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Abstract: The paper develops and discusses a theoretical model for collaborative design systems based on motivated learning agents, however, with a novel self-development module to help the system improve itself. Self-Development Agent based on previous work is an intelligent agent, which not only receives information from sensors in the environment, but also gives valuable suggestions that could help to improve the system. Two case studies with different system setups are described in detail to help better understand this model. The theoretical model is not limited to the specific systems described in this paper, but could be adapted to other collaboration systems as well. [ABSTRACT FROM AUTHOR]

This paper presents a novel clustering method this is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustering problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering processes. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase the accuracy of clustering problems. Experimental results from three different examples, including two benchmark data sets and one practical application, verify the effectiveness and applicability of the proposed work. [Copyright &y& Elsevier]

Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data. [ABSTRACT FROM AUTHOR]

• A comprehensive survey/review paper on computer-aided diagnosis (CAD) in medicine of 251 publications. • Referenced publications had to pass criteria to determine subject relevance and quality of publication. • It bridges a crucial gap that currently exists in the literature regarding an overview of CAD systems in medicine. • It provides a venue for people with different backgrounds to understand pertinent aspects in medicine and in computer science. • Provides a foundation for accelerating research in this vital field in the interface of medicine and computer science. Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine. Furthermore, CAD systems in medicine may process clinical data that can be complex and/or massive in size. They do so in order to infer new knowledge from data and use that knowledge to improve their diagnostic performance over time. Therefore, such systems can also be viewed as intelligent systems because they use a feedback mechanism to improve their performance over time. The main aim of the literature survey described in this paper is to provide a comprehensive overview of past and current CAD developments. This survey/review can be of significant value to researchers and professionals in medicine and computer science. There are already some reviews about specific aspects of CAD in medicine. However, this paper focuses on the entire spectrum of the capabilities of CAD systems in medicine. It also identifies the key developments that have led to today's state-of-the-art in this area. It presents an extensive and systematic literature review of CAD in medicine, based on 251 carefully selected publications. While medicine and computer science have advanced dramatically in recent years, each area has also become profoundly more complex. This paper advocates that in order to further develop and improve CAD, it is required to have well-coordinated work among researchers and professionals in these two constituent fields. Finally, this survey helps to highlight areas where there are opportunities to make significant new contributions. This may profoundly impact future research in medicine and in select areas of computer science. [ABSTRACT FROM AUTHOR]

Abstract: This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained. [Copyright &y& Elsevier]

Abstract: Railway scheduling has been a significant issue in the railway industry. Over the last few years, numerous approaches and tools have been developed to compute railway scheduling. However, robust solutions are necessary to absorb short disruptions. In this paper, we present the robustness problem from the point of view of railway operators and we propose analytical and simulation methods to measure robustness in a single railway line. In the analytical approach, we have developed some formulas to measure robustness based on the study of railway line infrastructure topology and buffer times. In the simulation approach, we have developed a software tool to assess the robustness for a given schedule. These methods have been inserted in MOM (More information can be found at the MOM web page http://www.dsic.upv.es/users/ia/gps/MOM), which is a project in collaboration with the Spanish Railway Infrastructure Manager (ADIF). [Copyright &y& Elsevier]

Abstract: Text retrieval has received a lot of attention in computer science. In the text retrieval field, the most widely-adopted similarity technique is using vector space models (VSM) to evaluate the weight of terms and using Cosine, Jaccard or Dice to measure the similarity between the query and the texts. However, these similarity techniques do not consider the effect of the sequence of the information. In this paper, we propose an integrated text retrieval (ITR) mechanism that takes the advantage of both VSM and longest common subsequence (LCS) algorithm. The key idea of the ITR mechanism is to use LCS to re-evaluate the weight of terms, so that the sequence and weight relationships between the query and the texts can be considered simultaneously. The results of mathematical analysis show that the ITR mechanism can increase the similarity on Jaccard and Dice similarity measurements when a sequential relationship exists between the query and the texts. [Copyright &y& Elsevier]

Abstract: Predicting the preferences of users and providing the personalized services or products based on their preferences are the important issues. However, the research considering users’ preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose an agent-based framework for providing the personalized services using context history on context-aware computing. Based on the proposed framework, we implement a prototype system to show the feasibility of the framework. Previous researches require that the users input their preference manually, but this research provides the personalized services extracting the relationship between users’ profile and services under the same context automatically. [Copyright &y& Elsevier]

This paper proposes an image interpolation model based on probabilistic neural network (PNN). The method adjusts automatically the smoothing parameters for varied smooth/edge image region, and takes into consideration both smoothness (flat region) and sharpness (edge region) characteristics at the same model. A single neuron, combined with PSO training, is used for sharpness/smoothness adaptation. Finally, we report the performance of these newly proposed methods in other image interpolation method. [Copyright &y& Elsevier]

For continuous systems with polytopic uncertainties, the mixed H 2/H ∞ robust output control problem is considered in this paper. The design objective is to seek a static output feedback controller that is able to stabilize the uncertain system and to minimize the mixed H 2/H ∞ norm. To this end, a hybrid algorithm mixed by the technique of Hurwitz testing matrix of family of polynomial, average performance constraint and genetic algorithm (GA) is proposed. Due to the fact that the algorithm is based on GAs, we use a hierarchical structure to merge the conditions of Hurwitz testing matrix technique and average performance constraint into a fitness function which is called Hierarchical Fitness Function Structure (HFFS). By replacing fitness function with HFFS, any GAs can be applied to constitute the algorithm. Also, the proposed approach does not need any assumptions, so it is much more relaxed than that of Lyapunov-based design methods. [Copyright &y& Elsevier]

Abstract: This paper experimentally examines the effects of domain complexity on the quality of knowledge acquired and the efficiency with which knowledge acquisition (KA) is accomplished. The experiment is performed using two of the most prominent KA methods: unstructured interviews and protocol analysis. One contribution of the research is that it employs an experimental strategy that pays particular attention to addressing problematic issues of measurement and control. The experimental results indicate that domain complexity strongly influences the quality of knowledge acquired when protocol analysis is used for KA. While finding the other hypotheses to be inconclusive, some interesting trends were also identified regarding the quality and efficiency of knowledge acquisition methods. [Copyright &y& Elsevier]

There are some problems that need expertise in order to get a satisfactory solution. Ferryboat carries goods, fresh water, diesel oil, luggage and storing rooms up to its permissible draft in order to maintain safety according to the international safety regulations. The best weight distribution on ferryboat needs human expertise to handle many variables, such as the amount of the bunker and fresh water that allow us to use more rooms for charging in order to maximize the profit. This sort of problems can be classified under Configuration Problem. In this paper, we address the development of a ferryboat expert systems (WDFB) using CommonKADS knowledge engineering methodology. We propose a reusable problem-solving approach, which is an enhancement of the structure-oriented approach, capable of solving the ferryboat configuration problem. The proposed model includes heuristics that make the search of suitable configuration more efficient, taking into consideration the transformation knowledge and the optimality criteria. The results of testing the system on a real-world data from National Navigation Company, Suez, Egypt, were satisfactory. [Copyright &y& Elsevier]