Miracle-dz/UniFAR-FTUs huggingface dataset:
in Computer

:The Neighbor Discovery is a process of identifying the nearest node. So the identification can done through Neighbor Discovery Protocol (NDP) is a protocol in the Internet Protocol Suite used with IPv6. It is responsible for address autoconfiguration of nodes, discovery of other nodes on the link, and maintaining reachability information about the paths to other active neighbor nodes. Node connectivity is subject to changes because of mobility in wireless communication, transmission power changes, or loss of synchronization between neighboring nodes. Hence, even after a sensor is aware of its immediate neighbors, it must continuously maintain its view, a process we call continuous neighbor discovery Each sensor employs a simple protocol in a coordinate effort to reduce power consumption without increasing the time required to detect hidden sensors.
The Relationship between Perceived Security, Perceived Privacy and Social Commerce Intention among University Students in Saudi Arabia: The Mediating Role of Trust, Attitude and Satisfaction.

: Social commerce intention has increasingly become more attractive and enhanced by the users of social networking sites that often received attitudinal beliefs from contemporary users. It is in view of this background that, this study is designed to examine the influence of attitudinal beliefs (perceived privacy and perceived security) and the mediating relationship between trust, attitude and satisfaction on social commerce intention among university students in Saudi Arabia. As a result, a framework is developed to guide the study and demonstrate the possible relationship among the examined constructs. Supportive literature to corroborate the conceptualized model in the context of Saudi Arabia is inclusive. Therefore, the study emphasizes that the utilization of complimentary social network sites by university students, exerts a robust consequence on their social commerce intention. Furthermore, the study establishes that this relationship is mediated by trust, attitude and satisfaction.
Cognitive Learning for Social Robot through Facial Expression from Video Input

: This paper presents research on Cognitive Learning through Facial Expression for making Artificial Intelligent Social Robot. In this paper we have made a system for Cognitive Learning through facial expression from continuous video input. The goal of this research paper is to train a machine with the help of facial expression of any person. In this we have we have used the new technology namely, MATLAB. Use of MATLAB have made the implementation task easier and also increased the performance like speed and accuracy. The past work is very complex and has slow working. Our model works on our own proposed algorithm which has made it easier to implement. In this paper we have reviewed the previous work, discussed current concepts, tools and purpose of further work with initial finding on new Robotic environment and concepts through simulation in MATLAB. The Model was created on the basis of Cognitive Learning through Facial Expression as a part of this research paper to support customizable sets of concepts and theory in order to easily interface with diverse sets of educational material. Developing new methods to improve recognition performance is a major concern of this research work. The results are discussed in terms of previous research and theory. Implications of the study for practice and suggestions for future research are also discussed.
Immunotherapy and Immunochemotherapy for Leishmaniasis: An Overview

Instead of relying on drugs to reduce the parasitic burden of leishmaniasis and waiting for the effecter immune response to develop in time to control the parasities, immunotherapy in conjugation with chemotherapy can rapidly induce the effector immune response. With a safe and potent drug plus an affordable therapeutic vaccine, which remains to be developed, a single visit by patient with leishmaniasis might be sufficient to induce a quick and lasting recovery. Human vaccination against leishmaniasis using live leishmania was used in middle east and Russia (1941-1980). First generation vaccines composed by killing parasites induce low efficacies (54%). Second generation vaccines using live genetically modified parasites or bacteria or viruses containing leishmania genes, the use of adjuvants increased vaccine efficacies of the purified antigens to 82%. Recombinant second generation vaccines and third generation DNA vaccines showed average values of parasitic load reduction of 68% and 59% in laboratory animal models respectively, but their success in field trials had not yet been reported. To date only three vaccines have been licenced for use one in Uzbekistan and two in Brazil. Drug toxicity and the emergence of resistance could also be dramatically reduced compared with present long term monotherapy. Immunotherapy could be an effective addition to chemotherapy for leishmaniasis.
Coordination of Solar PV and Battery Storage System of a Three – Level NPC Inverter Using FLC

In this paper, a novel configuration of a three level neutral-point-clamped (NPC) inverter that can integrate solar photovoltaic (PV) with battery storage in a grid-connected system is proposed. The energy of the proposed topology lies in a novel, lengthened unbalance three-level vector modulation technique that can engender the correct ac voltage under unbalanced dc voltage conditions. This paper presents the Fuzzy Logic controller design philosophy of the proposed configuration and the theoretical framework of the proposed modulation technique. An incipient Fuzzy Logic controller for the proposed system is additionally presented in order to control the power delivery between the solar PV, battery, and grid, which simultaneously provides extreme power point tracking (MPPT) operation for the solar PV. The efficacy of the proposed methodology is investigated by the simulation of several scenarios, including battery charging and discharging with different calibers of solar irradiation. The proposed methodology and topology is tested on MATLAB/SIMULINK Environment.
Harmonic Mitigation of a Solar FED Cascaded H-Bridge inverter using Artificial Neutral Network

