Subsethood-based Fuzzy Rule Models and their Application to Student Performance

K. Rasmani and Q. Shen. Subsethood-based Fuzzy Rule Models and their Application to Student Performance Classification. Proceedings of the 14th International Conference on Fuzzy Systems, pages 755-760, 2005.

Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers

K. Rasmani and Q. Shen. Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1679-1684, 2004

Encouraging Complementary Fuzzy Rules within Iterative Rule Learning

M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 15-22.

Hamilton?Jacobi?Bellman equations for quantum optimal feedback control

Gough, John; Belavkin, V.P.; Smolianov, O.G., (2005) 'Hamilton?Jacobi?Bellman equations for quantum optimal feedback control', Journal of Optics B: Quantum and Semiclassical Optics 7 pp.S237-S244 RAE2008

The Iranian Foreign Policy in the Persian Gulf Region under the Rule of President Hassan Rouhani: Continuity of Change

Although Iran borders with many states and has direct access to the Caspian Sea as well as the Indian Ocean, the Persian Gulf region seems to be the most vital area to its security and prosperity. Yet since the 70’s Iran’s relations with the Arab states in the region have been rather strained and complex. The main reason for that had been the success of the Islamic revolution in 1979 which later resulted in a new dimension of Sunni-Shia rivalry. Moreover, post-revolutionary Iranian authorities also intended to maintain the regional hegemony from the Imperial State of Iran period. As a result, successive Iranian governments competed for hegemony in the Persian Gulf with the littoral Arab states which consolidated their regional positions due to close links and intensive cooperation with the West especially with the United States. Despite some political and economic initiatives which were undertaken by President Mahmoud Ahmadinejad, this rivalry was also evident between 2005–2013. The main aim of this article is to find out whether Iranian foreign policy towards the Arab states in the Persian Gulf region has undergone any significant changes since Hassan Rouhani became the President of the Islamic Republic of Iran in August 2013. According to Mohammad Reza Deshiri, the Iranian foreign policy after 1979 can be divided into so-called waves of idealism and realism. During dominance of idealism values and spirituality are more important than pragmatism while during the realistic waves political as well as economic interests prevail over spirituality. Iranian idealism is connected with export of revolutionary ideas, Shia dominance as well as the restoration of unity among all muslims (ummah). On this basis both presidential terms of Mahmoud Ahmadinejad can be classified as ‘waves of idealism’, albeit some of his ideas were very pragmatic. The question is if Hassan Rouhani’s foreign policy represents a continuity or a change. Is the current Iran’s foreign policy towards the Persian Gulf region idealistic or rather realistic? The main assumption is that there will be no Arab-Iranian rapprochement in the Persian Gulf without a prior normalization of political relations between Iran and the West especially the United States.

Optimal Scheduling of Secondary Content for Aggregation in Video-on-Demand Systems

Dynamic service aggregation techniques can exploit skewed access popularity patterns to reduce the costs of building interactive VoD systems. These schemes seek to cluster and merge users into single streams by bridging the temporal skew between them, thus improving server and network utilization. Rate adaptation and secondary content insertion are two such schemes. In this paper, we present and evaluate an optimal scheduling algorithm for inserting secondary content in this scenario. The algorithm runs in polynomial time, and is optimal with respect to the total bandwidth usage over the merging interval. We present constraints on content insertion which make the overall QoS of the delivered stream acceptable, and show how our algorithm can satisfy these constraints. We report simulation results which quantify the excellent gains due to content insertion. We discuss dynamic scenarios with user arrivals and interactions, and show that content insertion reduces the channel bandwidth requirement to almost half. We also discuss differentiated service techniques, such as N-VoD and premium no-advertisement service, and show how our algorithm can support these as well.

