In this paper, we consider solving the all-pairs regular path problem on large graphs efficiently. Let G be a graph and r be a regular path query, and consider finding the answers of r on G. If G is so small that it fits in main memory, it suffices to load entire G into main memory and traverse G to find paths matching r. However, if G is too large and cannot fit in main memory, we need another approach. In this paper, we propose a novel approach based on external memory algorithm. Our algorithm finds the answers matching r by scanning the node list of G sequentially. We made a small experiment, which suggests that our algorithm can solve the problem efficiently.

In this paper, we present a new encoding/decoding method for dynamic multidimensional datasets and its implementation scheme. Our method encodes an n-dimensional tuple into a pair of scalar values even if n is sufficiently large. The method also encodes and decodes tuples using only shift and and/or register instructions. One of the most serious problems in multidimensional array based tuple encoding is that the size of an encoded result may often exceed the machine word size for large-scale tuple sets. This problem is efficiently resolved in our scheme. We confirmed the advantages of our scheme by analytical and experimental evaluations. The experimental evaluations were conducted to compare our constructed prototype system with other systems; (1) a system based on a similar encoding scheme called history-offset encoding, and (2) PostgreSQL RDBMS. In most cases, both the storage and retrieval costs of our system significantly outperformed those of the other systems.

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

In this letter, we present a meet-in-the-middle attack on the 7-round reduced block cipher Kalyna-b/2b, which has been approved as the new encryption standard of Ukraine (DSTU 7624:2014) in 2015. According to its designers, the cipher provides strength to several cryptanalytic methods after the fifth and sixth rounds of the versions with block length of 128 and 256 bits, respectively. Our attack is based on the differential enumeration approach, where we carefully deploy a four-round distinguisher in the first four rounds to bypass the effect of the carry bits resulting from the prewhitening modular key addition. We also exploit the linear relation between consecutive odd and even indexed round keys, which enables us to attack seven rounds and recover all the round keys incrementally. The attack on Kalyna with 128-bit block has a data complexity of 289 chosen plaintexts, time complexity of 2230.2 and a memory complexity of 2202.64. The data, time and memory complexities of our attack on Kalyna with 256-bit block are 2233, 2502.2 and 2170, respectively.

Modern web users may encounter a browser security threat called drive-by-download attacks when surfing on the Internet. Drive-by-download attacks make use of exploit codes to take control of user's web browser. Many web users do not take such underlying threats into account while clicking URLs. URL Blacklist is one of the practical approaches to thwarting browser-targeted attacks. However, URL Blacklist cannot cope with previously unseen malicious URLs. Therefore, to make a URL blacklist effective, it is crucial to keep the URLs updated. Given these observations, we propose a framework called automatic blacklist generator (AutoBLG) that automates the collection of new malicious URLs by starting from a given existing URL blacklist. The primary mechanism of AutoBLG is expanding the search space of web pages while reducing the amount of URLs to be analyzed by applying several pre-filters such as similarity search to accelerate the process of generating blacklists. AutoBLG consists of three primary components: URL expansion, URL filtration, and URL verification. Through extensive analysis using a high-performance web client honeypot, we demonstrate that AutoBLG can successfully discover new and previously unknown drive-by-download URLs from the vast web space.

In RFID-enabled supply chains, it is necessary to protect the contents of EPCs (Electronic Product Code) since an EPC contains sensitive information such as the product code and serial number and could be used for counterfeits. Although many protection schemes have been proposed, no scheme can limit the number of illegal attempts for discovering EPCs or notice whether an attacker exists. In this paper, we propose an illegal interrogation detectable products distribution scheme for RFID-enabled supply chains. The idea is to detect the attacker by forcing him/her to access an authentication server. Our scheme masks EPCs with random sequences. Masked EPCs are written into genuine tags on products while random sequences are placed on an authentication server with an access code. An access code is divided into shares with a secret sharing scheme and they are written into genuine tags. We also write bogus shares into extra off-the-shelf tags that are not attached to any products. Since an attacker who wants to know genuine EPCs may obtain a large number of access code candidates and must try each on the authentication server, the server can detect the attacker.

This paper realized the automatic recognition of Mycobacterium tuberculosis in Ziehl-Neelsen stained images by the conventional light microscopy, which can be used in the computer-aided diagnosis of the tuberculosis. We proposed a novel recognition method based on active shape model. First, the candidate bacillus objects are segmented by a method of marker-based watershed transform. Next, a point distribution model of the object shape is proposed to label the landmarks on the object automatically. Then the active shape model is performed after aligning the training set with a weight matrix. The deformation regulation of the object shape is discovered and successfully applied in recognition without using geometric and other commonly used features. During this process, a width consistency constraint is combined with the shape parameter to improve the accuracy of the recognition. Experimental results demonstrate that the proposed method yields high accuracy in the images with different background colors. The recognition accuracy in object level and image level are 92.37% and 97.91% respectively.