A concept of application of Artificial Neural Network (ANN) for estimating switching angle in an 11 level full bridge cascaded multilevel inverter with optimal pulse width modulation , which was powered by five varying dc input sources. A solar panel was connected to each cascaded inverter. For a given modulation index the optimal switching angles with lowest THD is generated using trained neural network by replacing look-up table is proposed in this paper. The odd harmonics( 5,7,11) in the inverter is eliminated by using the trained network. Theoretical concepts have been validated in simulation results using artificial neural network technique which shows the high performance and technical advantages of the developed system. keywordsActive power filter, distributed energy resources (DERs), harmonics injection, optimal pulse width modulation (OPWM), selective harmonics compensation (SHC), Artificial Neural Network(ANN)
Discover and Analyzes Whether Mobile Applications Downloaded From the Internet Are Good or Bad

Android Malware is pernicious software. It is configured to attack the hardware such as android or mobile phone or smart phone. It is designed to exploit the flaw in specific mobile phone software technologies and operating systems. Nowadays, the mobile phone is the number one most vulnerable to malware attacks. Malware can be in the form of adware, Trojans, viruses, root kits and spyware. They delete important documents or steal protected data or bring software that is not authorized by the user. To solve this problem you need to categorize the applications on the mobile. The techniques used in machine learning are used here to differentiate between applications in mobile as good or bad. In this paper, present two methods as using the Genetic algorithm for feature selection and the Nearest Neighbor for classification.
Implementation of Link Fingerprint Verification and Data Provenance using RSSI

Due to constrained assets and adaptability, security protocol for Internet of Things (IoT) should be light-weighted. The cryptographic solutions are not possible to apply on little and low-power devices of IoT in view of their power and space impediments. In this paper, a light-weight protocol to verify the information and accomplishing information provenance is introduced for multi-hop IoT arrange. The Received Signal Strength Indicator (RSSI) of conveying IoT nodes are utilized to produce the connection fingerprints. The connection fingerprints are coordinated at the server to process the relationship coefficient. Higher the estimation of connection coefficient, higher the rates of verified information move. Lower worth gives the recognition of ill-disposed node in the middle of a particular connection. Information provenance has additionally been accomplished by examination of pocket header with all the accessible connection fingerprints at the server. The time unpredictability is processed at the node and server level, which is O(1). The power scattering is determined for IoT nodes and overall network. The outcomes demonstrate that the power utilization of the framework and time complexity. Exploratory outcomes show that up to 97% connection is accomplished when no attacker node is available in the IoT network.
1:4 Microstrip Compact Patch Antenna Array

The paper presented here is dealing with the design of compact patch antenna array . The array of 1:4 patch antenna with microstrip line feeding technique is proposed on operating frequency of 5GHz. The antenna proposed is conventionally using microstrip rectangular patch, the designed antenna is fed with the matching lines inset fed then the power divider has been utilized to design an array of 1:4. Inhibiting characteristics of a single microstrip patch, like low gain and smaller bandwidth, make it more popular for array configuration. the microstrip patch antenna array is able to provide frequency agility, feed line flexibility, broad band-width, beam scanning omnidirectional patterning. A systematic approach has been taken to design the antenna array. The design and measurement results of the antenna are discussed below. Keywords— microstrip patch antenna, antenna array, s11 parameter, 5Ghz, IE3D simulation
Big Data: The New Challenges in Data Mining

—Big Data is a new term used to identify the datasets but due to their large size and complexity, we cannot manage them with our current methodologies or data mining software tools. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This paper represents a broad overview of the topic, Big Data challenges, Data Mining Challenges with Big Data, Big Data processing framework and forecast to the future.
An Object-Face Liveness Detection based on Local Feature Descriptors Using Fuz-SVM Classifier

: In day to day life, the Object Face liveness detection of specific image became a critical task in user authentication and other security applications. However, it is important to develop a system which can improve in processing of user authentication and also accomplished with identification of spoofing object from its genuine objects. In practically, most of the existing systems were fails in distinguish of genuine and fake objects especially in case of blur images. In order to overcome this situations and identify genuine object live faces, we proposed An Object Face Liveness Detection based on Local Feature Descriptors using Fuz-SVM Classifier. In the proposed system we used Histogram of Oriented Gradients (HOG) combined with Local Phase Quantization (LPQ) so that the HOG will extract features from normal images and LPQ will extract from blurred images. The proposed system also concentrates on individual differences among several objects, allows to select specific part of whole object based on Region of Interest (ROI). One of the major advantage with this system is reduces the processing time, and increases recognizes rate of specific face of the object.
Gender, Age and Class in School Differences in Internet Addiction and Psychological Distress among Adolescents in a Nigerian Urban City

