The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.

A multicode transmission (MC) system can transmit multiple data streams at one time. However, the amplitude of the transmission signal has sharp fluctuations. To avoid this problem, constant amplitude (CA) signaling schemes were studied, and some MC systems were developed such as the MC system with CA signaling (MC-CA) and the parallel combinatory MC system with CA signaling (PCMC-CA). In this paper, extension systems of PCMC-CA system are developed. In particular, two demodulation methods are discussed for the extension systems. Then, the bit error rate (BER) and data transmission rate are theoretically analyzed. The results shows that the extension systems has a better performance than the MC-CA system in both of the BER and data transmission rate.

Most existing deterministic multithreading systems are costly on pipeline parallel programs due to load imbalance. In this letter, we propose a Load-Balanced Deterministic Runtime (LBDR) for pipeline parallelism. LBDR deterministically takes some tokens from non-synchronization-intensive threads to synchronization-intensive threads. Experimental results show that LBDR outperforms the state-of-the-art design by an average of 22.5%.

In recent years, there has been a significant growth in the importance of data mining of graph-structured data due to this technology's rapid increase in both scale and application areas. Many previous studies have investigated decision tree learning on Semantic Web-based linked data to uncover implicit knowledge. In the present paper, we suggest a new random forest algorithm for linked data to overcome the underlying limitations of the decision tree algorithm, such as local optimal decisions and generalization error. Moreover, we designed a parallel processing environment for random forest learning to manage large-size linked data and increase the efficiency of multiple tree generation. For this purpose, we modified the previous candidate feature searching method of the decision tree algorithm for linked data to reduce the feature searching space of random forest learning and developed feature selection methods that are adjusted to linked data. Using a distributed index-based search engine, we designed a parallel random forest learning system for linked data to generate random forests in parallel. Our proposed system enables users to simultaneously generate multiple decision trees from distributed stored linked data. To evaluate the performance of the proposed algorithm, we performed experiments to compare the classification accuracy when using the single decision tree algorithm. The experimental results revealed that our random forest algorithm is more accurate than the single decision tree algorithm.

The dynamic routing and wavelength assignment (RWA) problem in wavelength division multiplexing (WDM) optical networks with sparse wavelength conversion has been a hot research topic in recent years. An optimized algorithm based on a multiple-layered interconnected graphic model (MIG) for the dynamic RWA is presented in this paper. The MIG is constructed to reflect the actual WDM network topology. Based on the MIG, the link cost is given by the conditions of available lightpath to calculate an initial solution set of optimal paths, and by combination with path length, the optimized solution using objective function is determined. This approach simultaneously solves the route selection and wavelength assignment problem. Simulation results demonstrate the proposed MIG-based algorithm is effective in reducing blocking probability and boosting wavelength resource utilization compared with other RWA methods.

Although password-only authenticated key exchange (PAKE) in the three-party setting has been widely studied in recent years, it remains a challenging area of research. A key challenge in designing three-party PAKE protocols is to prevent insider dictionary attacks, as evidenced by the flaws discovered in many published protocols. In this letter, we revisit Abdalla and Pointcheval's three-party PAKE protocol from FC 2005 and demonstrate that this protocol, named 3PAKE, is vulnerable to a previously unpublished insider offline dictionary attack. Our attack is dependant on the composition of 3PAKE and the higher-level protocol that uses the established session key.

Secret sharing (SS) has been extensively studied as for both secure data storage and a fundamental building block for multiparty computation (MPC). Recently, Kikuchi et al. proposed a passively and unconditionally secure conversion protocol that converts from a share of a ramp scheme to another of homomorphic SS scheme. The share-size of the ramp scheme is small, and the homomorphic SS scheme is a class of SS schemes that includes Shamir's and replicated SS schemes, which are convenient for MPC. Therefore, their protocol is a conversion from an SS scheme whose share-size is small to MPC-friendly SS schemes, and can be applied to reduce the amount of data storage while maintaining extendibility to MPC. We propose five unconditionally and actively secure protocols in the honest majority. In this paper, we consider a privacy and correctness as security requirement and does not consider a robustness: A cheat caused by an active adversary must be detected. These protocols consist of two conversion protocols, two reveal protocols and a protocol generating specific randomness. Main protocols among them are two conversion protocols for bilateral conversion between a ramp scheme and linear SS scheme, and the others are building blocks of the main protocols. Linear SS scheme is a subset of homomorphic SS scheme but includes both Shamir's and replicated SS schemes. Therefore, these main protocols are conversions between an SS scheme whose share-size is small to MPC-friendly SS schemes. These main protocols are unconditionally and actively secure so if MPC protocols used after the conversion are actively secure, the whole system involving SS scheme, conversion, and MPC protocols can be unconditionally and actively secure by using our main protocols. One of our two main protocols is the first to convert from MPC-friendly SS schemes to the ramp scheme. This enhances applications, such as secure backup, of the conversion protocol. Other than the two main protocols, we propose a protocol for generating specific randomnesses and two reveal protocols as building blocks. The latter two reveal protocols are actively and unconditionally secure in the honest majority and requires O(n||F||)-bit communication per revealing, and we believe that it is independently interest.

