Reviewerly/exHarmony huggingface dataset:
A Video Surveillance System for Unmanned Surveillance of Cantonment Boundary

Since last few years, the Incidents that breach internal security and attack on the security forces are increasing day by day. These are security issues are becoming challenging to handle manually due to economical restrictions. This paper proposes an application for video surveillance to handle and monitor the intrusive incidents. The proposed application includes then human detection in no men’s land around the boundary of the army cantonment. The human detection approach is proposed in this paper is developed with integration of the object detection using background subtraction, feature extraction using CNN and object classification into human and non human using SVM. The proposed approach achieves 95.6% accuracy in human detection. Application proposed in this paper is useful for unmanned surveillance of cantonment boundary.
Impedance Cytometry for Detection of Particle and Counting using Low Phase Noise DDFS – LUT

The biotechnology is widely growing with many technologies, still we see a large gap in real-time implementation of complete blood counting. To increase the resolution and accuracy of the measurements advanced communication DDFS can be used. The elements in Direct Digital Frequency Synthesizers (DDFS) involved are: phase accumulator, a phase to amplitude converter which also called look up table (LUT), a digital to analog converter along with active filter. Direct digital frequency synthesis is a method for generating complex high - frequency waveforms for specific applications. This DDFS generates frequency resolution which makes it ideal components use in radar system, software defined radio, modern wireless communicating system, advanced satellite navigation purpose. Use cases for high frequency we get interrupt with spurious noise, larger ROM size, and high power consumption of DDFS signal. In this paper we are proposing the use of signal generated from DDFS to impedance cytometry in which the number of particles gets detected by getting the output frequency different from the input frequency. Due to use of small frequency range of signal spurious noise, power consumption and ROM size will be less with effective performance.
The Unseen Web, an Enormous Mass of Internet

Deep web is the part of the internet; it holds 99% of the internet rest of the internet is known assurface web and it is access able by standard search engines, Deep web is famous by different namesand can be called as invisible and hidden web but it can’t be accessed by the standard exploratoryengines this is due to the un indexed contents by common exploratory engines. Deep web content isinvisible under HTML form. Deep web provides anonymous internet platform for hackers, governmentagencies and others. Due to this no one can trace the location of the user and don’t have anykind of information about the web surfing of the user. Dark web is the part of Deep web, the reasonbehind it commonly used for the black market i.e. Drug sale, ammunition sale, and many otherillegal activities. It is the consist of millions of websites some of which are very informative. Whileon the other hand some of the portion is legal to visit and rest is illegal. These websites can beaccessed by using the popular tools, i.e. TOR, I2P which provide anonymity. About 2 million peopleuse the TOR browser to browse deep and dark web.
MRI images series segmentation using the geodesic deformable model

Image segmentation is regarded as the most crucial medical imaging process because it extracts the region of interest and thus helps upgrade medical diagnosis. The Geodesic deformable model is a curve that deforms within digital images to extract object shapes. The Geodesic deformable model has been used very successfully in the process of single image segmentation but it fails to segment a series of images. In this research, the Geodesic deformable model is developed to overcome its limitation by controlling the speed of the curve deformation. The developed model is implemented on several series of MRI images that include tumors of varying shape complexity. Experimental results show that the developed model performed very well and successfully segmented the series of MRI images which outperform the baseline model.
Data retrieval based on the smart contract within the blockchain

Blockchain technology appears to be the ideal solution for storing data in a transparent and decentralized manner.It also allows open access to data and enhances its immutable nature.This technology has helped prove its usefulness in several industries so far, however, distributed ledger technology does not work as a pure database.Therefore, some problems occur in accessing data.Querying data in the blockchain leads to performance and bandwidth problems.This primarily occurs because the blockchain does not have a primary query language, unlike regular databases.The distributed nature of the blockchain is in this case an obstacle.In this paper, a safe and fast method will be proposed to retrieve consistent data from the blockchain-based on the smart contract that will be opened after completing the transaction procedures.All nodes will sign the proposed transaction (by adding a special hash to each node resulting from the transaction information and node data).Upon completion of Transaction procedures, A smart contract will be opened (in which a QR is placed) resulting from converting the signatures in the transaction to QR When the smart contract data is retrieved, the QR for each transaction will be used All node signatures and transaction data will be extracted .The data will be retrieved by the QR generated for each transaction after it is stored in all nodes servers participating in the system.A new method was proposed to generate a hash for each node present in the system.The proposed method was tested in terms of time and complexity, and the algorithm was statistically analyzed, and all the results proved successful.
Extended Local Binary Pattern Features based Face Recognition using Multilevel SVM Classifier

