Embedding Technical Analysis Into Neural Network Based Trading Systems
We have recently proposed a promising trading system for the S P 500 index which consists of a feature selection component and a simple filter for data preprocessing two specialized neural networks for return prediction and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data filter ing and return integration leads to further improvements in the system. The new system uses a well known technical indicator to split the data and an additional indicator for reducing the number of unprofitable trades. Several system combinations are explored and tested over a 5 year trading period. The most promising system yielded an annual rate of return ARR of 15.99% with 54 trades. This compares favorably to the ARR for the buy and hold strategy 11.05% and to the best results obtained using the system with no technical analysis knowl edge embedded 13.35% with 126 trades.

title: Performance of Group Key Agreement Protocols over Multiple Operations
abstract: ∗Shanyu Zheng and Jim Alves-FossCenter for Secure and Dependable SystemsUniversity of IdahoMoscow, ID 83844,  LeeDepartment of StatisticsUnversity of IdahoMoscow, ID 83844,  stevel@uidaho.eduABSTRACTA few group key protocols are analyzed, implemented anddeployed, but the costs associated with them have beenpoorly understood. Their analysis of group key agreementsperformance is based on the cost of performing a single op-eration. In this paper we extend this analysis to examine theperformance behavior of ﬁve group key protocols after ex-ecution of multiple operation. We report our experimentalresults for 100 operations consist of combinations of join,leave, mass join, mass leave, merge, and partition. In orderto thoroughly compare the performance of ﬁve protocols,we simulate three group operations: join-leave-mass join-mass leave, merge-partition, and join-leave-mass join-massleave-merge-partition to observe what is not apparent fromthe theoretical analysis.KEY WORDSgroup key management, group communications, crypto-graphic protocols

title: Motif analysis of noisy regular textures
abstract: A regular texture contains a periodic arrangement of a determinable pattern. Motifs of a regular texture are the minimal parallelogrammic sub-regions that can reproduce the texture by tiling. A motif extracted from a regular texture can be used for texture reproduction, texture recognition, and texture compression. However, it is not simple to accurately capture a motif of a regular texture, as the motif may be non-rectangular or arranged in an arbitrary direction. Moreover, when the texture contains additive noises, such as are created by the scanning process, motif analysis becomes more difficult. In this paper, we present an algorithm to obtain a motif of a noisy regular texture in O( mn log mn ) time, where m × n is the size of the texture. The analysis examples demonstrate that the proposed algorithm can robustly derive motifs of noisy regular textures.

title: Analog on-chip-learning for active noise canceling
abstract: Active noise canceling is demonstrated by analog neuro-chips with on-chip learning capability. The developed neuro-chip faithfully incorporates the error-backpropagation learning rule. Without any digital signal processor the developed system successfully compensated for nonlinear distortion of loudspeakers as well as for acoustic multi-path fading and random noises in real time.

title: Design and implementation of a multi-threaded TMN agent system
abstract: A telecommunications management network adopts the OSI manager-agent model in which an agent system plays a more important role than a manager system. Because the existing agent systems sequentially perform a CMIS operation on selected managed objects, it cannot guarantee quick response time when many managed objects are selected. We propose a multi-threaded process structure for the agent system which performs CMIS operations concurrently on managed objects. We implemented our agent system using OSIMIS-4.O TMN platform in Solaris. We measured response time to a CMIS operation to evaluate the performance of our system. Experimental results show that the performance of our system is not so good when network resources reside within the agent system, but very good when network resources reside outside the agent system.

title: Mobility prediction and routing in ad hoc wireless networks
abstract: By exploiting non-random behaviors for the mobility patterns that mobile users exhibit, we can predict the future state of network topology and perform route reconstruction proactively in a timely manner. Moreover, by using the predicted information on the network topology, we can eliminate transmissions of control packets otherwise needed to reconstruct the route and thus reduce overhead. In this paper, we propose various schemes to improve routing protocol performances by using mobility prediction. We then evaluate the effectiveness of using mobility prediction via simulation.

title: Training Algorithm with Incomplete Data for Feed-Forward Neural Networks
abstract: A new algorithm is developed to train feed-forward neural networks for non-linear input-to-output mappings with small incomplete data in arbitrary distributions. The developed Training-EStimation-Training (TEST) algorithm consists of 3 steps, i.e., (1) training with the complete portion of the training data set, (2) estimation of the missing attributes with the trained neural networks, and (3) re-training the neural networks with the whole data set. Error back propagation is still applicable to estimate the missing attributes. Unlike other training methods with missing data, it does not assume data distribution models which may not be appropriate for small training data. The developed TEST algorithm is first tested for the Iris benchmark data. By randomly removing some attributes from the complete data set and estimating the values latter, accuracy of the TEST algorithm is demonstrated. Then it is applied to the Diabetes benchmark data, of which about 50% contains missing attributes. Compared with other existing algorithms, the proposed TEST algorithm results in much better recognition accuracy for test data.