IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Volume E100.D , Issue 10
Showing 1-50 articles out of 50 articles from the selected issue
Special Section on Security, Privacy and Anonymity in Computation, Communication and Storage Systems
  • Guojun Wang
    2017 Volume E100.D Issue 10 Pages 2265-2266
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS
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  • Liang-Chun CHEN, Chien-Lung HSU, Nai-Wei LO, Kuo-Hui YEH, Ping-Hsien L ...
    Type: INVITED PAPER
    2017 Volume E100.D Issue 10 Pages 2267-2274
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.

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  • Weina NIU, Xiaosong ZHANG, Guowu YANG, Ruidong CHEN, Dong WANG
    Type: PAPER
    Subject area: Operating system and network Security
    2017 Volume E100.D Issue 10 Pages 2275-2286
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Advanced Persistent Threat (APT) is one of the most serious network attacks that occurred in cyberspace due to sophisticated techniques and deep concealment. Modeling APT attack process can facilitate APT analysis, detection, and prediction. However, current techniques focus on modeling known attacks, which neither reflect APT attack dynamically nor take human factors into considerations. In order to overcome this limitation, we propose a Targeted Complex Attack Network (TCAN) model for APT attack process based on dynamic attack graph and network evolution. Compared with current models, our model addresses human factors by conducting a two-layer network structure. Meanwhile, we present a stochastic model based on states change in the target network to specify nodes involved in the procedure of this APT. Besides, our model adopts time domain to expand the traditional attack graph into dynamic attack network. Our model is featured by flexibility, which is proven through changing the related parameters. In addition, we propose dynamic evolution rules based on complex network theory and characteristics of the actual attack scenarios. Finally, we elaborate a procedure to add nodes by a matrix operation. The simulation results show that our model can model the process of attack effectively.

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  • Na RUAN, Mingli WU, Shiheng MA, Haojin ZHU, Weijia JIA, Songyang WU
    Type: PAPER
    Subject area: Operating system and network Security
    2017 Volume E100.D Issue 10 Pages 2287-2294
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    As a new generation voice service, Voice over LTE (VoLTE) has attracted worldwide attentions in both the academia and industry. Different from the traditional voice call based on circuit-switched (CS), VoLTE evolves into the packet-switched (PS) field, which has long been open to the public. Though designed rigorously, similar to VoIP services, VoLTE also suffers from SIP (Session Initiation Protocal) flooding attacks. Due to the high performance requirement, the SIP flooding attacks in VoLTE is more difficult to defend than that in traditional VoIP service. In this paper, enlightened by Counting Bloom Filter (CBF), we design a versatile CBF-like structure, PFilter, to detect the flooding anomalies. Compared with previous relevant works, our scheme gains advantages in many aspects including detection of low-rate flooding attack and stealthy flooding attack. Moreover, not only can our scheme detect the attacks with high accuracy, but also find out the attackers to ensure normal operation of VoLTE by eliminating their negative effects. Extensive experiments are performed to well evaluate the performance of the proposed scheme.

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  • Toshihiro YAMAUCHI, Yuta IKEGAMI, Yuya BAN
    Type: PAPER
    Subject area: Operating system and network Security
    2017 Volume E100.D Issue 10 Pages 2295-2306
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Recently, there has been an increase in use-after-free (UAF) vulnerabilities, which are exploited using a dangling pointer that refers to a freed memory. In particular, large-scale programs such as browsers often include many dangling pointers, and UAF vulnerabilities are frequently exploited by drive-by download attacks. Various methods to prevent UAF attacks have been proposed. However, only a few methods can effectively prevent UAF attacks during runtime with low overhead. In this paper, we propose HeapRevolver, which is a novel UAF attack-prevention method that delays and randomizes the timing of release of freed memory area by using a memory-reuse-prohibited library, which prohibits a freed memory area from being reused for a certain period. The first condition for reuse is that the total size of the freed memory area is beyond the designated size. The threshold for the conditions of reuse of the freed memory area can be randomized by HeapRevolver. Furthermore, we add a second condition for reuse in which the freed memory area is merged with an adjacent freed memory area before release. Furthermore, HeapRevolver can be applied without modifying the target programs. In this paper, we describe the design and implementation of HeapRevolver in Linux and Windows, and report its evaluation results. The results show that HeapRevolver can prevent attacks that exploit existing UAF vulnerabilities. In addition, the overhead is small.

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  • Dongyang ZHAN, Lin YE, Binxing FANG, Xiaojiang DU, Zhikai XU
    Type: PAPER
    Subject area: Operating system and network Security
    2017 Volume E100.D Issue 10 Pages 2307-2318
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Protecting critical files in operating system is very important to system security. With the increasing adoption of Virtual Machine Introspection (VMI), designing VMI-based monitoring tools become a preferential choice with promising features, such as isolation, stealthiness and quick recovery from crash. However, these tools inevitably introduce high overhead due to their operation-based characteristic. Specifically, they need to intercept some file operations to monitor critical files once the operations are executed, regardless of whether the files are critical or not. It is known that file operation is high-frequency, so operation-based methods often result in performance degradation seriously. Thus, in this paper we present CFWatcher, a target-based real-time monitoring solution to protect critical files by leveraging VMI techniques. As a target-based scheme, CFWatcher constraints the monitoring into the operations that are accessing target files defined by users. Consequently, the overhead depends on the frequency of target files being accessed instead of the whole filesystem, which dramatically reduces the overhead. To validate our solution, a prototype system is built on Xen with full virtualization, which not only is able to monitor both Linux and Windows virtual machines, but also can take actions to prevent unauthorized access according to predefined policies. Through extensive evaluations, the experimental results demonstrate that the overhead introduced by CFWatcher is acceptable. Especially, the overhead is very low in the case of a few target files.

