In data sharing privacy has become one of the main concerns particularly when sharing datasets involving individuals contain private sensitive information. A model that is widely used to protect the privacy of individuals in publishing micro-data is k-anonymity. It reduces the linking confidence between private sensitive information and specific individual by generalizing the identifier attributes of each individual into at least k-1 others in dataset. K-anonymity can also be defined as clustering with constrain of minimum k tuples in each group. However, the accuracy of the data in k-anonymous dataset decreases due to huge information loss through generalization and suppression. Also most of the current approaches are designed for numerical continuous attributes and for categorical attributes they do not perform efficiently and depend on attributes hierarchical taxonomies, which often do not exist. In this paper we propose a new model for k-anonymization, which is called Similarity-Based Clustering (SBC). It is based on clustering and it measures similarity and calculates distances between tuples containing numerical and categorical attributes without hierarchical taxonomies. Based on this model a bottom up greedy algorithm is proposed. Our extensive study on two real datasets shows that the proposed algorithm in comparison with existing well-known algorithms offers much higher data utility and reduces the information loss significantly. Data utility is maintained above 80% in a wide range of k values.

As semiconductor technologies have advanced, the reliability problem caused by soft-errors is becoming one of the serious issues in LSIs. Moreover, multiple component errors due to single soft-errors also have become a serious problem. In this paper, we propose a method to synthesize multiple component soft-error tolerant application-specific datapaths via high-level synthesis. The novel feature of our method is speculative resource sharing between the retry parts and the secondary parts for time overhead mitigation. A scheduling algorithm using a special priority function to maximize speculative resource sharing is also an important feature of this study. Our approach can reduce the latency (schedule length) in many applications without deterioration of reliability and chip area compared with conventional datapaths without speculative resource sharing. We also found that our method is more effective when a computation algorithm possesses higher parallelism and a smaller number of resources is available.

Performance evaluation of an improved multiband impulse radio ultra-wideband (MIR UWB) system based on sub-band selection is proposed in this paper. In the improved scheme, a data mapping algorithm is introduced to a conventional MIR UWB system, and out of all the sub-bands, only partial ones are selected to transmit information data, which can improve the flexibility of sub-bands/spectrum allocation, avoid interference and provide a variety of data rates. Given diagrams of a transmitter and receiver, the exact bit error rate (BER) of the improved system is derived. A comparison of system performance between the improved MIR UWB system and the conventional MIR UWB system is presented in different channels. Simulation results show that the improved system can achieve the same data rate and better BER performance than the conventional MIR UWB system under additive white Gaussian noise (AWGN), multipath fading and interference coexistence channels. In addition, different data transmission rates and BER performances can be easily achieved by an appropriate choice of system parameters.

Because wavelet transforms have the characteristic of decomposing signals that are similar to the human acoustic system, speech enhancement algorithms that are based on wavelet shrinkage are widely used. In this paper, we propose a new speech enhancement algorithm of hearing aids based on wavelet shrinkage. The algorithm has multi-band threshold value and a new wavelet shrinkage function for recursive noise reduction. We performed experiments using various types of authorized speech and noise signals, and our results show that the proposed algorithm achieves significantly better performances compared with other recently proposed speech enhancement algorithms using wavelet shrinkage.

SM3 is a hash function standard defined by China. Unlike SHA-1 and SHA-2, it is hard for SM3 to speed up the throughput because it has more complicated compression function than other hash algorithm. In this paper, we propose a 4-round-in-1 structure to reduce the number of rounds, and a logical simplifying to move 3 adders and 3 XOR gates from critical path to the non-critical path. Based in SMIC 65nm CMOS technology, the throughput of SM3 can achieve 6.54Gbps which is higher than that of the reported designs.

A parallel Aho-Corasick (AC) approach, named PAC-k, is proposed for string matching in deep packet inspection (DPI). The proposed approach adopts graphic processing units (GPUs) to perform the string matching in parallel for high throughput. In parallel string matching, the boundary detection problem happens when a pattern is matched across chunks. The PAC-k approach solves the boundary detection problem because the number of characters to be scanned by a thread can reach the longest pattern length. An input string is divided into multiple sub-chunks with k characters. By adopting the new starting position in each sub-chunk for the failure transition, the required number of threads is reduced by a factor of k. Therefore, the overhead of terminating and reassigning threads is also decreased. In order to avoid the unnecessary overlapped scanning with multiple threads, a checking procedure is proposed that decides whether a new starting position is in the sub-chunk. In the experiments with target patterns from Snort and realistic input strings from DEFCON, throughputs are enhanced greatly compared to those of previous AC-based string matching approaches.

