Reviewerly/exHarmony huggingface dataset:
An Augmented Reality Multimedia (ARM): A Dynamic Multimedia Encyclopedia on Mobile Device

In the recent days, there are rapid advancements in the web 2.0 contents (texts, images and videos) and in the computational complexity. The next generation in computing is predictably in the mobile arena. A lot of applications are being developed to cater the needs of mobile device users. This research is based on the question: How can we benefit from the rapidly proliferating web 2.0 contents in the mobile device with staggering technological developments? This paper presents a surprisingly impressive novel dynamic multimedia encyclopedia, multimedia, that augments reality on the mobile device. This system supersedes the traditional texts or maps retrieved. A group of estimation and sensors to track objects and keep information on them used. An augmented reality based multimedia encyclopedia on mobile device which is dynamic is presented. It is dynamic and mines resources on the web. We demonstrate how this device can be utilized to bring relevant information to users in a much more enriched manner than traditional texts. ARM is dynamic and is distinctive in 3 ways. It fetches multimedia contents for users through automated intuition and dynamically updates content from the web. It uses recent multimedia content to produce answers which are versatile. New mediums like videos &amp; languages can be incorporated into the framework. This system takes advantage of the mobile camera, location and orientation sensors to augment reality. Live camera view and retrieve dynamic data. A Combination of algorithms is used to analyze and demonstrate our ARM.
Mobility Pattern Probing of Mobile Users using Call Data Record Dataset

Colossal measures of information are currently being gathered because of the expanded use of portable media communications. The aims of the mobile phone clients can't be watched, their expectations are reflected in the call information which characterize use designs. Over some undefined time, frame, an individual telephone produces an enormous example of utilization. In this paper, we examine the solo learning possibilities of two neural systems for the profiling of brings made by clients over a time allotment in a versatile media transmission arrange. Our inquest gives a similar examination to client call information records so as to direct a clear information mining on clients call designs. Our examination demonstrates the preparation capacity of the two systems to segregate client call designs. The arranged highlights can later be deciphered and marked dependent on explicit necessities of the versatile specialist organization. Along these lines, suspicious call practices are separated inside the portable media transmission organize. We give results utilizing covered call information from a genuine portable media transmission arranges.
Modelling Team Cohesion during Military Conscription: a Multidimensional Model for Task Cohesion

The quality of freshly harvested herbs is heavily influenced by multiple factors, namely weather conditions, harvesting, transport, drying, storage, and many more.Our main goal here is to identify models that are able to predict spore contaminations on different types of herbs on the basis of these factors as well as to find optimal processing parameters, which shall lead to lower contaminations of herbs as well as lower costs for contamination prevention represents.The here presented workflow utilizes two different approaches, which in combination shall lead to a reliable contamination prediction and prevention mechanism.For the prediction part we learn ensembles of machine learning models using the processing parameters as features to predict the risk for spore contamination a priori of labor analysis data.Using tree-based modelling algorithms we already achieved a spore contamination prediction accuracy of 86.21% for the herb nettle.In Addition to that, we use descriptive statistics to provide information on the relevant parameters which could be responsible for the occurred contamination.Here we already achieve a p-value smaller than 0.01 for a few processing parameters.In the future we want to expand this workflow by improving the modelling process using different modelling algorithms.Additionally, we are working on an online life system, which combine these two methods, to not only present a farmer the information whether a contamination is probably, but also provide him the information which processing parameters lead to a contamination and how they should be affected to lower the risk.
The Characteristics of Big Data in Successful of Software Development Life Cycle for Mobile Application

This paper aims to software development life cycle for big data, software development and mobile application for the new environment of the real world. The big data is a new design in the world for the Internet devices to connected life in new environment. The paper reviews 6Vs such as (Volume, Variety, Velocity, Variability, Veracity and Value) for big data an environment application used in the mobile application for students and instructors in university. In fact, for the digital content is to efficiency and effectively for accessed the contents with anytime and everywhere to capture the speeding and capacity data. Current research on software development on application for students and instructors is limited to readiness and awareness. This research extends the need for empirical findings from system analyst and top management of digital contents for big data approach.
Automated Detection of White Blood Cells Cancer Diseases