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  • Yuji UNAGAMI, Natsume MATSUZAKI, Shota YAMADA, Nuttapong ATTRAPADUNG, ...
    Type: PAPER
    Subject area: Operating system and network Security
    2017 Volume E100.D Issue 10 Pages 2319-2326
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    In this paper, we propose a similarity searchable encryption in the symmetric key setting for the weighted Euclidean distance, by extending the functional encryption scheme for inner product proposed by Bishop et al. [4]. Our scheme performs predetermined encoding independently of vectors x and y, and it obtains the weighted Euclidean distance between the two vectors while they remain encrypted.

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  • Takeshi KUMAKI, Takeshi FUJINO
    Type: PAPER
    Subject area: Privacy, anonymity, and fundamental theory
    2017 Volume E100.D Issue 10 Pages 2327-2338
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    This paper presents a hierarchical-masked image filtering method for privacy-protection. Cameras are widely used for various applications, e.g., crime surveillance, environment monitoring, and marketing. However, invasion of privacy has become a serious social problem, especially regarding the use of surveillance cameras. Many surveillance cameras point at many people; thus, a large amount of our private information of our daily activities are under surveillance. However, several surveillance cameras currently on the market and related research often have a complicated or institutional masking privacy-protection functionality. To overcome this problem, a Hierarchical-Masked image Filtering (HMF) method is proposed, which has unmaskable (mask reversal) capability and is applicable to current surveillance camera systems for privacy-information protection and can satisfy privacy-protection related requirements. This method has five main features: unmasking of the original image from only the masked image and a cipher key, hierarchical-mask level control using parameters for the length of a pseudorandom number, robustness against malicious attackers, fast processing on an embedded processor, and applicability of mask operation to current surveillance camera systems. Previous studies have difficulty in providing these features. To evaluate HMF on actual equipment, an HMF-based prototype system is developed that mainly consists of a USB web camera, ultra-compact single board computer, and notebook PC. Through experiments, it is confirmed that the proposed method achieves mask level control and is robust against attacks. The increase in processing time of the HMF-based prototype system compared with a conventional non-masking system is only about 1.4%. This paper also reports on the comparison of the proposed method with conventional privacy protection methods and favorable responses of people toward the HMF-based prototype system both domestically and abroad. Therefore, the proposed HMF method can be applied to embedded systems such as those equipped with surveillance cameras for protecting privacy.

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  • Yoshinori AONO, Takuya HAYASHI, Le Trieu PHONG, Lihua WANG
    Type: PAPER
    Subject area: Privacy, anonymity, and fundamental theory
    2017 Volume E100.D Issue 10 Pages 2339-2347
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.

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  • Ki-Woong PARK, Sung Hoon BAEK
    Type: PAPER
    Subject area: Privacy, anonymity, and fundamental theory
    2017 Volume E100.D Issue 10 Pages 2348-2356
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Emerging digital payment services, also known as FinTech, have enabled various types of advanced payment transactions (such as Google Wallet, Apple Pay, Samsung Pay, etc.). However, offline peer-to-peer cash transactions still make up about 25.6% of the overall financial transactions in everyday life. By investigating existing online and offline payment systems, we identify three key challenges for building a digital cash transaction system with core features of the offline cash transactions: self-verifiability of digital cash; user anonymity; atomic cash transfer for double spending/depositing protection. In this paper, we propose OPERA, an offline peer-to-peer digital cash transaction system that addresses the three challenges. It newly introduces a concept of ‘one-time-readable memory(ORM)’ and ‘digital token’ which is a unit of self-verifiable digital cash. The one-time readability from ORM and three-stage token exchange protocol enable OPERA to provide uniqueness to digital cash and to allow a complete offline digital payment. OPERA devices are enhanced with TCPA technology to ensure the integrity of the physical device package. To evaluate the feasibility and resilience of the OPERA design, we built a prototype on a customized embedded board.

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  • Tomoyoshi ONO, Kazuki YONEYAMA
    Type: PAPER
    Subject area: Privacy, anonymity, and fundamental theory
    2017 Volume E100.D Issue 10 Pages 2357-2367
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Group signature (GS) schemes guarantee anonymity of the actual signer among group members. Previous GS schemes assume that randomness in signing is never exposed. However, in the real world, full randomness exposure can be caused by implementation problems (e.g., using a bad random number generator). In this paper, we study (im)possibility of achieving anonymity against full randomness exposure. First, we formulate a new security model for GS schemes capturing full randomness exposure. Next, we clarify that it is impossible to achieve full-anonymity against full randomness exposure without any secure component (e.g., a tamper-proof module or a trusted outside storage). Finally, we show a possibility result that selfless-anonymity can be achieved against full randomness exposure. While selfless-anonymity is weaker than full-anonymity, it is strong enough in practice. Our transformation is quite simple; and thus, previous GS schemes used in real-world systems can be easily replaced by a slight modification to strengthen the security.

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  • Fei XU, Pinxin LIU, Jing XU, Jianfeng YANG, S.M. YIU
    Type: PAPER
    Subject area: Privacy, anonymity, and fundamental theory
    2017 Volume E100.D Issue 10 Pages 2368-2372
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Bloom Filter is a bit array (a one-dimensional storage structure) that provides a compact representation for a set of data, which can be used to answer the membership query in an efficient manner with a small number of false positives. It has a lot of applications in many areas. In this paper, we extend the design of Bloom Filter by using a multi-dimensional matrix to replace the one-dimensional structure with three different implementations, namely OFFF, WOFF, FFF. We refer the extended Bloom Filter as Feng Filter. We show the false positive rates of our method. We compare the false positive rate of OFFF with that of the traditional one-dimensional Bloom Filter and show that under certain condition, OFFF has a lower false positive rate. Traditional Bloom Filter can be regarded as a special case of our Feng Filter.