Let G be a probabilistic graph, in which the vertices fail independently with known probabilities. Let K represent a specified subset of vertices. The K-terminal reliability of G is defined as the probability that all vertices in K are connected. When |K|=2, the K-terminal reliability is called the 2-terminal reliability, which is the probability that the source vertex is connected to the destination vertex. The problems of computing K-terminal reliability and 2-terminal reliability have been proven to be #P-complete in general. This work demonstrates that on multi-tolerance graphs, the 2-terminal reliability problem can be solved in polynomial-time and the results can be extended to the K-terminal reliability problem on bounded multi-tolerance graphs.

It is a well-known and useful problem to find a matching in a given graph G whose size is at most a given parameter k and whose weight is maximized (over all matchings of size at most k in G). In this paper, we consider two natural extensions of this problem. One is to find t disjoint matchings in a given graph G whose total size is at most a given parameter k and whose total weight is maximized, where t is a (small) constant integer. Previously, only the special case where t=2 was known to be fixed-parameter tractable. In this paper, we show that the problem is fixed-parameter tractable for any constant t. When t=2, the time complexity of the new algorithm is significantly better than that of the previously known algorithm. The other is to find a set of vertex-disjoint paths each of length 1 or 2 in a given graph whose total length is at most a given parameter k and whose total weight is maximized. As interesting applications, we further use the algorithms to speed up several known approximation algorithms (for related NP-hard problems) whose approximation ratio depends on a fixed parameter 0<ε<1 and whose running time is dominated by the time needed for exactly solving the problems on graphs in which each connected component has at most ε-1 vertices.

Left-weight sequences (LW-sequences for short) are in common currency for encoding binary trees. In [16], Wu et al. proposed an algorithm associated with tree rotations for listing all binary trees in diverse representations including LW-sequences. In particular, such a list of LW-sequences is generated in Gray-code order. In this paper, based on this ordering, we present efficient ranking and unranking algorithms. For binary trees with n internal nodes, the time complexity and the space requirement in each of our ranking and unranking algorithms are O(n2) and O(n), respectively.

In this paper, a convolution theorem which is analogous to the theorem for Fourier transform is shown among a certain type of polynomials. We establish a fast method of the multiplication in a special class of quotient rings of multivariate polynomials over q-element finite field GF(q). The polynomial which we treat is one of expressing forms of the multiple-valued logic function from the product of the semigroups in GF(q) to GF(q). Our results can be applied to the speedup of both software and hardware concerning multiple-valued Boolean logic.

In this paper we discuss approximation algorithms for the ELEMENT-DISJOINT STEINER TREE PACKING problem (Element-STP for short). For a graph G=(V,E) and a subset of nodes T⊆V, called terminal nodes, a Steiner tree is a connected, acyclic subgraph that contains all the terminal nodes in T. The goal of Element-STP is to find as many element-disjoint Steiner trees as possible. Element-STP is known to be APX-hard even for |T|=3 [1]. It is also known that Element-STP is NP-hard to approximate within a factor of Ω(log |V|) [3] and there is an O(log |V|)-approximation algorithm for Element-STP [2],[4]. In this paper, we provide a $lceil rac{|T|}{2} ceil$-approximation algorithm for Element-STP on graphs with |T| terminal nodes. Furthermore, we show that the approximation ratio of 3 for Element-STP on graphs with five terminal nodes can be improved to 2.

In the 3-slope ski-rental problem, the player is asked to determine a strategy, that is, (i) whether to buy a ski wear and then a ski set separately, or to buy them at once for a discount price, and (ii) when to buy these goods. If the player has not got any thing, he/she can rent it for some price. The objective is to minimize the total cost, under the assumption that the player does not know how many times he/she goes skiing in the future. We reveal that even with a large discount for buying at once available, there is some price setting for which to buy the goods separately is a more reasonable choice. We also show that the performance of the optimal strategy may become arbitrarily worse, when a large discount is offered.