High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the coding accuracy, HEVC adopts sample adaptive offset (SAO), which reduces the distortion of reconstructed pixels using classification based non-linear filtering. In the traditional coding tree unit (CTU) grain based VLSI encoder implementation, during the pixel classification stage, SAO cannot use the raw samples in the boundary of the current CTU because these pixels have not been processed by deblocking filter (DF). This paper proposes a hardware-oriented category determination algorithm based on estimating the deblocking strengths on CTU boundaries and selectively adopting the promising samples in these areas during SAO classification. Compared with HEVC test mode (HM11.0), experimental results indicate that the proposed method achieves an average 0.13%, 0.14%, and 0.12% BD-bitrate reduction (equivalent to 0.0055dB, 0.0058dB, and 0.0097dB increases in PSNR) in CTU sizes of 64 × 64, 32 × 32, and 16 × 16, respectively.

In recent years, society has experienced several changes in its ways and methods of consuming. Nowadays, the diversity and the customization of products and services have provoked that the consumer needs continuously change. Hence, the database systems support e-business processes are required to be timeliness and adaptable to the changing preferences. Autonomous Decentralized Database System (ADDS), has been proposed in order to satisfy the enhanced requirements of current on-line e-business applications. Autonomy and decentralization of subsystems help to achieve short response times in highly competitive situations and an autonomous Coordination Mobile Agent (CMA) has been proposed to achieve flexibility in a highly dynamic environment. However, a problem in ADDS is as the number of sites increases, the distribution and harmonization of product information among the sites are turning difficult. Therefore, many users cannot be satisfied quickly. As a result, system timeliness is inadequate. To solve this problem, a self configuration technology is proposed. This technology can configure the system to the evolving situation dynamically for achieving high response. A simulation shows the effectiveness of the proposed technology in a large-scale system. Finally, an implementation of this technology is presented.

Web Service Composition (WSC) has been well recognized as a convenient and flexible way of service sharing and integration in service-oriented application fields. WSC aims at selecting and composing a set of initial services with respect to the Quality of Service (QoS) values of their attributes (e.g., price), in order to complete a complex task and meet user requirements. A major research challenge of the QoS-aware WSC problem is to select a proper set of services to maximize the QoS of the composite service meeting several QoS constraints upon various attributes, e.g. total price or runtime. In this article, a fast algorithm based on QoS-aware sampling (FAQS) is proposed, which can efficiently find the near-optimal composition result from sampled services. FAQS consists of five steps as follows. 1) QoS normalization is performed to unify different metrics for QoS attributes. 2) The normalized services are sampled and categorized by guaranteeing similar number of services in each class. 3) The frequencies of the sampled services are calculated to guarantee the composed services are the most frequent ones. This process ensures that the sampled services cover as many as possible initial services. 4) The sampled services are composed by solving a linear programming problem. 5) The initial composition results are further optimized by solving a modified multi-choice multi-dimensional knapsack problem (MMKP). Experimental results indicate that FAQS is much faster than existing algorithms and could obtain stable near-optimal result.

Distributed antenna systems (DASs) combined with multi-user multiple-input multiple-output (MU-MIMO) transmission techniques have recently attracted significant attention. To establish MU-MIMO DASs that have wide service areas, the use of a dynamic clustering scheme (CS) is necessary to reduce computation in precoding. In the present study, we propose a simple method for dynamic clustering to establish a single cell large-scale MU-MIMO DAS and investigate its performance. We also compare the characteristics of the proposal to those of other schemes such as exhaustive search, traditional location-based adaptive CS, and improved norm-based CS in terms of sum rate improvement. Additionally, to make our results more universal, we further introduce spatial correlation to the considered system. Computer simulation results indicate that the proposed CS for the considered system provides better performance than the existing schemes and can achieve a sum rate close to that of exhaustive search but at a lower computational cost.

East Japan Railway Company has created new businesses such as life-style business and information technology business on the basis of railway business for sustainable growth. These businesses generate and provide synergy to one another effectively because each business is autonomous decentralized system based on diversified infrastructure. The infrastructure includes not just structure but management, technology, operation and maintenance: we call this “MTOMI Model.” The MTOMI Model is the key concept of JR East's businesses and can generate JR East's ecosystem.

Some networks, such as wireless sensor networks, vehicle networks, etc., are often disconnected and thus fail to provide an end-to-end route for transmission. As a result, a new kind self-organized wireless network, i.e., Delay Tolerant Network (DTN) is proposed to transmit messages using a store-carry-forward method. To efficiently process aggregation queries, this paper proposes a subscription aggregation query processing method that combines query processing and transfer protocols. The basic idea is reducing the number of redundant copy transmissions, increasing the message delivery rate and reducing the transmission delay by matrix summation. Theoretical and experimental results show that the method can attain a good performance in the delay tolerant networks.