The Face recognition method is one of the authoritative biometric system in recognition methods to recognize the individual, because face is a distinctive biometric trait of an human being and it is the superior method of recognition. This paper proposes a novel Face recognition method by using extended LBP features. The pre-processing is carried out to extract the face area using viola-johns algorithm and all images are resized to 100x100. The LBP operator is applied on resized face images by rotating the each image by 15 degrees, i.e., at 7 degree left and 7 degree right and at zero degree to extract the feature vectors and final features are obtained by applying histogram technique. The SVM classifier is used for matching the database images with test images to measure the performance such as TSR, FAR, FRR &amp; EER. The performance parameters are compared with existing algorithms for YALE and FERET database.
Reconfigurable FPGA Architecture for Cryptographic Hashing Algorithms

Nowadays, security is the most significant thing in the communication field. Most of the data transmitted over the communication channel are highly confidential so it needs more security. But this confidential data are easily stolen by hackers and it affects the users’ privacy. Nowadays, so many encryption algorithms have been established to protect the original information of the users. But, Hash Function (HF) security is the most important primitive which used for data authentication and data integrity. The reconfigurable cryptography integrated with chip which used for cryptography. Hashing algorithm is used to generate the random number which also used as a key value for cryptography. In this paper, different kind of Secured Hash Algorithms (SHA) such as SHA-0, SHA-1, SHA-2, and SHA-3 in that the SHA-2 has different family (SHA-2F) such as SHA-224, SHA-256, SHA-384, and SHA-512 is studied.
Cross-Project Change Prediction Using Meta-Heuristic Techniques

Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction.
A new method of event relation identification

Aiming at the problem that the traditional event relation identification cannot be considered semantic relation of event structural characteristics; this paper proposes a method of semantic relation based on dependency and co-occurrences. Dividing the text into event representation, using the distribution characteristics of the event elements, the phenomenon of the co-occurrences elements and the dependence relation between the text events excavate clues of semantic relevant events. Then, clustering the event set with the related thread by the improved AP algorithm. Experiments show that the semantic role of the event (six elements) can be more accurate to calculate the degree of dependence and the co-occurrences overlap ratio of event elements between the candidate relation events which is helpful to the more abundant candidate related event set, so as to improve the recognition ability of the matter.
Values of Success and Adaptive Capabilities of the Modern Individual at the Beginning of the Youth Period (Based on the Example of Students in Educational Organizations of the Ministry of Internal Affairs)

The article provides theoretical and empirical evidence of the relationship between adaptive capacity and the value of individual success. The relevance of the scientific issue lies in the search for new approaches to understanding and exploring the adaptability and success of individuals in today’s world. The novelty of the study is to validate the concept of the life success of the individual as a&nbsp;metaresource in the system of self-regulation, which realizes the function of reflective comparisons of social and individual values in order to construct its own concept of successful life and acts as a&nbsp;system of interrelated mechanisms, properties and states. The principles of regulatory, subjective, and resource-­based approaches are the methodological bases for understanding life success. Particular attention is paid to the value of success in life and its relationship to the adaptive capacity of the individual. Value bases provide basic coordinates for the formation and realization of adaptive capabilities of the individual. The aim of the research is to study the intensity of the values and adaptive capacities of the individual and to identify their interrelationship in a&nbsp;sample of respondents at the beginning of their youth period (using the example of participants in educational organizations of the Ministry of Internal Affairs). The empirical study revealed a&nbsp;problem: in a&nbsp;sample with high success rates, adaptive capacity and a&nbsp;well-established hierarchy of values for life success, only some values have significant links with adaptive capacity and can be both a&nbsp;resource factor and guide adaptation in general, as well as a&nbsp;brake on adaptation. The results have increased knowledge of perceptions of successful youth, of priority values and of the participation of these values in the processes of personal adaptation.
On Beyond-Birthday-Bound Security: Revisiting the Development of ISO/IEC 9797-1 MACs

