Problem solving as intelligent retrieval from distributed knowledge sources

Distributed computing in intelligent systems is investigated from a different perspective. From the viewpoint that problem solving can be viewed as intelligent knowledge retrieval, the use of distributed knowledge sources in intelligent systems is proposed

Expanding Database Keyword Search for Database Exploration

AbstractDatabase keyword search (DB KWS) has received a lot of attention in database research community. Although much of the research has been motivated by improving performance, recent research has also paid increased attention to its role in database contents exploration or data mining. In this paper we explore aspects related to DB KWS in two steps: First, we expand DB KWS by incorporating ontologies to better capture users’ intention. Furthermore, we examine how KWS or ontology-enriched KWS can offer useful hints for better understanding of the data and in-depth analysis of the data contents, or data mining

Creating NoSQL Biological Databases with Ontologies for Query Relaxation

AbstractThe complexity of building biological databases is well-known and ontologies play an extremely important role in biological databases. However, much of the emphasis on the role of ontologies in biological databases has been on the construction of databases. In this paper, we explore a somewhat overlooked aspect regarding ontologies in biological databases, namely, how ontologies can be used to assist better database retrieval. In particular, we show how ontologies can be used to revise user submitted queries for query relaxation. In addition, since our research is conducted at today's “big data” era, our investigation is centered on NoSQL databases which serve as a kind of “representatives” of big data. This paper contains two major parts: First we describe our methodology of building two NoSQL application databases (MongoDB and AllegroGraph) using GO ontology, and then discuss how to achieve query relaxation through GO ontology. We report our experiments and show sample queries and results. Our research on query relaxation on NoSQL databases is complementary to existing work in big data and in biological databases and deserves further exploration

Learning about Learners: System Learning in Virtual Learning Environment

Virtual learning is not just about a set of useful IT tools for learning. From an examination on where virtual learning stands in the overall learning spectrum, we point out the important impact of natural computing on virtual learning. We survey and analyze selected literature on important role of natural computing aspects, such as emergence (using swarm intelligence to achieve collective intelligence) and emotion, to virtual learning. In addition, in order to effectively incorporate these aspects into virtual learning, we propose using infrastructural support for virtual learning through system learning: The virtual learning environment not only provides facilities for learners, but also observes the behavior of learners and takes actions, so that its own performance can be improved (i.e., to better serve the learners). In this sense, system learning is concerned with learning about learners. Consequently, a virtual learning environment is a true human-machine symbiosis, paired by human learning and system learning

An iterative initial-points refinement algorithm for categorical data clustering

The original k-means clustering algorithm is designed to work primarily on numeric data sets. This prohibits the algorithm from being directly applied to categorical data clustering in many data mining applications. The k-modes algorithm [Z. Huang, Clustering large data sets with mixed numeric and categorical value, in: Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference. World Scientific, Singapore, 1997, pp. 21–34] extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, as is the case with most data clustering algorithms, the algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. The differences on the initial points often lead to considerable distinct cluster results. In this paper we present an experimental study on applying Bradley and Fayyad\u27s iterative initial-point refinement algorithm to the k-modes clustering to improve the accurate and repetitiveness of the clustering results [cf. P. Bradley, U. Fayyad, Refining initial points for k-mean clustering, in: Proceedings of the 15th International Conference on Machine Learning, Morgan Kaufmann, Los Altos, CA, 1998]. Experiments show that the k-modes clustering algorithm using refined initial points leads to higher precision results much more reliably than the random selection method without refinement, thus making the refinement process applicable to many data mining applications with categorical data

Genetic Algorithm Based Approach for Nucleic Acid Pattern Extraction

Finding a common pattern among nucleic acid sequences in a given database is an important yet relatively difficult problem in computational biology. Such a pattern is useful for describing the characteristics of a certain family of nucleic acid sequences, and can also be used for classification purposes as well as examine the closeness of two organisms. In this paper, we present a global pattern extraction tool named GAPE which can be applicable in computational biology to describe a certain family of nucleic acid sequences with common features. The algorithm utilizes an optimized Genetic Algorithm (GA) framework to drive the evolution of desirable patterns. A specialized pair-wise alignment algorithm is also introduced to efficiently examine the closeness of a sequence to a regular expression pattern. Experimental results using real biological data are shown to indicate the effectiveness of the tool