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  • Sang-Hoon CHOI, Joobeom YUN, Ki-Woong PARK
    Type: LETTER
    2017 Volume E100.D Issue 10 Pages 2373-2376
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    The secret document leakage incidents have raised awareness for the need to better security mechanisms. A leading cause of the incidents has been due to accidental disclosure through via removable storage devices. As a remedy to the issue, many organizations have been employing private cloud platform or virtual desktop infrastructure (VDI) to prevent the leakage of the secret documents. In spite of the various security benefits of cloud-based infrastructure, there are still challenges to prevent the secret document leakage incidents. In this paper, we present a novel scheme, called Doc-Trace, to provide an end-to-end traceability for the secret documents by inserting steganographic pattern into unused regions of the secret documents on private cloud and VDI platforms. We devise a computationally efficient storage scanning mechanism for providing end-to-end traceability for the storage scanning can be performed in an event-driven manner since a steganographic mark are encoded into a well-regulated offset address of the storage, which decrease the computation overhead drastically. To evaluate the feasibility of the proposed scheme, this work has been undertaken on a real cloud platform based on OpenStack.

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  • Toshihiro YAMAUCHI, Yohei AKAO
    Type: LETTER
    2017 Volume E100.D Issue 10 Pages 2377-2381
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    An operating system is an essential piece of software that manages hardware and software resources. Thus, attacks on an operating system kernel using kernel rootkits pose a particularly serious threat. Detecting an attack is difficult when the operating system kernel is infected with a kernel rootkit. For this reason, handling an attack will be delayed causing an increase in the amount of damage done to a computer system. In this paper, we propose Kernel Rootkits Guard (KRGuard), which is a new method to detect kernel rootkits that monitors branch records in the kernel space. Since many kernel rootkits make branches that differ from the usual branches in the kernel space, KRGuard can detect these differences by using the hardware features of commodity processors. Our evaluation shows that KRGuard can detect kernel rootkits that involve new branches in the system call handler processing with small overhead.

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Special Section on Advanced Log Processing and Office Information Systems
  • Shiro Uesugi
    2017 Volume E100.D Issue 10 Pages 2382
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS
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  • Toshihiko WAKAHARA, Toshitaka MAKI, Noriyasu YAMAMOTO, Akihisa KODATE, ...
    Type: INVITED PAPER
    2017 Volume E100.D Issue 10 Pages 2383-2390
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    The Life Intelligence and Office Information System (LOIS) Technical Committee of the Institute of Electronics, Information and Communication Engineers (IEICE) dates its origin to May 1986. This Technical Committee (TC) has covered the research fields of the office related systems for more than 30 years. Over this time, this TC, under its multiple name changes, has served as a forum for research and provided many opportunities for not only office users but also ordinary users of systems and services to present ideas and discussions. Therefore, these advanced technologies have been diffused from big enterprises to small companies and home users responsible for their management and operation. This paper sums up the technology trends and views of the office related systems and services covered in the 30 years of presentations of the LOIS Technical Committees by using the new literature analysis system based on the IEICE Knowledge Discovery system (I-Scover system).

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  • Yasushi YAMAZAKI, Tetsushi OHKI
    Type: INVITED PAPER
    2017 Volume E100.D Issue 10 Pages 2391-2398
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    With the rapid spread of smart devices, such as smartphones and tablet PCs, user authentication is becoming increasingly important because various kinds of data concerning user privacy are processed within them. At present, in the case of smart devices, password-based authentication is frequently used; however, biometric authentication has attracted more attention as a user authentication technology. A smart device is equipped with various sensors, such as cameras, microphones, and touch panels, many of which enable biometric information to be obtained. While the function of biometric authentication is available in many smart devices, there remain some problems to be addressed for more secure and convenient user authentication. In this paper, we summarize the current problems with user authentication on smart devices and propose a novel user authentication system based on the concept of context awareness to resolve these problems. We also present our evaluation of the performance of the system by using biometric information that was acquired from smart devices. The evaluation demonstrates the effectiveness of our system.

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  • Toru KOBAYASHI, Kenichi ARAI, Hiroyuki SATO, Shigeaki TANIMOTO, Atsush ...
    Type: PAPER
    2017 Volume E100.D Issue 10 Pages 2399-2410
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Smart education environment, that is a learning environment utilizing the Information Communication Technology (ICT), has attracted a great deal of attention. In order to expand this environment, we need a system that can establish the learning environment armed cloud systems to reduce a significant strain on teaching staff. The important issue for such system is extensibility because the system should be adapted to many kinds of original digital learning material with minimum modification. Therefore, this paper proposes “An Application Framework for Smart Education System: SES Framework”. In this Smart Education System, multi-aspect information concerning to a technical term embedded in the original digital learning material can be retrieved from different social media automatically. They can be also displayed on multi-screen devices according to user's operation. It is implemented based on “Transforming Model” which enables the migration of the original digital learning material to the smart education environment. It also has an easy operation flow for trainees named “three-step selection flow”. SES Framework derived from Model-View-Controller (MVC) pattern is based on the system architecture that enables triple mashup against the original digital learning material, external social media, and screen devices in front of users. All these functionalities have been implemented on cloud systems. We show SES Framework through the implementation example. We also demonstrate the effectiveness of SES Framework by indicating the system modification case study.

