In our previous work, we introduced new concepts of secure scan design; shift register equivalent circuits (SR-equivalents, for short) and strongly secure circuits, and also introduced generalized shift registers (GSRs, for short) to apply them to secure scan design. In this paper, we combine both concepts of SR-equivalents and strongly secure circuits and apply them to GSRs, and consider the synthesis problem of strongly secure SR-equivalents using GSRs. We also consider the enumeration problem of GSRs that are strongly secure and SR-equivalent, i.e., the cardinality of the class of strongly secure SR-equivalent GSRs to clarify the security level of the secure scan architecture.

For subgraph enumeration problems, very efficient algorithms have been proposed whose time complexities are far smaller than the number of subgraphs. Although the number of subgraphs can exponentially increase with the input graph size, these algorithms exploit compressed representations to output and maintain enumerated subgraphs compactly so as to reduce the time and space complexities. However, they are designed for enumerating only some specific types of subgraphs, e.g., paths or trees. In this paper, we propose an algorithm framework, called the frontier-based search, which generalizes these specific algorithms without losing their efficiency. Our frontier-based search will be used to resolve various practical problems that include constrained subgraph enumeration.

Attribute-based encryption (ABE) enables flexible data access control based on attributes and policies. In ciphertext-policy ABE (CP-ABE), a secret key is associated with a set of attributes and a policy is associated with a ciphertext. If the set of attributes satisfies the policy, the ciphertext can be decrypted. CP-ABE can be applied to a variety of services such as access control for file sharing systems and content distribution services. However, a CP-ABE scheme usually has larger costs for encryption and decryption than conventional public-key encryption schemes due to flexible policy setting. In particular, wildcards, which mean that certain attributes are not relevant to the ciphertext policy, are not essential for a certain service. In this paper, we propose a partially wildcarded CP-ABE scheme with a lower encryption and decryption cost. In our scheme, user's attributes are separated into those requiring wildcards and those not requiring wildcards. Our scheme embodies a CP-ABE scheme with a wildcard functionality and an efficient CP-ABE scheme without wildcard functionality. We show that our scheme is provably secure under the DBDH assumption. Then, we compare our scheme with the conventional CP-ABE schemes and describe a content distribution service as an application of our scheme. Also, we implement our scheme on a PC and measure the processing time. The result shows that our scheme can reduce all of the costs for key generation, encryption, and decryption as much as possible.

Structured signatures are digital signatures where relationship between signers is guaranteed in addition to the validity of individually generated data for each signer, and have been expected for the digital right management. Nevertheless, we mention that there is no scheme with a tight security reduction, to the best of our knowledge. Loosely speaking, it means that the security is downgraded against an adversary who obtains a large amount of signatures. Since contents are widely utilized in general, achieving a tighter reduction is desirable. Based on this background, we propose the first structured signature scheme with a tight security reduction in the conventional public key cryptography and the one with a rigorous reduction proof in the ID-based cryptography via our new proof method. Moreover, the security of our schemes can be proven under the CDH assumption which is the most standard. Our schemes are also based on bilinear maps whose implementation can be provided via well-known cryptographic libraries.

Cryptographic protocols enable participating parties to compute any function of their inputs without leaking any information beyond the output. A card-based protocol is a cryptographic protocol implemented by physical cards. In this paper, for constructing protocols with small numbers of shuffles, we introduce a new type of cards, regular polygon cards, and a new protocol, oblivious conversion. Using our cards, we construct an addition protocol on non-binary inputs with only one shuffle and two cards. Furthermore, using our oblivious conversion protocol, we construct the first protocol for general functions in which the number of shuffles is linear in the number of inputs.

This paper presents a mathematical model for the oscillator-based true random number generator (TRNG) to study the influence of some key parameters to the randomness of the output sequence. The output of the model is so close to the output of the real design of the TRNG that the model can generate the random bits instead of the analog simulation for research. It will cost less time than the analog simulation and be more convenient for the researchers to change some key parameters in the design. The authors give a method to improve the existing design of the oscillator-based TRNG to deal with the possible bias of the key parameters. The design is fabricated with a 55-nm CMOS process.

