Creating a large and natural facial expression database is a prerequisite for facial expression analysis and classification. It is, however, not only time consuming but also difficult to capture an adequately large number of spontaneous facial expression images and their meanings because no standard, uniform, and exact measurements are available for database collection and annotation. Thus, comprehensive first-hand data analyses of a spontaneous expression database may provide insight for future research on database construction, expression recognition, and emotion inference. This paper presents our analyses of a multimodal spontaneous facial expression database of natural visible and infrared facial expressions (NVIE). First, the effectiveness of emotion-eliciting videos in the database collection is analyzed with the mean and variance of the subjects' self-reported data. Second, an interrater reliability analysis of raters' subjective evaluations for apex expression images and sequences is conducted using Kappa and Kendall's coefficients. Third, we propose a matching rate matrix to explore the agreements between displayed spontaneous expressions and felt affective states. Lastly, the thermal differences between the posed and spontaneous facial expressions are analyzed using a paired-samples t-test. The results of these analyses demonstrate the effectiveness of our emotion-inducing experimental design, the gender difference in emotional responses, and the coexistence of multiple emotions/expressions. Facial image sequences are more informative than apex images for both expression and emotion recognition. Labeling an expression image or sequence with multiple categories together with their intensities could be a better approach than labeling the expression image or sequence with one dominant category. The results also demonstrate both the importance of facial expressions as a means of communication to convey affective states and the diversity of the displayed manifestations of felt emotions. There are indeed some significant differences between the temperature difference data of most posed and spontaneous facial expressions, many of which are found in the forehead and cheek regions.

Yellow pages search is a popular service that provides a means for finding businesses close to particular locations. The efficient search of yellow pages is becoming a rapidly evolving research area. The underlying data maintained in yellow pages search engines are typically labeled according to Standard Industry Classification (SIC) categories, and users can search yellow pages with categories according to their interests. Categorizing yellow pages queries into a subset of topical categories can help to improve search experience and quality. However, yellow pages queries are usually short and ambiguous. In addition, a yellow pages query taxonomy is typically organized by a hierarchy of a fairly large number of categories. These characteristics make automatic yellow pages query categorization difficult and challenging. In this article, we propose a flexible yellow pages query categorization approach. The proposed technique is built based on a TF-IDF similarity taxonomy matching scheme that is able to provide more accurate query categorization than previous keyword-based matching schemes. To further improve the categorization performance, we design several filtering schemes. Through extensive experimentation, we demonstrate encouraging results. We obtain F1 measures of about 0.5 and 0.3 for categorizing yellow pages queries into 19 coarse categories and 244 finer categories, respectively. We investigate different components in the proposed approach and also demonstrate the superiority of our approach over a hierarchical support vector machine classifier.

The negative database (NDB) is a complement of the corresponding database. The NDB could protect the privacy of the data, but it should be complete and hard-to-reverse. However, existent techniques cannot generate the complete and hard-to-reverse negative database. In this paper, a hybrid method is proposed to generate single negative databases. The proposed hybrid method includes two phases. Firstly, a complete negative database with a small size is generated by the transformation of the prefix algorithm. Secondly, a hard-to-reverse negative database, which is generated with the q-hidden method, is added into the small complete negative database. Therefore, the hybrid negative database is both complete and hard-to-reverse. Experiment results show that the NDB generated by the hybrid method is better than the NDB generated by the typical q-hidden method. Especially, the NDB generated by the q-hidden method can be reversed on average when the string length is 300. However, the NDB generated by the hybrid method cannot be reversed on average when the string length is 150.

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been conducted and the experiment results demonstrate that IR-tree outperforms the state-of-the-art approaches for geographic document searches.

To date, most facial expression analysis has been based on visible and posed expression databases. Visible images, however, are easily affected by illumination variations, while posed expressions differ in appearance and timing from natural ones. In this paper, we propose and establish a natural visible and infrared facial expression database, which contains both spontaneous and posed expressions of more than 100 subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions. The posed database includes the apex expressional images with and without glasses. As an elementary assessment of the usability of our spontaneous database for expression recognition and emotion inference, we conduct visible facial expression recognition using four typical methods, including the eigenface approach [principle component analysis (PCA)], the fisherface approach [PCA + linear discriminant analysis (LDA)], the Active Appearance Model (AAM), and the AAM-based + LDA. We also use PCA and PCA+LDA to recognize expressions from infrared thermal images. In addition, we analyze the relationship between facial temperature and emotion through statistical analysis. Our database is available for research purposes.