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  • Keisuke TSUNODA, Akihiro CHIBA, Kazuhiro YOSHIDA, Tomoki WATANABE, Osa ...
    Type: PAPER
    2017 Volume E100.D Issue 10 Pages 2411-2419
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    In this paper, we propose a low-invasive framework to predict changes in cognitive performance using only heart rate variability (HRV). Although a lot of studies have tried to estimate cognitive performance using multiple vital data or electroencephalogram data, these methods are invasive for users because they force users to attach a lot of sensor units or electrodes to their bodies. To address this problem, we proposed a method to estimate cognitive performance using only HRV, which can be measured with as few as two electrodes. However, this can't prevent loss of worker productivity because the workers' productivity had already decreased even if their current cognitive performance had been estimated as being at a low level. In this paper, we propose a framework to predict changes in cognitive performance in the near future. We obtained three principal contributions in this paper: (1) An experiment with 45 healthy male participants clarified that changes in cognitive performance caused by mental workload can be predicted using only HRV. (2) The proposed framework, which includes a support vector machine and principal component analysis, predicts changes in cognitive performance caused by mental workload with 84.4 % accuracy. (3) Significant differences were found in some HRV features for test participants, depending on whether or not their cognitive performance changes had been predicted accurately. These results lead us to conclude that the framework has the potential to help both workers and managerial personnel predict what their performances will be in the near future. This will make it possible to proactively suggest rest periods or changes in work duties to prevent losses in productivity caused by decreases of cognitive work performance.

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  • Kenta NOMURA, Masami MOHRI, Yoshiaki SHIRAISHI, Masakatu MORII
    Type: PAPER
    2017 Volume E100.D Issue 10 Pages 2420-2431
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Internet of Things (IoT) has been widely applied in various fields. IoT data can also be put to cloud, but there are still concerns regarding security and privacy. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is attracted attention in cloud storage as a suitable encryption scheme for confidential data share and transmission. In CP-ABE, the secret key of a user is associated with a set of attributes; when attributes satisfy the access structure, the ciphertext is able to be decrypted. It is necessary that multiple authorities issue and manage secret keys independently. Authorities that generate the secret key can be regarded as managing the attributes of a user in CP-ABE. CP-ABE schemes that have multiple authorities have been proposed. The other hand, it should consider that a user's operation at the terminals is not necessary when a user drop an attribute and key is updated and the design of the communication system is a simple. In this paper, we propose CP-ABE scheme that have multiple key authorities and can revoke attribute immediately with no updating user's secret key for attribute revocation. In addition, the length of ciphertext is fixed. The proposed scheme is IND-CPA secure in DBDH assumption under the standard model. We compare the proposed scheme and the other CP-ABE schemes and show that the proposed scheme is more suitable for cloud storage.

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  • Yoshiaki SHIRAISHI, Kenta NOMURA, Masami MOHRI, Takeru NARUSE, Masakat ...
    Type: PAPER
    2017 Volume E100.D Issue 10 Pages 2432-2439
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for data access control on cloud storage systems. In ABE, to revoke users' attributes, it is necessary to make them unable to decrypt ciphertexts. Some CP-ABE schemes for efficient attribute revocation have been proposed. However, they have not been given a formal security proof against a revoked user, that is, whether they satisfy forward secrecy has not been shown or they just do not achieve fine-grained access control of shared data. We propose an attribute revocable attribute-based encryption with the forward secrecy for fine-grained access control of shared data. The proposed scheme can use both “AND” and “OR” policy and is IND-CPA secure under the Decisional Parallel Bilinear Diffie-Hellman Exponent assumption in the standard model.

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  • Yoshiaki SHIRAISHI, Masanori HIROTOMO, Masami MOHRI, Taisuke YAMAMOTO
    Type: PAPER
    2017 Volume E100.D Issue 10 Pages 2440-2448
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    The application of Intelligent Transport Systems (ITS) transmits data with road-to-vehicle communication (RVC) and inter-vehicle communication (IVC). Digital signature is essential to provide security for RVC and IVC. The public key certificate is used to verify that a public key belongs to an individual prover such as user or terminal. A certificate revocation list (CRL) is used for verifying validity of the public key certificate. A certificate authority (CA) publishes a CRL and distributes it to vehicles. CRL distribution traffic disturbs ITS application traffic because of sharing wireless channel between them. To distribute it on low bit rate will help to ease the disturbance. Although multiplex transmitting is effective in reliable communication, a duplication of received packets is waste of bandwidth as a consequence. This paper proposes a CRL distribution scheme based on random network coding which can reduce duplicate packets. The simulation results show that the number of duplicate packets of the proposed scheme is less than that of a simple error correction (EC)-based scheme and the proposed one can distribute CRL to more vehicles than EC-based ones.

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  • Yoshiaki SHIRAISHI, Masaki KAMIZONO, Masanori HIROTOMO, Masami MOHRI
    Type: PAPER
    2017 Volume E100.D Issue 10 Pages 2449-2457
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    In the case of drive-by download attacks, most malicious web sites identify the software environment of the clients and change their behavior. Then we cannot always obtain sufficient information appropriate to the client organization by automatic dynamic analysis in open services. It is required to prepare for expected incidents caused by re-accessing same malicious web sites from the other client in the organization. To authors' knowledge, there is no study of utilizing analysis results of malicious web sites for digital forensic on the incident and hedging the risk of expected incident in the organization. In this paper, we propose a system for evaluating the impact of accessing malicious web sites by using the results of multi-environment analysis. Furthermore, we report the results of evaluating malicious web sites by the multi-environment analysis system, and show how to utilize analysis results for forensic analysis and risk hedge based on actual cases of analyzing malicious web sites.

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  • Takashi HATASHIMA, Yasuhisa SAKAMOTO
    Type: LETTER
    2017 Volume E100.D Issue 10 Pages 2458-2461
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    We surveyed employees who use personal devices for work. Residual analysis for cross-tabulation was carried out for three groups classified based on company rules and regulations established for mobile work. We show that the behavior of employees working with personal devices to process business data changes due to the presence or absence of the company rules and regulations.

