Reviewerly/exHarmony huggingface dataset:
A Novel approach for Denoising palm leaf manuscripts using Image Gradient approximations

This paper developed a novel algorithm to denoise old palm leaf manuscript using local Otsu thresholding and Sobel operator-based image gradient approximations. Initially an intense literature review is conducted on the existing document noise removal algorithms and implemented some of the algorithms which promised to give good PSNR values. Based on the results obtained, background noise removal algorithm using local features, gave the best results for denoising palm leaf manuscripts in terms of Peak signal to noise ratio and Mean Square Error. The values obtained are 14.64 db, 0.002 and 0.03 respectively. But the performance of this algorithm heavily depends on the threshold value used for filtering the document and is having a high computational complexity and overhead. So novel denoising algorithm is developed which can solve the drawbacks of the existing, document noise removal using local features algorithm. Using this enhanced algorithm, the PSNR, SSIM and MSE values obtained are 14.69, 0.003 and 0.02 respectively. So based on these parameter values, this novel approach proves best for palm leaves manuscript as compared to the various other algorithms existing for this purpose.
Handwritten hiragana classifier with minimal training data utilizing convolutional neural networks

A robust algorithm for Japanese handwritten hiragana character classifier is proposed using a machine learning approach for minimal training data to reduce computational power and time consumption. The proposed algorithm utilizes image recognition techniques to process samples from a data set. Six different models involving convolutional neural networks are implemented using image templates that were previously processed, in order to achieve great results with the least possible amount of training data. Prediction results were evaluated separating the dataset in training and validation data at a ratio of 5:95 respectively, achieving 96.95% as the highest accuracy across different models, competing against state-of-the-art classifiers with 80:20 training ratio.
Blockchains and institutional competition in innovation systems

Purpose The purpose of this paper is to introduce the notion of blockchain as an institutional technology, defend the idea of National Innovation Systems as institutional systems, and then make use of the theory of institutional competition to characterise challenges posed by innovation public policy by blockchain technology. Design/methodology/approach The approach is to consider the nature of blockchain technology as an institutional technology, and to consider the nature of National Innovation Systems as institutional systems. The author then applies a theory of institutional competition developed elsewhere to appraise the interaction of the two. Findings The author expects for there to emerge sustained competition for National Innovation Systems from innovation systems implemented using blockchains. There will be pressure exerted by the latter upon the former to become more integrated, secure, usable and to greater support profit expectations for entrepreneurs. Originality/value The theory of institutional competition upon which this work is based makes use of cutting-edge behavioural and institutional economics. It has hitherto only been applied at a general level and has not been applied to a specific set of institutions such as National Innovation Systems.
Protection of Big Data Privacy on Multiple Cloud Providers by Asymmetric Security Scheme

Big data is the name that defines data which has enormous size and unstructured. Due to the file size is pretty huge. It is impracticable to store a large file in one storage volume. However, cloud computing is a solution to this impossible. Data owner can store the file in a cloud storage provider (CSP). Nevertheless, the new dilemma has arisen. Relying on single cloud storage may generate trouble for the customer. A CSP may stop its service anytime. Moreover, the CSP is the third party that user have to trust without verification. In that case, the privacy or unauthorized accessing of data may be violated without notice. To overcome this risk, we propose secure data storage scheme for big data storing on multiple CSPs. The one big data file is split into chunks and distributed to multiple cloud storage provider. After splitting the file, metadata is generated. Metadata is a place to keep chunks information, includes; chunk locations, access paths, username and password of the data owner, methods to connect each CSP. The metadata is encrypted and transferred to the user who requests to access the file. The user utilizes the metadata and chunks of the file to compose the original file. This method will minimize the risk of privacy. The goal of this paper is to provide the method to protect the privacy of data stored on multiple cloud storage providers. Furthermore, we discuss and analyze how this data storage scheme promote the protection of big data privacy.
Analysis of Firefly-Fuzzy Hybrid Controller for Wheeled Mobile Robot

At present, many intelligent techniques have been developed by various researchers in the field of mobile robot navigation. It is the task of getting proper movement in the environment to attain said goal by detecting and avoiding the obstacle with less human interference. For effective navigational strategy, Firefly-Fuzzy hybrid algorithm is analyzed over the unstructured environment because individual technique may not guaranty for an optimal solution over all configurations. The key advantage of the proposed hybrid controller is that it combines the multiple features of the different approaches into a single controller. The simulation and experimental results are presented at the end of paper over various environmental conditions and it is compared with the standalone algorithm; it is observed that the hybrid Firefly-Fuzzy controller performs well in terms of path and time optimization.
Mask Guided Fusion for Group Activity Recognition in Images

