For the first stage of the multi-sensitive bucketization (MSB) method, the l-diversity grouping for multiple sensitive attributes is incomplete, causing more information loss. To solve this problem, we give the definitions of the l-diversity avoidance set for multiple sensitive attributes and the avoiding of a multiple dimensional bucket, and propose a complete l-diversity grouping (CLDG) algorithm for multiple sensitive attributes. Then, we improve the first stages of the MSB algorithms by applying the CLDG algorithm to them. The experimental results show that the grouping ratio of the improved first stages of the MSB algorithms is significantly higher than that of the original first stages of the MSB algorithms, decreasing the information loss of the published microdata.

This paper presents the first attribute-based signature (ABS) scheme in which the correspondence between signers and signatures is captured in an arithmetic model of computation. Specifically, we design a fully secure, i.e., adaptively unforgeable and perfectly signer-private ABS scheme for signing policies realizable by arithmetic branching programs (ABP), which are a quite expressive model of arithmetic computations. On a more positive note, the proposed scheme places no bound on the size and input length of the supported signing policy ABP's, and at the same time, supports the use of an input attribute for an arbitrary number of times inside a signing policy ABP, i.e., the so called unbounded multi-use of attributes. The size of our public parameters is constant with respect to the sizes of the signing attribute vectors and signing policies available in the system. The construction is built in (asymmetric) bilinear groups of prime order, and its unforgeability is derived in the standard model under (asymmetric version of) the well-studied decisional linear (DLIN) assumption coupled with the existence of standard collision resistant hash functions. Due to the use of the arithmetic model as opposed to the boolean one, our ABS scheme not only excels significantly over the existing state-of-the-art constructions in terms of concrete efficiency, but also achieves improved applicability in various practical scenarios. Our principal technical contributions are (a) extending the techniques of Okamoto and Takashima [PKC 2011, PKC 2013], which were originally developed in the context of boolean span programs, to the arithmetic setting; and (b) innovating new ideas to allow unbounded multi-use of attributes inside ABP's, which themselves are of unbounded size and input length.

To enhance the user's privacy in electronic ID, anonymous credential systems have been researched. In the anonymous credential system, a trusted issuing organization first issues a certificate certifying the user's attributes to a user. Then, in addition to the possession of the certificate, the user can anonymously prove only the necessary attributes. Previously, an anonymous credential system was proposed, where CNF (Conjunctive Normal Form) formulas on attributes can be proved. The advantage is that the attribute proof in the authentication has the constant size for the number of attributes that the user owns and the size of the proved formula. Thus, various expressive logical relations on attributes can be efficiently verified. However, the previous system has a limitation: The proved CNF formulas cannot include any negation. Therefore, in this paper, we propose an anonymous credential system with constant-size attribute proofs such that the user can prove CNF formulas with negations. For the proposed system, we extend the previous accumulator for the limited CNF formulas to verify CNF formulas with negations.

Deep neural network (DNN)-based speech synthesis became popular in recent years and is expected to soon be widely used in embedded devices and environments with limited computing resources. The key intention of these systems in poor computing environments is to reduce the computational cost of generating speech parameter sequences while maintaining voice quality. However, reducing computational costs is challenging for two primary conventional DNN-based methods used for modeling speech parameter sequences. In feed-forward neural networks (FFNNs) with maximum likelihood parameter generation (MLPG), the MLPG reconstructs the temporal structure of the speech parameter sequences ignored by FFNNs but requires additional computational cost according to the sequence length. In recurrent neural networks, the recursive structure allows for the generation of speech parameter sequences while considering temporal structures without the MLPG, but increases the computational cost compared to FFNNs. We propose a new approach for DNNs to acquire parameters captured from the temporal structure by backpropagating the errors of multiple attributes of the temporal sequence via the loss function. This method enables FFNNs to generate speech parameter sequences by considering their temporal structure without the MLPG. We generated the fundamental frequency sequence and the mel-cepstrum sequence with our proposed method and conventional methods, and then synthesized and subjectively evaluated the speeches from these sequences. The proposed method enables even FFNNs that work on a frame-by-frame basis to generate speech parameter sequences by considering the temporal structure and to generate sequences perceptually superior to those from the conventional methods.

Pedestrian attribute information is important function for an advanced driver assistance system (ADAS). Pedestrian attributes such as body pose, face orientation and open umbrella indicate the intended action or state of the pedestrian. Generally, this information is recognized using independent classifiers for each task. Performing all of these separate tasks is too time-consuming at the testing stage. In addition, the processing time increases with increasing number of tasks. To address this problem, multi-task learning or heterogeneous learning is performed to train a single classifier to perform multiple tasks. In particular, heterogeneous learning is able to simultaneously train a classifier to perform regression and recognition tasks, which reduces both training and testing time. However, heterogeneous learning tends to result in a lower accuracy rate for classes with few training samples. In this paper, we propose a method to improve the performance of heterogeneous learning for such classes. We introduce a rarity rate based on the importance and class probability of each task. The appropriate rarity rate is assigned to each training sample. Thus, the samples in a mini-batch for training a deep convolutional neural network are augmented according to this rarity rate to focus on the classes with a few samples. Our heterogeneous learning approach with the rarity rate performs pedestrian attribute recognition better, especially for classes representing few training samples.