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  • Arinobu NIIJIMA, Takahiro KUSABUKA, Soichiro UCHIDA, Tomoki WATANABE, ...
    Type: LETTER
    2017 Volume E100.D Issue 10 Pages 2462-2464
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    We present a new simple Internet of Things (IoT) device that we call “Smart Bottle Cap”, which enables a standard bottle to become a user-controllable liquid pouring system. It consists of a mini vacuum pump to start the liquid flowing, a microcontroller to control the liquid flow, a BLE module to connect it to a smartphone, an accelerometer to detect the tilt angle of the bottle, an LED for drawing the attention of users, and a 3.7 V LiPo battery. The device's novel point is that a flow control mechanism built into a standard bottle cap makes the system suitable for general use and enables it to be easily extended.

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  • Jae-Yoon JUNG, Gyunyoung HEO, Kyuhyup OH
    Type: LETTER
    2017 Volume E100.D Issue 10 Pages 2465-2469
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Smart card payment systems provide a convenient billing mechanism for public transportation providers and passengers. In this paper, a smart card-based transit log is used to reveal functionally related regions in a city, which are called zones. To discover significant zones based on the transit log data, two algorithms, minimum spanning trees and agglomerative hierarchical clustering, are extended by considering the additional factors of geographical distance and adjacency. The hierarchical spatial geocoding system, called Geohash, is adopted to merge nearby bus stops to a region before zone discovery. We identify different urban zones that contain functionally interrelated regions based on passenger trip data stored in the smart card-based transit log by manipulating the level of abstraction and the adjustment parameters.

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Regular Section
  • Qiong WANG, Mohamed EL-HADEDY, Kevin SKADRON, Ke WANG
    Type: PAPER
    Subject area: Computer System
    2017 Volume E100.D Issue 10 Pages 2470-2477
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Motif searching, i.e., identifying meaningful patterns from biological data, has been studied extensively due to its importance in the biomedical sciences. In this work, we seek to improve the performance of Weeder, a widely-used tool for automatic de novo motif searching. Weeder consists of several functions, among which we find that the function oligo_scan, which handles the pattern matching, is the bottleneck, especially when dealing with large datasets. Motivated by this observation, we adopt the Micron Automata Processor (AP) to accelerate the pattern-matching stage of Weeder. The AP is a massively-parallel, non-von-Neumann semiconductor architecture that is purpose-built for symbolic pattern matching. Relying on the fact that AP is capable of performing matching for thousands of patterns in parallel, we develop an AP-accelerated Weeder implementation in this work. In particular, we describe how to map Weeder's pattern matching to the AP chip and use the high-end FPGA on the AP board to postprocess the output from AP. Our experiment shows that the AP-accelerated Weeder achieves 751x speedup on pattern matching, compared to a single-threaded CPU implementation.

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  • Naohisa FUKASE, Yasuyuki MIURA, Shigeyoshi WATANABE, M.M. HAFIZUR RAHM ...
    Type: PAPER
    Subject area: Computer System
    2017 Volume E100.D Issue 10 Pages 2478-2492
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    The high performance network-on-chip (NoC) router using minimal hardware resources to minimize the layout area is very essential for NoC design. In this paper, we have proposed a memory sharing method of a wormhole routed NoC architecture to alleviate the area overhead of a NoC router. In the proposed method, a memory is shared by multiple physical links by using a multi-port memory. In this paper, we have proposed a partial link-sharing method and evaluated the communication performance using the proposed method. It is revealed that the resulted communication performance by the proposed methods is higher than that of the conventional method, and the progress ratio of the 3D-torus network is higher than that of 2D-torus network. It is shown that the improvement of communication performance using partial link sharing method is achieved with slightly increase of hardware cost.

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  • Takashi NAKADA, Tomoki HATANAKA, Hiroshi UEKI, Masanori HAYASHIKOSHI, ...
    Type: PAPER
    Subject area: Software System
    2017 Volume E100.D Issue 10 Pages 2493-2504
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Improving energy efficiency is critical for embedded systems in our rapidly evolving information society. Near real-time data processing tasks, such as multimedia streaming applications, exhibit a common fact that their deadline periods are longer than their input intervals due to buffering. In general, executing tasks at lower performance is more energy efficient. On the other hand, higher performance is necessary for huge tasks to meet their deadlines. To minimize the energy consumption while meeting deadlines strictly, adaptive task scheduling including dynamic performance mode selection is very important. In this work, we propose an energy efficient slack-based task scheduling algorithm for such tasks by adapting to task size variations and applying DVFS with the help of statistical analysis. We confirmed that our proposal can further reduce the energy consumption when compared to oracle frame-based scheduling.

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  • Shan DING, Gang ZENG, Ryo KURACHI, Ruifeng HUANG
    Type: PAPER
    Subject area: Software System
    2017 Volume E100.D Issue 10 Pages 2505-2514
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    As a next-generation CAN (Controller Area Network), CAN FD (CAN with flexible data rate) has attracted much attention recently. However, how to use the improved bus bandwidth efficiently in CAN FD is still an issue. Contrasting with existing methods using greedy approximate algorithms, this paper proposes a genetic algorithm for CAN FD frame packing. It tries to minimize the bandwidth utilization by considering the different periods of signals when packing them in the same frame. Moreover, it also checks the schedulability of packed frames to guarantee the real-time constraints of each frame and proposed a merging algorithm to improve the schedulability for signal set with high bus load. Experimental results validate that the proposed algorithm can achieve significantly less bandwidth utilization and improved schedulability than existing methods for a given set of signals.