ISO/IEC 9797-1 is an international standard for block-cipher-based Message Authentication Code (MAC). The current version ISO/IEC 9797-1:2011 specifies six single-pass CBC-like MAC structures that are capped at the birthday bound security. For a higher security that is beyond-birthday bound, it recommends to use the concatenation combiner of two single-pass MACs. In this paper, we reveal the invalidity of the suggestion, by presenting a birthday bound forgery attack on the concatenation combiner, which is essentially based on Joux’s multi-collision. Notably, our new forgery attack for the concatenation of two MAC Algorithm 1 with padding scheme 2 only requires 3 queries. Moreover, we look for patches by revisiting the development of ISO/IEC 9797-1 with respect to the beyond-birthday bound security. More specifically, we evaluate the XOR combiner of single-pass CBC-like MACs, which was used in previous version of ISO/IEC 9797-1.
Experimental quantum conference key agreement

We report a proof-of-principle demonstration of four-party quantum confer ence agreement using photonic GHZ (Greenberger-Horne-Zeilinger) states transmitted in fiber. Our results demonstrate the viability of multi-user entangled-based quantum key distribution beyond the two-party paradigm.
Errors in Statistical Programming

titled "Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer," published online February 2, 2017, and also in the September 2017 issue of JAMA Oncology, 1 an error occurred in the statistical programming of the analysis.All analyses were recalculated, which resulted in changes to the population-attributable risk proportion estimates for some of the risk factors and combinations of risk factors, 2 most of which did not alter the conclusions.This article was corrected online.
PRÁTICAS AVANÇADAS EM SAÚDE SOB A LUZ DAS TEORIAS E SISTEMATIZAÇÃO DA ENFERMAGEM

This paper aims to reflect on the use of systematization of nursing care and nursing theories as a subsidy to advanced health practices.This is a reflective theoretical study with a qualitative approach, carried out in January 2021 with the support of scientific literature and experiences of clinical practice.In the analysis of the data, it was observed that the advanced health practice, together with the systematization of nursing care and nursing theories provide the nurse with a differentiated clinical management.Such an approach promotes a clinical evaluation, as well as a critical and thorough evaluation, avoiding fragmented assistance based on the biomedical model.The integrative approach, with greater properties as to the choice of conduct for the care to be offered, favors autonomy and professional valorization.
Cybercrime groups working together

There has been a shift in how cybercrime gangs operate, with increased collaboration and more targeted campaigns, according to new research from Accenture.
The EM algorithm for multi-dimensional Gaussian mixture model

One of the research hotspots of the expectationmaximization (EM) algorithm is the parameter estimation of mixture models.Taking the Gaussian mixture model (GMM) in a multi-dimensional case as an example, we use the EM algorithm to estimate its parameters, and get the corresponding iterative formulas.Then, use R software to carry out numerical simulation experiments on the EM algorithm, and explore its convergence and sensitivity.
PATH LEARNING ALGORITHM FOR FULLY INDIGENOUS MULTIMEDIA AND GUIDE ROBOT

A fully indigenous guide and multimedia robot, is designed to welcome guests and visitors and guide them for campus-tour with appropriate multimedia information.Path leaning algorithm is developed for the robot to make it follow a defined route in the University campus.It can interact with visitors and play multimedia on screen.ZIGBEE nodes are placed at few predefined locations and when robot reaches in its range, it starts playing the information regarding that location.To reach the predefined location robot uses obstacle avoidance using bubble rebound algorithm.It also uses training data set I for the calculation of distance and time with respect to routeand training data set II to get the heading in degree from magnetic compass as machine learning.A robotic Arm is connected to welcome guest by positioning 'NAMASKAR'.Obstacle avoiding algorithm is applied to avoid collision in the way.Design of robot and verification of results has been performed and result shows no error in the path learning.Paper covers the complete technical specifications and steps to design the robot.
A system of privacy preserving public auditing for secure cloud storage system

The cloud computer is the technology which enables services to be shared on the internet. It allows users to run the application on any device with Internet or Access to Internal network without any programme being installed and users can access their personal application files. Many people use databases in the cloud for recent storage. It allows users to save data, and users can access it whenever they want it. Cloud computing is the next generation technology to transform the software’s operating environment in information and applications. Furthermore, the user should only be able to use cloud storage as if it were local without worrying that this paper needs to be thoroughly reviewed, use and combine the public key homomorphic authenticator with the random masking to securely protect the private cloud data control mechanisms that fulfil the above requirements. Data processes and real public auditability do not work in this regard. The auditing task tracks, inserts and deletions of data. The model is designed to help public auditing procedures, data dynamics, and multiple TPAs. We also expand our definition to include ring signatures that use HARS. Merkle Hash Tree is used for the authentication of block levels. We also increase the result to allow the TPA to simultaneously audit many users by Collection.
A Systematic Learning on Variety of Recommender Systems for Online Commodities