Identity-based encryption (IBE) is an advanced form of public key encryption and one of the most important cryptographic primitives. Of the many constructions of IBE schemes, the one proposed by Boneh and Boyen (in Eurocrypt 2004) is quite important from both practical and theoretical points of view. The scheme was standardized as IEEE P1363.3 and is the basis for many subsequent constructions. In this paper, we investigate its multi-challenge security, which means that an adversary is allowed to query challenge ciphertexts multiple times rather than only once. Since single-challenge security implies multi-challenge security, and since Boneh and Boyen provided a security proof for the scheme in the single-challenge setting, the scheme is also secure in the multi-challenge setting. However, this reduction results in a large security loss. Instead, we give tight security reduction for the scheme in the multi-challenge setting. Our reduction is tight even if the number of challenge queries is not fixed in advance (that is, the queries are unbounded). Unfortunately, we are only able to prove the security in a selective setting and rely on a non-standard parameterized assumption. Nevertheless, we believe that our new security proof is of interest and provides new insight into the security of the Boneh-Boyen IBE scheme.

As the role of wireless communication is becoming more important for realizing a future connected society for not only humans but also things, spectrum scarcity is becoming severe, because of the huge numbers of mobile terminals and many types of applications in use. In order to realize sustainable wireless connection under limited spectrum resources in a future wireless world, a new dynamic spectrum management scheme should be developed that considers the surrounding radio environment and user preferences. In this paper, we discuss a new spectrum utilization framework for a future wireless world called the “smart spectrum.” There are four main issues related to realizing the smart spectrum. First, in order to recognize the spectrum environment accurately, spectrum measurement is an important technology. Second, spectrum modeling for estimating the spectrum usage and the spectrum environment by using measurement results is required for designing wireless parameters for dynamic spectrum use in a shared spectrum environment. Third, in order to effectively gather the measurement results and provide the spectrum information to users, a measurement-based spectrum database can be used. Finally, smart spectrum management that operates in combination with a spectrum database is required for realizing efficient and organized dynamic spectrum utilization. In this paper, we discuss the concept of the smart spectrum, fundamental research studies of the smart spectrum, and the direction of development of the smart spectrum for targeting the future wireless world.

Crowd modeling and simulation is an active research field that has drawn increasing attention from industry, academia and government recently. In this paper, we present a generic data-driven approach to generate crowd behaviors that can match the video data. The proposed approach is a bi-layer model to simulate crowd behaviors in pedestrian traffic in terms of exclusion statistics, parallel dynamics and social psychology. The bottom layer models the microscopic collision avoidance behaviors, while the top one focuses on the macroscopic pedestrian behaviors. To validate its effectiveness, the approach is applied to generate collective behaviors and re-create scenarios in the Informatics Forum, the main building of the School of Informatics at the University of Edinburgh. The simulation results demonstrate that the proposed approach is able to generate desirable crowd behaviors and offer promising prediction performance.

To drastically increase the splitting ratio of extended-reach (40km span) time- and wavelength-division multiplexed passive optical networks (WDM/TDM-PONs), we modify the gain control scheme of our automatic gain controlled semiconductor optical amplifiers (AGC-SOAs) that were developed to support upstream transmission in long-reach systems. While the original AGC-SOAs are located outside the central office (CO) as repeaters, the new AGC-SOAs are located inside the CO and connected to each branch of an optical splitter in the CO. This arrangement has the potential to greatly reduce the costs of CO-sited equipment as they are shared by many more users if the new gain control scheme works properly even when the input optical powers are low. We develop a prototype and experimentally confirm its effectiveness in increasing the splitting ratio of extended-reach systems to 512.

This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on gender-age, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI.

The widespread usage of computers and communication networks affects people's social activities effectively in terms of intercommunication and the communication generally begins with domain name resolutions which are mainly provided by DNS (Domain Name System). Meanwhile, continuous cyber threats to DNS such as cache poisoning also affects computer networks critically. DNSSEC (DNS Security Extensions) is designed to provide secure name resolution between authoritative zone servers and DNS full resolvers. However high workload of DNSSEC validation on DNS full resolvers and complex key management on authoritative zone servers hinder its wide deployment. Moreover, querying clients use the name resolution results validated on DNS full resolvers, therefore they only get errors when DNSSEC validation fails or times out. In addition, name resolution failure can occur on querying clients due to technical and operational issues of DNSSEC. In this paper, we propose a client based DNSSEC validation system with adaptive alert mechanism considering minimal querying client timeout. The proposed system notifies the user of alert messages with answers even when the DNSSEC validation on the client fails or timeout so that the user can determine how to handle the received answers. We also implemented a prototype system and evaluated the features on a local experimental network as well as in the Internet. The contribution of this article is that the proposed system not only can mitigate the workload of DNS full resolvers but also can cover querying clients with secure name resolution, and by solving the existing operation issues in DNSSEC, it also can promote DNSSEC deployment.