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  • Jiang ZHU, Bai WANG, Bin WU, Weiyu ZHANG
    Type: PAPER
    Subject area: Data Engineering, Web Information Systems
    2017 Volume E100.D Issue 10 Pages 2515-2525
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Community detection is a pivotal task in data mining, and users' emotional behaviors have an important impact on today's society. So it is very significant for society management or marketing strategies to detect emotional communities in social networks. Based on the emotional homophily of users in social networks, it could confirm that users would like to gather together to form communities according to emotional similarity. This paper exploits multivariate emotional behaviors of users to measure users' emotional similarity, then takes advantage of users' emotional similarity as edge weight to remodel an emotional network and detect communities. The detailed process of detecting emotional communities is as follows: 1) an emotional network is constructed and emotional homophily in experimental dataset is verified; 2) both CNM and BGLL algorithms are employed to detect emotional communities in emotional network, and emotional characters of each community are analyzed; 3) in order to verify the superiority of emotional network for detecting emotional communities, 1 unweighted network and 3 other weighted and undirected networks are constructed as comparison. Comparison experiments indicate that the emotional network is more suitable for detecting emotional communities, the users' emotional behaviors are more similar and denser in identical communities of emotional network than the contrastive networks' communities.

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  • Yuki YAMAGISHI, Kazuo AOYAMA, Kazumi SAITO, Tetsuo IKEDA
    Type: PAPER
    Subject area: Data Engineering, Web Information Systems
    2017 Volume E100.D Issue 10 Pages 2526-2536
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    This paper presents an efficient similarity search method utilizing as an index a complete binary tree (CBT) based on optimized pivots for a large-scale and high-dimensional data set. A similarity search method, in general, requires high-speed performance on both index construction off-line and similarity search itself online. To fulfill the requirement, we introduce novel techniques into an index construction and a similarity search algorithm in the proposed method for a range query. The index construction algorithm recursively employs the following two main functions, resulting in a CBT index. One is a pivot generation function that obtains one effective pivot at each node by efficiently maximizing a defined objective function. The other is a node bisection function that partitions a set of objects at a node into two almost equal-sized subsets based on the optimized pivot. The similarity search algorithm employs a three-stage process that narrows down candidate objects within a given range by pruning unnecessary branches and filtering objects in each stage. Experimental results on one million real image data set with high dimensionality demonstrate that the proposed method finds an exact solution for a range query at around one-quarter to half of the computational cost of one of the state-of-the-art methods, by using a CBT index constructed off-line at a reasonable computational cost.

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  • Youngin KIM, Cheong Hee PARK
    Type: PAPER
    Subject area: Artificial Intelligence, Data Mining
    2017 Volume E100.D Issue 10 Pages 2537-2546
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    In data stream analysis, detecting the concept drift accurately is important to maintain the classification performance. Most drift detection methods assume that the class labels become available immediately after a data sample arrives. However, it is unrealistic to attempt to acquire all of the labels when processing the data streams, as labeling costs are high and much time is needed. In this paper, we propose a concept drift detection method under the assumption that there is limited access or no access to class labels. The proposed method detects concept drift on unlabeled data streams based on the class label information which is predicted by a classifier or a virtual classifier. Experimental results on synthetic and real streaming data show that the proposed method is competent to detect the concept drift on unlabeled data stream.

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  • Taiki OGATA, Naoki HIGO, Takayuki NOZAWA, Eisuke ONO, Kazuo YANO, Koji ...
    Type: PAPER
    Subject area: Human-computer Interaction
    2017 Volume E100.D Issue 10 Pages 2547-2555
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    People's body movements in daily face-to-face communication influence each other. For instance, during a heated debate, the participants use more gestures and other body movements, while in a calm discussion they use fewer gestures. This “coevolution” of interpersonal body movements occurs on multiple time scales, like minutes or hours. However, the multi-time-scale coevolution in daily communication is not clear yet. In this paper, we explore the minute-to-minute coevolution of interpersonal body movements in daily communication and investigate the characteristics of this coevolution. We present quantitative data on upper-body movements from thousand test subjects from seven organizations gathered over several months via wearable sensors. The device we employed measured upper-body movements with an accelerometer and the duration of face-to-face communication with an infrared ray sensor on a minute-by-minute basis. We defined a coevolution measure between two people as the number of per-minute changes of their body movement and compared the indices for face-to-face and non-face-to-face situations. We found that on average, the amount of people's body movements changed correspondingly for face-to-face communication and that the average rate of coevolution in the case of face-to-face communication was 3-4% higher than in the case of non-face-to-face situation. These results reveal minute-to-minute coevolution of upper-body movements between people in daily communication. The finding suggests that the coevolution of body movement arises in multiple time scales.

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  • Corentin JOUAULT, Kazuhisa SETA, Yuki HAYASHI
    Type: PAPER
    Subject area: Educational Technology
    2017 Volume E100.D Issue 10 Pages 2556-2566
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    The purpose of this research is to support learners in self-directed learning on the Internet using automatically generated support using the current state of the semantic web. The main issue of creating meaningful content-dependent questions automatically is that it requires the machine to understand the concepts in the learning domain. The originality of this work is that it uses Linked Open Data (LOD) to enable meaningful content-dependent support in open learning space. Learners are supported by a learning environment, the Semantic Open Learning Space (SOLS). Learners use the system to build a concept map representing their knowledge. SOLS supports learners following the principle of inquiry-based learning. Learners that request help are provided with automatically generated questions that give them learning objectives. To verify whether the current system can support learners with fully automatically generated support, we evaluated the system with three objectives: judge whether the LOD based support was feasible and useful, whether the question support improved the development of historical considerations in the learners' mind and whether the engagement of learners was improved by the question support. The results showed that LOD based support was feasible. Learners felt that the support provided was useful and helped them learn. The question support succeeded in improving the development of learners' deep historical considerations. In addition, the engagement and interest in history of learners was improved by the questions. The results are meaningful because they show that LOD based question support can be a viable tool to support self-directed learning in open learning space.

