and categorization which main topics are sorted and categorized. To develop ontology, it applies knowledge engineering concept and using Protégé Ontology Editor version 5.5 beta to process the data for the accurate system development according to user-centered design concept. The study findings were the developed system is able to support information exchange, information retrieval, and research search and to decrease research redundancy and unnecessary budgeting. Moreover, the system could possible enhance conducting more innovations, confirming experts in the area, showing research trends, and showing research results on related fields, strategies, and problems which leads to the Southern developments extensively. After the implementation, the users’ overall opinions and satisfaction was at a high level and information retrieval efficiency of system was at a good level with 0.70 accuracy, 0.78 recall, 0.88 precision, and 0.82 F-measure.
Reversible Data Hiding Based on Prediction Error Histogram Shifting and Pixel-Based PVO

As already known, prediction error histogram shifting (PEHS) is a remarkable reversible data hiding method in which a sequence E of prediction errors of pixels is constructed and data is embedded by histogram shifting (HS) on E. In this paper, we propose a method that is a combination of PEHS and PPVO method. In this scheme, after dividing the host image into equal-sized blocks, the block is considered based on its flat property. If it is flat, it embeds a data bit based on the PESH method. In remaining case, it embeds a data bit based on PPVO. Experimental results show that the proposed method outperforms some state-of-the-art methods in terms of embedding capacity and stego image quality.