Maintaining awareness using policies; Enabling agents to identify relevance of information

The field of computer supported cooperative work aims at providing information technology models, methods, and tools that assist individuals to cooperate. The presented paper is based on three main observations from literature. First, one of the problems in utilizing information technology for cooperation is to identify the relevance of information, called awareness. Second, research in computer supported cooperative work proposes the use of agent technologies to aid individuals to maintain their awareness. Third, literature lacks the formalized methods on how software agents can identify awareness. This paper addresses the problem of awareness identification. The main contribution of this paper is to propose and evaluate a formalized structure, called Policy-based Awareness Management (PAM). PAM extends the logic of general awareness in order to identify relevance of information. PAM formalizes existing policies into Directory Enabled Networks-next generation structure and uses them as a source for awareness identification. The formalism is demonstrated by applying PAM to the space shuttle Columbia disaster occurred in 2003. The paper also argues that efficacy and cost-efficiency of the logic of general awareness will be increased by PAM. This is evaluated by simulation of hypothetical scenarios as well as a case study.     

SpectralCAT: Categorical spectral clustering of numerical and nominal data

Data clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. Although many clustering algorithms have been proposed, most of them deal with clustering of one data type (numerical or nominal) or with mix data type (numerical and nominal) and only few of them provide a generic method that clusters all types of data. It is required for most real-world applications data to handle both feature types and their mix. In this paper, we propose an automated technique, called SpectralCAT, for unsupervised clustering of high-dimensional data that contains numerical or nominal or mix of attributes. We suggest to automatically transform the high-dimensional input data into categorical values. This is done by discovering the optimal transformation according to the Calinski–Harabasz index for each feature and attribute in the dataset. Then, a method for spectral clustering via dimensionality reduction of the transformed data is applied. This is achieved by automatic non-linear transformations, which identify geometric patterns in the data, and find the connections among them while projecting them onto low-dimensional spaces. We compare our method to several clustering algorithms using 16 public datasets from different domains and types. The experiments demonstrate that our method outperforms in most cases these algorithms.     

PDDRA: A new pre-fetching based dynamic data replication algorithm in data grids

In recent years, grid technology has had such a fast growth that it has been used in many scientific experiments and research centers. A large number of storage elements and computational resources are combined to generate a grid which gives us shared access to extra computing power. In particular, data grid deals with data intensive applications and provides intensive resources across widely distributed communities. Data replication is an efficient way for distributing replicas among the data grids, making it possible to access similar data in different locations of the data grid. Replication reduces data access time and improves the performance of the system. In this paper, we propose a new dynamic data replication algorithm named PDDRA that optimizes the traditional algorithms. Our proposed algorithm is based on an assumption: members in a VO (Virtual Organization) have similar interests in files. Based on this assumption and also file access history, PDDRA predicts future needs of grid sites and pre-fetches a sequence of files to the requester grid site, so the next time that this site needs a file, it will be locally available. This will considerably reduce access latency, response time and bandwidth consumption. PDDRA consists of three phases: storing file access patterns, requesting a file and performing replication and pre-fetching and replacement. The algorithm was tested using a grid simulator, OptorSim developed by European Data Grid projects. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, effective network usage, total number of replications, hit ratio and percentage of storage filled.     

Resolving Ambiguity: The Effect of Experience on Interpretation of Ambiguous Events in Generalized Social Phobia.

The authors examined the effect of learning on multiple encounters with sources of social ambiguity in individuals with generalized social phobia (GSP). The authors modified G. B. Simpson and H. Kang's (1994) paradigm and presented prime-target word pairs to individuals with GSP and nonanxious controls (NACs) to prime threat and nonthreat meanings of homographs and examine the effect of this priming on later encounters with that homograph. Consistent with previous research, NACs showed faster response latencies naming a target primed by a homograph with the same meaning activated in two successive trials than naming the same target primed by an unrelated word. Furthermore, NACs showed slower response latencies naming a target when a different meaning of the homograph prime was activated in successive trials than naming a target primed by an unrelated word. GSP participants did not show this pattern in learning a nonthreat meaning of a homograph. These results support the hypothesis that a faulty learning mechanism may be involved in social anxiety. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Study on morphology,thermal, mechanical and Cd(II) adsorption properties of PVC/α-MnO<Subscript>2</Subscript>-stearic acid nanocomposites: production and application

Alpha manganese dioxide nanorods (α-MnO₂) were successfully functionalized with stearic acid (SA) by solvothermal method to prevent agglomeration. The α-MnO₂-SA nanorods were employed as a filler for the preparation of poly(vinyl chloride) (PVC) nanocomposite (NC) films with different percentages (1, 3 and 5 wt%). The morphology, mechanical and thermal properties of the obtained NCs were investigated. The results showed that α-MnO₂-SA can effectively improve the properties of PVC. The enhancement in properties of the NCs was attributed to the improved interfacial bonding by modification. Also, these NCs were used as adsorbent for removal of cadmium ions. Our finding suggests that the PVC/α-MnO₂-SA NCs are good candidates for efficient Cd(??) removal from the wastewater.     

Mathematical Modeling and Genetic Algorithm Optimization of Reactive Absorption of H2S

A rate‐based mathematical model was developed for the reactive absorption of H₂S in NaOH, with NaOCl or H₂O₂ as the chemical oxidant solutions in a packed column. A modified mass transfer coefficient in the gas phase was obtained by genetic algorithm and implemented in the model to correct the assumption of instantaneous reactions. The effects of different operating variables including the inlet H₂S concentration, inlet gas mass flux, initial NaOH, concentrations of the chemical oxidants in the scrubbing solutions, and liquid‐to‐gas ratio on the H₂S removal efficiency were studied. A genetic algorithm was employed to optimize the operating variables in order to obtain maximum removal efficiency of H₂S. The model results were in good agreement with the experimental data.     

A new evaluation measure for color image segmentation based on genetic programming approach

One of the greatest challenges while working on image segmentation algorithms is a comprehensive measure to evaluate their accuracy. Although there are some measures for doing this task, but they can consider only one aspect of segmentation in evaluation process. The performance of evaluation measures can be improved using a combination of single measures. However, combination of single measures does not always lead to an appropriate criterion. Besides its effectiveness, the efficiency of the new measure should be considered. In this paper, a new and combined evaluation measure based on genetic programming (GP) has been sought. Because of the nature of evolutionary approaches, the proposed approach allows nonlinear and linear combinations of other single evaluation measures and can search within many and different combinations of basic operators to find a good enough one. We have also proposed a new fitness function to make GP enable to search within search space effectively and efficiently. To test the method, Berkeley and Weizmann datasets besides several different experiments have been used. Experimental results demonstrate that the GP based approach is suitable for effective combination of single evaluation measures.     

Mechanism of Martensitic to Equiaxed Microstructure Evolution during Hot Deformation of a Near-Alpha Ti Alloy

Metallurgical and Materials Transactions A - In this study, mechanisms of microstructural evolution during hot deformation of Ti-1100 were investigated by EBSD analysis. Misorientation angle...