Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Cross-Site Scripting Vulnerability and performance comparison using C-Time and E-Time

Several works are in progress in the direction of web communication. The major threats are content sniffing, Cross-Site Scripting (XSS) and SQL Injection attacks. In content sniffing data is altered from any unauthorized script. XSS is a variant of this where malicious programs/scripts are executed from the client node for fake presence and steals the data. In SQL injection malicious SQL statements are inserted to monitor the database from the outside environment. The main aim of this paper is to detect the XSS attack and prevent the data from the final alteration. For this we are considering two types of time evaluation. First time is time to translating JSP script to java programs for data sending which is called C-Time and second time is for identification of vulnerable outputs that is called E-Time. Based on the timing comparison we will prove that our methodology has better detection in comparison to the traditional system.
A Marker Controlled Watershed Algorithm with Priori Shape Information for Segmentation of Clustered Nuclei

: Microscopy cell image analysis is a fundamental tool for biological research. This analysis is used in studies of different aspects of cell cultures. Visual inspection of individual cells is very time consuming, insufficient to detect or describe delicate changes in cellular morphology. The main challenges in segmenting nuclei in histometry are due to the fact that the specimen is a 2-D section of a 3-D tissue sample. The 2-D sectioning can result in partially imaged nuclei, sectioning of nuclei at odd angles, and damage due to the sectioning process. Furthermore, sections have finite thickness resulting in aggregating or overlapping nuclei in planar images. Hence a set of image objects that differ considerably from the ideal of round blob-like shapes occur. Their sizes and shapes in images can be irregular. The classic methodology for cell detection is image segmentation, which is a fundamental and difficult problem in computer vision. Image segmentation is a fundamental and difficult problem in computer vision. The difficulty in automatic segmentation of images of cells is often uneven due to auto fluorescence from the tissue and fluorescence from out-of-focus objects. This unevenness makes the separation of foreground and background a non-trivial task. The intensity variations within the nuclei further complicate the segmentation as the nuclei may be split into more than one object, leading to over-segmentation. Due to the cell nuclei are often clustered, make it difficult to separate the individual nuclei. Hence an automatic segmentation of cell nuclei is an essential step in image histometry and cytometry. This paper presents a robust method to segment clustered overlapping or aggregating nuclei cells using priori information of shape markers and marking function in a watershed-like algorithm. The shape markers are extracted using adaptive H-minima transform and prior information about the usual shape of normal/pathological nuclei cells. A new marking function based on outer distance transform, to avoid jagged boundaries of segmented objects is created. Thus the right sets of markers and a marking function are used to accurately separate clustered nuclei.
A Review on Shoulder Surfing Attack in Authentication Technique

- Security is a important factor in computer system now a days. Users of computer system give a primary preference to security. Authentication is a process used to provide security to the user. Authentication is a process of identifying the person’s identity or conforming the identity of a particular person. There are various authentication method, but most commonly used method is textual password. Combination of alphabet and number create a secure password. But it has some drawbacks i.e. it can be easily guess by third person also called attacker. If it make complex then it could be hard to memorize. Also it is susceptible to various attacks such as brute force attack, dictionary attack, social engineering attack, eves dropping, etc. to overcome the drawback of textual password graphical password system introduced. Graphical system is easy to memorize but it undergo shoulder surfing attack which is quite big problem. in which any entity or person can observe users password directly or by using any device. This paper survey the various shoulder surfing attacks in graphical password approach.
Non Minitia Fingerprint Recognition based on Segmentation

—The biometric identification of a person has an advantage over traditional technique. Widely used biometric is Fingerprint to identify and authenticate a person. In this paper we propose Non Minutia Fingerprint Recognition based on Segmentation (NMFRS) algorithm. The variance of each block is determined by segmenting the finger print into 8*8 blocks. Area of Interest (AOI) is obtained by removing the blocks with minimum variance. Features of Finger Print is obtained by applying Discrete Cosine Transform (DCT) on AOI and converted to major and minor non-overlapping blocks to determine variance. The percentage recognition rate is better in the proposed algorithm compared to the existing algorithms.
Prediction of Crack Growth in Steam Generator Tubes Using Monte Carlo Simulation