Operating swarm robots has the virtues of improved performance, fault tolerance, distributed sensing, and so on. The problem is, high overall system costs are the main barrier in managing a system of foraging swarm robots. Moreover, its control algorithm should be scalable and reliable as the foraging (search) spaces become wider. This paper analyzes a nature-inspired cooperative method to reduce the operating costs of the foraging swarm robots through simulation experiments. The aim of this research is to improve efficiency of mechanisms for reducing the cost by developing a new algorithm for the synergistic cooperation of the group. In this paper, we set the evaluation index of energy efficiency considering that the mission success rate as well as energy saving is important. The value is calculated as the number of successful operations against the total consumption of energy in order to also guarantee optimized for the work processing power than the one simple goal of energy savings. The method employs a behavioral model of a honey bee swarm to improve the energy efficiency in collecting crops or minerals. Experiments demonstrate the effectiveness of the approach. The experiment is set a number of strategies to combine the techniques to the proposed and conventional methods. Considering variables such as the area of search space and the size of a swarm, the efficiency comparison test is performed. As the result, the proposed method showed the enhanced energy efficiency of the average 76.9% as compared to the conventional simple model that means reduction of the recharging cost more than 40%.

Since 2008, three different classes of Boolean functions with optimal algebraic immunity have been proposed by Carlet and Feng [2], Wang et al.[8] and Chen et al.[3]. We call them C-F functions, W-P-K-X functions and C-T-Q functions for short. In this paper, we propose three affine equivalent classes of Boolean functions containing C-F functions, W-P-K-X functions and C-T-Q functions as a subclass, respectively. Based on the affine equivalence relation, we construct more classes of Boolean functions with optimal algebraic immunity. Moreover, we deduce a new lower bound on the nonlinearity of C-F functions, which is better than all the known ones.

The classification of warheads and decoys is a core technology in the defense of the ballistic missile. Usually, a high range resolution is favorable for the development of the classification algorithm, which requires a high sampling rate in fast time, and thus leads to a heavy computation burden for data processing. In this paper, a novel method based on compressed sensing (CS) is presented to improve the range resolution of the target with low computational complexity. First, a tool for electromagnetic calculation, such as CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering of the target. Second, the range-resolved signal of the target is acquired by further processing. Third, a greedy algorithm is applied to this signal. By the iterative search of the maximum value from the signal rather than the calculation of the inner product for raw echo, the scattering coefficients of the target can be reconstructed efficiently. A series of experimental results demonstrates the effectiveness of our method.

We present circuit implementations for computing exponentials and logarithms suitable for rapid single-flux-quantum (RSFQ) logic. We propose hardware algorithms based on the sequential table-lookup (STL) method using the radix-2 signed-digit representation that achieve high-throughput, digit-serial calculations. The circuits are implemented by processing elements formed in systolic-array-like, regularly-aligned pipeline structures. The processing elements are composed of adders, shifters, and readouts of precomputed constants. The iterative calculations are fully overlapped, and throughputs approach the maximum throughput of serial processing. The circuit size for calculating significand parts is estimated to be approximately 5-10 times larger than that of a bit-serial floating-point adder or multiplier.

We propose an exact algorithm to determine the satisfiability of oblivious read-twice branching programs. Our algorithm runs in $2^{left(1 - Omega(rac{1}{log c}) ight)n}$ time for instances with n variables and cn nodes.

Designing a backbone IP network, especially to support both unicast and multicast traffic under delay constraints, is a difficult problem. Real network design must consider cost, performance and reliability. Therefore, a simulator can help a network designer to test the functionality of the network before the implementation. This paper proposes a heuristic design algorithm called D-MENTOR, and the algorithm was developed by programming based on Mesh Network Topological Optimization and Routing Version 2 (MENTOR-II) to integrate as a new module of DElite tool. The simulation results show that, in almost all test cases, the proposed algorithm yields lower installation cost.

Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization operator and an intelligent allocation operator. A directed perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to speed up the convergence. In addition, with a new geometric sensor detection model instead of the old probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively.Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value.

This paper presents a novel data compression method for testing integrated circuits within the selective dictionary coding framework. Due to the inverse value of dictionary indices made use of for the compatibility analysis with the heuristic algorithm utilized to solve the maximum clique problem, the method can obtain a higher compression ratio than existing ones.