Background: Reports had revealed that internet addiction is a newly emerging social and mental health issue among the youths and has attracted much attention around the world. Furthermore excessive use of the internet has negatively affected the daily life routines of adolescents. Objective: This study assessed gender differences, internet addiction and psychological distress among adolescents in a Nigerian urban city. Original Research Article Okwaraji et al.; INDJ, 4(3): 123-131, 2015; Article no.INDJ. 2015.034 124 Study Design: This is a descriptive cross sectional study design. Place and duration of study: This study was carried out in Enugu, south east Nigeria between February and April 2015. Method: The young Internet Addiction Test (IAT), the General health questionnaire (GHQ-12) and a socio demographic questionnaire were used to access 480 adolescents (240 males and 240 females) in a Nigerian urban city for prevalence of internet addiction and psychological distress. Results: Result revealed that many adolescents showed presence of internet addiction and psychological distress. There was difference in internet addiction and psychological difference among the age groups, gender and class in school. Conclusion: This study has observed the prevalence of internet addiction and psychological distress among adolescents in a Nigerian urban city. It is recommended that there is need for government to make laws regulating the rate of usage of the internet by young people especially adolescents.
Hedge Ratio Estimates and Hedging Effectiveness: Evidence for Nifty 50 Index Futures

This study investigates the hedge ratio and hedging effectiveness for NIFTY 50 Index on NSE in India. For accomplishing the objective NIFTY 50 Index traded on NSE India is considered for a period from April 2005 to December 2015. The sample used in this study includes daily future close prices and spot closing prices for NIFTY 50 Index on NSE in India (www.nseindia.com). Since most of the trading activity takes place in near month contracts, only near month contracts are studied using econometric tools unit root test, OLS, Cointegration Bi-VAR, Vector Error Correction Method and GARCH(1,1). The analysis reveals OLS model can be used to calculate the risk reduction and help the hedgers to compare and take advantage for a given position from the different future position.
Frequent Itemset Mining Algorithms for Knowledge Discovery – A Compendious Review of Various Approaches

Frequent Itemset Mining is a Data Mining task that has drawn the attention of researchers over the years. This concept is used in Market Basket Analysis in particular and Decision Support problems in general. In this paper, we have focused on the developments in this area so far. We start with an account of the algorithms that generate Candidate Itemsets . This class of algorithms proves costly, particularly in cases where there exist a large number of Itemsets . Then we describe the tree based Frequent Pattern algorithm that does not require Candidate Itemset Generation , thereby bringing the cost down. After that, we introduce lattice based algorithms in which fewer database scans are needed and hence I/O cost gets reduced. Then we discuss an algorithm that uses a single recursive function and simplifies its structure without worrying too much about the speed. Then we move on to have a look at an algorithm that uses hyperlinks and saves time and space. Then we describe computationally efficient algorithms for Closed Itemsets . Finally, we discuss some algorithms that leverage the inherent advantages of some special data representations to enhance efficiency. While keeping the nub and essence intact, we have avoided the original algorithmic and mathematical notations to keep it perspicuous, coherent, and comprehensible. However, a simplified block diagram has been given, wherever the nature of the algorithm permits it, to summarize the functioning of the algorithm briefly.
Comparative Assessment on Privacy Preservation in Health Care Sectors coupled with IoT

: Safe and high-quality healthcare service is of supreme significance to patients. Security and patients’ privacy of healthcare data are imperative problems that will have a large impact on the upcoming accomplishment of Healthcare with IoT. A major problem in the IoT dependent healthcare system is the fortification of privacy. Usually, a healthcare service contributor receives data from its patients and distributes them with healthcare experts or registered clinics. The contributor may perhaps share out the data to pharmaceutical companies and health insurance companies. Hence, for overcoming the challenges existing in security, this paper has come out with a privacy-preserving technique with significant data extraction from IoT devices linked with healthcare sector. According to the adopted scheme, the information obtained from IoT devices is processed for preserving the sensitive data, such that unknown people are prohibited to access them. Here, Grey Wolf Optimization (GWO) scheme is proposed to recognize the optimal key. The objective of the proposed scheme is to minimize hiding failure rate, modification degree, and true positive value for better preservation of sensitive data. Moreover, the implemented technique is distinguished with conventional schemes like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Bee Colony (ABC), Firefly (FF) and Differential Evolution (DE) algorithms in terms of performance. Also, the statistical analysis of the presented method is measured for three test cases, and the effectiveness of the implemented method is revealed.
FACTORS AFFECTING THE CONVERSION TO ORGANIC FARMING IN IRAN: A CASE STUDY OF MAZANDARAN RICE PRODUCERS

