The current paper presents an integral cryptanalysis in the single-key setting against light-weight block-cipher LBlock reduced to 22 rounds. Our attack uses the same 15-round integral distinguisher as the previous attacks, but many techniques are taken into consideration in order to achieve comprehensive understanding of the attack; choosing the best balanced-byte position, meet-in-the-middle technique to identify right key candidates, partial-sum technique, relations among subkeys, and combination of the exhaustive search with the integral analysis. Our results indicate that the integral cryptanalysis is particularly useful for LBlock like structures. At the end of this paper, which factor makes the LBlock structure weak against the integral cryptanalysis is discussed. Because designing light-weight cryptographic primitives is an actively discussed topic, we believe that this paper returns some useful feedback to future designs.

The traditional reinforcement learning (RL) methods can solve Markov Decision Processes (MDPs) online, but these learning methods cannot effectively use a priori knowledge to guide the learning process. The exploration of the optimal policy is time-consuming and does not employ the information about specific issues. To tackle the problem, this paper proposes heuristic function negotiation (HFN) as an online learning framework. The HFN framework extends MDPs and introduces heuristic functions. HFN changes the state-action dual layer structure of traditional RL to the triple layer structure, in which multiple heuristic functions can be set to meet the needs required to solve the problem. The HFN framework can use different algorithms to let the functions negotiate to determine the appropriate action, and adjust the impact of each function according to the rewards. The HFN framework introduces domain knowledge by setting heuristic functions and thus speeds up the problem solving of MDPs. Furthermore, user preferences can be reflected in the learning process, which improves the flexibility of RL. The experiments show that, by setting reasonable heuristic functions, the learning results of the HFN framework are more efficient than traditional RL. We also apply HFN to the air combat simulation of unmanned aerial vehicles (UAVs), which shows that different function settings lead to different combat behaviors.

Multi-operand adders that calculate the summation of more than two operands usually consist of compressor trees, which reduce the number of operands to two without any carry propagation, and carry-propagate adders for the two operands in the ASIC implementation. Compressor trees that consist of full adders and half adders cannot be implemented efficiently on LUT-based FPGAs, and carry-chains or dedicated structures have been utilized to produce multi-operand adders on FPGAs. Recent studies indicate that compressor trees can be implemented efficiently on LUTs using Generalized Parallel Counters (GPCs) as the building blocks of compressor trees. This paper addresses the problem of synthesizing compressor trees based on GPCs. Based on the observation that characteristics such as the area, power, and delay correlate roughly to the total number and the maximum level of GPCs, the target problem can be regarded as a minimization problem for the total number of GPCs and the maximum levels of the GPCs, for which an ILP-based approach is proposed. The key point of our formulation is not to model the problem based on the structures of compressor trees like the existing approach, but instead the compression process itself is used to reduce the number of variables and constraints in the ILP formulation. The experimental results demonstrate the advantage of our formulation in terms of the quality and runtime.

Learning an appearance model for person re-identification from multiple images is challenging due to the corrupted images caused by occlusion or false detection. Furthermore, different persons may wear similar clothes, making appearance feature less discriminative. In this paper, we first introduce the concept of multiple instance to handle corrupted images. Then a novel pairwise comparison based multiple instance learning framework is proposed to deal with visual ambiguity, by selecting robust features through pairwise comparison. We demonstrate the effectiveness of our method on two public datasets.

Our prior papers proposed a traffic engineering scheme to further localize traffic in peer-assisted content delivery networks (CDNs). This scheme periodically combines the content files and allows them to obtain the combined content files while keeping the price unchanged from the single-content price in order to induce altruistic clients to download content files that are most likely to contribute to localizing network traffic. However, the selection algorithm in our prior work determined which and when content files should be combined according to the cache states of all clients, which is a kind of unrealistic assumption in terms of computational complexity. This paper proposes a new concept of virtual local server to reduce the computational complexity. We could say that the source server in our mechanism has a virtual caching network inside that reflects the cache states of all clients in the ‘actual’ caching network and combines content files based on the virtual caching network. In this paper, without determining virtual caching network according to the cache states of all clients, we approximately estimated the virtual caching network from the cache states of the virtual local server of the local domain, which is the aggregated cache state of only altruistic clients in a local domain. Furthermore, we proposed a content selection algorithm based on a virtual caching network. In this paper, we used news life-cycle model as a content model that had the severe changes in cache states, which was a striking instance of dynamic content models. Computer simulations confirmed that our proposed algorithm successfully localized network traffic.

