206618709
Building an Ontology for Financial Investment
Intelligent agent technologies hold great promise for the financial services and investment industries such as portfolio management. In financial investment, a multi-agent system approach is natural because of the multiplicity of information sources and the different expertise that must be brought to bear to produce a good recommendation (such as a stock buy or sell decision). The agents in a multi-agent system need to coordinate, cooperate or communicate with each other to solve a complex problem. However, ontologies are a key component in how different agents in a multi-agent system can communicate effectively, and how the knowledge of agents can develop. This paper presents a case study in building an ontology in financial investment. The lessons we learned from the construction process are discussed. Based on our ontology development experience and the current development of ontologies, a framework of next generation ontology construction tools, which is aimed to facilitate the ontology construction, is proposed.
45852164
An Agent-Based Hybrid Intelligent System for Financial Investment Planning
Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. however, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. this paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. this system currently consists of 13 different agents. the experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. the system is very flexible and robust. the success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.
6709890
An agent-based hybrid framework for database mining
While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.
1383532
New directions in the theory of identification via channels
Studies two problems in the theory of identification via channels. The first problem concerns the identification via channels with noisy feedback. Whereas for Shannon's transmission problem the capacity of a discrete memoryless channel does not change with feedback, it is known that the identification capacity is affected by feedback. The authors study its dependence on the feedback channel. They prove both, a direct and a converse coding theorem. Although a gap exists between the upper and lower bounds provided by these two theorems, the known result for channels without feedback and the known result for channels with complete feedback, are both special cases of these two new theorems, because in these cases the bounds coincide. The second problem is the identification via wiretap channels. A secrecy identification capacity is defined for the wiretap channel. A "dichotomy theorem" is proved which says that the second-order secrecy identification capacity is the same as Shannon's capacity for the main channel as long as the secrecy transmission capacity of the wiretap channel is not zero, and zero otherwise. Equivalently, one can say that the identification capacity is not lowered by the presence of a wiretapper as long as 1 bit can be transmitted (or identified) correctly with arbitrarily small error probability. This is in strong contrast to the case of transmission. >
3877390
Distributed meetings: a meeting capture and broadcasting system
The common meeting is an integral part of everyday life for most workgroups. However, due to travel, time, or other constraints, people are often not able to attend all the meetings they need to. Teleconferencing and recording of meetings can address this problem. In this paper we describe a system that provides these features, as well as a user study evaluation of the system. The system uses a variety of capture devices (a novel 360° camera, a whiteboard camera, an overview camera, and a microphone array) to provide a rich experience for people who want to participate in a meeting from a distance. The system is also combined with speaker clustering, spatial indexing, and time compression to provide a rich experience for people who miss a meeting and want to watch it afterward.
8318108
Weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
28725102
A Framework for Component Reuse in a Metamodelling-Based Software Development
Reuse forms one key mechanism in improving system development productivity and quality. In this paper we suggest a systematic framework for reuse processes, which is driven by ideas and concepts based on metamodelling. The framework enables us to consider reuse in a comprehensive manner and to develop support for reuse processes within a metaCASE environment. By utilising metamodelling concepts we expand reuse situations to cover both design artefacts and method components. Accordingly, the reuse framework consists of a system development process model, a type level hierarchy, a classification of reuse situation types, a component granularity model, and a model of reuse operations. We demonstrate the usefulness of the framework by analysing reuse support in a metaCASE environment: MetaEdit+. We also examine how the framework can help to strategise over possible software development scenarios (patterns) that involve various levels of reuse in situations where software requirements change considerably and there is a considerable number of design assets to exploit.
5700504
Localized algorithm for aggregate fairness in wireless sensor networks
For data-collection applications in sensor networks, it is important to ensure all data sources have equal (or weighted) access to network bandwidth so that the base stations receive a complete picture about the monitored area. We point out the fairness problem in the current design of sensor networks, which may cause extremely biased bandwidth allocations. It is a challenge to design a fully distributed fairness solution due to the lack of global knowledge about the distribution of data sources and their routing paths. This paper proposes a new aggregate fairness model and a localized algorithm (called AFA) that implements the model. AFA is designed to work with any routing protocol. In particular, it allows the packets from a data source to follow an arbitrary set of forwarding paths to the base stations. This flexibility makes it considerably harder to allocate bandwidth fairly among different data sources. AFA solves the problem with only localized operations at the sensors. It is easy to implement, which is an attractive property for sensor networks. Moreover, the algorithm automatically adjusts a sensor's forwarding rate to avoid packet drops due to downstream congestion, which helps improve energy efficiency. We perform extensive simulations, demonstrating that the proposed algorithm can effectively improve end-to-end fairness.