Information Technology

The aim of present research is to determine and prioritize criteria and scales of evaluating cohesive training systems. Present paper attempts to provide a paradigm for better devising of universities' cohesive training systems by identifying such scales. In terms of its aim, this research is an applied one and in terms of data collection method, it is descriptive and survey one. Its population includes elite training experts in the colleges and institutes affiliated to University of Tehran. It attempts to study domestic and foreign papers and to identify criteria and scales for evaluating cohesive training systems. Library method is used to gather information on theoretical basics, literature and to identify aspects and scales. Identified aspects include: content, flexibility, organizational structure, usage convenience, usage services and apparent form. By using the experts opinions, importance ratio of each one of scales and aspects is determined. Also for evaluating the status of this scales in golestan system of Tehran university a questionnaire is compiled. that its admissibility and permanence is confirmed. Then the questionnaire in a sample (of our case) is distributed. In data analysis, T-test: statistical test is used to examine the status of scales and aspects. The results of this test indicated that the organizational structure scales and usage convenience have middle status, the status of flexibility, usage services and apparent form is undesirable, and the content have a desirable status.

Most of the attacks on web application occurs by hits and trial methods, analysis of previous methods, misguiding users etc. User’s are intelligent but could see only, that, which are visible. Internal configuration or working are hidden by the user, even by designer also, because web application has to cross various platforms, architectures and layers each having their own pattern or procedure, various web attacking classe are acting on web ,out of which phishing attack class has been discussed and which is the most threatening way to misguide user, Here Phishing attack has been discussed from where attacker generates exact replica of original site to get username and password.

The development and maintenance of user interface software is surely a challenging task. Organizations who use secured authentication system tolerate no leakage. Cryptographic primitives are useful tools but security of these primitives does not guarantee security of the system. Using patterns for authentication is a system that provides patterns as passwords to the users. Users tend to choose their passwords through random art generation which can be captured by malicious users by video capturing or photo clicking. In lieu of the traditional password based system, several attempts had been reported in literature about authentication schemes which are successful in increasing the strength of the system against some of the known attacks. In this paper, Random Pattern based Flexible User Interface for an effective Secured Authentication Protocol (SAP-RP) is presented. With iterations of random patterns, the users enter different passwords (which are based on the original images selected by them) for every login attempt that is converted to hashed value. It is compared with registered hashed value stored in the database which ensures confidentiality and authentication in the network plane using server and flexible user interface based authentication mechanism.

This paper presents three algorithms and one proposed algorithm to find relevant pages for a given Web page (URL). The first algorithm comes from the Pagerank analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of HITS to identify relevant pages more precisely and effectively. The third one shows the advantages and limitations of Weighted Pagerank algorithm and the final proposed algorithm based on Topic Sensitive Pagerank algorithm. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related researches.

Recognizing faces is something that people usually do effortlessly and without much conscious thought, yet it has remained a difficult problem in the area of computer vision, where some 20 years of research is just beginning to yield useful technological solutions. As a biometric technology, automated face recognition has a number of desirable properties that are driving research into practical techniques. The problem of face recognition can be stated as ‘identifying an individual from images of the face’ and encompasses a number of variations other than the most familiar Application of mug shot identification. One notable aspect of face recognition is the broad interdisciplinary nature of the interest in it: within computer recognition and pattern recognition; biometrics and security; multimedia processing; psychology and neuroscience. It is a field of research notable for the necessity and the richness of interaction between computer scientists and psychologists. The automatic recognition of human faces spans a variety of different technologies. At a highest level, the technologies are best distinguished by the input medium that is used, whether visible light, infra-red or 3-dimensional data from stereo or other range-finding technologies. Thus far, the field has concentrated on still, visible-light, photographic images, often black and white, though much interest is now beginning to be shown in the recognition of faces in colour video. Each input medium that is used for face recognition brings robustness to certain conditions, e.g. infra-red face imaging is practically invariant to lighting conditions while 3-dimensional data in theory is invariant to head pose. Imaging in the visible light spectrum, however, will remain the preeminent domain for research and application of face recognition because of the vast quantity of legacy data and the ubiquity and cheapness of photographic capture equipment.