The growth of stress corrosion cracks in steam generator tubes is predicted using the Monte Carlo simulation and statistical approaches. The statistical parameters that represent the characteristics of crack growth and crack initiation are derived from in-service inspection (ISI) non-destructive evaluation (NDE) data. Based on the statistical approaches, crack growth models are proposed and applied to predict crack distribution at the end of cycle (EOC). Because in-service inspection (ISI) crack data is different from physical crack data, a simple method for predicting the physical number of cracks from periodic in-service inspection data is proposed in this study. Actual number of cracks is easily estimated using the method, and the statistical crack growth is simulated using the Monte Carlo method. Probabilistic distributions of the number of cracks and maximum crack size at EOC are obtained from the simulation. Comparing the predicted EOC crack data with the known EOC data the usefulness of the proposed method is examined and satisfactory results are obtained. keyword: Statistical Assessment, POD (probability of detection), Effective POD, Steam Generator Tube, Structural Integrity, Monte Carlo method.
Ant Based Swarm Computing for Image Classification - A Brief Survey

The social insect metaphor for solving problems has become an emerging topic in the recent years. This approach emphasizes on direct or indirect interactions among simple agents. Swarm Intelligence is the collective behavior of decentralized [8], self-organized [4] system whereby the collective behavior of agent interacting locally with the environment causes coherent global pattern to emerge. Classification is a computational procedure that sorts the image into groups according to their similarities [5]. Images can be similar but to measure the similarity pixel-to-pixel comparison is made. Numerous methods for classification have been developed. Exploring new methods to increase classification accuracies have been a key topic. This paper explores the swarm computing methods called Ant Colony Optimization (ACO) to classify imagery. General Terms Classification, Swarm Intelligence, Artificial Intelligence, Ant Colony Optimization, Imagery.
Transmission of Data Using Arm Based Privacy Protection QR-code

—The authentication system used in banks, to protect the assets of users is at high risk of safety. In order to overcome the safety issues, we proposed a system that allows the user to safely enter credentials and information to transfer money after launching LIVE-CD on stand alone in place and avoiding the possibility of entering credit card details (send or receive money without using of credit cards). The entered information is encrypted by common key crypto system and stored in a QR (quick response) code. The QR code encrypts our credit card details and also generates a One-Time Password (OTP). The arm processor will be checking the QR code details and perform two kinds of operation. It either carries out the transaction (if the entered password is correct) or stops the transaction (if entered password is wrong). In order to ensure safety in case if the QR code is used by unknown person it sends the information immediately to the particular account holder through e-mail (or) text message.
Assessment of Phytochemicals and Antioxidant activities of Leucas indica aerial parts- A comparative study

Copyright: © 2016 | Author(s), This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial No Derivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. The exploration was to evaluate the phytochemical ingredients and antioxidant activities of annual herb Leucas indica methanolic leaves and flowers extracts. These were estimated by distinguished methods. Phytochemical constituents and non enzymatic antioxidants showed enhancement in increasing concentration from 25 mg/ml to 100 mg/ml. All the assays were carried out in triplicate and the results uttered as mean values ± standard deviations. The levels of phytochemical, explicitly phenolics, flavonoids, tannins, alkaloids, saponins, vitamin C, βCarotene and lycopene, evidently exposed that methanolic leave extract had elevated phytochemical constituents than methanolic flower extract, except alkaloids. Same pattern was observed for antioxidant capacity assays, specifically DPPH, FRAP, reducing power assay, Hydroxyl radical scavenging activity and ABTS radical scavenging activity, this may be due the presence of high phytochemical ingredients. Even though, leave extract had superior activities than flowers extract, they both have profound therapeutic potential.
Data Mining Algorithms for Classification of Diagnostic Cancer Using Genetic Optimization Algorithms

The breast tumor is the primary driver of female casualty every-where throughout the world and the real area of study from a long time but with slighter development than anticipated. Numerous establishments and associations are working in this field to prompt to a conceivable arrangement of the issue or to prompt to additionally comprehension of the issue. Numerous past in-quiries about the said were contemplated for improved comprehension of the issue and the research performed previously was to reduce dimensionality and to contribute to the betterment in the field of cancer, Wisconsin-Madison Diagnostic Breast cancer (WDBC) dataset was taken from learning repository of UCI database with 569 distinct instances for training by choosing finest features out of 32 different attributes. Different feature selection algorithms were used with data mining algorithms for better classification. Numerous enhancements in classification accuracy of WDBC were discovered by utilizing distinctive methodologies than the prior reviews directed in a similar field. The Logistic Regression, Linear Regression, and SVM algorithms showed better classification accuracy i.e. 98.24 %, 98.24 % and 98.07 % than the previous outcome results known for the said classification algorithms. The results were generated using 10 fold cross validation, by using different classification algorithms with feature selection and generation algorithms.
Comparative Study of Eucalyptus, Open Stack and Nimbus