The purpose of this research was to examine factors affecting the conversation to organic farming by rice producers in Mazandaran Province, north of Iran. This study used qualitative research methods. The research method was multiple-case study and purposive sampling method was used. The main instrument for data collection was an open-ended questionnaire (protocol). The sample size was 10 organic rice producers in Mazandaran province that received certification of healthy product from Standard Organization of Mazandaran Province. The data gathered until empirical saturation was reached. The results showed that factors affecting the conversation to organic farming consist of: Facilitating factors and barriers to conversion to organic farming. Facilitating factors include motivations and profits. Health and safety, environmental, Ideological and philosophical and economic motivations are the most important motivating factors mentioned by rice producers. Barriers to conversion to organic farming include challenges and costs. Lack of knowledge, lack of government supports, fear of the future and production are mentioned as the most important challenges of the conversion to organic farming.
Attack Graph Based Security Metrics: State of the Art

- In this paper, we discussed the development of Attack Graph-Based Security Metrics that will be used to evaluate the security of a network. Attack Graph-Based Security Metrics recently used simultaneously to form a Multiple Attack Graph-Based Security Metrics. Furthermore, Multiple Attack Graph-Based Security Metrics were used to evaluate the two networks so it can be inferred which network is more secure than other one.
Optimized Design of FIR Filter using Vedic Multiplier for Reconfigurable Applications

—The likelihood of realization of the block FIR filter in transpose form configuration for area-delay efficient realization of large order FIR filters is performed for both fixed and reconfigurable applications. FIR filter is the fundamental building block of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), signal processing and Image processing applications. The transposed form block FIR filter contains adder, inner product unit (multiplier), and Coefficient Storage Unit (CSU) and register units. The CSU stores coefficients of the considerable number of channels to be utilized for the reconfigurable application. The reconfigurable FIR filter is designed with an array multiplier. So it provides high die size (area) and high power utilization. To overcome this problem, the reduced Vedic multiplier is composed by urdhva tiryagbhyam technique. Vertical and horizontal multiplication is carried out to reduce the partial product generation stages. Finally, this reduced Vedic multiplier unit is applied into the transposed form block FIR filter to achieve the low area, delay and low power. This structure involves significantly less Area Delay Product (ADP) and less Energy Per Sample (EPS) than the transposed form block FIR filter using an array multiplier.
Role of post-translational modifications in regulation of tumor suppressor p53 function

As an important tumor suppressor, the inactivation and mutation of p53 is discovered in more than 50% of cancers. Repression of tumor progression by p53 is mainly through its function as a common transcription factor for regulation of its target genes, involved in cell growth regulation, DNA damage repair and apoptosis process. There are multiple types of post-transcriptional modifications (PTMs) on p53 protein, including phosphorylation, acetylation, mono- and di-methylation, ubiquitylation, sumoylation and so on. These modifications usually do not function alone; they always interplay with other PTMs and collectively regulate p53 function mainly through regulation of p53 stability and transactivity. This paper reviews the function and mechanism of major posttranslational modifications of p53 and the interaction between these posttranslational modifications.
The Big Data and Cloud Era Effects of E-port

Big data and cloud computing is the development trend of the global information technology exciting and challenges every day now have a large amount of data and information generated, it provides an opportunity for big data analysis. Cloud computing is the essence of all computing social resources together, to realize automatic management, make all kinds of service providers and users don't need to worry about details, to focus more on their own business; it is conducive to innovation and lower costs. E-port will be all kinds of import and export business enterprise data are centralized to the public data center, If will be use of in cloud big data and computing function to analysis the information, not just stop at passive understanding of import and export trade, can also be planning, guiding, developing more efficient foreign trade and industry development planning.
Pixel wise Authentication of Images using Fragile Watermarking

This paper proposes a novel pixel wise fragile watermarking scheme in spatial domain by which one can effectively locate the tamper in every pixel of the images accurately and scheme is also protected by user defined secret key which is given explicitly. Proposed watermarking algorithms has good PSNR value and have less complexity of algorithms. Proposed algorithms can also work on color images efficiently. Tamper detection rate of proposed approach is good and also able to achieve 100% effectiveness in some cases, which is good enough. Experimental results show the efficiency of proposed approach for various types of temporization attacks.