28414698
A limit theorem for n-phase Barker sequences (corresp.)
It is proved that for 3lLl19, except for L =6, the total number of normalized n-phase Barker sequences of length L increases without limit as n goes to infinity
29554681
Uniqueness of the generalized Barker sequence of length 6
It is proven that there is a unique generalized Barker sequence of length 6, modulo the group of Barker-preserving transformations. >
37809849
Sixty-phase generalized Barker sequences
The extension of the list of known generalized Barker sequences to length 19 is proposed. Examples for all lengths up to 19 where the terms of the sequence are sixtieth roots of unity are given. >
15560929
UNDER DEVELOPMENTUCD in Chinese IT enterprises
A zoom lens system includes a first lens unit having a negative optical power, located closer to an object side, and a second lens unit having a positive optical power, located farther from the object side. Zooming is accomplished by varying the spacing between the first and second lens units. The surface of at least one lens element in the two lens units is a diffractive optical surface. At least one surface of the lens element is an aspherical surface.
12573730
A secure proxy signature protocol for agent-based M-commerce applications
We present a novel agent-based threshold proxy signcryption (ATPS) protocol, which enables an agent owner to securely delegate his signing capabilities to a mobile agent so that the mobile agent can sign documents on behalf of its owner. The protocol incorporates three novel contributions: a proxy key generation method, a threshold proxy signcryption scheme and a proxy signature verification method. The most notable feature of the protocol is that it enables a mobile agent to securely, efficiently and autonomously sign documents on the owner's behalf in a remote host with nonrepudiation and fairness assurance for both the owner and the remote host. The security of the proposed protocol is analyzed and its performance against other related work is evaluated.
36918365
C3: A New Learning Scheme to Improve Classification of Rare Category Emails
This paper proposes C3, a new learning scheme to improve classification performance of rare category emails in the early stage of incremental learning. C3 consists of three components: the chief-learner, the co-learners and the combiner. The chief-learner is an ordinary learning model with an incremental learning capability. The chief-learner performs well on categories trained with sufficient samples but badly on rare categories trained with insufficient samples. The co-learners that are focused on the rare categories are used to compensate for the weakness of the chief-learner in classifying new samples of the rare categories. The combiner combines the outputs of both the chief-learner and the co-learner to make a finial classification. The chief-learner is updated incrementally with all the new samples overtime and the co-learners are updated with new samples from rare categories only. After the chief-learner has gained sufficient knowledge about the rare categories, the co-learners become unnecessary and are removed. The experiments on customer emails from an e-commerce company have shown that the C3 model outperformed the Naive Bayes model on classifying the emails of rare categories in the early stage of incremental learning.
17848781
A Flexible Approach to Secure and Fair Document Exchange
This paper presents a novel protocol for achieving secure and fair document exchange among multiparties. The protocol has the following characteristics: it employs no trusted (third) party when a majority of the parties involved behave honestly; it adopts a technique for integrity and originality verification of messages without use of either asymmetric (public key) or symmetric (conventional) encryption; and it maximises the use of hash and polynomial functions. In comparison with existing protocols, this new protocol has a number of advantages such as better flexibility and reliability. The protocol is also assessed with respect to a set of requirements such as atomicity and assurance.
445053
Prediction of Helix, Strand Segments from Primary Protein Sequences by a Set of Neural Networks
In prediction of secondary structure of proteins there are always some suspected segments. These suspected segments confuse people and lower the accuracy of prediction methods. To deal with this problem, a set of neural networks (NNs) are built based on helix, strand and coil segments selected from PDB. The test performance of these NNs on training data is perfect without surprise. However the prediction on test data is not good enough because the training data are lake of great representativeness. The results support the fact that closer neighbor vectors have the similar outputs of NNs. One can improve representativeness of training data without enlarging data scale as long as select less data from dense region and more from sparse region on condition that distribution of sample data has been known.