Reviewerly/exHarmony huggingface dataset:
Transmission of Watermarked Image in WSN Using ELSM Algorithm

To improve the copyright protection of the digital image, the watermarking technique is used. By changing the entropy, identify the location where the secret data gets embedded into the original image. It provides less distortion and high robustness. The watermarked image is send to the receiver through the wireless sensor network. In WSN, clustering is the best technique to save the energy. In cluster-based WSN, cluster head requires more energy to receive the data from the sensor nodes and transmitting it to the base station. To maintain the lifetime of WSN, the proper selection of CH is essential. In this paper, we propose the energy-based least squares multiple algorithms. Simulation results show that the ELSM algorithm is more efficient compared to the WLSM algorithm to improve the range of bandwidth, cluster overhead, skew rate, offset rate, carrier signal, reference signal and control output signal.
Uncertainty analysis, modeling, and forecasting for renewables and demands(iSPEC 2019)

This paper presents a method to research the relationship between the reflection film effect and environmental temperature of photovoltaic power station, and the relationship between the reflection film effect and the environment temperature of photovoltaic power station is carried out. Through the design of experimental model, the reflection film effect of photovoltaic power station was measured, the experimental data were extracted, It is found that the ambient temperature is basically inversely proportional to the increased power generation of the reflective film of the photovoltaic power station. When the ambient temperature is high enough, the addition of the reflective film does not increase the power generation, but consumes electricity.
A Systematic Approach to Horizontal Clustering Analysis on Embedded RSA Implementation

Side-channel attacks are a threat to cryptographic algorithms running on embedded devices. The exponent blinding is the main countermeasure which resists the classical forms of side-channel attacks. Horizontal side-channel analysis is an effective method to overcome this kind of countermeasures and recover the secret key through the analysis of a single trace. However, it is very difficult to exploit leakage from a single trace in practice, because the segment borders of crucial operation are hard to find and the single execution possibly contains a lot of noise in a real application environment. In this paper, a framework for systematic approach is presented to overcome these drawbacks. Firstly, we propose a method to use the Hilbert-Huang transform for the segment borders detection and the noise reduction. Then, an effective clustering method is used to recover the private key. The proposed horizontal clustering approach is applied to a protected implementation on embedded devices. The results show that the success rate of the key recovery could be up to 98%. Finally, we analyze the mechanism of leakage exploration.
Challenges of Information Infrastructures for Open Science and Academic Libraries

Purpose/Thesis: The paper reviews the main concepts of information infrastructure, information environment, and information behavior of researchers in the context of digital revolution. The concept of open science and new models of scholarly communication are considered.Approach/Methods: Related studies of information infrastructure and information behavior of researchers are analyzed. We report on the results of a series of qualitative studies of information behavior and of information ecology of the academic environment in Slovakia based on interviews with doctoral students, expert researchers and information managers. We apply a previously deve­loped original methodology of concept mapping.Results and conclusions: The results allow us to re-consider relevance assessment in the digital environment and to present a model of the information ecology in the information environment. We present the analyzed data represented by the concept maps which show the attitudes of rese­archers to open science, economic models of science and values of researchers. The final model of academic information ecologies is explained, and a new interactive model of the academic library is presented.Practical implications: We suggest the implications for the development of information science and for the education of information professionals.Originality/Value: Original models emphasize values of academic research, values of information, value-added services of academic libraries, and value-sensitive design of digital libraries.
GPS Devices to Monitor Cognitive Impairment People: Technical Implications of Personal Trackers for Use in Mexico

A behavioral manifestation of Alzheimer or cognitive impairment patients is their tendency to suffer spatial and temporal disorientation and getting themselves lost. Caregivers of these patients tackle this situation with the use of GPS devices, because it allows them to be peaceful and confident, knowing they can immediately track the patient in case they go missing. The present work describes, from a compilation of information of governmental health programs and websites of companies related to the monitoring of people and from personal conversations with geo-location service providers, the technical implications caregivers ought to know about the GPS technology for its use with Alzheimer and cognitive impairment patients.
The marketing policy in Russian libraries: The study findings within the context of the world experience

