16105685
A Hybrid Learning Approach for TV Program Personalization
The rapid growth of communication technologies and the invention of set-top-box (STB) and personal digital recorder (PDR) have enabled today’s television to receive and store tremendous programs. The abundance of TV programs precipitates a need for personalization tools to help people obtain programs that they really want to watch. User preference learning plays an important role in TV program personalization. In this paper, we introduce a hybrid user preference learning approach for TV program personalization. The learning architecture is designed to integrate multiple learning sources for preference learning, which are explicit input/modification, user viewing history, and user real-time feedback. Among those, learning from user viewing history and learning from user real-time feedback are described in detail. The experimental results proved that the hybrid learning approach outperforms the learning method merely adopting user real-time feedback.
34374182
A Multi-Agent System for Personalized and Private Service in PDR
105158
A ubiquitous personalized multimedia service model based on FSM
The ubiquitous computing environment and users' demand for multimedia personalization precipitate a need for ubiquitous personalized multimedia services (UPMSs). Delivering UPMSs is very complex because of diverse device capabilities, dynamic network characteristics, etc. This paper proposes a lifecycle management model based on FSM (finite state machines) for ubiquitous personalized multimedia services (UPMSs). By using this model, the management of UPMSs is very clear and easy, and the services are delivered more efficiently, effectively, and robustly.
1113559
Fuzzy Clustering for TV Program Classification
In order to achieve TV program group recommendation, an approach based on fuzzy clustering is proposed for program classification. We first describe the XML based program description metadata representation, in which both textual and symbolic information is included. Secondly it presents the program feature extraction and presentation method. A program is defined as two vectors, one is based on term statistics implying what the program is about, and the other reflects broadcasting characteristics of the program. Then the classifying approach based on fuzzy clustering is proposed. The approach goes: normalizing original data, building fuzzy similarity matrix, and then clustering. The final fuzzy similarity matrix is constructed by combining two fuzzy similarity matrices calculated from two different aspects.
8948329
A strategic model for supply chain design with logical constraints: formulation and solution
This paper proposes a strategic production-distribution model for supply chain design with consideration of bills of materials (BOM). Logical constraints are used to represent BOM and the associated relationships among the main entities of a supply chain such as suppliers, producers, and distribution centers. We show how these relationships are formulated as logical constraints in a mixed integer programming (MIP) model, thus capturing the role of BOM in the selection of suppliers in the strategic design of a supply chain. A test problem is presented to illustrate the effectiveness of the formulation and solution strategy. The results and their managerial implications are discussed.
5533159
Cycles of length 1 modulo 3 in graph
We prove a conjecture of Saito that if a graph G with ≥ 3 has no cycle of length 1 (mod 3), then G has an induced subgraph which is isomorphic to the Petersen graph. The above result strengthened the result by Dean et al. that every 2-connected graph with ≥ 3 has a (1 mod 3)-cycle if G is not isomorphic to the Petersen graph. ? 2001 Elsevier Science B.V. All rights reserved.
46151699
A Dynamic En-Route Scheme for Filtering False Data Injection in Wireless Sensor Networks
1959706
A key pre-distribution scheme using deployment knowledge for wireless sensor networks
Sensor networks pose security and privacy challenges. One challenge is how to bootstrap secure communications among nodes. Several key pre-distribution schemes have been proposed, however, they either have a high memory requirement for large networks or need a large transmission range to achieve the desired connectivity. In this paper, we propose a novel key pre-distribution scheme using deployment knowledge. In our scheme, sensor field is divided into multiple hexagonal grids and nodes are divided into groups, each of which is deployed into a grid. By using deployment knowledge, we can drastically reduce the number of potential groups from which neighbors of each node may come. Thus, a pair-wise key can be generated efficiently for each pair of neighbors. Compared to existing schemes, our scheme achieves a high connectivity with a lower memory requirement and a smaller transmission range and outperforms others in resilience against node capture attacks.
9807820
Norm and coefficient constraints for robust adaptive beamforming
Norm-constrained and coefficient-constrained adaptive filters are often used in robust adaptive beamforming to mitigate the target-signal cancellation problem caused by DOA (direction of arrival) mismatch. The norm and coefficient constraints are usually selected numerically based on computer simulations or real-time experiments, which is not enough for theoretical analysis that requires explicit mathematical expressions. This paper analytically derives these constraints using the Taylor series expansion of the array correlation matrix. The constraints that are expressed in terms of maximal DOA mismatch agree well with the simulation results, and most importantly they are quite useful in practical beamformer design and performance analysis.