Pseudo Power Gating (Pseudo PG) is one of gate level power reduction methods for combinational circuits by stopping unnecessary input changes of gates. In Pseudo PG, an extra control signal might be added to a gate and other input changes of the gate are deactivated when the control signal takes the controlling value. To improve the power reduction capability, the paper newly introduces dual-stage Pseudo PG with advanced clustering algorithm where up to two extra control signals are added to a gate if effective. The advanced clustering algorithm selects the first control signal to be compatible with the second control signal based on the propagation of controlling condition via a path, with which candidates of controllable gates excluded by the maximum depth constraint can be controlled. Experimental results show that the proposed dual-stage Pseudo PG method has obtained 23.23% average power reduction with 5.28% delay penalty with respect to the original circuits, and has obtained 10.46% more power reduction with 2.75% delay penalty compared with respect to circuits applying the original single-stage Pseudo PG.

Temporal Decorrelation source SEParation (TDSEP) is a blind separation scheme that utilizes the time structure of the source signals, typically, their periodicities. The advantage of TDSEP over non-Gaussianity based methods is that it can separate Gaussian signals as long as they are periodic. However, its shortcoming is that separation performance (SEP) heavily depends upon the values of the time shift parameters (TSPs). This paper proposes a method to automatically and blindly estimate a set of TSPs that achieves optimal SEP against periodic Gaussian signals. It is also shown that, selecting the same number of TSPs as that of the source signals, is sufficient to obtain optimal SEP, and adding more TSPs does not improve SEP, but only increases the computational complexity. The simulation example showed that the SEP is higher by approximately 20dB, compared with the ordinary method. It is also shown that the proposed method successfully selects just the same number of TSPs as that of incoming signals.

Pieces of personal information, such as personal names and relationships, are crucial in text mining applications. Obituaries are good sources for this kind of information. This study proposes an effective method for extracting various facts about people from obituary Web pages. Experiments show that the proposed method achieves high performance in terms of recall and precision.

In this paper, we propose a new buffer map notification scheme for Peer-to-Peer Video-on-Demand systems (P2P VoDs) which support VCR operations such as fast-forward, fast-backward, and seek. To enhance the fluidity of such VCR operations, we need to refine the size of each piece as small as possible. However, such a refinement significantly degrades the performance of buffer map notification schemes with respect to the overhead, piece availability and the efficiency of resource utilizations. The basic idea behind our proposed scheme is to use a piece-based buffer map with a segment-based buffer map in a complementary manner. The result of simulations indicates that the proposed scheme certainly increases the accuracy of the information on the piece availability in the neighborhood with a sufficiently low cost, which reduces the intermittent waiting time of each peer by more than 40% even under a situation in which 50% of peers conduct the fast-forward operation over a range of 30% of the entire video.

This paper derives the average symbol and bit weight distributions for the irregular non-binary cluster low-density parity-check (LDPC) code ensembles. Moreover, we give the exponential growth rates of the average weight distributions in the limit of large code length. We show the condition that the typical minimum distances linearly grow with the code length.

A removable HDD “iVDR” is an international standardized medium, which has HDD features such as a large capacity and high-speed data transfer, and also is removable and compatible. We discuss the concepts of the hardware-specifications designed by the iVDR Consortium and the history of the international standardization activities for iVDR. We also discuss application expansions through these standardization activities.

This letter presents a scheme to improve the running time of exemplar-based image inpainting, first proposed by Criminisi et al. In the exemplar-based image inpainting, a patch that contains unknown pixels is compared to all the patches in the known region in order to find the best match. This is very time-consuming and hinders the practicality of Criminisi's method to be used in real time. We show that a simple bounding algorithm can significantly reduce number of distance calculations, and thus the running time. Performance of the bounding algorithm is affected by the order of patches that are compared, as well as the order of pixels in a patch. We present pixel and patch ordering schemes that improve the performance of bounding algorithms. Experiments with well-known images used in inpainting literature show that the proposed reordering scheme can reduce running time of the bounding algorithm up to 50%.