Mechanized analysis of white platelets malignant growth infections, for example, Leukemia and Myeloma is a difficult biomed-ical inquire about point. Our methodology introduces out of the blue another best in class application that helps with diagnosing the white platelets infections. we break these sicknesses into two classifications, every classification contains like side effects infections that may confound in diagnosing. In light of the specialist's determination, one of two methodologies is actualized. Each methodology is connected on one of the two maladies classification by processing distinctive highlights. At last, Random Forest classifier is connected for ultimate choice. The proposed methodology means to early disclosure of white platelets malignancy, decrease the misdiagnosis cases notwithstanding improve the framework learning approach. In addition, permitting the specialists just to have the last tuning on the outcome acquired from the framework. The proposed methodology accomplished an exactness of 93% in the principal classification and 95% in the second class.
The Analysis of Student's Science Metacognition Ability in Information System and Computer Technology Courses

This study aims to analyze and describe students' scientific metacognition abilities in the Computer Information Technology Course.Through a quantitative approach, data collection using a survey model and involving 156 students at the State Islamic University of Sulthan Thaha Saifuddin Jambi and the Islamic Institute of Nusantara Muara Bulian.Based on the data and discussion, it can be seen that in general, students' science metacognition ability is in the "medium" category as much as 48.62%, the "high" category is 40.38%, and the "low" category is 11%.More in-depth data analysis shows that the ability to identify problems (128,20), plan learning activities (152,88), and make conclusions (156,26) is in the "high" category.The ability to consider the implications of a concept (112,53), construct the relationship between previous knowledge and the knowledge to be studied (113,14), monitor every step to be taken (96,27), and identify sources of error (88,87 ) is in the "medium" category.Meanwhile, the ability to identify concepts with an average score (78.52) is in the "low" category.Therefore, these abilities and skills must remain the attention and focus of educators (teachers and lecturers) in using appropriate learning models to improve scientific metacognition skills, including learning courses in information technology and computer systems and others.
Integrating Extended Gaia Semantic Information Retrieval Model with Android Based System using Native API

In present scenario software industry becomes more advanced. We all know that for developing software system there are many latest technologies available like Agile Software development , Software Agent, Semantic Web ,IOT , Cloud Computing etc. In this paper author tries to provide implementation of Extended GAIA Semantic information Retrieval System. E.G.S.I.R. is basically combination of software agent and semantic web features.
Valuation of rare earth elements in ore deposits in the Murmansk Region

The Kola rare-earth metallogenic province is the biggest in Russia.The largest resources of rare earth elements (REE) are confined to the Khibiny apatite deposits and loparite and eudialyte deposits of the Lovozero massif.In order to compare the prospects of different Kola deposits and occurrences, we estimated the value of rare-earth oxides contained in ores pursuant to average market prices of certain rare-earth oxides.Heavy REE (Dy, Yb, Tm, Tb, Lu) and Nd make the largest contribution to the value of REE-bearing ores despite the fact that light REE (La and Ce) make up the majority of the REE resources of almost all deposits in the region.Given mineral processing technologies and infrastructure available, we have identified the most important objectives to advance the REE industry in the region: (1) REE recovery from the currently produced minerals, namely, apatite of the Khibiny deposits and baddeleyite of the Kovdor deposit-high-level processing of the Kovdor baddeleyite concentrate can make Russia a dominant player on scandium market; (2) launching mining operations in the multicomponent (Ti-Ta-Nb)-REE-Zr Lovozero eudialyte deposit (in the first place, the Alluaiv site); (3) revaluation of the Afrikanda deposit in the light of new technologies for processing perovskite-titanomagnetite ores, as well as due to the fact that Russia currently lacks domestic production of titanium raw materials; (4) prospecting and exploration of the Keivy occurrences in alkaline granites and pegmatites and the development of processing technologies for these complex ores.
Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.
New Trends in the Development of Media Literacy Education in Ukraine