The routing efficiency of structured overlay networks depends on the consistency of pointers between nodes, where a pointer maps a node identifier to the corresponding address. This consistency can, however, break temporarily when some overlay nodes fail, since it takes time to repair the broken pointers in a distributed manner. Conventional solutions utilize “backpointers” to quickly discover any failure among the pointing nodes, which allow them to fix the pointers in a short time. Overlay nodes are, however, required to maintain backpointers for every pointing node, which incurs significant memory and consistency check overhead. This paper proposes a novel light-weight protocol; an overlay node gives a “living will” containing its acquaintances (backpointers) only to its successor, thus other nodes are freed from the need to maintain it. Our carefully-designed protocol guarantees that all acquaintances are registered via the living will, even in the presence of churn, and the successor notifies the acquaintances for the deceased. Even if the successor passes away and the living will is lost, the successor to the successor can identify the acquaintances with a high success ratio. Simulations show that our protocol greatly reduces memory overhead as well as the detection time for node failure with the cost being a slight increase in messaging load.

The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

An on-chip monitoring circuit using a sub-sampling scheme, which consists of a 6-bit flash analog-to-digital converter (ADC) and a 51-phase phase-locked loop (PLL)-based frequency synthesizer, is proposed to analyze the signal integrity of a single-ended 8-Gb/s octal data rate (ODR) chip-to-chip interface with a source synchronous clocking scheme.

This paper introduces low-power and small area injection-locking clock and data recovery circuit (CDR) for the wireline and wireless proximity link. By using signal conversion from differential input to common-mode output, the newly proposed edge detector can eliminate the usually used delay line and XOR-based edge detector, and provided low power operation and a small circuit area. The CDR test chip fabricated in a 65-nm CMOS process consumes 30mW from a 1.2- V supply at 12.5Gbps. The fabricated CDR achieved a BER lower than 10-12 and the recovered clock had an rms jitter of 0.87ps. The CDR area is 0.165mm2.

A novel activation method for a B dopant implanted in a Si substrate using a soft X-ray undulator was examined. As the photon energy of the irradiated soft X-ray approached the energy of the core level of Si 2p, the activation ratio increased. The effect of soft X-ray irradiation on B activation was remarkable at temperatures lower than 400°C. The activation energy of B activation by soft X-ray irradiation (0.06 eV) was lower than that of B activation by furnace annealing (0.18 eV). The activation of the B dopant by soft X-ray irradiation occurs at low temperature, although the activation ratio shows small values of 6.2×10-3 at 110°C. The activation by soft X-ray is caused not only by thermal effects, but also electron excitation and atomic movement.

The availability is an important issue of software-defined networking (SDN). In this paper, the experiments based on a SDN testbed showed that the resource utilization of the data plane and control plane changed drastically when DDoS attacks happened. This is mainly because the DDoS attacks send a large number of fake flows to network in a short time. Based on the observation and analysis, a DDoS defense mechanism based on legitimate source and destination IP address database is proposed in this paper. Firstly, each flow is abstracted as a source-destination IP address pair and a legitimate source-destination IP address pair database (LSDIAD) is established by historical normal traffic trace. Then the proportion of new source-destination IP address pair in the traffic per unit time is cumulated by non-parametric cumulative sum (CUSUM) algorithm to detect the DDoS attacks quickly and accurately. Based on the alarm from the non-parametric CUSUM, the attack flows will be filtered and redirected to a middle box network for deep analysis via south-bound API of SDN. An on-line updating policy is adopted to keep the LSDIAD timely and accurate. This mechanism is mainly implemented in the controller and the simulation results show that this mechanism can achieve a good performance in protecting SDN from DDoS attacks.

Coordinated Multi-point (CoMP) transmission has long been known for its ability to improve cell edge throughput. However, in a CoMP cellular network, fixed CoMP clustering results in cluster edges where system performance degrades due to non-coordinated clusters. To solve this problem, conventional studies proposed dynamic clustering schemes. However, such schemes require a complex backhaul topology and are infeasible with current network technologies. In this paper, small power base stations (BSs) are introduced instead of dynamic clustering to solve the cluster edge problem in CoMP cellular networks. This new cell topology is called the diamond cellular network since the resultant cell structure looks like a diamond pattern. In our novel cell topology, we derive the optimal locations of small power base stations and the optimal resource allocation between the CoMP base station and small power base stations to maximize the proportional fair utility function. By using the proposed architecture, in the case of perfect user scheduling, a more than 150% improvement in 5% outage throughput is achieved, and in the case of successive proportional fair user scheduling, nearly 100% improvement of 5% outage throughput is achieved compared with conventional single cell networks.