Recognizing group activities from still images is a challenging problem since images lack motion and temporal information that makes it easier to differentiate foreground from background. Nevertheless, images present rich spatial content that can be effectively leveraged for better feature representation and recognition. In this paper, we propose a two-stream convolutional neural network approach for group activity recognition. Our proposed approach is based on using person segment mask images to guide feature learning process. Our method is capable of inferring group relations without the need of bottom-up approaches and low-level annotations. To this end, we utilize three ways of fusing RGB and person segment mask feature maps. Experimental results demonstrate that person mask guidance provides a complementary learning process by outperforming previous methods with a large margin.
Investigation and mapping strategy on influence of surface tilting in diffractive pattern correlation profilometry

A new full-field profilometry based on diffraction image correlation (DIC) was developed in which the technique bases on calibrated database of reference diffractive images (RDIs) to estimate surface depth information. This technique has the advantage of removing vertical scanning for achieving high measurement efficiency in microscopic surface profilometry. However, as the diffractive images not only depend on the tested height but also the local surface tilt. Thus, the pre-built image database may not match the measured diffractive image and lead to a systematic measured error incurred by the surface tilting condition. Thus, in the article, the influence of surface tilt to the diffraction images is investigated and analyzed to understand the relationship between the surface tilt and the image variation. A lateral drifting phenomenon depends on the tilt angle and tilt direction was also quantified to estimate the impact of the tested surface height. Meanwhile, a detection algorithm to determine the center of diffractive image was developed to position the shifting quantity of the image. Moreover, the information coupling problem between height and tilting parameters, such as the tilt angle and tilt direction was studied to decouple these parameters from the tested height, so the tested surface can be reconstructed accurately. To realize the proposed method, some data processing strategies were also proposed to decouple the depth information from multi-surface parameters such as surface tilt angles (pitch and yaw) and direction effectively.
Reaching Out Towards Fully Verified Autonomous Systems

Autonomous systems such as “self-driving” vehicles and closed-loop medical devices increasingly rely on learning-enabled components such as neural networks to perform safety critical perception and control tasks. As a result, the problem of verifying that these systems operate correctly is of the utmost importance. We will briefly examine the role of neural networks in the design and implementation of autonomous systems, and how various verification approaches can contribute towards engineering verified autonomous systems. In doing so, we examine promising initial solutions that have been proposed over the past three years and the big challenges that remain to be tackled.
High-accuracy surface measurement through modelling of the surface transfer function in interference microscopy

Surfaces featuring complex topographies, such as high slope angles, large curvatures and high aspect-ratio structures on both macro- and micro-scales, present significant challenges to optical measuring instruments. Here we demonstrate a method to characterise and correct the three-dimensional surface transfer function (3D STF) of a coherence scanning interferometer (CSI). Slope-dependent errors present in the original measurements are reduced after phase inversion of the 3D STF, and the final results agree with traceable contact stylus measurements within the 30 nm reproducibility of the stylus measurements. This method enables in-situ compensation for errors related to aberrations, defocus and diffraction.
Averaged one-dependence inverted specific-class distance measure for nominal attributes

Scores of machine learning algorithms depend on a good distance measure to achieve high performance. The inverted specific-class distance measure, simply ISCDM, is proposed to find reasonable distance measure between each pair of instances with nominal attributes only. ISCDM does not depend on the attribute value of the training instance, which makes it less sensitive to missing values in the training set and more robust to non-class attribute noise. However, in ISCDM, all attributes are assumed to be fully independent. It is obvious that the attribute independence assumption in ISCDM is rarely true in reality, which would harm its performance in the applications with complex attribute dependencies. In this paper, we single out an improved inverted specific-class distance measure by relaxing its unrealistic attribute independence assumption. We call it averaged one-dependence inverted specific-class distance measure, simply AODISCDM. We experimentally tested it on 29 classification problems from the University of California at Irvine (UCI) repository and found that it significantly outperforms ISCDM in terms of the negative conditional log likelihood (-CLL) and the root relative squared error (RRSE). Besides, the proposed AODISCDM maintains the computational simplicity (no search involved) and robustness that characterise ISCDM.
On the depth spectrum of repeated-root constacyclic codes over finite chain rings

