Artificial Intelligence

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John McCarthy, coined the term “Artificial Intelligence” in 1955. He defines it as "the science and engineering of making intelligent machines." (as quoted in Poole, Mackworth & Goebel, 1998) AI research is highly technical and specialized, and has been characterized as “deeply divided into subfields that often fail to communicate with each other.” (McCorduck, Pamela (2004), Machines Who Think (2nd ed.) These divisions are attributed to both technical and social factors. The research cited here looks at the divisions and viewpoints and includes an overview of the current science of artificial intelligence, also known as intelligent computing.

  • Usha Gayatri, P.; Neeraja S.; Leela Poornima, Ch.; Chandra Sekharaiah, K.; Yuvaraj, M., "Exploring Cyber Intelligence Alternatives For Countering Cyber Crime," Computing for Sustainable Global Development (INDIACom), 2014 International Conference on , vol., no., pp.900,902, 5-7 March 2014. (ID#:14-1621) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6828093&isnumber=6827395 In this paper, a case study of cyber crime is presented in the context of JNTUHJAC website. CERT-In is identified as the organization relevant to handling this kind of cybercrime. This paper is an attempt to find and do away with the lacunae in the prevailing cyber laws and the I.T. Act 2000 and the related amendment act 2008 such that law takes cognizance of all kinds of cybercrimes perpetrated against individuals/societies/nations. It is found that ICANN is an organization to control the cyberspace by blocking the space wherein the content involves cognizable offence. Keywords: Artificial intelligence; Computer crime; Context; Cyberspace; Handheld computers; Internet; Organizations; Artificial Intelligence (AI);Collective Intelligence (CI);Information Technology (IT); Information and Communication Technologies (ICTs); Web Intelligence (WI)
  • Mijumbi, Rashid; Gorricho, Juan-Luis; Serrat, Joan; Claeys, Maxim; De Turck, Filip; Latre, Steven, "Design and evaluation of learning algorithms for dynamic resource management in virtual networks," Network Operations and Management Symposium (NOMS), 2014 IEEE , vol., no., pp.1,9, 5-9 May 2014. (ID#:14-1622) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6838258&isnumber=6838210 Network virtualization is considerably gaining attention as a solution to ossification of the Internet. However, the success of network virtualization will depend in part on how efficiently the virtual networks utilize substrate network resources. In this paper, we propose a machine learning-based approach to virtual network resource management. We propose to model the substrate network as a decentralized system and introduce a learning algorithm in each substrate node and substrate link, providing self-organization capabilities. We propose a multiagent learning algorithm that carries out the substrate network resource management in a coordinated and decentralized way. The task of these agents is to use evaluative feedback to learn an optimal policy so as to dynamically allocate network resources to virtual nodes and links. The agents ensure that while the virtual networks have the resources they need at any given time, only the required resources are reserved for this purpose. Simulations show that our dynamic approach significantly improves the virtual network acceptance ratio and the maximum number of accepted virtual network requests at any time while ensuring that virtual network quality of service requirements such as packet drop rate and virtual link delay are not affected. Keywords: Bandwidth; Delays; Dynamic scheduling; Heuristic algorithms; Learning (artificial intelligence);Resource management; Substrates; Artificial Intelligence; Dynamic Resource Allocation; Machine Learning; Multiagent Systems; Network virtualization; Reinforcement Learning; Virtual Network Embedding
  • Lamperti, G.; Zhao, X., "Diagnosis of Active Systems by Semantic Patterns," Systems, Man, and Cybernetics: Systems, IEEE Transactions on , vol.PP, no.99, pp.1,1, January 2014. (ID#:14-1623) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6725692&isnumber=6376248 A gap still exists between complex discrete-event systems (DESs) and the effectiveness of the state-of-the-art diagnosis techniques, where faults are defined at component levels and diagnoses incorporate the occurrences of component faults. All these approaches to diagnosis are context-free, in as much diagnosis is anchored to components, irrespective of the context in which they are embedded. By contrast, since complex DESs are naturally organized in hierarchies of contexts, different diagnosis rules are to be defined for different contexts. Diagnosis rules are specified based on associations between context-sensitive faults and regular expressions, called semantic patterns. Since the alphabets of such regular expressions are stratified, so that the semantic patterns of a context are defined based on the interface symbols of its subcontexts only, separation of concerns is achieved, and the expressive power of diagnosis is enhanced. This new approach to diagnosis is bound to seemingly contradictory but nonetheless possible scenarios: a DES can be normal despite the faulty behavior of a number of its components; also, it can be faulty despite the normal behavior of all its components. Keywords: Automata; Circuit faults; Context; History; Monitoring; Semantics; Syntactics; Artificial intelligence; decision support systems; discrete-event systems (DESs);fault diagnosis ;intelligent systems 
