One of the fast approximate similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used when undertaking a constant-time similarity search. The number of accesses to the hash table, however, increases when the number of bits lengthens. In this paper, we consider a method that does not access data with a long Hamming radius by using multiple binary codes. Further, we attempt to integrate the proposed approach and the existing multi-index hashing (MIH) method to accelerate the performance of the similarity search in the Hamming space. Then, we propose a learning method of the binary hash functions for multiple binary codes. We conduct an experiment on similarity search utilizing a dataset of up to 50 million items and show that our proposed method achieves a faster similarity search than that possible with the conventional linear scan and hash table search.

Up until now, the best public key encryption with multi-dimensional range query (PKMDRQ) scheme has two problems which need to be resolved. One is that the scheme is selectively secure. The other is that the time of decryption is long. To address these problems, we present a method of converting a predicate encryption supporting inner product (IPE) scheme into a PKMDRQ scheme. By taking advantage of this approach, an instance is also proposed. The comparison between the previous work and ours shows that our scheme is more efficient over the time complexity. Moreover, our scheme is adaptively secure.

A cloud system is defined as a large scale computer system that contains running high performance computers and responds to a large number of incoming tasks over the Internet. In this paper, we consider the problem to schedule computational jobs efficiently regarding system resource constraint and introduce a cuckoo search (CS) algorithm. Experimental results show that CS outperforms the genetic algorithm in terms of fitness value.

Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners motivated to access the large-scale computing resources and economic savings. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. So how to design an efficient, in the two aspects of accuracy and efficiency, searchable encryption scheme over encrypted cloud data is a very challenging task. In this paper, for the first time, we propose a practical, efficient, and flexible searchable encryption scheme which supports both multi-keyword ranked search and parallel search. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search results. To improve search efficiency, we design a tree-based index structure which supports parallel search to take advantage of the powerful computing capacity and resources of the cloud server. With our designed parallel search algorithm, the search efficiency is well improved. We propose two secure searchable encryption schemes to meet different privacy requirements in two threat models. Extensive experiments on the real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting multi-keyword ranked parallel searches.

The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm's application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.

In this paper, we propose an efficient method for computing the weight spectrum of LDPC convolutional codes based on circulant matrices of quasi-cyclic codes. In the proposed method, we reduce the memory size of their parity-check matrices with the same distance profile as the original codes, and apply a forward and backward tree search algorithm to the parity-check matrices of reduced memory. We show numerical results of computing the free distance and the low-part weight spectrum of LDPC convolutional codes of memory about 130.

Presentation slide composition is an important job for knowledge workers. Instead of starting from scratch, users tend to make new presentation slides by reusing existing ones. A primary challenge in slide reuse is to select desired materials from a collection of existing slides. The state-of-the-art solution utilizes texts and images in slides as well as file names to help users to retrieve the materials they want. However, it only allows users to choose an entire slide as a query but does not support the search for a single element such as a few keywords, a sentence, an image, or a diagram. In this paper, we investigate content-based search for a variety of elements in presentation slides. Users may freely choose a slide element as a query. We propose different query processing methods to deal with various types of queries and improve the search efficiency. A system with a user-friendly interface is designed, based on which experiments are performed to evaluate the effectiveness and the efficiency of the proposed methods.

With the development of the Internet, there are more and more shared resources on the Web. Personalized search becomes increasingly important as users demand higher retrieval quality. Personalized search needs to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags (features) and thus provide a powerful way for organizing, retrieving and sharing different types of social resources. To capture and understand user preferences, a user is typically modeled as a vector of tag: value pairs (i.e., a tag-based user profile) in collaborative tagging systems. In such a tag-based user profile, a user's preference degree on a group of tags (i.e., a combination of several tags) mainly depends on the preference degree on every individual tag in the group. However, the preference degree on a combination of tags (a tag-group) cannot simply be obtained from linearly combining the preference on each tag. The combination of a user's two favorite tags may not be favorite for the user. In this article, we examine the limitations of previous tag-based personalized search. To overcome their problems, we model a user profile based on combinations of tags (tag-groups) and then apply it to the personalized search. By comparing it with the state-of-the-art methods, experimental results on a real data set shows the effectiveness of our proposed user profile method.

In this paper, we consider multi-source multi-relay power allocation in cooperative wireless networks. A new intelligent optimization algorithm, multi-objective free search (MOFS), is proposed to efficiently allocate cooperative relay power to better support multiple sources transmission. The existence of Pareto optimal solutions is analyzed for the proposed multi-objective power allocation model when the objectives conflict with each other, and the MOFS algorithm is validated using several test functions and metrics taken from the standard literature on evolutionary multi-objective optimization. Simulation results show that the proposed scheme can effectively get the potential optimal solutions of multi-objective power allocation problem, and it can effectively optimize the tradeoff between network sum-rate and fairness in different applications by selection of the corresponding solution.

This paper proposes a robust and fast lyric search method for music information retrieval (MIR). The effectiveness of lyric search systems based on full-text retrieval engines or web search engines is highly compromised when the queries of lyric phrases contain incorrect parts due to mishearing. To improve the robustness of the system, the authors introduce acoustic distance, which is computed based on a confusion matrix of an automatic speech recognition experiment, into Dynamic-Programming (DP)-based phonetic string matching to identify the songs that the misheard lyric phrases refer to. An evaluation experiment verified that the search accuracy is increased by 4.4% compared with the conventional method. Furthermore, in this paper a two-pass search algorithm is proposed to realize real-time execution. The algorithm pre-selects the probable candidates using a rapid index-based search in the first pass and executes a DP-based search process with an adaptive termination strategy in the second pass. Experimental results show that the proposed search method reduced processing time by more than 86.2% compared with the conventional methods for the same search accuracy.