Let R be a finite commutative chain ring and γ be a fixed generator of the maximal ideal of R. For any unit w in R, (1+wγ)-constacyclic codes over R as a generalization of negacyclic codes over Z4 are a class of important linear codes. In this paper, based on algebraic structure, the generator polynomials of all torsion codes of (1+wγ)-constacyclic codes of any length over R are first given. Then, by using these torsion codes, we completely determine the depth spectrums of (1+wγ)-constacyclic codes over R of any length.
The use of electronic educational environment for interdisciplinary relations on the example of professional study on business – informatics bachelor program

Purpose of the study. The aim of the study is to analyze the optimal conditions for the use of electronic educational environment and competence-activity approach in the educational process of the university for the training of bachelors in the direction of “business informatics”. The urgency of the problems outlined in this article is determined by the need to develop human resources in the field of information technology and improve the quality of training in this area. The object of the research is the training of full-time students of educational institutions of higher professional education, carried out in the conditions of education informatization. Materials and research methods. The article analyzes the possibility of effective use of electronic educational resources in the educational process in terms of informatization of higher education, as well as the impact of the use of the competence-activity approach and interdisciplinary connections on the development of students’ professional competencies. Results. Taking into account the analysis carried out on the use of electronic educational environment at NUST “MISiS”, it is shown that it is promising to be shared with the competence-activity approach in order to bring the training of IT specialists in line with modern requirements at the shortage of time resources. Conclusion. In the transition to a digital economy in our country, it is necessary to form the approaches to the training of IT industry specialists. The use of the electronic educational environment “LMS Canvas” at the NUST “MISiS” and the competence-activity approach, which implies not the message, but the formation of students’ knowledge in the process of implementing the project development, has proved its effectiveness in preparing bachelors in the direction of “business informatics”. This approach made it possible, through effective use of interdisciplinary connections in the e-learning environment, to involve students in the process of active learning, to improve the process of key competencies forming, to improve substantially the quality of diploma works and their practical significance, as well as to increase the level of compliance with global trends in the field of higher education informatization.
Photonic Processor for Fully Discretized Neural Networks

Machine learning is now moving towards, and will become prevalent in, fog-computing and real-time computing environments. To this end, much machine-learning-at-the-edge research has focused on efficient neural network architectures, giving rise to efficient approximations of fixed-point neural networks, called discretized neural networks. While higher performing than their fixed and floating-point counterparts, discretized neural networks still have an existing bottleneck at the neuron's accumulation of products, called the popcount. This bottleneck sets an upper bound on performance regardless of neural network architecture. We address the popcount bottleneck by introducing a photonic discretized neural network processor. This processor minimizes the popcount bottleneck, thereby maximizing neural network computational throughput. Additionally, it offers potential for performance enhancement through simultaneous convolution operations enabled by wavelength division multiplexing. We show that the photonic architecture is capable of increasing performance by 700% and 100% when compared to state-of-the-art digital and analog architectures, respectively.
Effect of moving pictures on visual task performance and fatigue using 4K OLED and LCD TVs

Abstract This study compared the effects of 4K organic light‐emitting diode (OLED) and liquid‐crystal display (LCD) TVs on human visual task performance and fatigue when viewing moving pictures. In experiments conducted, subjects were asked to locate 35 icons among a total of 276 on‐screen icons at four moving picture speeds. From the results obtained, it was determined that OLED TVs (emissive type) exhibit superior characteristics for hit rate and false alarm rate compared with LCD TVs (non‐emissive‐type). The evaluation results of a simulator sickness questionnaire administered to ascertain fatigue levels also indicated that OLED TVs induce relatively little fatigue at fast‐moving picture speeds of 10 ppf or more compared with LCD TVs. Thus, this study confirms that OLED TVs, given their ultrafast response time characteristics, are superior in terms of moving picture images to LCD TVs.
Bi-Level Masked Multi-scale CNN-RNN Networks for Short Text Representation

Representing short text is becoming extremely important for a variety of valuable applications. However, representing short text is critical yet challenging because it involves lots of informal words and typos (i.e. the noise problem) but only few vocabularies in each text (i.e. the sparsity problem). Most of existing work on representing short text relies on noise recognition and sparsity expansion. However, the noises in short text are with various forms and changing fast, but, most of the current methods may fail to adaptively recognize the noise. Also, it is hard to explicitly expand a sparse text to a high-quality dense text. In this paper, we tackle the noise and sparsity problems in short text representation by learning multi-grain noise-tolerant patterns and then embedding the most significant patterns in a text as its representation. To achieve this goal, we propose a bi-level multi-scale masked CNN-RNN network to embed the most significant multi-grain noise-tolerant relations among words and characters in a text into a dense vector space. Comprehensive experiments on five large real-world data sets demonstrate our method significantly outperforms the state-of-the-art competitors.
Eye Disease Detection System Use Forward Chaining Methods