The author of the article singles out the main stages of media education development in Ukraine, which directly depend on the political situation (change of the political course of Ukraine from pro-Russian to pro-Western), which stimulates changes in the social, economic, educational, spiritual spheres of life.The article analyzes the leading Ukrainian media education centers.The article identifies a number of trends in media education development in Ukraine at the present stage, when media education has become more popular in Ukraine than before 2014, there is a practical introduction of mass forms of media education for different categories of the population, however, at the same time, it is increasingly becoming a tool for propaganda and ideological manipulation based on the ideological and protectionist theory.Among current trends in media education in Ukraine, the following are highlighted:-The positioning of Russia as a kind of its stimulator of sharp interest and development of mass (for different categories of population) media education in Ukraine;-reliance on ideological (absorbing nationalist ideas) and protectionist theory of media education;-terminological and substantive inconsistency in the position of modern Ukrainian researchers in the field of media education.-use of media education by some scientists not related to this branch of pedagogy as a means to achieve their own interests (popularization of themselves in the scientific community, commercial goals, etc.).
An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.
Bayesian machine learning of frequency-bin CNOT

We analyze the first experimental two-photon frequency-bin gate: a coincidence-basis CNOT. A novel characterization approach based on Bayesian machine learning is developed to estimate the gate performance with measurements in the logical basis alone.
Identification of Epigenetic Modulators that Inhibit Melanin Synthesis in B16/F10 Melanoma Cells

Nowadays, many anti-phishing systems are being developed to identify phishing contents in online communication systems. Despite the availability of myriads anti-phishing systems, phishing continues unabated due to inadequate detection of a zero-day attack, superfluous computational overhead and high false rates. Although Machine Learning approaches have achieved promising accuracy rate, the choice and the performance of the feature vector limit their effective detection. In this work, an enhanced machine learning-based predictive model is proposed to improve the efficiency of anti-phishing schemes. The predictive model consists of Feature Selection Module which is used for the construction of an effective feature vector. These features are extracted from the URL, webpage properties and webpage behaviour using the incremental component-based system to present the resultant feature vector to the predictive model. The proposed system uses Support Vector Machine and Naïve Bayes which have been trained on a 15-dimensional feature set. The experiments were based on datasets consisting of 2541 phishing instances and 2500 benign instances. Using 10-fold cross-validation, the experimental results indicate a remarkable performance with 0.04% False Positive and 99.96% accuracy for both SVM and NB predictive models.
VMS Mean and FFD VM Allocation Algorithm for Cloud Datacenter

The datacenter power consumption is increasing speedily, because of the scalable and dynamic provisioning of resources to the remote consumers. Not efficiently utilizing the resource of host machine is the main reason for increase in power consumption. By efficiently placing the number of virtual machine in fewer active host and shutdown the inactive host could reduce the consumption of power in datacenter. In this paper VMs Mean and FFD Bin Packing Virtual Machine Placement Algorithm is proposed.FFD preprocess VM’s and Host’s by sorting all the VM’s by MIPS and Host’s by their power. To place the VM’s in host machine VMs Mean method is used which finds the lowest CPU utilizing VM. The main objective is to increase the host machine utilization by efficiently placing the VM’s into few active Host’s and also reduced the power consumption. To test this algorithm CloudSim Toolkit is been used. The parameter such as EC, SLAV, PDM, ESV, VM migration and SLATAH is used as a metric to evaluate the Algorithm efficiency.
Recommender System for Geo-Social Access Control Framework

A malicious attack or threat can happen within any organization, from their own employees, administrators, contractors or former employees, who pose the important resources of a company such as database, physical laboratories and financial resources. In an organization insider attacks are most common as well as most costly affair. According to United States cyber security 2018 statistics, insider threat holds the risk of 74% out of surveyed organizations. The insider threat has caused immense loss to data as well as monetary assets. Among the surveyed organization by US cyber securities, 53% of organization claimed their remediation cost was around $ 100000 and in 2018 the number raised to 66%. Higher number of organization claimed insider attackers were most costly attacks in comparison with external attacks. Some of the probable reasons, why it is difficult to stop an insider attack are, firstly insider threat may be unintentional and all of sudden. Second is distinguishing regular work by employee and malicious work is difficult. Third is most of the insider attackers are technologically sound to mask their intentional activities or easily erase the intentional activity signs from the system before anyone observes it. Lastly and the worst case is employees simply say their intentional act was by mistake and escape from scenario. To avoid such malicious insider attacks lots of research is done on access control. Access control is a method or technique to control the access of an insider to the organizations valuable resources. There are different types of access control models, having their own access control policies and criteria to grant the authority, to have an access to specific resources of an organization. In this paper we discuss the different types of technical access control models that have been developed with certain parameters and their advantages and limitations.
Usability Tests of Thai Mobile Banking UI Design

