One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.

This paper presents the current state of this research work, aimed to develop a methodology for designing Adaptive Virtual Organizations. This paper includes both completed and remaining work on this topic.

The While my affiliation is to the Robotic Embedded Systems patch of interest was tagged with a GPStracked drifter and Lab at USC, I have worked with my advisor Prof. Gaurav the AUV surveyed within the Lagrangian frame of reference Sukhatme to build up a collaboration with biologists of the advecting patch (Das et al. 2010a). We are investigating and oceanographers both at USC and at the Monterey Bay a multi-criteria utility based technique to acquire discrete Aquarium Research Institute (MBARI).

Behaviour recognition aims to infer the particular behaviours of the inhabitant in a smart home from a series of sensor readings from around the house. There are many reasons to recognise human behaviours; one being to monitor the elderly or cognitively impaired and detect potentially dangerous behaviours. We view the behaviour recognition problem as the task of mapping the sensory outputs to a sequence of recognised activities. This research focuses on the development of machine learning methods to find an approximation to the mapping between sensor outputs and behaviours. However, learning the mapping raises an important issue, which is that the training data is not necessarily annotated with exemplar behaviours of the inhabitant. This doctoral study takes several steps towards addressing the problem of finding an approximation to this mapping, beginning with separate investigations on current methods proposed in the literature, identifying useful sensory outputs for behaviour recognition, and concluding by proposing two directions: one using supervised learning on annotated sensory stream and one using unsupervised learning on unannotated ones.

To encompass them, several extensions of Dung's argumentation framework (AF) [Dung, This extended research abstract describes an 1995] have been proposed, but the most general, as shown in argumentation-based approach to modelling articulated [Baroni et al., 2011], is the Argumentation Framework with decision making contexts. The approach Recursive Attacks (AF RA) formalism [Baroni et al., 2009b; encompasses a variety of argument and attack 2011]. In[Baroni et al., 2009a; 2010b] we showed how to organise schemes aimed at representing basic knowledge arguments that are instances of argument schemes in and reasoning patterns for decision support.

My work focuses on building computational agents that assist people in managing their activities in environments in which tempo and complexity outstrip people’s cognitive capacity,such as in coordinating rescue teams in the aftermath of a disaster, or in helping people with dementia manage their everyday lives. A critical challenge faced in such environments is not only that individuals must factor complicated constraints into deciding how and when to act on their own goals, but also that their decisions are further constrained by choices made by others with whom they interact, such as between cooperating teams in disaster relief or between patients and caregivers in an assisted-living facility. An additional challenge in such situations is that the interests of individuals, such as privacy and autonomy, along with slow, costly, uncertain,or otherwise problematic communication may further limitindividuals’ abilities to work together. My work assumes that a computational agent is associated with each individual, and that these agents will work together efficiently to manage individual and joint activities, while maintaining autonomy and privacy to the extent possible.

Linear classification is a useful tool for dealing with large-scale data in applications such as document classification and natural language processing. Recent developments of linear classification have shown that the training process can be efficiently conducted. However, when the data size exceeds the memory capacity, most training methods suffer from very slow convergence due to the severe disk swapping. Although some methods have attempted to handle such a situation, they are usually too complicated to support some important functions such as parameter selection. In this paper, we introduce a block minimization framework for data larger than memory. Under the framework, a solver splits data into blocks and stores them into separate files. Then, at each time, the solver trains a data block loaded from disk. Although the framework is simple, the experimental results show that it effectively handles a data set 20 times larger than the memory capacity.

Many people read online reviews written by other users to learn more about a product or venue. However, the overwhelming amount of user- generated reviews and variance in length, detail and quality across the reviews make it difficult to glean useful information. In this paper, we present a summarization system called Review Spotlight. It provides a brief overview of reviews by using adjective- noun word pairs extracted from the review text. The system also allows the user to click any word pair to read the original sentences from which the word pair was extracted. We present our system implementation as a Google Chrome browser extension, and an evaluation on how two word pair scoring methods (TF and TF-IDF) affect the identification of useful word pairs.

Weighted Pairwise Classification (OWPC) loss [Usunier et al., 2009] which has been shown to be state-of-the-art on Image annotation datasets are becoming larger and (small) text retrieval tasks. WARP uses stochastic gradient larger, with tens of millions of images and tens descent and a novel sampling trick to approximate ranks resulting of thousands of possible annotations. We propose in an efficient online optimization strategy which we a strongly performing method that scales to show is superior to standard stochastic gradient descent applied such datasets by simultaneously learning to optimize to the same loss, enabling us to train on datasets that precision at the top of the ranked list of annotations do not even fit in memory.

Artificial intelligence has a long history of learning from domain problems ranging from chess to jeopardy. In this work, we look at a problem stemming from social science, namely, how do social relationships influence communication content and vice versa. The tools used to study communication content (content analysis) have rarely been combined with those used to study social relationships (social network analysis). Furthermore, there is even less work addressing the longitudinal characteristics of such a combination. This paper presents a general framework for measuring the dynamic bi-directional influence between communication content and social networks. The framework leverages the idea that knowledge about both kinds of networks can be represented using the same knowledge representation. In particular, through the use of Semantic Web standards, the extraction of networks is made easier. The framework is applied to two use-cases: online forum discussions and conference publications. The results provide a new perspective over the dynamics involving both social networks and communication content.