In the recent decades, libraries have been constantly pressurized to justify the value of their staff, services, and their very existence to their stakeholders and users who have changed their informational behavior. The key to the libraries’ success lies in the flexibility of their internal environments, as well as in the ability to predict, manage, and use changes in the external environment. To a great extent, it depends on whether the library has developed its marketing policy as well as on the understanding of its necessity and on implementation principles. In the early 2019, the specialists of SPSTL SB RAS conducted a large-scale survey “Modern trends in the development of library resources in the web space”, that comprises several types of libraries in 23 countries of the world. The author addresses the study in the part that considers organization of library marketing divisions in the world. She substantiates the need for them and identifies the common vectors in this area. The study findings demonstrate that despite theoretical understanding of marketing efforts significance and acceptance of the single concept (marketing policy) of audience capture, the few libraries are ready to allocate additional resources, in particular, labor, to practical implementation of such theoretical understanding. The need for the promotion coordinated concept makes significant difference in the responses of domestic and foreign respondents. For Russian libraries, the coordinated promotion concept being introduced by the administration, is more acceptable, while foreign libraries most of time permit their divisions to choose independently how to promote their services, resources, and events. Besides, among the libraries that follow a single coordinated concept, the percentage of those lacking a special promotion unit, is lower by 10–20%. From this perspective, the single concept has a positive impact on building administration awareness of the need for marketing efforts which, however, does not necessarily result in systematic marketing efforts of libraries.
Method for Constructing Data Model Of Enterprise Relationship Graph Based on Industrial and Commercial Data

The method relies on the commercial bank system architecture, reuses the external business data query service of the commercial banking system, integrates the data warehouse data processing capability, provides the association data to the map presentation tool, and constructs the enterprise relationship map data model. Thereby a relational graph model generates and provides the bank with the data needed under the customized query. The core technology of the method is to design the relational graph database model and explore the national publicity data. The construction of the relationship map helps to improve the bank's ability to acquire new customers, as well as to fully understand existing customers, create customer portraits, and help banks prevent group customers' group risks. In the field of procurement business, in order to avoid the bidding and stringing between suppliers, the method establishes the actual controller algorithm to understand whether there is a relationship between suppliers, what is the relationship, provide data support for procurement decisions.
Hybrid Approach to Enhance Data Security on Cloud

Cloud computing is a label for the delivery of hosted services on the Internet. The first challenge in the cloud computing is security, and the second challenge is the large size of the file stored on the cloud. Today, cloud is in young age and used too much for storing the data. So, security is a major concern. Security endures a primary concern for businesses regarding cloud selection, usually public cloud selection. Public cloud service providers share their hardware infrastructure among the numerous customers, as the public cloud has an environment of multi-tenant. Multi-tenancy is an important feature of the cloud computing but also is prone to several vulnerabilities. From the end-user aspect, cloud computing looks very insecure from the perspective of privacy. In this paper, a technique REM (RSA algorithm, Elgamal algorithm, MD5 technique) is proposed by which we can provide better security to our data over the cloud. Unlike the previous techniques, which maintained only one pillar of security, this paper maintains two pillars of security, i.e., confidentiality and integrity along with reducing the size of the file.
Compact Video Analysis Human Action Recognition Approach

Human action recognition is one of the most challenging computer vision topics and an important realistic yet open research task in many relevant applications such as security, content retrieval and video surveillance. In this paper, we proposed a robust approach for human action recognition in different video scenes. The architecture of the approach consists of shot boundary detection, shot frame rate resampling, extracting compact feature vectors by emphasizing variation and classification. Extracting strong patterns in feature vectors before classification affect the chosen approach high performance compared with latest state-of-art work using four widely used realistic data sets: Hollywood-2, UCF11(YouTube actions), UCF Sports and KTH.
The Effect Analysis of Crossover and Selection Methods on the Performance of GenClust++ Algorithm

In the K-Means algorithm, the determination of the center point coordinates (centroid) directly affects the quality of the clustering process. Determination of the coordinates of the center point (centroid) is generally done by generating random numbers and each instance will then be placed based on proximity to random numbers generated. Determining a good centroid will prevent the occurrence of local optima problems in K-Means. The GenClust++ algorithm is a pretty good algorithm in determining centroid. However, what needs to be considered is the influence of the selection and crossover process on the performance of clustering. This study will discuss the combination of selection and crossover processes that are good in the process of determining the centroid. Performance measurement method will be based on the measurement of Mean Square Error and distance calculation using Euclidean Distance. The results show that the Roulette Wheel Selection and Whole Arithmetic Crossover selection processes will give the best performance for the GenClust++ Algorithm.
Analyses of Multi-collection Corpora via Compound Topic Modeling