Polymorphic gates are special kinds of logic gates that can exhibit different functions under the control of environmental parameters, such as light, temperature, and VDD. These polymorphic gates can be used to build polymorphic circuits that perform different functions under different environments. Because polymorphic gates are different from traditional logic gates, the existent completeness theory for the traditional logic gate set is not suitable for the polymorphic gate set. So far, only the definition of the complete polymorphic gate set is given. There is no approach to judging whether a given polymorphic gate set is complete. The contributions of this article include three aspects. First, the impact of logic-1 and logic-0 on the completeness of the polymorphic gate set is discussed. Second, the theory and two related algorithms for judging the completeness of polymorphic gate sets with two modes are given. Finally, the theory and related algorithms for complete polymorphic gate sets with more than two modes are proposed.

In this paper, an eXtended Stepwise Dimension Reduction approach (XSDR) to evolutionary design of relatively large combinational logic circuits is proposed. In our previous work, a Stepwise Dimension Reduction approach (SDR) is introduced. The SDR divides a circuit into several layers. The layers are evolved one after another. However, some layers are difficult to be evolved. The XSDR improves the SDR by decomposing the original truth table of a layer to two truth tables. The new truth tables after decomposing are easy to be evolved. The proposed method has been tested with multipliers and the circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The experimental results demonstrate that the XSDR extensively improves the performance of the SDR in terms of the number of fitness evaluations and the computational time.

Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.

A novel self-adaption strategy for the parameter epsiv in epsiv-MOEA is proposed in this paper based on the analyses of the relationship between the value of epsiv and the maximum number of non-dominated solutions. Then this novel strategy is applied in epsiv-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter s, epsiv-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper.

Inspired by biological immune principles, a novel Immune Genetic Programming based on Register-Stack structure (rs-IGP) is proposed in this paper. In rs-IGP, an antigen represents a problem to be solved, and an antibody represents a candidate solution. A flexible and efficient antibody representation based on register-stack structure is designed for rs-IGP. Three populations are adopted in rs-IGP, i.e. the common population, the elitist population and the self set. The immune genetic operators are also developed, including clone operator, recombination operator, mutation operator, hypermutation operator, crossover operator and negative selection operator. The experimental results demonstrate that rs-IGP has better performance.

The mechanism for the formation of small-world networks is important but still unsolved. We proposed a network evolution model based on local interaction among rational individuals with fixed network dimensions. This model extends Barabasi's preferential attachment mechanism to consider two more realistic factors when choosing opponent to interact. Prisoner's dilemma game are utilized to model such local interaction between individuals. The edges of the network are regulated by one simple rule proposed which strengthen the edges with good interaction while weaken those with bad ones. Numerical results show that small-world network structure could be evolved.

The assignment of the computation and communication is one of the important problems in the design of peer-to-peer (P2P) massively multi-player online game (MMOG). A reputation mechanism for P2P MMOG is proposed in this paper to avoid the problems in P2P design. The communications among peers are described by whether the messages are received timely and correctly in the process of synchronization, and then the reliabilities of peers are quantified. The concept of local reputation is proposed, based on which the adaptive adjusting algorithm for the function of peers and the algorithm for getting update peer selecting reliable computing peers are brought forward to assign the computation and communication of the system. According to the experiments, the algorithms are effective for the distribution of the computation and communication.

Predicting IEC users’ evaluation characteristics is one way of reducing users’ fatigue. However, users’ relative evaluation appears as noise to the algorithm which learns and predicts the users’ evaluation characteristics. This paper introduces the idea of absolute scale to improve the performance of predicting users’ subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users’ fatigue. We first evaluate the effectiveness of the proposed method using seven benchmark functions instead of a human user. The experimental results show that the convergence speed of an IEC using the proposed absolute rating data-trained predictor is much faster than that of an IEC using a conventional predictor training with relative rating data. Next, the proposed algorithm is used in an individual emotion fashion image retrieval system. Experimental results of sign tests demonstrate that the proposed algorithm can alleviate user fatigue and has a good performance in individual emotional image retrieval.

The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (evolution strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.

Using immune regulation mechanisms that include density regulation and network regulation, this paper proposes a novel immune genetic algorithm. Its core idea is that all individuals compose an antibody network, and it utilizes a density regulation mechanism to adjust individual diversity at an individual level and network regulation mechanism to achieve dynamic balance between individual diversity and population convergence. The dynamic regulative ability of this algorithm is analyzed and the approach to choosing the parameters is also given. As a novel adaptive resolving algorithm, it can be used to solve many complex optimization problems. This paper discusses solution of the frequency assignment problem and analyzes the parameters' influence upon the performance of this algorithm. The experimental results prove that this algorithm has good performance and can properly maintain the balance between individual diversity and population convergence.