Games have grown to be an important part of our society and naturally, its incorporation with education has become a rising trend Together with the strong need for STEM talents, this paper proposed that STEM sandbox game-based learning video games could enhance students' interest and performance in the four disciplines. It is further argued that simulation realism, creativity and interactivity are three important components for this purpose. To promote their development, Planetary Marching Cubes (PMC), a development basis for STEM games has been discussed, along with a game developed with it, HelloPlanet. Students in primary and secondary schools have been invited to play HelloPlanet, and one of the main contributions of this paper is to demonstrate the improvement in both students' interest and performance in STEM. Students and teachers feedback are both very positive, implying that the education sector is fully ready to integrate game-based learning. When comparing with other methods of planet generation, most students picked PMC. This consolidates the argument of the importance of the three components, while also demonstrates the potential of PMC as a development basis for educational sandbox games.

Local pooling (LP) in configuration (feature) space proposed by Boureau et al. explicitly restricts similar features to be aggregated, which can preserve as much discriminative information as possible. At the time it appeared, this method combined with sparse coding achieved competitive classification results with only a small dictionary. However, its performance lags far behind the state-of-the-art results as only the zero-order information is exploited. Inspired by the success of high-order statistical information in existing advanced feature coding or pooling methods, we make an attempt to address the limitation of LP. To this end, we present a novel method called high-order LP (HO-LP) to leverage the information higher than the zero-order one. Our idea is intuitively simple: we compute the first- and second-order statistics per configuration bin and model them as a Gaussian. Accordingly, we employ a collection of Gaussians as visual words to represent the universal probability distribution of features from all classes. Our problem is naturally formulated as encoding Gaussians over a dictionary of Gaussians as visual words. This problem, however, is challenging since the space of Gaussians is not a Euclidean space but forms a Riemannian manifold. We address this challenge by mapping Gaussians into the Euclidean space, which enables us to perform coding with common Euclidean operations rather than complex and often expensive Riemannian operations. Our HO-LP preserves the advantages of the original LP: pooling only similar features and using a small dictionary. Meanwhile, it achieves very promising performance on standard benchmarks, with either conventional, hand-engineered features or deep learning-based features.

Combined arms are the appropriate combinations to achieve mutually complementary effects in real time strategy (RTS) game. However, it remains the difficulty of planning the sub-unit maneuver, positioning and engagement. Following the research of multi-agents potential fields that provides an effective and efficient way to handle unit maneuver in various environment, we adopted fuzzy measure and integral into potential field and extended the normal additive property to non-additive property in potential field. A new approach for various maneuver planning in RTS game is proposed. The model explains the basic principles that must guide the combined arms in the movement and conduct human-like unit maneuver. We implemented our methodology in a well-known RTS game platform, Warcraft III. In our experiment, fuzzy measure and integral showed the ability to perform various and human-like intelligence in game.

The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs) which have been proposed to handle this essential and crucial problem in the social network analysis. However, they can not adequately consider the so-called local information: the degrees of x and y, the number of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words, not any LILPA takes into account these local information simultaneously. This limits the performances of LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different LILPAs with aggregation weights which are determined with maximum entropy method. The final experimental results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual LILPAs.

With the rapid development of digital imaging and communication technologies, image set-based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set using the gallery face image sets. The set-to-set distance-based methods ignore the relationship between gallery sets, whereas representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set-based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image-based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.

The of behavioral patterns in online social media are often reflecting the happenings in our society. These patterns, which can be considered as opinions, are often correlated with public opinion polling. However, many correlation analyses done previously were for subsequent discoveries and not being able to handle short interval polling opinions. For opinions obtained from tracking polling with short opinion collection interval, like rolling polling, it cannot perform well in tracing the latest trends. This paper describes an extended correlation model for such kind of polling in examining the correlation between opinion in online social media and the public opinion from tracking poll. It has been tested with a recent rolling polling and it outperformed the previous correlation models.

In many cases, news articles are shared in Online Social Network (OSN) sites by their members. Disparate kinds of information are often propagated from the news media to the online social networks. Little efforts were devoted to measure or estimate the social influences of news media on the online social networks when compared to the identifications of their connectivity. In this paper, we took references on measurements of social influences among individuals in online social network and derived absolute, relative and combined measurements in order to deduce the social influences of single news article. Those measurements can be extended to a set of news articles and a social medium hence they can be used to evaluate and compare the social influences of the numerous news media. Experiments to compare the performances of the proposed measurements will also be demonstrated and discussed.

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Local-feature-based face recognition (FR) methods, such as Gabor features encoded by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases such as FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for many practical FR applications. In this paper, we propose a new and efficient local feature extraction scheme, namely monogenic binary coding (MBC), for face representation and recognition. Monogenic signal representation decomposes an original signal into three complementary components: amplitude, orientation, and phase. We encode the monogenic variation in each local region and monogenic feature in each pixel, and then calculate the statistical features (e.g., histogram) of the extracted local features. The local statistical features extracted from the complementary monogenic components (i.e., amplitude, orientation, and phase) are then fused for effective FR. It is shown that the proposed MBC scheme has significantly lower time and space complexity than the Gabor-transformation-based local feature methods. The extensive FR experiments on four large-scale databases demonstrated the effectiveness of MBC, whose performance is competitive with and even better than state-of-the-art local-feature-based FR methods.