Object Detection at the Optimal Scale with Hidden State Shape Models

Hidden State Shape Models (HSSMs) [2], a variant of Hidden Markov Models (HMMs) [9], were proposed to detect shape classes of variable structure in cluttered images. In this paper, we formulate a probabilistic framework for HSSMs which provides two major improvements in comparison to the previous method [2]. First, while the method in [2] required the scale of the object to be passed as an input, the method proposed here estimates the scale of the object automatically. This is achieved by introducing a new term for the observation probability that is based on a object-clutter feature model. Second, a segmental HMM [6, 8] is applied to model the "duration probability" of each HMM state, which is learned from the shape statistics in a training set and helps obtain meaningful registration results. Using a segmental HMM provides a principled way to model dependencies between the scales of different parts of the object. In object localization experiments on a dataset of real hand images, the proposed method significantly outperforms the method of [2], reducing the incorrect localization rate from 40% to 15%. The improvement in accuracy becomes more significant if we consider that the method proposed here is scale-independent, whereas the method of [2] takes as input the scale of the object we want to localize.

Rule Extraction, Fuzzy ARTMAP, and Medical Databases

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.

Dynamic Programming for Optimal Control of Set-Up Scheduling with Neural Network Modifications

This paper demonstrates an optimal control solution to change of machine set-up scheduling based on dynamic programming average cost per stage value iteration as set forth by Cararnanis et. al. [2] for the 2D case. The difficulty with the optimal approach lies in the explosive computational growth of the resulting solution. A method of reducing the computational complexity is developed using ideas from biology and neural networks. A real time controller is described that uses a linear-log representation of state space with neural networks employed to fit cost surfaces.

Genetically Generated Neural Networks II: Searching for an Optimal Representation

Genetic Algorithms (GAs) make use of an internal representation of a given system in order to perform optimization functions. The actual structural layout of this representation, called a genome, has a crucial impact on the outcome of the optimization process. The purpose of this paper is to study the effects of different internal representations in a GA, which generates neural networks. A second GA was used to optimize the genome structure. This structure produces an optimized system within a shorter time interval.

Impact of non-optimal grounding of the CC2420 RFIC on a 802.15.4 Tyndall sensor wireless mote

The performance of an RF output matching network is dependent on integrity of the ground connection. If this connection is compromised in anyway, additional parasitic elements may occur that can degrade performance and yield unreliable results. Traditionally, designers measure Constant Wave (CW) power to determine that the RF chain is performing optimally, the device is properly matched and by implication grounded. It is shown that there are situations where modulation quality can be compromised due to poor grounding that is not apparent using CW power measurements alone. The consequence of this is reduced throughput, range and reliability. Measurements are presented on a Tyndall Mote using a CC2420 RFIC todemonstrate how poor solder contact between the ground contacts and the ground layer of the PCB can lead tothe degradation of modulated performance. Detailed evaluation that required the development of a new measurement definition for 802.15.4 and analysis is presented to show how waveform quality is affected while the modulated output power remains within acceptable limits.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

Optimal assessment of the ability of children with recurrent respiratory tract infections to produce anti-polysaccharide antibodies.

Specific anti-polysaccharide antibody deficiency (SPAD) is an immune disorder. Diagnostic criteria have not yet been defined clearly. One hundred and seventy-six children evaluated for recurrent respiratory tract infections were analysed retrospectively. For each subject, specific anti-pneumococcal antibodies had been measured with two enzyme-linked immunosorbent assays (ELISAs), one overall assay (OA) using the 23-valent pneumococcal polysaccharide vaccine (23-PPSV) as detecting antigen and the other purified pneumococcal polysaccharide serotypes (serotype-specific assay, SSA) (serotypes 14, 19F and 23F). Antibody levels were measured before (n = 176) and after (n = 93) immunization with the 23-PPSV. Before immunization, low titres were found for 138 of 176 patients (78%) with OA, compared to 20 of 176 patients (11%) with the SSA. We found a significant correlation between OA and SSA results. After immunization, 88% (71 of 81) of the patients considered as responders in the OA test were also responders in the SSA; 93% (71 of 76) of the patients classified as responders according to the SSA were also responders in the OA. SPAD was diagnosed in 8% (seven of 93) of patients on the basis of the absence of response in both tests. Thus, we propose to use OA as a screening test for SPAD before 23-PPSV immunization. After immunization, SSA should be used only in case of a low response in OA. Only the absence of or a very low antibody response detected by both tests should be used as a diagnostic criterion for SPAD.