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  • Ryosuke ONDA, Yuki HIRAI, Kay PENNY, Bipin INDURKHYA, Keiichi KANEKO
    Type: PAPER
    Subject area: Educational Technology
    2017 Volume E100.D Issue 10 Pages 2567-2577
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    We developed a system called DELTA that supports the students' use of backward chaining (BC) to prove the congruence of two triangles. DELTA is designed as an interactive learning environment and supports the use of BC by providing hints and a function to automatically check the proofs inputted by the students. DELTA also has coloring, marking, and highlighting functions to support students' attempts to prove the congruence of two triangles. We evaluated the efficacy of DELTA with 36 students in the second grade of a junior high school in Japan. We found that (1) the mean number of problems, which the experimental group (EG) completely solved, was statistically higher than that of the control group on the post-test; (2) the EG effectively used the BC strategy to solve problems; and (3) the students' attempt to use both the forward chaining strategy and the BC strategy led to solving the problems completely.

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  • Zhixian MA, Jie ZHU, Weitian LI, Haiguang XU
    Type: PAPER
    Subject area: Pattern Recognition
    2017 Volume E100.D Issue 10 Pages 2578-2586
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.

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  • Younggi KIM, Younghee LEE
    Type: PAPER
    Subject area: Pattern Recognition
    2017 Volume E100.D Issue 10 Pages 2587-2596
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Human activity prediction has become a prerequisite for service recommendation and anomaly detection systems in a smart space including ambient assisted living (AAL) and activities of daily living (ADL). In this paper, we present a novel approach to predict the next-activity set in a multi-user smart space. Differing from the majority of the previous studies considering single-user activity patterns, our study considers multi-user activities that occur with a large variety of patterns. Its complexity increases exponentially according to the number of users. In the multi-user smart space, there can be inevitably multiple next-activity candidates after multi-user activities occur. To solve the next-activity problem in a multi-user situation, we propose activity set prediction rather than one activity prediction. We also propose activity sequence partitioning to reduce the complexity of the multi-user activity pattern. This divides an activity sequence into start, ongoing, and finish zones based on the features in the tendency of activity occurrences. The majority of the activities in a multi-user environment occur at the beginning or end, rather than the middle, of an activity sequence. Furthermore, the types of activities typically occurring in each zone can be sufficiently distinguishable. Exploiting these characteristics, we suggest a two-step procedure to predict the next-activity set utilizing a long short-term memory (LSTM) model. The first step identifies the zones to which current activities belong. In the next step, we construct three different LSTM models to predict the next-activity set in each zone. To evaluate the proposed approach, we experimented using a real dataset generated from our campus testbed. Our experiments confirmed the complexity reduction and high accuracy in the next-activity set prediction. Thus, it can be effectively utilized for various applications with context-awareness in a multi-user smart space.

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  • Lu SUN, Mineichi KUDO, Keigo KIMURA
    Type: PAPER
    Subject area: Pattern Recognition
    2017 Volume E100.D Issue 10 Pages 2597-2604
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Multi-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several problems, such as ignoring label outliers and label correlations. In addition, most of them emphasize on conducting dimension reduction in an unsupervised or supervised way, therefore, unable to utilize the label information or a large amount of unlabeled data to improve the performance. In order to cope with these problems, we propose a novel method termed Robust sEmi-supervised multi-lAbel DimEnsion Reduction, shortly READER. From the viewpoint of empirical risk minimization, READER selects most discriminative features for all the labels in a semi-supervised way. Specifically, the ℓ2,1-norm induced loss function and regularization term make READER robust to the outliers in the data points. READER finds a feature subspace so as to keep originally neighbor instances close and embeds labels into a low-dimensional latent space nonlinearly. To optimize the objective function, an efficient algorithm is developed with convergence property. Extensive empirical studies on real-world datasets demonstrate the superior performance of the proposed method.

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  • Yuichi YOSHIDA, Tsuyoshi TOYOFUKU
    Type: PAPER
    Subject area: Image Processing and Video Processing
    2017 Volume E100.D Issue 10 Pages 2605-2613
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Descriptor aggregation techniques such as the Fisher vector and vector of locally aggregated descriptors (VLAD) are used in most image retrieval frameworks. It takes some time to extract local descriptors, and the geometric verification requires storage if a real-valued descriptor such as SIFT is used. Moreover, if we apply binary descriptors to such a framework, the performance of image retrieval is not better than if we use a real-valued descriptor. Our approach tackles these issues by using a dual representation descriptor that has advantages of being both a real-valued and a binary descriptor. The real value of the dual representation descriptor is aggregated into a VLAD in order to achieve high accuracy in the image retrieval, and the binary one is used to find correspondences in the geometric verification stage in order to reduce the amount of storage needed. We implemented a dual representation descriptor extracted in semi-real time by using the CARD descriptor. We evaluated the accuracy of our image retrieval framework including the geometric verification on three datasets (holidays, ukbench and Stanford mobile visual search). The results indicate that our framework is as accurate as the framework that uses SIFT. In addition, the experiments show that the image retrieval speed and storage requirements of our framework are as efficient as those of a framework that uses ORB.

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  • Takahiro OGAWA, Akira TANAKA, Miki HASEYAMA
    Type: PAPER
    Subject area: Image Processing and Video Processing
    2017 Volume E100.D Issue 10 Pages 2614-2626
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    A Wiener-based inpainting quality prediction method is presented in this paper. The proposed method is the first method that can predict inpainting quality both before and after the intensities have become missing even if their inpainting methods are unknown. Thus, when the target image does not include any missing areas, the proposed method estimates the importance of intensities for all pixels, and then we can know which areas should not be removed. Interestingly, since this measure can be also derived in the same manner for its corrupted image already including missing areas, the expected difficulty in reconstruction of these missing pixels is predicted, i.e., we can know which missing areas can be successfully reconstructed. The proposed method focuses on expected errors derived from the Wiener filter, which enables least-squares reconstruction, to predict the inpainting quality. The greatest advantage of the proposed method is that the same inpainting quality prediction scheme can be used in the above two different situations, and their results have common trends. Experimental results show that the inpainting quality predicted by the proposed method can be successfully used as a universal quality measure.