Popular probabilistic topic models have typically centered on one single text collection, which is deficient for comparative text analyses. We consider a setting where we have partitionable corpora. Each subcollection shares a single set of topics, but there exists relative variation in topic proportions among collections. We propose the compound latent Dirichlet allocation (cLDA) model that encourages generalizability, depends less on user-input parameters, and includes any prior knowledge corpus organization structure. For parameter estimation, we study Markov chain Monte Carlo (MCMC) and variational inference approaches extensively and suggest an efficient MCMC method. We evaluate cLDA using both synthetic and real-world corpora and cLDA shows superior performance over the state-of-the-art models.
Performance analysis of Satellite Image Super Resolution using Deep Learning Techniques

Super-resolution has gained significant importance recently owing to its finer sampling image details. Deep learning algorithms have remarked as an entity for developing single image high-quality reconstruction. Super-resolution with Deep Learning algorithms has demonstrated state of the art approaches for reconstructing sharper and more accurate images. Satellite images are highly prone to lose minute details of the image when subjected to algorithmic modeling. Thus, it is necessary to preserve the details of image. In this paper, an attempt is made to incorporate the state of the art approaches for reconstructing the satellite images. This requires careful conditioning of validating parameters like bias value, weights, appropriate usage of filters and scaling factors. The existing super-resolution algorithms such as Bicubic interpolation, Super resolution convolutional neural network (SRCNN), fast Super resolution convolutional neural network (FSRCNN) and Deep Laplacian Pyramid (LapSRN) are simulated to reconstruct the satellite images obtained from benchmark data sets of Indian and International satellite sensors. An extensive quantitative and qualitative evaluation of the super-resolution algorithms shows that the Deep Laplacian Pyramid networks perform favorably against the other state-of-the-art methods exclusively for satellite images.
Ensemble Feature Selection to Improve Classification Accuracy in Human Activity Recognition

Real-time data with redundant and irrelevant features can degrade the performance of the classifier. Dataset with more number of features also increases the noise of the data and increases the time complexity of the learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the dimensions of the data. In existing feature selection methods, the resultant feature sets can lead to local optima in the space of feature subsets. In this paper, ensemble-based feature selection approach is proposed to reduce size of the dataset and to improve classification accuracy. Results show that the proposed ensemble approach enhances the classifier performance, with reduced number of features.
Classification Methods to Improve Performance in Breast Cancer Screening

Breast cancer is a very aggressive type of cancer with a very low median survival. Today the deaths of women in the age group 15-55 are increasing because of malignant cells are increasing in breast. For the death of women it is the main cause. So, the possibility of improvement is only the early diagnosis of patients. Machine Learning (ML) techniques can assist the physicians by expanding tools for detection at initial stage and analysis of breast cancer thus increasing the probability of patient's survival [1]. At present, mammography is the best imaging strategy utilized by radiologist for screening breast tumours. In this paper, author proposes a system using different classification method like Support Vector Machine (SVM), Naive Bayes, Decision tree and MLP (Multi-Layer Perceptron) for early detection of cancer. Propose system extracts the texture based features and shape based features using LBP, GLCM, Otsu, Compactness, Fourier Transform. The main focus of the presented work is on application of MLP for breast cancer classification. In addition medical images data has been used to improve accuracy. Proposed system will do the comparative study between both datasets by extracting the feature with and without removing pectoral muscles.
Component Fusion Image Encryption Method Based on Composite Chaotic Model

In order to improve the security and encryption performance of component fusion image encryption, a method of component fusion image encryption is proposed based on composite chaotic model. A component fusion image is decomposed into three grayscale images, and a composite chaotic model is constructed by using Logistic chaotic map and Hyperhenon chaotic map, and the chaotic algorithm is combined with traditional cyclic encryption. The chaotic system with dynamic initial conditions is used to generate three pseudorandom sequences to encrypt the fused image. The encryption key generated by the logistic chaotic sequence is replaced by the encryption key in the Hyperhenon chaotic sequence to generate the image encryption sequence. The vector quantization coding of image encryption is carried out by piecewise linear chaotic mapping, and the optimal design of image encryption algorithm is realized. The simulation results show that the proposed method has good fusion performance and strong anti-attack ability to ensure the security and stability of image transmission.
The Teaching of Programming is not the Future but the Present