-Cloud computing is a Service Oriented Architecture which reduces information technology overhead for the end-user and provides great flexibility, reduced total cost of ownership, on-demand services and many other benefits. Hence it delivers all IT related capabilities as services rather than product . Services on cloud are divided into three broad categories: Software as a Service, Infrastructure as a Service & Platform as a Service. Same as services cloud is also classified as Private Cloud, Public Cloud & Hybrid Cloud. Private cloud is gaining popularity, not only among large organizations but also small and medium enterprises. To deploy public or private cloud there are many open source software platforms available such as Eucalyptus, Nimbus, OpenStack, Open Nebula, Cloud Stack and Amazon Web Services. In this paper, we provide a comparative study of three open source cloud management platforms: Eucalyptus, OpenStack and Nimbus. We believe that the comparison presented in this paper would benefit enterprises as well as research institutes in selecting best open source platforms to meet their technology demands.
RFID Based Application Algorithms for Communication System: A Literature Review

— Radio Frequency Identification (RFID) technology is being most popular in the field of communication, privacy and security for modern society. Today’s high demand of fast, accurate, cost effective, easy to implement, stable and reliable system. RFID has shown its potential and it can overcome the limitation of existing system. As per the requirement it can integrate with key technologies and may used for accurate real-time indoor positioning and tracking. This review discusses the various existing algorithms and their applications. The comparison of existing algorithms is given in terms of applicability, measuring parameters, advantages and limitation. This literature review concludes with the best algorithm for accurate real-time indoor RFID positioning and tracking.
To reduce data leakage in horizontally distributed database using association rules

— The mainstay of this project is to propose a protocol for to reduce data leakage in horizontally distributed database using association rule. The current leading protocol is that of kantarcioglu and Clifton in [1]. My protocol is based on the Fast Distributed Mining (FDM) algorithm, which is an unsecured distributed version of the Apriori algorithm. This protocol offers enhanced privacy with respect to the protocol. In addition it is simpler and is significantly more efficient in terms of communications rounds, communication cost and computational cost.
Differential Detection of Space-Time Spreading with Two Transmit Antennas

A differential detection scheme for space-time spreading with two transmit antennas is proposed. The scheme does not require channel state information at either the transmitter or the receiver. With segmentation and preamble symbols padded at the transmitter, the receiver recovers the information using differential detection. Both phase-shift keying (PSK) and quadrature amplitude modulation (QAM) signals are considered. The proposed scheme achieves two-level transmit diversity gain with low complexity and saves the use of channel estimation, while having about 3 dB performance loss as compared to the coherent detection scheme. When multiple receive antennas exist, additional receive diversity gain can be achieved along with the transmit diversity gain. The scheme works fine under block-fading channel as well as slow Rayleigh fading channel, which is a popular scenario for highrate data communications. The system performance for different segment sizes, channel fading speeds, modulation methods, and numbers of receive antennas is studied through simulations.
Deep Convolutional Neural Networks for Real Time Object Recognition

This paper focuses on Object recognition using YOLO, a real-time object detection model developed to run on portable devices such as a laptop or web camera. The model was first trained on the dataset then on the COCO dataset, when we look at the old . 5 IOU mAP detection metric YOLOv3 is quite good. This paper presents a new Object detection method based on transfer learning and sample enhancement under the framework of YOLO network. This method takes full advantages of the real-time feature of YOLO, as well as the enhancement of the generalization ability brought by transfer learning and sample enhancements
MECHANICS OF MEMORY – A REVIEW

The interaction of living organisms between themselves and with the environment is essential for survival. The communication among different living species involves the integrity of central nervous system which generates brain activity such as arousal, attention, learning and memory. Moreover, face perception and recognition of faces are fundamental brain processes for human relationship. The ability to hold objects in memory is essential to intelligent behavior, but its neural basis still remains poorly understood. Advances in neuroscience research for past two decades have contributed to clarify the intricate puzzle about brain recognizing objects.
COMPARATIVE ANALYSIS OF DIFFERENT METHODOLOGIES FOR LOCAL CLIMATE ZONE CLASSIFICATION