Recently, the compound element pseudo transient analysis, CEPTA, method is regarded as an efficient practical method to find DC operating points of nonlinear circuits when the Newton-Raphson method fails. In the previous CEPTA method, an effective SPICE3 implementation algorithm was proposed without expanding the Jacobian matrix. However the limitation of step size was not well considered. Thus, the non-convergence problem occurs and the simulation efficiency is still a big challenge for current LSI nonlinear cicuits, especially for some practical large-scale circuits. Therefore, in this paper, we propose a new SPICE3 implementation algorithm and an embedding algorithm, which is where to insert the pseudo capacitors, for the CEPTA method. The proposed implementation algorithm has no limitation for step size and can significantly improve simulation efficiency. Considering the existence of various types of circuits, we extend some possible embedding positions. Numerical examples demonstrate the improvement of simulation efficiency and convergence performance.

Optical Code Division Multiplexing (OCDM) is a multiplexing technology for constructing future all-optical networks. Compared with other multiplexing technologies, it can be easily controlled and can establish lightpaths of smaller granularity. However, previous research has revealed that OCDM networks are vulnerable to cycle attacks. Cycle attacks are caused by multi-access interference (MAI), which is crosstalk noise on the same wavelength in OCDM networks. If cycle attacks occur, they disrupt all network services immediately. Previous research has proposed a logical topology design that is free of cycle attacks. However, this design assumes that path assignment is centrally controlled. It also does not consider the delay between each node and the centralized controller. In this paper, we propose novel logical topology designs that are free of cycle attacks and methods of establishing paths. The basic concepts underlying our methods are to autonomously construct a cycle-attack-free logical topology and to establish lightpaths by using a distributed controller. Our methods can construct a logical network and establish lightpaths more easily than the previous method can. In addition, they have network scalability because of their distributed control. Simulation results show that our methods have lower loss probabilities than the previous method and better mean hop counts than the centralized control approach.

This paper studies the performance of code-aided (CA) soft-information based carrier phase recovery, which iteratively exploits the extrinsic information from channel decoder to improve the accuracy of phase synchronization. To tackle the problem of strong coupling between phase recovery and decoding, a semi-analytical model is proposed to express the distribution of extrinsic information as a function of phase offset. Piecewise approximation of the hyperbolic tangent function is employed to linearize the expression of soft symbol decision. Building on this model, open-loop characteristic and closed-loop performance of CA iterative soft decision-directed (ISDD) carrier phase synchronizer are derived in closed-form. Monte Carlo simulation results corroborate that the proposed expressions are able to characterize the performance of CA ISDD carrier phase recovery for systems with different channel codes.

Relay selection is a promising technique with which to achieve remarkable gains in multi-relay cooperative networks. Opportunistic relaying (OR) and selection cooperation (SC) are two major relay selection schemes for dual-hop decode-and-forward cooperative networks; they have been shown to be globally outage-optimal under an aggregate power constraint. However, due to channel fluctuations, the channel state information (CSI) used in the selection process may become outdated and differ from the CSI during the actual transmission of data. In this work, we study the effect of outdated CSI on OR and threshold-based SC (TSC) schemes under independent but not necessarily identically distributed Rayleigh fading channels. The source can possibly cooperate with the best relay for data transmission, with the destination performing maximal ratio combining of the signals from the source and the relay. In particular, we analyze the average symbol error probability (ASEP) of OR and TSC with outdated CSI by deriving approximate but tight closed-form expressions for the moment generating function of the end-to-end signal-to-noise ratio. We also investigate the asymptotic behavior of the ASEP. The results show that the diversity orders of OR and TSC reduce to one and two, respectively, due to the outdated CSI. However, TSC achieves full spatial diversity order when the relay-to-destination CSI is perfect. Finally, to verify the analytical results Monte Carlo simulations are performed, in which OR attains better ASEP than TSC in a perfect CSI scenario, while TSC is less susceptible to outdated CSI.

The throughput rate of Viterbi decoding (VD) is not limited by the speed of functional units when look-ahead computation techniques are used. The disadvantages of the look-ahead computation in VD are the hardware complexity and the decode latency. In this paper, implementation methods of the look-ahead ACS computation are proposed to improve the hardware efficiency and reduce the latency where the hardware efficiency and the latency can be balanced with a single parameter.

When attackers compromise a client system, they can steal user input. We propose a distributed one-time keyboard system to prevent information leakage via keyboard typing. We define the problem of secure keyboard arrangement over distributed multi-devices and channels. An analytical model is proposed for the optimal keyboard layout.

This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.