Using Optimization-Based Classification Method for Massive Datasets

Optimization-based algorithms, such as Multi-Criteria Linear programming (MCLP), have shown their effectiveness in classification. Nevertheless, due to the limitation of computation power and memory, it is difficult to apply MCLP, or similar optimization methods, to huge datasets. As the size of today’s databases is continuously increasing, it is highly important that data mining algorithms are able to perform their functions regardless of dataset sizes. The objectives of this paper are: (1) to propose a new stratified random sampling and majority-vote ensemble approach, and (2) to compare this approach with the plain MCLP approach (which uses only part of the training set), and See5 (which is a decision-tree-based classification tool designed to analyze substantial datasets), on KDD99 and KDD2004 datasets. The results indicate that this new approach not only has the potential to handle arbitrary-size of datasets, but also outperforms the plain MCLP approach and achieves comparable classification accuracy to See5

Modeling of Characteristics on Artificial Intelligence IQ Test: a Fuzzy Cognitive Map-Based Dynamic Scenario Analysis

This research article uses a Fuzzy Cognitive Map (FCM) approach to improve an earlier proposed IQ test characteristics of Artificial Intelligence (AI) systems. The defuzzification process makes use of fuzzy logic and the triangular membership function along with linguistic term analyses. Each edge of the proposed FCM is assigned to a positive or negative influence type associated with a quantitative weight. All the weights are based on the defuzzified value in the defuzzification results. This research also leverages a dynamic scenario analysis to investigate the interrelationships between driver concepts and other concepts. Worst and best-case scenarios have been conducted on the correlation among concepts. We also use an inference simulation to examine the concepts importance order and the FCM convergence status. The analysis results not only examine the FCM complexity, but also draws insightful conclusions

Modeling of Characteristics on Artificial Intelligence IQ Test: a Fuzzy Cognitive Map-Based Dynamic Scenario Analysis

This research article uses a Fuzzy Cognitive Map (FCM) approach to improve an earlier proposed IQ test characteristics of Artificial Intelligence (AI) systems. The defuzzification process makes use of fuzzy logic and the triangular membership function along with linguistic term analyses. Each edge of the proposed FCM is assigned to a positive or negative influence type associated with a quantitative weight. All the weights are based on the defuzzified value in the defuzzification results. This research also leverages a dynamic scenario analysis to investigate the interrelationships between driver concepts and other concepts. Worst and best-case scenarios have been conducted on the correlation among concepts. We also use an inference simulation to examine the concepts importance order and the FCM convergence status. The analysis results not only examine the FCM complexity, but also draws insightful conclusions

Intelligent Offloading in Blockchain-Based Mobile Crowdsensing Using Deep Reinforcement Learning

Mobile Crowdsensing (MCS) utilizes sensing data collected from users' mobile devices (MDs) to provide high-quality and personalized services, such as traffic monitoring, weather prediction, and service recommendation. In return, users who participate in crowdsensing (i.e., MCS participants) get payment from cloud service providers (CSPs) according to the quality of their shared data. Therefore, it is vital to guarantee the security of payment transactions between MCS participants and CSPs. As a distributed ledger, the blockchain technology is effective in providing secure transactions among users without a trusted third party, which has found many promising applications such as virtual currency and smart contract. In a blockchain, the proof-of-work (PoW) executed by users plays an essential role in solving consensus issues. However, the complexity of PoW severely obstructs the application of blockchain in MCS due to the limited computational capacity of MDs. To solve this issue, we propose a new framework based on Deep Reinforcement Learning (DRL) for offloading computation-intensive tasks of PoW to edge servers in a blockchain-based MCS system. The proposed framework can be used to obtain the optimal offloading policy for PoW tasks under the complex and dynamic MCS environment. Simulation results demonstrate that our method can achieve a lower weighted cost of latency and power consumption compared to benchmark methods