An optimal design method of linear processors intended for a multi-input multi-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay network is presented under the condition of spatial-domain self-interference nulling. This method is designed to suit the availability of channel state information (CSI). If full CSI of source station (SS)-relay station (RS), RS-RS (self-interference channel), and RS-destination station (DS) links are available, the instantaneous end-to-end capacity is maximized. Otherwise, if CSI of the RS-DS link is either partially available (only covariance is known), or not available, while CSI of the other links is known, then the ergodic end-to-end capacity is maximized. Performance of the proposed FD-AF relay system is demonstrated through computer simulations, especially under various correlation conditions of the RS-DS link.

The Boolean network can be used as a mathematical model for gene regulatory networks. An attractor, which is a state of a Boolean network repeating itself periodically, can represent a stable stage of a gene regulatory network. It is known that the problem of finding an attractor of the shortest period is NP-hard. In this article, we give a fixed-parameter algorithm for detecting a singleton attractor (SA) for a Boolean network that has only AND and OR Boolean functions of literals and has bounded treewidth k. The algorithm is further extended to detect an SA for a constant-depth nested canalyzing Boolean network with bounded treewidth. We also prove the fixed-parameter intractability of the detection of an SA for a general Boolean network with bounded treewidth.

In this paper, we present a new cryptanalytic tool that can reduce the complexity of integral analysis against Addition-Rotation-XOR (ARX) based designs. Our technique is based on the partial-sum technique proposed by Ferguson et al. at FSE 2000, which guesses subkeys byte to byte in turn, and the data to be analyzed is compressed for each key guess. In this paper, the technique is extended to ARX based designs. Subkeys are guessed bit by bit, and the data is compressed with respect to the value of the guessed bit position and carry values to the next bit position. We call the technique bitwise partial-sum. We demonstrate this technique by applying it to reduced-round versions of HIGHT, which is one of the ISO standard 64-bit block ciphers. Another contribution of this paper is an independent improvement specific to HIGHT. By exploiting linear computations inside the round function, the number of guessed bits during the key recovery phase can be greatly reduced. Together with the bitwise partial-sum, the integral analysis on HIGHT is extended from previous 22 rounds to 26 rounds, while full HIGHT consists of 32 rounds.

Computation in the brain is realized in complicated, heterogeneous, and extremely large-scale network of neurons. About a hundred billion neurons communicate with each other by action potentials called “spike firings” that are delivered to thousands of other neurons from each. Repeated integration and networking of these spike trains in the network finally form the substance of our cognition, perception, planning, and motor control. Beyond conventional views of neural network mechanisms, recent rapid advances in both experimental and theoretical neuroscience unveil that the brain is a dynamical system to actively treat environmental information rather passively process it. The brain utilizes internal dynamics to realize our resilient and efficient perception and behavior. In this paper, by considering similarities and differences of the brain and information networks, we discuss a possibility of information networks with brain-like continuing internal dynamics. We expect that the proposed networks efficiently realize context-dependent in-network processing. By introducing recent findings of neuroscience about dynamics of the brain, we argue validity and clues for implementation of the proposal.

For a large-sized touch screen, we designed and evaluated a real-time touch microarchitecture using a field-programmable gate array (FPGA). A high-speed hardware accelerator based on a parallel touch algorithm is suggested and implemented in this letter. The touch controller also has a timing control unit and an analog digital convert (ADC) control unit for analog touch sensing circuits. Measurement results of processing time showed that the touch controller with its proposed microarchitecture is five times faster than the 32-bit reduced instruction set computer (RISC) processor without the touch accelerator.

We previously proposed a query-by-sketch image retrieval system that uses an edge relation histogram (ERH). However, it is difficult for this method to retrieve partial objects from an image, because the ERH is a feature of the entire image, not of each object. Therefore, we propose an object-extraction method that uses edge-based features in order to enable the query-by-sketch system to retrieve partial images. This method is applied to 20,000 images from the Corel Photo Gallery. We confirm that retrieval accuracy is improved by using the edge-based features for extracting objects, enabling the query-by-sketch system to retrieve partial images.

This paper proposes a disaster recovery method for transport networks. In a scenario of recovery from a disaster, a network is repaired through multiple restoration stages because repair resources are limited. In a practical case, a network should provide the reachability of important traffic in transient stages, even as service interruption risks and/or operational overheads caused by transport paths switching are suppressed. Then, we define the multi-objective optimization problem: maximizing the traffic recovery ratio and minimizing the number of switched transport paths at each stage. We formulate our problem as linear programming, and show that it yields pareto-optimal solutions of traffic recovery versus the number of switched paths. We also propose a heuristic algorithm for applying to networks consisting of a few hundred nodes, and show that it can produce sub-optimal solutions that differ only slightly from optimal solutions.

Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners motivated to access the large-scale computing resources and economic savings. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. So how to design an efficient, in the two aspects of accuracy and efficiency, searchable encryption scheme over encrypted cloud data is a very challenging task. In this paper, for the first time, we propose a practical, efficient, and flexible searchable encryption scheme which supports both multi-keyword ranked search and parallel search. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search results. To improve search efficiency, we design a tree-based index structure which supports parallel search to take advantage of the powerful computing capacity and resources of the cloud server. With our designed parallel search algorithm, the search efficiency is well improved. We propose two secure searchable encryption schemes to meet different privacy requirements in two threat models. Extensive experiments on the real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting multi-keyword ranked parallel searches.

Speeded up robust features (SURF) can detect/describe scale- and rotation-invariant features at high speed by relying on integral images for image convolutions. However, the time taken for matching SURF descriptors is still long, and this has been an obstacle for use in real-time applications. In addition, the matching time further increases in proportion to the number of features and the dimensionality of the descriptor. Therefore, we propose a fast matching method that rearranges the elements of SURF descriptors based on their entropies, divides SURF descriptors into sub-descriptors, and sequentially and analytically matches them to each other. Our results show that the matching time could be reduced by about 75% at the expense of a small drop in accuracy.

In this paper, we propose a novel ranking method called VisualTextualRank which ranks media data according to the relevance between the data and specified keywords. We apply our method to the system of video shot ranking which aims to automatically obtain video shots corresponding to given action keywords from Web videos. The keywords can be any type of action such as “surfing wave” (sport action) or “brushing teeth” (daily activity). Top ranked video shots are expected to be relevant to the keywords. While our baseline exploits only visual features of the data, the proposed method employs both textual information (tags) and visual features. Our method is based on random walks over a bipartite graph to integrate visual information of video shots and tag information of Web videos effectively. Note that instead of treating the textual information as an additional feature for shot ranking, we explore the mutual reinforcement between shots and textual information of their corresponding videos to improve shot ranking. We validated our framework on a database which was used by the baseline. Experiments showed that our proposed ranking method, VisualTextualRank, improved significantly the performance of the system of video shot extraction over the baseline.

Secret sharing scheme (SS) has been extensively studied since SSs are important not only for secure data storage but also as a fundamental building block for multiparty computation (MPC). For an application to secure data storage, the share size of SS is an important factor. For an application to a building block for MPC, the extendibility to MPC is needed. Computationally secure SSs and a ramp scheme have a small share size but there have been few studies concerning their MPC. In contrast, there have been many studies about MPC on Shamir's and replicated SSs while their share size is large. We consider an application scenario of SS such as applying SSs to secure data storage service with MPC. In this application, users store their data in servers through SS, and sometimes the servers perform MPC as an optional feature. In this case, the extendibility to MPC is needed and good code-efficiency is preferable. We propose a new computational SS, and show how to convert shares of our SS and a ramp SS to those of multiparty-friendly SS such as Shamir's and replicated SS. This enables one to secretly-share data compactly and extend secretly-shared data to MPC if needed.

A design strategy (the required ECC strength and the judgment method of the dominant error mode) of error-prediction low-density parity-check (EP-LDPC) error-correcting code (ECC) and error-recovery schemes for scaled NAND flash memories is discussed in this paper. The reliability characteristics of NAND flash memories are investigated with 1X, 2X and 3Xnm NAND flash memories. Moreover, the system-level reliability of SSDs is analyzed from the acceptable data-retention time of the SSD. The reliability of the NAND flash memory is continuously degrading as the design rule shrinks due to various problems. As a result, future SSDs will not be able to maintain system-level reliability unless advanced ECCs with signal processing are adopted. Therefore, EP-LDPC and error-recovery (ER) schemes are previously proposed to improve the reliability. The reliability characteristics such as the bit-error rate (BER) versus the data-retention time and the effect of the cell-to-cell interference on the BER are measured. These reliability characteristics obtained in this paper are stored in an SSD as a reliability table, which plays a principal role in EP-LDPC scheme. The effectiveness of the EP-LDPC scheme with the scaling of the NAND flash memory is also discussed by analyzing the cell-to-cell interference. An interference factor $alpha$ is proposed to discuss the impact of the cell-to-cell coupling. As a result, the EP-LDPC scheme is assumed to be effective down to 1Xnm NAND flash memory. On the other hand, the ER scheme applies different voltage pulses to memory cells, according to the dominant error mode: program-disturb or data-retention error dominant mode. This paper examines when the error mode changes, corresponding to which pulse should be applied. Additionally, the estimation methods of the dominant error mode by ER scheme are also discussed. Finally, as a result of the system-level reliability analysis, it is concluded that the use of the EP-LDPC scheme can maintain the reliability of the NAND flash memory in 1Xnm technology node.

In consideration of the image noise captured by photoelectric cameras at nighttime, a robust motion detection algorithm based on sparse representation is proposed in this study. A universal dictionary for arbitrary scenes is presented. Realistic and synthetic experiments demonstrate the robustness of the proposed approach.