Choquet Integral (CI), which is known as a fuzzy measure-based technique, has been a general aggregation tool for multi-criteria decision making problem. In this paper, we apply Choquet Integral to unit selection problem in Real Time Strategy (RTS) game. In addition, three new fuzzy integrals named Mean based Fuzzy Integral (Me-based FI), Max-based Fuzzy Integral (Ma-based FI), and Order-based Fuzzy Integral (Or-based FI) are developed, which relax the monotonicity requirement of the traditional fuzzy measures and consider different properties of game play. We compare the performance of Choquet Integral and the new proposed ones on this practical application with highly non-monotonic data. Experiments show that the proposed new fuzzy integrals achieved better learning performance and testing result.

Unit formation planning and target of attack is the core of micro management in real time strategy (RTS) game. It is more complicated than macro management, such as building and unit production sequence. It consists of a great quantity of possibility. Multiple targets and the non-additive property of unit formation leads the micro-management remains a problem. Traditional tree searching or A* searching is unable to handle these two properties. They are time consuming in runtime and difficult to manage in the AI programming development as there are too many weightings and each of them will interact with the others. In this paper, we applied potential field, fuzzy measure and integral to solve the micro-management. Potential field is suitable for complicated and various environment with multiple targets. However, it does not consider non-additive property. We integrated it with fuzzy measure and integral to extend simple additive property to non-additive property and provide the ability to handle interaction among different targets.

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l1-regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l1-norm minimization leads to a sparse solution, the proposed method is called metasample-based SR classification (MSRC). Extensive experiments on publicly available gene expression data sets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.

CBR systems that are built for the classification problems are called CBR classifiers. This paper presents a novel and fast approach to building efficient and competent CBR classifiers that combines both feature reduction (FR) and case selection (CS). It has three central contributions: 1) it develops a fast rough-set method based on relative attribute dependency among features to compute the approximate reduct, 2) it constructs and compares different case selection methods based on the similarity measure and the concepts of case coverage and case reachability, and 3) CBR classifiers built using a combination of the FR and CS processes can reduce the training burden as well as the need to acquire domain knowledge. The overall experimental results demonstrating on four real-life data sets show that the combined FR and CS method can preserve, and may also improve, the solution accuracy while at the same time substantially reducing the storage space. The case retrieval time is also greatly reduced because the use of CBR classifier contains a smaller amount of cases with fewer features. The developed FR and CS combination method is also compared with the kernel PCA and SVMs techniques. Their storage requirement, classification accuracy, and classification speed are presented and discussed.

In this article we propose a case‐base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case‐based reasoning (CBR) system. A machine‐learning technique, fuzzy decision‐tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature‐weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.

In this paper, implementation and evaluation of a printed circuit board assembly (PCBA) manufacturing system model based on fuzzy colored Petri nets (FCPN) modeling was performed. From the Petri nets simulation results, two contributions were claimed: (1) The successful application of our approach to evaluate systems that consist of complicated concurrent processes with embedded data structures and uncertainty reasoning. (2) An approach in automatic determination of the threshold values in the fuzzy production rules using FCPN. The details of the assembling processes and various manufacturing data were gathered from computer manufacturers located in China. Different resource allocation strategies were introduced in the experiments, and simulation results were obtained. The resources included screen printers, different pick and place machines, infrared soldering machines and component insertion robots. All the experiments were built by using the Petri net tool DESIGN/CPN. A number of hierarchy pages, color sets, places, arcs and transitions representing the PCBA and fuzzy production rules were constructed in the experiments. Simulation runs of the Petri nets model were carried out. The result shows that our approach could be used to guide decision-makers in the design and selection of a suitable PCBA manufacturing strategy. Future extension of our work is described.

It has been increasingly recognized over recent years that expert systems which combine one or more techniques greatly increase the problem solving capability and help overcome some of the shortcomings associated with any single technique. The verification of these expert systems requires methods which could tackle the multiple knowledge representation paradigms and integrated inference mechanisms used. The paper provides a formal description technique for verifying the correctness of hybrid expert systems (HES) that emphasizes an integration of object hierarchy, property inheritance and production rules. The main idea is to convert the HES into a state controlled coloured Petri net (SCCPN) where the object hierarchy, property inheritance and production rules are modelled as separated components in the same SCCPN. The detection and analysis of the anomalies in the system are done by constructing and examining the reachability tree spanned by the knowledge inference. This provides a formal basis for automating the deduction process and a means of verifying HES. A set of propositions is formulated to verify errors and anomalies in HES. Lastly, future extension of the approach is discussed.