Metaheuristic methods have been studied for combinational optimization problems for some time. Recently, a Consultant-Guided Search (CGS) has been proposed as a metaheuristic method for the Traveling Salesperson Problem (TSP). This approach is an algorithm in which a virtual person called a client creates a solution based on consultation with a virtual person called a consultant. In this research, we propose a parallel algorithm which uses the Ant Colony System (ACS) to create a solution with a consultant in a Consultant-Guided Search, and calculate an approximation solution for the TSP. Finally, we execute a computer experiment using the benchmark problems (TSPLIB). Our algorithm provides a solution with less than 2% error rate for problem instances using less than 2000 cities.

This paper proposes a new algorithm for substring searching. Our algorithm is an improvement on the famous BM algorithm. When a mismatch happens while searching a substring (pattern), the BM algorithm will use two strategies to calculate shifting distances of the substring respectively and selects the larger one. In comparison, our algorithm uses each of the two strategies for their most suitable cases separately without a selection operation. Experimental results demonstrated that our algorithm is more efficient than the BM algorithm and the Quick Search algorithm, especially for binary strings and DNA strings.

We consider the problems of access control and encrypted keyword search for cryptographic cloud storage in such a way that they can be implemented for a multiple users setting. Our fine-grained access control aware multi-user secure keyword search approach interdependently harmonizes these two security notions, access control and encrypted keyword search. Owing to the shrinkage of the cloud server's search space to the user's decryptable subset, the proposed scheme both decreases information leakage and is shown to be efficient by the results of our contrastive performance simulation.

Face recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images - more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.

In this study, we have developed a translation repair method to automatically improve the accuracy of translations. Machine translation (MT) supports multilingual communication; however, it cannot achieve high accuracy. MT creates only one translated sentence; therefore, it is difficult to improve the accuracy of translated sentences. Our method creates multiple translations by adding personal pronouns to the source sentence and by using a word dictionary and a parallel corpus. In addition, it selects an accurate translation from among the multiple translations using the results of a Web search. As a result, the translation repair method improved the accuracy of translated sentences, and its accuracy is greater than that of MT.

A lot of work has been conducted on time series classification and similarity search over the past decades. However, the classification of a time series with high accuracy is still insufficient in applications such as ubiquitous or sensor systems. In this paper, a novel textual approximation of a time series, called TAX, is proposed to achieve high accuracy time series classification. l-TAX, an extended version of TAX that shows promising classification accuracy over TAX and other existing methods, is also proposed. We also provide a comprehensive comparison between TAX and l-TAX, and discuss the benefits of both methods. Both TAX and l-TAX transform a time series into a textual structure using existing document retrieval methods and bioinformatics algorithms. In TAX, a time series is represented as a document like structure, whereas l-TAX used a sequence of textual symbols. This paper provides a comprehensive overview of the textual approximation and techniques used by TAX and l-TAX

In this paper, a new technique is proposed to reduce the frequency of cell search by user equipment (UE) in the presence of femtocells. A new common signal (CS) and a separate set of primary synchronization signals (PSSs) are employed to facilitate efficient cell search in a next-geration LTE-based system. The velocity of the UE is also utilized to determine cell search mode. A slow UE recognizes the presence of femtocells using the CS, so that it can make separate searches for macrocells and femtocells. A fast UE will not search for femtocells since the coverage of femtocells is restricted to a small region. The fast UE detects the macrocell boundary using the PSSs transmitted from neighboring macrocells, so that it can search for macrocells only at the macrocell boundary. The effects of CS and UE velocity on the number of cell searches are analyzed. The performance of the proposed technique is evaluated by computer simulations.

We consider Monte Carlo tree search problem, a variant of Min-Max tree search problem where the score of each leaf is the expectation of some Bernoulli variables and not explicitly given but can be estimated through (random) playouts. The goal of this problem is, given a game tree and an oracle that returns an outcome of a playout, to find a child node of the root which attains an approximate min-max score. This problem arises in two player games such as computer Go. We propose a simple and efficient algorithm for Monte Carlo tree search problem.

Biometric identification has recently attracted attention because of its convenience: it does not require a user ID nor a smart card. However, both the identification error rate and response time increase as the number of enrollees increases. In this paper, we combine a score level fusion scheme and a metric space indexing scheme to improve the accuracy and response time in biometric identification, using only scores as information sources. We firstly propose a score level indexing and fusion framework which can be constructed from the following three schemes: (I) a pseudo-score based indexing scheme, (II) a multi-biometric search scheme, and (III) a score level fusion scheme which handles missing scores. A multi-biometric search scheme can be newly obtained by applying a pseudo-score based indexing scheme to multi-biometric identification. We secondly propose the NBS (Naive Bayes search) scheme as a multi-biometric search scheme and discuss its optimality with respect to the retrieval error rate. We evaluated our proposal using the datasets of multiple fingerprints and face scores from multiple matchers. The results showed that our proposal significantly improved the accuracy of the unimodal biometrics while reducing the average number of score computations in both the datasets.

This paper is concerned with a method to reduce the computation time of the Discrete Ray Tracing Method (DRTM) which was proposed to numerically analyze electromagnetic fields above Random Rough Surfaces (RRSs). The essence of DRTM is firstly to search rays between source and receiver and secondly to compute electric fields based on the traced rays. In the DRTM, the method discretizes not only RRSs but also ray tracing procedure. In order to reduce computation time for ray searching, the authors propose to modify the conventional algorithm discretizing RRSs with equal intervals to a new one which discretizes them with unequal intervals according to their profiles. The authors also use an approximation of Fresnel function which enables us to reduce field computation time. The authors discuss the reduction rate for computation time of the DRTM from the numerical view points of ray searching and field computation. Finally, this paper shows how much computation time is reduced by the new method.