Intrusion Detection System (IDS) has been used as a vital instrument in defending the network from this malicious activity. With the ability to analyze network traffic and recognize incoming and on- going network attack, majority of network administrator has turn to IDS to help them in detecting anomalies in network traffic. The gathering of information and analysis on the anomalies activity can be classified into fast and slow attack. Since fast attack activity make a connection in few second and uses a large amount of packet, detecting this early connection provide the administrator one step ahead in deflecting further damages towards the network infrastructure. This paper describes IDS that detects fast attack intrusion using time based detection method. The time based detection method calculates the statistic of the frequency event which occurs between one second time intervals for each connection made to a host thus providing the crucial information in detecting fast attack.

The computer revolution is now well advanced, but although we see a starting preparation of computer machines in many forms of work people do, the domain of computers is still significantly small because of the specialized training needed to use them and the lack of intelligence in computer systems. In the history of computer science five generations have passed by, each adding a new innovative technology that brought computers nearer and nearer to the people. Now it is sixth generation, whose prime objective is to make computers more intelligent i.e., to make computer systems that can think as humans. The fifth generation was aimed at using conventional symbolic Artificial Intelligence techniques to achieve machine intelligence. But it failed. Statistical modeling and Neural Networks falls in really sixth generation. The goal of work in Artificial Intelligence is to build the machines that perform tasks normally requiring human intelligence. Its true, but speech recognition, seeing and walking doesn’t require “intelligence”, but human perceptual ability and motor control.

Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. A cognitive radio is an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. Spectrum shortage was born the idea for cognitive radios. These devices utilize advanced radio and signal processing technology along with novel spectrum allocation policies to support new wireless users operating in the existing crowded spectrum, without degrading the performance of entrenched users. In this paper we present a Cognitive Radio approach for usage of Virtual Unlicensed Spectrum , a vision of a Cognitive Radio (CR) based approach that uses allocated spectrum in a opportunistic manner to create “virtual unlicensed bands” i.e. bands that are shared with the primary (often licensed) users on a non-interfering basis. Dynamic spectrum management techniques are used to adapt immediate local spectrum availability. One of the most important components of the cognitive radio concept is the ability to measure, sense, learn, and be aware of the parameters related to the radio channel characteristics, availability of spectrum and power, radio’s operating environment, user requirements and applications, available networks (infrastructures) and nodes, local policies and other operating restrictions. In cognitive radio terminology, primary users can be defined as the users who have higher priority or legacy rights on the usage of a specific part of the spectrum.

This paper organized as such: In the first section authors mention about all aspects of cloud computing – basic definitions, benefits, important features, workability and application, and so on. In the second section of this paper, authors mention Microsoft decisions about applying of cloud computing onto their newest products, such as Azure OS. In the third section of this paper, authors reveal new innovative algorithm for overcoming malfunctions and spyware actions from cloud platforms and resources. In this section, they purpose a new adaptively reduced-order extended Kalman filter for using as an estimator and predictor in cloud platforms. Authors claim that, if this algorithm were implemented on cloud gateways and resources, cloud service providers were able to track and trace lots of spyware and Trojans and also predict and estimate amount of power consumptions.

Rough Set Theory provides a formal framework for data mining. Feature Selection or Attribute Reduction is a preprocessing step in data mining, and it is very effective in reducing dimensionality, reducing irrelevant data, increasing learning accuracy and improving comprehensibility. The fuzzy-rough feature selection algorithm was used to handle the continuous real-valued data as well as to handle noisy data. It was implemented by standard fuzzification techniques enabling linguistic labels to be associated with attribute values. It also provides uncertainty modeling by allowing the possibility of the membership value to more than one fuzzy label. In this paper, we use an Improved Quickreduct algorithm by redefining the lower and upper approximations based on fuzzy set theory. The membership degrees of feature values to fuzzy sets are exploited in the process of dimensionality reduction. The experiments are carried out on public domain datasets available in UCI machine learning repository and real Tuberculosis data set.