Expert system for the diagnosis of eye diseases in humans using forward chaining method aims to trace the symptoms that are displayed in the form of questions in order to diagnose the type of disease with the software. Expert system software can recognize the type of eye disease after consultation with answering some of the questions that is displayed by the application of an expert system and can infer some kind of eye disease suffered by the patient. Data recognizable disease customize the rules are made to be able to match the symptoms of eye disease and give value to determine the percentage of the value of the patient's disease type approach
Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences

Using video sequences to restore 3D human poses is of great significance in the field of motion capture. This paper proposes a novel approach to estimate 3D human action via end-to-end learning of deep convolutional neural network to calculate the parameters of the parameterized skinned multi-person linear model. The method is divided into two main stages: (1) 3D human pose estimation based on a single frame image. We use 2D/3D skeleton point constraints, human height constraints, and generative adversarial network constraints to obtain a more accurate human-body model. The model is pre-trained using open-source human pose datasets; (2) Human-body pose generation based on video streams. Combined with the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos to generate a smoother 3D pose. In addition, we compared the proposed 3D human pose recovery method with the commercial motion capture platform to prove the effectiveness of the proposed method. To make a contrast, we first built a motion capture platform through two Kinect (V2) devices and iPi Soft series software to obtain depth-camera video sequences and monocular-camera video sequences respectively. Then we defined several different tasks, including the speed of the movements, the position of the subject, the orientation of the subject, and the complexity of the movements. Experimental results show that our low-cost method based on RGB video data can achieve similar results to commercial motion capture platform with RGB-D video data.
Application of Classification Algorithm C4.5 in Recommendations for Natural Tourism Development in District Simalungun

Abstract A tour of human activities to relieve fatigue after a week or a month in carrying out daily activities both as a worker or employee. This activity is carried out to entertain every human being and relax the mind. Besides that, tourism can also help people around the location of tourism objects as a livelihood to meet the needs of life. Simalungun Regency has several natural attractions scattered in several sub-districts which can attract the attention of visitors to travel, but there are still many that have not been well managed by the government or the private sector. To develop the tourism industry in Simalungun Regency, many obstacles faced by the management start from the location, transportation, costs and facilities available and this is also faced by visitors to visit a tourist attraction. In this study, we will choose a location that can be developed based on a questionnaire from several communities / tourists to assess a tourist site that is more supportive to develop. From the results of the election using the C4.5 Algorithm. Then it has been found that the Nature Tourism object can be developed with a level of accuracy that reaches 90% and a recall of 83.33%.
Deceiving Face Recognition Neural Network with Samples Generated by Deepfool

Abstract Image-based identity authentication systems have been extensively used recently. However, it holds the risk of illegal use of the images for authentication by others, which will cause a severe threat to personal privacy and security protection. The concept of adversarial samples, which is often utilized in the field of deep learning to deceive classification models, has become more and more popular, and has accumulated lots of significant efforts. Meanwhile, previous work mainly focused on interpreting images by features rather than by the structural characteritics of the human face, which made the deception of face recognition model ineffective. To solve the problem, face recognition neural network deceiving method that is based on Deepfool algorithm is proposed. For a specific face recognition neural network, white-box attack is used to generate adversarial samples, and Euclidean distance is utilized to optimize adversarial samples to obtain face photos with misleading attributes. The proposed approach can be used for privacy and security protection. Experiments on several face testing data sets verify effeteness of our proposed approach.
Prioritized Control of Multivariate Process using Lexicographic Ordering Approach: A Simulation Study

Design of a multivariate control system is a challenging task owing to inherent process nonlinearities, multivariable interactions, and unstable zeros in the process dynamics. Prioritization of multiple control objectives is conventionally achieved using scalarization approach by appropriate weighting factors in the augmented objective function. However, the optimality of these parameters does not hold in presence of unmeasured disturbance and different setpoints. Hence, prioritization in the objectives is not achieved in such circumstances. On the other hand, priority in various objectives can effectively be realized in lexicographic optimization approach. We use the lexicographic optimization approach to address setpoint tracking control problem in linear MPC. To validate the efficacy of the lexicographic ordering approach, multivariate quadruple tank process is considered. Tuning issues with conventional MPC and its remedies using lexicographic approach are discussed in the present work.