Activity recognition plays an important role in many areas such as smart environments by offering unprecedented opportunities for assisted living, automation, security and energy efficiency. It’s also an essential component for planning and plan recognition in smart environments. One challenge of activity recognition is the need for collecting and annotating huge amounts of data for each new physical setting in order to be able to carry out the conventional activity discovery and recognition algorithms. This extensive initial phase of data collection and annotation results in a prolonged installation process and excessive time investment for each new space. In this paper we propose a new method of transferring learned knowledge of activities to a new physical space in order to leverage the learning process in the new environment. Our method called ”Home to Home Transfer Learning” (HHTL) is based on using a semi EM framework and modeling activities using structural, temporal and spatial features. This method allows us to avoid the tedious task of collecting and labeling huge amounts of data in the target space, and allows for a more accelerated and more scalable deployment cycle in the real world. It also allows us to exploit the insights learned in previous spaces. To validate our algorithms, we use the data collected in several smart apartments with different physical layouts.

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.

In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. ISBN 9781608454921, 103 pages.

Iris recognition technology, used to identify individuals by photographing the iris of their eye, has become popular in security applications because of its ease of use, accuracy, and safety in controlling access to high-security areas. Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. The proposed method combines the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for feature extraction. The output from these three algorithms is normalized and their score are fused to decide whether the user is genuine or imposter. This new strategies is discussed in this paper, in order to compute a multimodal combined score.

Applying Software Engineering to Agent Development

On Tuesday morning, July 12, the program chairs will welcome attendees, and conference and AAAI awards will be presented. The awards ceremony will be followed by the AAAI-10 keynote address, to be include 199 oral presentations in the is the definitive point of interaction delivered by Leslie Pack Kaelbling main track, as well as 75 additional between entertainment software developers (Massachusetts Institute of Technology) presentations in the special tracks on interested in AI and academic entitled "Intelligent Interaction Bioinformatics, AI and the Web, Challenges and industrial AI researchers. AAAI-10 has an in AI, Integrated Intelligence, by AAAI, the conference is targeted outstanding program of invited presentations, Physically Grounded AI, Nectar, and at both the research and featuring Carla P. Gomes Senior Member, as well as poster presentations commercial communities, promoting (Cornell University), Barry O'Sullivan by a select number of exceptional AI research and practice in the context (University College Cork), David C. technical papers, short papers, of interactive digital entertainment Parkes (Harvard University), and student abstracts, and doctoral systems with an emphasis on commercial Michael Thielscher (The University of consortium abstracts. Registration information with Jay M. Tenenbaum (CollabRx The week is filled with a host of and other program details will Inc.), the 2010 recipient of the other programs, including the AI be available on the AIIDE-10 website Robert S. Engelmore Memorial Lecture Video Competition, the AI Poker at www.aaai.org/aiide10 The IAAI-10 program Semantic Robot Vision Challenge, the Michael Youngblood (University of will also feature talks by Majd Alwan General Game Playing Competition, North Carolina Charlotte). Care Empowered by Applied AI," Registration for AAAI-10, IAAI-10, and Vernor Vinge (San Diego State and EAAI-10 is included in one joint University) on "Species of Mind." fee.

The Dendritic Cell Algorithm (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. In this research, and abstract model of DC behaviour is developed and subsequently used to form an algorithm, the DCA. The abstraction process was facilitated through close collaboration with laboratory- based immunologists, who performed bespoke experiments, the results of which are used as an integral part of this algorithm. The DCA is a population based algorithm, with each agent in the system represented as an 'artificial DC'. Each DC has the ability to combine multiple data streams and can add context to data suspected as anomalous. In this chapter the abstraction process and details of the resultant algorithm are given. The algorithm is applied to numerous intrusion detection problems in computer security including the detection of port scans and botnets, where it has produced impressive results with relatively low rates of false positives.

Semantic Web is actually an extension of the current one in that it represents information more meaningfully for humans and computers alike. It enables the description of contents and services in machine-readable form, and enables annotating, discovering, publishing, advertising and composing services to be automated. It was developed based on Ontology, which is considered as the backbone of the Semantic Web. In other words, the current Web is transformed from being machine-readable to machine-understandable. In fact, Ontology is a key technique with which to annotate semantics and provide a common, comprehensible foundation for resources on the Semantic Web. Moreover, Ontology can provide a common vocabulary, a grammar for publishing data, and can supply a semantic description of data which can be used to preserve the Ontologies and keep them ready for inference. This paper provides basic concepts of web services and the Semantic Web, defines the structure and the main applications of ontology, and provides many relevant terms are explained in order to provide a basic understanding of ontologies.

We present the sTeX+ system, a user-driven advancement of sTeX - a semantic extension of LaTeX that allows for producing high-quality PDF documents for (proof)reading and printing, as well as semantic XML/OMDoc documents for the Web or further processing. Originally sTeX had been created as an invasive, semantic frontend for authoring XML documents. Here, we used sTeX in a Software Engineering case study as a formalization tool. In order to deal with modular pre-semantic vocabularies and relations, we upgraded it to sTeX+ in a participatory design process. We present a tool chain that starts with an sTeX+ editor and ultimately serves the generated documents as XHTML+RDFa Linked Data via an OMDoc-enabled, versioned XML database. In the final output, all structural annotations are preserved in order to enable semantic information retrieval services.

We propose and evaluate an immuno-inspired approach to misbehavior detection in ad hoc wireless networks. Node misbehavior can be the result of an intrusion, or a software or hardware failure. Our approach is motivated by co-stimulatory signals present in the Biological immune system. The results show that co-stimulation in ad hoc wireless networks can both substantially improve energy efficiency of detection and, at the same time, help achieve low false positives rates. The energy efficiency improvement is almost two orders of magnitude, if compared to misbehavior detection based on watchdogs. We provide a characterization of the trade-offs between detection approaches executed by a single node and by several nodes in cooperation. Additionally, we investigate several feature sets for misbehavior detection. These feature sets impose different requirements on the detection system, most notably from the energy efficiency point of view.