Until relatively recently, technology has been used as a passive tool that makes the learner a user of various applications. However, this has been changing thanks to the widespread idea that all children should have the opportunity to learn a little computer skills from an early age, since they will live in a world where computing is everywhere. Aware that the teaching of programming is not the future but the present, countries such as the United States, Singapore, Estonia and even Spain, face this strategic challenge, which not only prepares students for an increasingly technological labour market, it encourages the development of useful skills for any student regardless of their future professional activity. With this idea in mind, this article describes the case study carried out in a classroom of Early Childhood Education, with students of 5 years of age, with which basic programming concepts have been worked out. The results obtained indicate that they have been able to master the new vocabulary, assimilate the concepts and work for themselves with the chosen tool to carry out the intervention.
Managing Information Systems Requirements Volatility in Development Projects: Mapping Research and Surveying Practices

This paper focuses on information systems (hereafter IS) requirements volatility during development projects. First, it provides a comprehensive research-based map that integrates measurements, antecedents, consequences and solutions for IS requirements volatility. Second, it offers insights into industry practices related to the key IS requirements volatility components identified in that map. Findings from a survey of 44 professionals leading IS development projects show that IS requirements volatility continues to be an issue most likely caused by requirements misspecification problems. Findings also present a ranking of the relative importance of possible solutions used to minimize that volatility. Implications for research and practice are discussed.
Collective Control of Scientific School Activities as a Multivariable Dynamic Object

The results of the analysis of the collective control of scientific school activities as a multivariable dynamic object have been presented. The synergetic effect of the collective intellectual interaction in decision-making has been revealed. This effect can be used in the future as an additional intellectual resource in controlling scientific school activities.
Malaria or typhoid co-infection in a tertiary care hospital of Bareilly, Uttar Pradesh, India

Background: Malaria is a protozoal disease caused by infection with parasite of genus Plasmodium. Typhoid is common with malarial infection.Methods: A cross sectional study was done to find out co-infection of typhoid and malaria. Study was done in central pathological lab of department of pathology, RMCH, Bareilly. Blood samples were collected in EDTA vial and plain vial. Blood smear was examined for malaria parasite within RBCs. Malaria rapid test was done for detection of Plasmodium species and Widal test was done for typhoid.Results: In this study found co-infection of malaria with typhoid was 15.64%. In malarial cases 54.50% were males, while maximum cases (26.92%) were in 21-30 yrs age group. Cases of P. vivax was maximum (86.28%) and maximum cases of P. vivax (29.42 %) was in 11-20 yrs age group while that of P. falciparum (22.22%) was in 11-20, 21-30, 41-50 yrs age group and maximum number (23.60%) of mixed malarial infection was in 31-40 yrs age group, While co-infection of malaria with typhoid was maximum (24.59%) was in 11-20 yrs age group and maximum (53.28%) in females. Maximum (79.51%) cases of typhoid were of P. vivax. Conclusions: Malaria and typhoid co-infection still remain a major public health problem in many developing countries. Concurrent infection with two agents can result in an illness having overlapping symptoms creating a diagnostic dilemma for the treating physician.
Designing a Smart Mobile Application to Detect Fraud Theft of E-Banking Access Based on SOA In Indonesia

Fraud detection applications become an important part for companies, banks are one of the companies that have confidential data assets that need to be protected because they contain financial information. In the digital era, the use of mobile banking is a utility function for the need to perform daily financial activities such as bill payment and fund transfer. Addressing potential fraud in mobile banking services is a challenge for banks to make IT security systems remain confident, integrity, and available. Potential attacks on IT security will occur from the internal side of the organization as well as from external parties. In maintaining the level of security and convenience of customers in using e-banking services, banks try to make a tool to be able to prevent the occurrence of a large risk of loss in future. The most potential that will become an attack is social engineering and data theft by internal employees, in this research the focus is to discuss the two potential attacks on accounts that have long been dormant and deal with them with an application with the SOA approach.
A Study of Script Language Effects in Deep Neural-Network-Based Scene Text Detection

This study is different from most of the recent text detection work which focuses on creating a robust text detector system. In this work we studied how script languages affect a text detector's performance by using a multi-language synthetic dataset-namely, the Synthetic Octa-Language (SOL) dataset. The effect of script languages continues to be largely unexplored. Previously, this kind of experiment was infeasible because too many factors influence the performance of a text detector. We really cannot tell what role the factor X plays, neither positive nor negative. To overcome these difficulties, we used controlled synthesized data, which allows us to explicitly control factors such as base image, script language, text content, text color, font face, and font size. With the SOL dataset, we were able to investigate the effect that script languages have on on deep neural-network (DNN)-based methods under different scenarios. Moreover, this dataset can be used in other script-language-related text detection research as well.