In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (γ-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, γ-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.

Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e. particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) for feature selection and ensemble of four tree-based classifiers, i.e. random forest (RF), naive bayes tree (NBT), logistic model trees (LMT), and reduces error pruning tree (REPT) for classification. Proposed approach is implemented on NSL-KDD dataset and from the experimental result, it significantly outperforms the existing methods in terms of accuracy and false alarm rate.

The user authorization query (UAQ) problem determines whether there exists an optimum set of roles to be activated to provide a set of permissions requested by a user. It has been deemed as a key issue for efficiently handling user's access requests in role-based access control (RBAC). Unfortunately, the weight is a value attached to a permission/role representing its importance, should be introduced to UAQ, has been ignored. In this paper, we propose a comprehensive definition of the weighted UAQ (WUAQ) problem with the role-weighted-cardinality and permission-weighted-cardinality constraints. Moreover, we study the computational complexity of different subcases of WUAQ, and show that many instances in each subcase are intractable. In particular, inspired by the idea of the genetic algorithm, we propose an algorithm to approximate solve an intractable subcase of the WUAQ problem. An important observation is that this algorithm can be efficiently modified to handle the other subcases of the WUAQ problem. The experimental results show the advantage of the proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale RBAC system.

Biomimetics is a new research field that creates innovation through the collaboration of different existing research fields. However, the collaboration, i.e., the exchange of deep knowledge between different research fields, is difficult for several reasons such as differences in technical terms used in different fields. In order to overcome this problem, we have developed a new retrieval platform, “Biomimetics image retrieval platform,” using a visualization-based image retrieval technique. A biological database contains a large volume of image data, and by taking advantage of these image data, we are able to overcome limitations of text-only information retrieval. By realizing such a retrieval platform that does not depend on technical terms, individual biological databases of various species can be integrated. This will allow not only the use of data for the study of various species by researchers in different biological fields but also access for a wide range of researchers in fields ranging from materials science, mechanical engineering and manufacturing. Therefore, our platform provides a new path bridging different fields and will contribute to the development of biomimetics since it can overcome the limitation of the traditional retrieval platform.

Recommender systems have attracted attention in both the academic and the business areas. They aim to give users more intelligent methods for navigating and identifying complex information spaces, especially in e-commerce domain. However, these systems still have to overcome certain limitations that reduce their performance, such as overspecialization of recommendations, cold-start, and difficulties when items with unequal probability distribution exist. A novel approach addresses the above issues through a case-based recommendation methodology which is a form of content-based recommendation that is well suited to many product recommendation domains, owing to the clear organization of users' needs and preferences. Unfortunately, the experience-based roots of case-based reasoning are not clearly reflected in case-based recommenders. In other words, the concept that product cases, which are usually fixed feature-based tuples, are experiential is not adopted well in case-based recommenders. To solve this problem as well as the recommenders' rating sparsity issue, one can use product reviews which are generated from users' experience with the product a basis of product information. Our approach adapts the use of sentiment scores along with feature similarity throughout the recommendation unlike traditional case-based recommender systems, which tend to depend entirely on pure similarity-based approaches. This paper models product cases with the products' features and sentiment scores at the feature level and product level. Thus, combining user experience and similarity measures improves the recommender performance and gives users more flexibility to choose whether they prefer products more similar to their query or better qualified products. We present the results using different evaluation methods for different case structures, different numbers of similar cases retrieved and multilevel sentiment-approaches. The recommender performance was highly improved with the use of feature-level sentiment approach, which recommends product cases that are similar to the query but favored for customers.

This letter proposes a class of polyphase zero correlation zone (ZCZ) sequence sets with low inter-set cross-correlation property. The proposed ZCZ sequence sets are constructed from DFT matrices and r-coincidence sequences. Each ZCZ sequence set is optimal, and the absolute value of the cross-correlation function of sequences from different sets is less than or equal to $rsqrt{N}$, where N denotes the length of each sequence. These ZCZ sequence sets are suitable for multiuser environments.