Visual tools for analysing evolution, emergence, and error in data streams
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
A smart proxy for a next generation web services transaction
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
Using links to aid web classification
- Authors: Xie, Wei , Mammadov, Musa , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 981-986
- Full Text:
- Description: In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
- Description: 2003005438
A fully automated CAD system using multi-category feature selection with restricted recombination
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
Image retrieval based on fuzzy mapping of image database and fuzzy similarity distance
- Authors: Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 812-817
- Full Text:
- Description: The on-line image retrieval process consists of a query example image, given by the user as an input, from which low-level image features are extracted. These image features are used to find images in the database which are most similar to the query image. A drawback, however, is that these low level image features are often too restricted to describe images on a conceptual or semantic level. The gap between the high level query from the user and low level features extracted by a computer is known as the semantic gap. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. This paper proposes a novel fuzzy approach for mapping the fuzzy database while extracting the colour features from image and assigning the weights to this fuzzy content when calculating the similarity between the query image and the images in database. Number of experiments was conducted on a small colour image database and promising results were obtained.
- Description: 2003005444
Virtual machine consolidation in cloud data centers using ACO metaheuristic C3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Calheiros, Rodrigo , Buyya, Rajkumar
- Date: 2014
- Type: Text , Conference paper
- Relation: 20th International Conference on Parallel Processing, Euro-Par 2014 Vol. 8632 LNCS, p. 306-317
- Full Text: false
- Reviewed:
- Description: In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.
Modeling of secured cloud network: - The case of an educational institute
- Authors: Bevinakoppa, Savitri , Sharma, Geetu , Venkatraman, Sitalakshmi
- Date: 2013
- Type: Text , Conference paper
- Relation: Recent researches in Infromation Science & Applications p. 150-155
- Full Text: false
- Reviewed:
Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions
- Authors: Ulhaq, Anwaar , Yin, Xiaoxia , Zhang, Yunchan , Gondal, Iqbal
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016; Lecce, Italy; 24th-27th October 2016; published in Advanced Conepts for Intelligent Vision Systems (Lecture Notes in Computer Science series) Vol. 10016 LNCS, p. 465-476
- Full Text: false
- Reviewed:
- Description: Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches. © Springer International Publishing AG 2016.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Dynamical analysis of neural networks with time-varying delays using the LMI approach
- Authors: Lakshmanan, Shanmugam , Lim, Cheepeng , Bhatti, Asim , Gao, David , Nahavandi, Saeid
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9491, p. 297-305
- Full Text: false
- Reviewed:
- Description: This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions. © Springer International Publishing Switzerland 2015.
Simplifying and improving ant-based clustering
- Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
- Date: 2011
- Type: Text , Conference paper
- Relation: 11th International Conference on Computational Science, ICCS 2011; Singapore, Singapore; 1st-3rd June 2011, published in Procedia Computer Science Vol. 4, p. 46-55
- Full Text:
- Reviewed:
- Description: Ant-based clustering (ABC) is a data clustering approach inspired from cemetery formation activities observed in real ant colonies. Building upon the premise of collective intelligence, such an approach uses multiple ant-like agents and a mixture of heuristics, in order to create systems that are capable of clustering real-world data. Many recently proposed ABC systems have shown competitive results, but these systems are geared towards adding new heuristics, resulting in increasingly complex systems that are harder to understand and improve. In contrast to this direction, we demonstrate that a state-of-the-art ABC system can be systematically evaluated and then simplified. The streamlined model, which we call SABC, differs fundamentally from traditional ABC systems as it does not use the ant-colony and several key components. Yet, our empirical study shows that SABC performs more effectively and effciently than the state-of-the-art ABC system.
A mapreduce based technique for mining behavioral patterns from sensor data
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9492, p. 145-153
- Full Text: false
- Reviewed:
- Description: WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © Springer International Publishing Switzerland 2015.
Frequency decomposition based gene clustering
- Authors: Rahman, Md Abdur , Chetty, Madhu , Bulach, Dieter , Wangikar, Pramod
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9490, p. 170-181
- Full Text: false
- Reviewed:
- Description: Gene expressions have been commonly applied to understand the inherent underlying mechanism of known biological processes. Although the microarray gene expressions usually appear aperiodic, with proper signal processing techniques, its periodic components can be easily obtained. Thus, if expressions of interconnected (regulatory and regulated) genes are decomposed, at least one common frequency component will appear in these genes. Exploiting this novel concept, we propose a frequency decomposition approach for gene clustering to better understand the gene interconnection topology. This method, based on Hilbert Huang Transform (HHT) enables us to segregate every periodic component of the gene expressions. Next, a multilevel clustering is performed based on these frequency components. Unlike existing clustering algorithms, the proposed method assimilates a meaningful knowledge of the gene interactions topology. The information related to underlying gene interactions is vital and can prove useful in many existing evolutionary optimisation algorithms for genetic network reconstruction. We validate the entire approach by its application to a 15-gene synthetic network. © Springer International Publishing Switzerland 2015.
A technique for parallel share-frequent sensor pattern mining from wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Full Text:
- Reviewed:
- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
Reinforcement learning of pareto-optimal multiobjective policies using steering
- Authors: Vamplew, Peter , Issabekov, Rustam , Dazeley, Richard , Foale, Cameron
- Date: 2015
- Type: Text , Conference paper
- Relation: 28th Australasian Joint Conference on Artificial Intelligence, AI 2015; Canberra, ACT; 30th November-4th December 2015 Vol. 9457, p. 596-608
- Full Text: false
- Reviewed:
- Description: There has been little research into multiobjective reinforcement learning (MORL) algorithms using stochastic or non-stationary policies, even though such policies may Pareto-dominate deterministic stationary policies. One approach is steering which forms a nonstationary combination of deterministic stationary base policies. This paper presents two new steering algorithms designed for the task of learning Pareto-optimal policies. The first algorithm (w-steering) is a direct adaptation of previous approaches to steering, and therefore requires prior knowledge of recurrent states which are guaranteed to be revisited. The second algorithm (Q-steering) eliminates this requirement. Empirical results show that both algorithms perform well when given knowledge of recurrent states, but that Q-steering provides substantial performance improvements over w-steering when this knowledge is not available. © Springer International Publishing Switzerland 2015.
iPod therefore I can : Enhancing the learning of children with intellectual disabilities through emerging technologies
- Authors: Marks, Genee , Milne, Jay
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at ICICTE 2008: International Conference on Information Communication Technologies in Education, Corfu, Greece : 10th-12th July 2008
- Full Text:
- Description: This paper explores the pedagogical and social potential of emerging technologies, in particular the iPod, in facilitating the learning of young Australians with severe intellectual and social disabilities. The study, which was carried out in a segregated educational setting in Victoria, Australia, sought to establish whether the intrinsic portable, multi-media capabilities of the iPod particularly lent themselves to a practical application for students with severe disabilities. It was concluded that such new technology has considerable power and potential as an emerging pedagogy with students with severe intellectual and physical disabilities.
- Description: 2003006449