Nowadays, many banking customers process their transactions via mobile banking applications because of the convenience.This study examines five well-known Thai mobile banking applications to measure their usability and compares their UI design from the perspective of nine main functions.An analytical tool for prototypes and the System Usability Scale (SUS) were used to collect and analyse data.The usability test of the five mobile banking applications in this paper involves three usability metrics: effectiveness showing the completion rate, efficiency showing the time used to successfully complete a task, and satisfaction using SUS to evaluate users' satisfaction with the product.This paper presents problems found in poor UI design and suggests how to improve mobile banking UI design.
Optimal Biclustering using Hybrid Swarm Intelligence for Web usage Mining

Web usage mining is used to analyze the user browsing behavior among the web pages which can be further utilized in other applications like recommender system, personalized web pages, providing insight for better business functionality. Since this type of mining does not only depends on the user or web pages, conventional clustering techniques may not suit very well for the analysis. Biclustering techniques are used to discover the subset in the form of submatrices as objects and attributes of objects are considered symmetrically. Finding optimal biclusters is a critical research issue. This research proposes a hybrid swarm intelligence-based method having Particle Swarm Optimization combined with Leader Clustering method along with Uniform Crossover operator. The experimental study shows that the proposed method performs well than traditional biclustering techniques in terms of evaluation metrics.
Fpga Implementation of Precise Convolutional Neural Network for Extreme Learning Machine

Feed-forward neural networks can be trained based on a gradient-descent based backpropagation algorithm. But, these algorithms require more computation time. Extreme Learning Machines (ELM’s) are time-efficient, and they are less complicated than the conventional gradient-based algorithm. In previous years, an SRAM based convolutional neural network using a receptive – field Approach was proposed. This neural network was used as an encoder for the ELM algorithm and was implemented on FPGA. But, this neural network used an inaccurate 3-stage pipelined parallel adder. Hence, this neural network generates imprecise stimuli to the hidden layer neurons. This paper presents an implementation of precise convolutional neural network for encoding in the ELM algorithm based on the receptive - field approach at the hardware level. In the third stage of the pipelined parallel adder, instead of approximating the output by using one 2-input 15-bit adder, one 4-input 14-bit adder is used. Also, an additional weighted pixel array block is used. This weighted pixel array improves the accuracy of generating 128 weighted pixels. This neural network was simulated using ModelSim-Altera 10.1d and synthesized using Quartus II 13.0 sp1. This neural network is implemented on Cyclone V FPGA and used for pattern recognition applications. Although this design consumes slightly more hardware resources, this design is more accurate compared to previously existing encoders.
Cover Processing-based Steganographic Model with Improved Security

Steganography uses specialised techniques to conceal messages in different cover objects such as image or video so that only the sender and receiver know of the message's existence and are able to decipher it. Previous research conducted in the area has mainly focused on steganography and steganalysis techniques. This paper proposes a new model for steganography called Cover Processing-based Steganographic Model (CPSM) that processes the cover objects and transmits them in a way to improve the security of steganographic objects. A comprehensive demonstration based on information theoryproves that CPSM provides improved security in terms of lower relative entropy as compared to previous models from the literature.Moreover, experimental tests show a decrease of the relative error between the cover and steganographic objects of up to 14%.
Features of Students Training: Qualifications and Technology

The article is based on the study of technologies and recommendations developed by the authors as a result of the experience accumulated in teaching a set of disciplines aimed at gaining knowledge and skills in the field of investigating certain types of crimes, performed at the Department of Investigation of the Lukyanov Orel Law Institute of the Ministry of the Interior of Russia from 2010 to 2019. The article examines the issues of preparation and selection of the most optimal training model, and the applied part provides the algorithm of a lesson, as well as options for assessing the work done by teams.