DSpace Community:http://hdl.handle.net/2440/142017-10-17T15:03:14Z2017-10-17T15:03:14ZSocially inspired algorithms for the traveling thief problemBonyadi, M.Michalewicz, Z.Przybyłek, M.Wierzbicki, A.http://hdl.handle.net/2440/1086392017-10-17T01:45:22Z2014-01-01T00:00:00ZTitle: Socially inspired algorithms for the traveling thief problem
Author: Bonyadi, M.; Michalewicz, Z.; Przybyłek, M.; Wierzbicki, A.
Abstract: Many real-world problems are composed of two or more problems that are interdependent on each other. The interaction of such problems usually is quite complex and solving each problem separately cannot guarantee the optimal solution for the overall multi-component problem. In this paper we experiment with one particular 2-component problem, namely the Traveling Thief Problem (TTP). TTP is composed of the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP).We investigate two heuristic methods to deal with TTP. In the first approach we decompose TTP into two sub-problems, solve them by separate modules/ algorithms (that communicate with each other), and combine the solutions to obtain an overall approximated solution to TTP (this method is called CoSolver ). The second approach is a simple heuristic (called density-based heuristic, DH) method that generates a solution for the TSP component first (a version of Lin-Kernighan algorithm is used) and then, based on the fixed solution for the TSP component found, it generates a solution for the KP component (associated with the given TTP). In fact, this heuristic ignores the interdependency between sub-problems and tries to solve the sub-problems sequentially. These two methods are applied to some generated TTP instances of different sizes. Our comparisons show that CoSolver outperforms DH specially in large instances.2014-01-01T00:00:00ZOn-line algorithms for 2-space bounded cube and hypercube packingZhao, X.Shen, H.http://hdl.handle.net/2440/1086172017-10-16T23:36:38Z2014-01-01T00:00:00ZTitle: On-line algorithms for 2-space bounded cube and hypercube packing
Author: Zhao, X.; Shen, H.
Abstract: We consider the problem of packing ddimensional cubes into the minimum number of unit cubes with 2-space bounded, as the generalization of the classic bin packing problem. Given a sequence of items, each of which is a d-dimensional (d ≥ 3) hypercube with side length not greater than 1 and an infinite number of d-dimensional (d ≥ 3) hypercube bins with unit length on each side, we want to pack all items in the sequence into a minimum number of bins. The constraint is that only two bins are active at anytime during the packing process. Each item should be orthogonally packed without overlapping with others. Items are given in an on-line manner which means each item comes without knowing any information about the subsequent items. We extend the technique of brick partitioning in paper [1] for square packing and obtain two results: a three dimensional box partitioning scheme for cube packing and a d-dimensional hyperbox partitioning scheme for hypercube packing. We give a 5.43-competitive algorithm for cube packing and a 32 21 · 2dcompetitive algorithm for hypercube packing. To the best of our knowledge these are the first known results on 2-space bounded cube and hypercube packing.2014-01-01T00:00:00ZExploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approachesYao, L.Ruan, W.Sheng, Q.Li, X.Falkner, N.http://hdl.handle.net/2440/1086162017-10-16T23:35:57Z2014-01-01T00:00:00ZTitle: Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches
Author: Yao, L.; Ruan, W.; Sheng, Q.; Li, X.; Falkner, N.
Abstract: RFID-based localization and tracking has some promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization problem. To track a moving subject, we propose GMM based Hidden Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demonstrate the effectiveness and accuracy of our approach.2014-01-01T00:00:00ZA secure anonymous authentication scheme for electronic medical records systemsLei, W.Li, Y.Sang, Y.Shen, H.http://hdl.handle.net/2440/1086152017-10-16T23:34:24Z2016-01-01T00:00:00ZTitle: A secure anonymous authentication scheme for electronic medical records systems
Author: Lei, W.; Li, Y.; Sang, Y.; Shen, H.
Abstract: Based on smart devices and wireless communication infrastructure, the Electronic Medical Records (EMR) systems become more and more popular in hospitals by reason of its convenient operation, remote access, data integrity, facilitated resources sharing, statistical analysis and many other advantages. When entering an EMR system, patients can be treated by many kinds of services and also leave their privacy to the providers. It is a viable method to protect a patient’s privacy in EMR by anonymous authentication, where identity of the patient is verified without demonstrating the true identity to the authenticator. This paper proposes an enhanced secure anonymous authentication scheme, based on smart card and biometrics with key agreement. According to security analysis, our scheme is secure against many types of attacks, including those not prevented by previous work, such as the smart card loss attack and impersonation attack. The performance of our scheme is also analyzed and compared with previous work, which shows that our scheme is more efficient in the computation cost.2016-01-01T00:00:00Z