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  • Yuki KAWANA, Norimichi UKITA
    Type: PAPER
    Subject area: Image Recognition, Computer Vision
    2017 Volume E100.D Issue 10 Pages 2627-2634
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    This paper proposes a method for human pose estimation in still images. The proposed method achieves occlusion-aware appearance modeling. Appearance modeling with less accurate appearance data is problematic because it adversely affects the entire training process. The proposed method evaluates the effectiveness of mitigating the influence of occluded body parts in training sample images. In order to improve occlusion evaluation by a discriminatively-trained model, occlusion images are synthesized and employed with non-occlusion images for discriminative modeling. The score of this discriminative model is used for weighting each sample in the training process. Experimental results demonstrate that our approach improves the performance of human pose estimation in contrast to base models.

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  • Kento OHTANI, Kenta NIWA, Kazuya TAKEDA
    Type: PAPER
    Subject area: Music Information Processing
    2017 Volume E100.D Issue 10 Pages 2635-2643
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    A single-dimensional interface which enables users to obtain diverse localizations of audio sources is proposed. In many conventional interfaces for arranging audio sources, there are multiple arrangement parameters, some of which allow users to control positions of audio sources. However, it is difficult for users who are unfamiliar with these systems to optimize the arrangement parameters since the number of possible settings is huge. We propose a simple, single-dimensional interface for adjusting arrangement parameters, allowing users to sample several diverse audio source arrangements and easily find their preferred auditory localizations. To select subsets of arrangement parameters from all of the possible choices, auditory-localization space vectors (ASVs) are defined to represent the auditory localization of each arrangement parameter. By selecting subsets of ASVs which are approximately orthogonal, we can choose arrangement parameters which will produce diverse auditory localizations. Experimental evaluations were conducted using music composed of three audio sources. Subjective evaluations confirmed that novice users can obtain diverse localizations using the proposed interface.

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  • Sanghyun YOON, Dong-Ah LEE, Eunji PAK, Taeho KIM, Junbeom YOO
    Type: LETTER
    Subject area: Software System
    2017 Volume E100.D Issue 10 Pages 2644-2647
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    Qplus-AIR is a real-time operating system for avionics, and its safety and correctness should be analyzed and guaranteed. We performed model checking a version of Qplus-AIR with the Times model checker and identified one abnormal case that might result in safety-critical situations.

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  • Van-Quyet NGUYEN, Kyungbaek KIM
    Type: LETTER
    Subject area: Data Engineering, Web Information Systems
    2017 Volume E100.D Issue 10 Pages 2648-2652
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    A widely-used query on a graph is a regular path query (RPQ) whose answer is a set of tuples of nodes connected by paths corresponding to a given regular expression. Traditionally, evaluating an RPQ on a large graph takes substantial memory spaces and long response time. Recently, several studies have focused on improving response time for evaluating an RPQ by splitting an original RPQ into smaller subqueries, evaluating them in parallel and combining partial answers. In these works, how to choose split labels in an RPQ is one of key points of the performance of RPQ evaluation, and rare labels of a graph can be used as split labels. However there is still a room for improvement, because a rare label cannot guarantee the minimum evaluation cost all the time. In this paper, we propose a novel approach of selecting split labels by estimating evaluation cost of each split subquery with a unit-subquery cost matrix (USCM), which can be obtained from a graph in prior to evaluate an RPQ. USCM presents the evaluation cost of a unit-subquery which is the smallest possible subquery, and we can estimate the evaluation cost of an RPQ by decomposing into a set of unit-subqueries. Experimental results show that our proposed approach outperforms rare label based approaches.

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  • Lixing XUE, Decheng ZUO, Zhan ZHANG, Na WU
    Type: LETTER
    Subject area: Dependable Computing
    2017 Volume E100.D Issue 10 Pages 2653-2658
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    This paper proposes a component ranking method to identify important components which have great impact on the system reliability. This method, which is opposite to an existing method, believes components which frequently invoke other components have more impact than others and employs component invocation structures and invocation frequencies for making important component ranking. It can strongly support for improving the reliability of software systems, especially large-scale systems. Extensive experiments are provided to validate this method and draw performance comparison.

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  • Sang-Chul LEE, Christos FALOUTSOS, Dong-Kyu CHAE, Sang-Wook KIM
    Type: LETTER
    Subject area: Artificial Intelligence, Data Mining
    2017 Volume E100.D Issue 10 Pages 2659-2663
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.

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  • Kangru WANG, Lei QU, Lili CHEN, Jiamao LI, Yuzhang GU, Dongchen ZHU, X ...
    Type: LETTER
    Subject area: Pattern Recognition
    2017 Volume E100.D Issue 10 Pages 2664-2668
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    In this paper, a novel approach is proposed for stereo vision-based ground plane detection at superpixel-level, which is implemented by employing a Disparity Texture Map in a convolution neural network architecture. In particular, the Disparity Texture Map is calculated with a new Local Disparity Texture Descriptor (LDTD). The experimental results demonstrate our superior performance in KITTI dataset.

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  • Masaki MURATA, Yuki ABE
    Type: LETTER
    Subject area: Natural Language Processing
    2017 Volume E100.D Issue 10 Pages 2669-2672
    Published: October 01, 2017
    Released: October 01, 2017
    JOURNALS FREE ACCESS

    We propose a method for automatic emphasis estimation using conditional random fields. In our experiments, the value of F-measure obtained using our proposed method (0.31) was higher than that obtained using a random emphasis method (0.20), a method using TF-IDF (0.21), and a method based on LexRank (0.26). On the contrary, the value of F-measure of obtained using our proposed method (0.28) was slightly worse as compared with that obtained using manual estimation (0.26-0.40, with an average of 0.35).

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