Maximizing the profit of datacenter networks (DCNs) demands to satisfy more flows' requirements simultaneously, but existing schemes always allocate resource based on single flow attribute, which cannot carry out accurate resource allocation and make many flows failed. In this letter, we propose Highest Priority Flow First (HPFF) to maximize DCN profit, which allocates resource for flows according to the priority. HPFF employs a utility function that considers multiple flow attributes, including flow size, deadline and demanded bandwidth, to calculate the priority for each flow. The experiments on the testbed show that HPFF can improve the network profit by 6.75%-19.7% and decrease the number of failed flow by 26.3%-83.3% compared with existing schemes under real DCN workloads.

Facial age estimation requires accurately capturing the mapping relationship between facial features and corresponding ages, so as to precisely estimate ages for new input facial images. Previous works usually use one-layer regression model to learn this complex mapping relationship, resulting in low estimation accuracy. In this letter, we propose a new gender-specific regression model with a two-layer structure for more accurate age estimation. Different from recent two-layer models that use a global regressor to calculate cumulative attributes (CA) and use CA to estimate age, we use gender-specific ones to calculate CA with more flexibility and precision. Extensive experimental results on FG-NET and Morph 2 datasets demonstrate the superiority of our method over other state-of-the-art age estimation methods.

To enhance user privacy, anonymous credential systems allow the user to convince a verifier of the possession of a certificate issued by the issuing authority anonymously. In the systems, the user can prove relations on his/her attributes embedded into the certificate. Previously, a pairing-based anonymous credential system with constant-size proofs in the number of attributes of the user was proposed. This system supports the proofs of the inner product relations on attributes, and thus can handle the complex logical relations on attributes as the CNF and DNF formulas. However this system suffers from the computational cost: The proof generation needs exponentiations depending on the number of the literals in OR relations. In this paper, we propose a pairing-based anonymous credential system with the constant-size proofs for CNF formulas and the more efficient proof generation. In the proposed system, the proof generation needs only multiplications depending on the number of literals, and thus it is more efficient than the previously proposed system. The key of our construction is to use an extended accumulator, by which we can verify that multiple attributes are included in multiple sets, all at once. This leads to the verification of CNF formulas on attributes. Since the accumulator is mainly calculated by multiplications, we achieve the better computational costs.

This paper presents a technique that analyzes pedestrians' attributes such as gender and bag-possession status from surveillance video. One of the technically challenging issues is that we use only top-view camera images to protect privacy. The shape features over the frames are extracted by bag-of-features (BoF) using histogram of oriented gradients (HoG) vectors. In order to enhance the classification accuracy, a two-staged classification framework is presented. Multiple classifiers are trained by changing the parameters in the first stage. The outputs from the first stage is further trained and classified in the second stage classifier. The experiments using 60-minute video captured at Haneda Airport, Japan, show that the accuracies for the gender classification and the bag-possession classification were 95.8% and 97.2%, respectively, which is a significant improvement from our previous work.

An anonymous credential system enables individuals to selectively prove their attributes while all other knowledge remains hidden. We considered the applicability of such a system to large scale infrastructure systems and perceived that revocations are still a problem. Then we contrived a scenario to lessen the number of revocations by using more attributes. In this scenario, each individual needs to handle a huge number of attributes, which is not practical with conventional systems. In particular, each individual needs to prove small amounts of attributes among a huge number of attributes and the manager of the system needs to certify a huge number of attributes of individuals periodically. These processes consume extremely large resources. This paper proposes an anonymous credential system in which both a user's proving attributes set, which is included in a huge attribute set, and manager's certifying attributes are very efficient. Conclusion Our proposal enables an anonymous credential system to be deployed as a large scale infrastructure system.

Accurate estimation of accentual attribute values of words, which is required to apply rules of Japanese word accent sandhi to prosody generation, is an important factor to realize high-quality text-to-speech (TTS) conversion. The rules were already formulated by Sagisaka et al. and are widely used in Japanese TTS conversion systems. Application of these rules, however, requires values of a few accentual attributes of each constituent word of input text. The attribute values cannot be found in any public database or any accent dictionaries of Japanese. Further, these values are difficult even for native speakers of Japanese to estimate only with their introspective consideration of properties of their mother tongue. In this paper, an algorithm was proposed, where these values were automatically estimated from a large amount of data of accent types of accentual phrases, which were collected through a long series of listening experiments. In the proposed algorithm, inter-speaker differences of knowledge of accent sandhi were well considered. To improve the coverage of the estimated values over the obtained data, the rules were tentatively modified. Evaluation experiments using two-mora accentual phrases showed the high validity of the estimated values and the modified rules and also some defects caused by varieties of linguistic expressions of Japanese.

This paper discusses an object-oriented approach to temporal multimedia data modeling in OMEGA; a multimedia database management under development at the University of Library and Information Science. An object-orientated approach is necessary to integrate various types of heterogeneous multimedia data, but it has become clear that current object-oriented data models are not sufficient to represent multimedia data, particularly when they are temporal. For instance, the current object-oriented data models cannot describe objects whose attribute values change time-dependently. Also, they cannot represent temporal relationships among temporal multimedia objects. We characterize temporal objects as instances of a subclass of class TimeInterval with the temporal attributes and the temporal relationships. This temporal multimedia data model is designed upward compatible with the ODMG-93 standard object model. To organize a temporal multimedia database, a five temporal axes model for representing temporal multimedia objects is also introduced. The five temporal axes--an absolute, an internal, a quasi-, a physical, and a presentation time axis--are necessary to describe time-dependent properties of multimedia objects in modeling, implementing and use. A concrete example of this organization method is also illustrated.