This paper presents a new class of games: location puzzle games. It combines puzzle games with the use of the geographical location. The game class is closely related to location-based games except that no physical movement in the real world is needed as in most mobile location-based games. For example, we present a game called Puzzle-Mopsi, which asks users to match a given set of images with the locations shown on the map. In addition to local knowledge, the game requires logical skills as the number of possible matches grows exponentially with the number of images. Small-scale experiments show that the players found the game interesting and that the difficulty increases with the number of targets and decreases with the player’s familiarity with the area.

Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a co-supervisor but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.

Affective music composition systems are known to trigger emotions in humans. However, the design of such systems to stimulate users‟ emotions continues to be a challenge because, studies that aggregate existing literature in the domain to help advance research and knowledge is limited. This study presents a systematic literature review on affective algorithmic composition systems. Eighteen primary studies were selected from IEEE Xplore, ACM Digital Library, SpringerLink, PubMed, ScienceDirect, and Google Scholar databases following a systematic review protocol. The findings revealed that there is a lack of a unique definition that encapsulates the various types of affective algorithmic composition systems. Accordingly, a unique definition is provided. The findings also show that most affective algorithmic composition systems are designed for games to provide background music. The generative composition method was the most used compositional approach. Overall, there was rather a low amount of research in the domain. Possible reasons for these trends are the lack of a common definition for affective music composition systems and also the lack of detailed documentation of the design, implementation and evaluation of the existing systems.

Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.

In this paper, optimal control of an inverted pendulum on a cart system is studied. Since the nonlinear structure of the system is complex, and in the presence of random disturbances, optimization and control of the motion of the system become more challenging. For handling this system, a discrete-time stochastic optimal control problem for the system is described, where the external force is considered as the control input. By defining a loss function, namely, the mean squared errors to be minimized, the stochastic approximation (SA) approach is applied to estimate the state dynamics. In addition, the Hamiltonian function is defined, and the first-order necessary conditions are derived. The gradient of the cost function is determined so that the SA approach is employed to update the control sequences. For illustration, considering the values of the related parameters in the system, the discrete-time stochastic optimal control problem is solved iteratively by using the SA algorithm. The simulation results show that the state estimation and the optimal control law design are well performed with the SA algorithm, and the motion of the inverted pendulum cart is addressed satisfactorily. In conclusion, the efficiency of the SA approach for solving the inverted pendulum on a cart system is verified.

In clustering, the cost function that is commonly used involves calculating all-pairwise squared distances. In this paper, we formulate the cost function using mean squared error and show that this leads to more balanced clustering compared to centroid-based distance functions, like the sum of squared distances in k-means. The clustering method has been formulated as a cut-based approach, more intuitively called Squared cut (Scut). We introduce an algorithm for the problem which is faster than the existing one based on the Stirling approximation. Our algorithm is a sequential variant of a local search algorithm. We show by experiments that the proposed approach provides better overall optimization of both mean squared error and cluster balance compared to existing methods.