In a sophisticated high-end product market, all firms often come up with a vast number of goods to partake the market shares. Owing to the availability of enough information of various products that enters the market or due to lack of right information, customers are prone to the state of dilemma in comparing and choosing the most appropriate ones. In most of the cases, the product specifications are mentioned, still whether these features suit the customers need is a concern. Online reviews tend to benefit the consumers and the goods developers. Here again, finding out the more supportive reviews become a challenge. Considering these factors, this article intends to be particular in reviewing the existing evaluation strategies and recommender systems that have grown progressively favorable in present era and are employed widely for casual to commercial items.
Research on intelligent obstacle avoidance control method for mobile robot in multi-barrier environment

In a current multi-obstacle environment, mobile robots are needed to control obstacle avoidance ability intelligently. Therefore, a method of mobile robot obstacle avoidance control in multi-obstacle environment is proposed based on the minimum risk index. This method first segments the path of obstacle environment according to certain rules. Then, robots are constrained through the constrained time according to requirements of robot obstacle avoidance. Therefore, calculating motion obtains necessary and sufficient conditions with no collision avoidance relationship. Then, impact danger level of each movement stage for the robot is evaluated by considering the spatial distance, inertia, and relative movement speed of multiple obstacle environments. The global safety path is planned, the intelligent avoidance function is calculated, and intelligent avoidance control is implemented. Finally, simulation results show that our proposed method is a common method in intelligent obstacle avoidance, timely tracking of moving target, and makes full use of space.
Assessing the Impact of Authentic Leadership and Team Climate on Innovative Work Behavior with in Self-Managed Teams in IT Organizations

Purpose: The main aim of this study is to assess the role played by Authentic Leaders in the follower’s perspective, The study constructs of Team Climate on Innovative Work Behaviour within Self manged teams in IT organisations. Design/Methodology/Approach: The sample data has been obtained from five major IT organizations in Chennai and Bangalore region. Selected organizations mainly comprise of the Software Developer’s Team under the Self-Managed Team base. Findings: Authentic leadership develops the feeling of trust among the team members and facilitates a conducive climate. Only in such scenarios it would lead to affirmative discussion and optimistic criticism which would enable to overcome lacunas if any. Conducive climate develops the trust and confidence among the team members where by employees gain the confidence of trying innovative ideologies. Implications: To create such an environment organization should create an ideal structure with maximum work autonomy along with accountability and responsibility. In other words, hygiene factors coined by Herzberg should be existing only then we can motivate the employees. Scope: The study involves only one particular form a leadership style whereas the study can be examined under various forms of leadership styles, Ekvall and Arvonen in the year 1994 identified a blend of leadership style which exhibited the leadership style possessing traces of three different leadership styles.
Students' Research Competence Formation of the Quality of Open and Distance Learning

This chapter is about theoretical aspects, practical instruments and methods for providing the quality of educational services in the system of open and distance learning according to ENQA approaches. The quality management principles, based on the concept of Total Quality Management (TQM) and standards ISO, and approaches to quality policy in universities in the implementation of open and distance learning systems are offered in it. The main problems in the implementation of quality management systems are analyzed. Approaches to the of the result effectiveness evaluation in terms of the educational process quality management are discussed. Models quality assessments and the basic directions in the formation of the knowledge system quality criteria, adopted in accordance with the requirements of the digital economy, are considered.
Novel Utility Procedure for Filtering High Associated Utility Items from Transactional Databases

In data mining, mining and analysis of data from different transactional data sources is an aggressive concept to explore optimal relations between different item sets. In recent years number of algorithms/methods was proposed to mine associated rule based item sets from transactional databases. Mining optimized high utility (like profit) association rule based item sets from transactional databases is still a challenging task in item set extraction in terms of execution time. We propose High Utility based Association Pattern Growth (HUAPG) approach to explore high association utility item sets from transactional data sets based on user item sets. User related item sets to mine associated items using utility data structure (UP-tree) with respect to identification of item sets in proposed approach. Proposed approach performance with compared to hybrid and existing methods worked on synthetic related data sets. Experimental results of proposed approach not only filter candidate item sets and also reduce the run time when database contain high amount of data transactions.