Mapping and classification of local climate zones is the most essential area of research in remote sensing for observing the environmental processes and understanding the relationship between natural phenomena and human beings for decision making. LCZ classification maps are beneficial for the climatologists to compare and classify rural and urban areas for temperature and urban heat island studies worldwide and can improve accuracy in reporting climatic changes. The basic motive of LCZ classification system is to ease the process of selecting the site and reporting metadata by improving the description of rural and urban surface conditions. Other applications include: defining urban heat island magnitude, climate modeling, weather forecasting, mapping land cover/use and historical temperature analysis. Many cities applied LCZ classification to classify land and selecting site for study. Local climate zone classification system was introduced in 2012 by Oke and Stewart which is now used as a standard wall over the world. Several techniques are being used to map local climate zones in recent years. They differ on the basis of basis of the study area, data sets used, spatial and spectral data, selecting features, sensors used, post classification filtering, software used, training data, training method, size of the training set, classification method and classifiers used for generating the final LCZ maps. Every algorithm has its own pros and cons. Numerous factors may affect the accuracy and efficiency of the methodology adopted for classification (such as atmospheric conditions and image resolution) and no one is applicable and perfect in every case. There is a need for knowing and developing a best-generalized classification method that can work efficiently under different conditions to facilitate researchers with limited resources. In this study, a comparative analysis of different techniques used for local climate zone mapping is done and their results are compared. The results show that each method produces different results for mapping LCZs in case of overall accuracy and kappa coefficient values. Some methods are efficient in saving time consumption, some for producing accurate results of classification, others may be advantageous in requiring efforts. The integration of best properties of different techniques can be done to develop a new technique that is more useful and can enhance the classification performance in any area of interest.
Design a Token Based On-demand Hybrid Multipath Routing Algorithm for Mobile Ad-hoc Network

— In Mobile Ad Hoc Network (MANET) it’s a challenging to design an efficient routing protocol, due to the dynamic network topology and resource constraint. AODV and DSR are most widely studied on-demand ad hoc routing protocols because of their low overheads. This paper explores the network redundancy through multipath routing. The paper is based on Token On-Demand Multipath Routing Protocol . The main purpose of the project is to reduce flooding in network. The path is only set between the nodes which have token. The algorithm is to overcome the disadvantages of AODV and DSR algorithm. TOHMR algorithm is used to reduce the flooding and network traffic.
A Proposed System of Trust Based ECG Monitoring for Ubiquitous Healthcare Applications

Ubiquitous computing technologies can be used to provide better solutions for healthcare of patients at home or hospital. Moreover, ubiquitous computing is also characterized by partial views over the state of the global environment, implying that we cannot guarantee that an environment can always verify the properties of the mobile entity that it has just received. Secure in this context encompasses both the need for cryptographic security and the need for trust, on the part of both parties, that the interaction is functioning as expected. In this paper, we propose a trust-based system of ECG Monitoring for ubiquitous healthcare applications. A ubiquitous trust domain is introduced to collaborate entities making of autonomous trust-based decision for ECG monitoring using wireless sensors. Wireless controller that can send monitored information to the service centre and provide a feedback, if necessary, to the patient from medic. If an emergency is detected the monitoring services gives alarm condition to doctor’s personal digital assistant.
Impact of Segment Types on Critical Path Delay Characteristics in FPGA Routing

: Hierarchical routing resources play vital role in FPGA routing. Better routability options can be obtained using segmented approach of wires thus enabling routing optimization. Source and sink logic blocks can be connected via wire segments such that the overall wire length and switching transistors inside the switch box can be saved over an extent. This paper presents an experimental approach of study of wire segments types affecting the routability. Widely used segment types are considered for experiments. A routing algorithm containing the cost equation based on routing segment types is proposed. Results show that at least 15% less critical path delay can be achieved tested against 12 largest MCNC benchmark circuits.
A Review on Structural Analysis of Overhead Crane Girder Using FEA Technique

41 Abstract:The aim of this paper is for review on structural analysis of overhead crane girder using Finite Element Analysis (FEA) technique. Overhead crane girder is subjected to various types of load. Girder is the critical assembly component of overhead crane. Currently research is being carried out to improve the strength structure of overhead crane girder. These efforts help to overcome overhead crane girder failure. Finite Element Analysis (FEA) software offers inexpensive solutions to overhead crane girder failure problem. In this study the researchers used Finite Element Analysis (FEA) technique using different types of approach.
Mining road traffic accident data to improve safety on road-related factors for classification and prediction of accident severity

A Road Traffic Accident is very serious matter of life. The World Health Organization (WHO) reports that about 1.24 million people of the world die annually on the roads. The Institute for Health Metrics and Evaluation (IHME) estimated about 907,900, 1.3 million and 1.4 million deaths from road traffic injuries in 1990, 2010 and 2013, respectively. Uttar Pradesh in particular one of the state of India, experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this paper, we applied data mining technologies to link recorded road characteristics to accident severity and developed a set of rules that could be used by the Indian Traffic Agency to improve safety and could help to save precious life. Key Words: Traffic Accident, Data Mining, Naive Bayes, Classification, Prediction. Language: en