  • Wolff, J.G., "Big Data and the SP Theory of Intelligence," Access, IEEE , vol.2, no., pp.301,315, 2014. (ID#:14-1624) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6782396&isnumber=6705689 This paper is about how the SP theory of intelligence and its realization in the SP machine may, with advantage, be applied to the management and analysis of big data. The SP system-introduced in this paper and fully described elsewhere-may help to overcome the problem of variety in big data; it has potential as a universal framework for the representation and processing of diverse kinds of knowledge, helping to reduce the diversity of formalisms and formats for knowledge, and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualization of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it. Keywords: Big Data; data analysis; data compression; data mining; data structures; natural language processing; unsupervised learning; Bid Data analysis; Big Data management; SP machine; SP theory of intelligence; data structure discovery; error management; high-parallel open-source version; inferential processes; knowledge structure visualization; lossless compression; natural language production; pattern recognition; streaming data analysis; unsupervised learning; Cognition; Computers; Data handling; Data storage systems; Information management; Licenses; Natural languages; Artificial intelligence; big data; cognitive science; computational efficiency; data compression; data-centric computing; energy efficiency; pattern recognition; uncertainty; unsupervised learning
  • Zhu, B.B.; Yan, J.; Guanbo Bao; Maowei Yang; Ning Xu, "Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems," Information Forensics and Security, IEEE Transactions on, vol.9, no.6, pp.891,904, June 2014. (ID#:14-1625) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6775249&isnumber=6803967 Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security. Keywords: artificial intelligence; security of data; CaRP password; Captcha as graphical passwords; PassPoints; artificial intelligence; automatic online guessing attacks; dual-view technologies; hard AI problems; hard mathematical problems; image hotspot problem; online security; password choices; relay attacks; search set; security primitives; shoulder-surfing attacks; Animals; Artificial intelligence; Authentication; CAPTCHAs; Usability; Visualization; CaRP; Captcha; Graphical password; dictionary attack; hotspots; password; password guessing attack; security primitive
  • Chaudhary, A.; Kumar, A.; Tiwari, V.N., "A reliable solution against Packet dropping attack due to malicious nodes using fuzzy Logic in MANETs," Optimization, Reliabilty, and Information Technology (ICROIT), 2014 International Conference on , vol., no., pp.178,181, 6-8 Feb. 2014. (ID#:14-1626) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6798326&isnumber=6798279 The recent trend of mobile ad hoc network increases the ability and impregnability of communication between the mobile nodes. Mobile ad Hoc networks are completely free from pre-existing infrastructure or authentication point so that all the present mobile nodes which are want to communicate with each other immediately form the topology and initiates the request for data packets to send or receive. For the security perspective, communication between mobile nodes via wireless links make these networks more susceptible to internal or external attacks because any one can join and move the network at any time. In general, Packet dropping attack through the malicious node (s) is one of the possible attacks in the mobile ad hoc network. This paper emphasized to develop an intrusion detection system using fuzzy Logic to detect the packet dropping attack from the mobile ad hoc networks and also remove the malicious nodes in order to save the resources of mobile nodes. For the implementation point of view Qualnet simulator 6.1 and Mamdani fuzzy inference system are used to analyze the results. Simulation results show that our system is more capable to detect the dropping attacks with high positive rate and low false positive. Keywords: fuzzy logic; inference mechanisms; mobile ad hoc networks; mobile computing; security of data; MANET; Mamdani fuzzy inference system; Qualnet simulator 6.1;data packets; fuzzy logic; intrusion detection system; malicious nodes; mobile ad hoc network; mobile nodes; packet dropping attack; wireless links; Ad hoc networks; Artificial intelligence; Fuzzy sets; Mobile computing; Reliability engineering; Routing; Fuzzy Logic; Intrusion Detection System (IDS);MANETs Security Issues; Mobile Ad Hoc networks (MANETs); Packet Dropping attack

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