Showing papers in "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences in 2017"

Frontier-Based Search for Enumerating All Constrained Subgraphs with Compressed Representation

Abstract: For the subgraph enumeration problem, there have been proposed very efficient algorithms whose time complexities are far less than the number of subgraphs. Although the number of subgraphs can increase exponentially with the graph size, they exploit compressed representations to maintain enumerated subgraphs compactly so as to reduce the time complexity. However, they are designed for enumerating a specific type of subgraphs only, e.g., paths or trees. In this paper, we propose a novel algorithm framework, called frontierbased search, which generalizes these specific algorithms without losing their efficiency. We believe that our frontier-based search will be used to resolve various practical problems that include complicated subgraph enumeration.

Another Fuzzy Anomaly Detection System Based on Ant Clustering Algorithm

Abstract: Attacks against computer networks are evolving rapidly. Conventional intrusion detection system based on pattern matching and static signatures have a significant limitation since the signature database should be updated frequently. The unsupervised learning algorithm can overcome this limitation. Ant Clustering Algorithm (ACA) is a popular unsupervised learning algorithm to classify data into different categories. However, ACA needs to be complemented with other algorithms for the classification process. In this paper, we present a fuzzy anomaly detection system that works in two phases. In the first phase, the training phase, we propose ACA to determine clusters. In the second phase, the classification phase, we exploit a fuzzy approach by the combination of two distance-based methods to detect anomalies in new monitored data. We validate our hybrid approach using the KDD Cup’99 dataset. The results indicate that, compared to several traditional and new techniques, the proposed hybrid approach achieves higher detection rates and lower false alarm rate.

Model Based Fallback Control for Networked Control System via Switched Lyapunov Function

Abstract: This paper aims to propose a fallback system isolated from man-in-the-middle attack (MITM) for networked control systems. In this paper, fallback is an incident response of the control system in which facilities are preferentially protected and a minimal system operates during MITM. Since the communication contents of the networked control systems can be tampered with by MITM, the proposed system named a "fallback system" is installed in a control target side, and the fallback system detects MITM without the communication contents. This system detects MITM based on the bilinear observer and switched Lyapunov function. When MITM is detected, normal operation is switched to fallback operation automatically. As a first step of this study, a selector and a fallback unit of the fallback system are developed. A practical experiment simulating a simple defective discriminator is shown.

Packed compact tries: A fast and efficient data structure for online string processing

Abstract: In this paper, we present a new data structure called the packed compact trie (packed c-trie) which stores a set $S$ of $k$ strings of total length $n$ in $n \log\sigma + O(k \log n)$ bits of space and supports fast pattern matching queries and updates, where $\sigma$ is the size of an alphabet. Assume that $\alpha = \log_\sigma n$ letters are packed in a single machine word on the standard word RAM model, and let $f(k,n)$ denote the query and update times of the dynamic predecessor/successor data structure of our choice which stores $k$ integers from universe $[1,n]$ in $O(k \log n)$ bits of space. Then, given a string of length $m$, our packed c-tries support pattern matching queries and insert/delete operations in $O(\frac{m}{\alpha} f(k,n))$ worst-case time and in $O(\frac{m}{\alpha} + f(k,n))$ expected time. Our experiments show that our packed c-tries are faster than the standard compact tries (a.k.a. Patricia trees) on real data sets. As an application of our packed c-trie, we show that the sparse suffix tree for a string of length $n$ over prefix codes with $k$ sampled positions, such as evenly-spaced and word delimited sparse suffix trees, can be constructed online in $O((\frac{n}{\alpha} + k) f(k,n))$ worst-case time and $O(\frac{n}{\alpha} + k f(k,n))$ expected time with $n \log \sigma + O(k \log n)$ bits of space. When $k = O(\frac{n}{\alpha})$, by using the state-of-the-art dynamic predecessor/successor data structures, we obtain sub-linear time construction algorithms using only $O(\frac{n}{\alpha})$ bits of space in both cases. We also discuss an application of our packed c-tries to online LZD factorization.

On asymptotically good ramp secret sharing schemes

Abstract: We coin the term of asymptotically good sequences of ramp secret sharing schemes. These are sequences such that when the number of participants goes to infinity, the information rate approaches some fixed positive number while the worst case information leakage of a fixed fraction of information, relative to the number of participants, attains another fixed number. A third fixed positive number describes the ratio of participants that are guaranteed to be able to recover all – or almost all – of the secret. By a non-constructive proof we demonstrate the existence of asymptotically good sequences of schemes with parameters arbitrarily close to the optimal ones. Moreover, we demonstrate how to concretely construct asymptotically good sequences of schemes from sequences of algebraic geometric codes related to a tower of function fields. Our study involves a detailed treatment of the relative generalized Hamming weights of the involved codes.

A Histogram-Based Quality Model for HTTP Adaptive Streaming

Abstract: HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. Because of throughput variations, video quality fluctuates during a streaming session. Therefore, a main challenge in HAS is how to evaluate the overall video quality of a session. In this paper, we explore the impacts of quality values and quality variations in HAS. We propose to use the histogram of segment quality values and the histogram of quality gradients in a session to model the overall video quality. Subjective test results show that the proposed model has very high prediction performance for different videos. Especially, the proposed model provides insights into the influence factors of the overall quality, thus leading to suggestions to improve the quality of streaming video. key words: video quality model, adaptive streaming, subjective test, histogram

Zigzag Decodable Fountain Codes

Abstract: This paper proposes a fountain coding system which has lower space decoding complexity and lower decoding erasure rate than the Raptor coding systems. The main idea of the proposed fountain code is employing shift and exclusive OR to generate the output packets. This technique is known as the zigzag decodable code, which is efficiently decoded by the zigzag decoder. In other words, we propose a fountain code based on the zigzag decodable code in this paper. Moreover, we analyze the overhead for the received packets, decoding erasure rate, decoding complexity, and asymptotic overhead of the proposed fountain code. As the result, we show that the proposed fountain code outperforms the Raptor codes in terms of the overhead and decoding erasure rate. Simulation results show that the proposed fountain coding system outperforms Raptor coding system in terms of the overhead and the space decoding complexity.

Error Recovery for Massive MIMO Signal Detection via Reconstruction of Discrete-Valued Sparse Vector

Abstract: In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP),we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method. key words: massive MIMO, signal detection, sum-of-absolute-value optimization, restricted isometry property

Quantum Stabilizer Codes Can Realize Access Structures Impossible by Classical Secret Sharing

Abstract: We show a simple example of a secret sharing scheme encoding classical secret to quantum shares that can realize an access structure impossible by classical information processing with limitation on the size of each share. The example is based on quantum stabilizer codes.