Aesthetic evaluation of Chinese calligraphy is one of the most challenging tasks in Artificial Intelligence. This paper attempts to solve this problem by proposing a number of aesthetic feature representations and feeding them into Artificial Neural Networks. Specifically, 22 global shape features are presented to describe a given handwritten Chinese character from different aspects according to classical calligraphic rules, and a new 10-dimensional feature vector is introduced to represent the component layout information using sparse coding. Moreover, a Chinese Handwriting Aesthetic Evaluation Database (CHAED) is also built by collecting 1000 Chinese handwriting images with diverse aesthetic qualities and inviting 33 subjects to evaluate the aesthetic quality for each calligraphic image. Finally, back propagation neural networks are constructed with the concatenation of the proposed features as input and then trained on our CHAED database for the aesthetic evaluation of Chinese calligraphy. Experimental results demonstrate that the proposed AI system provides a comparable performance with human evaluation. Through our experiments, we also compare the importance of each individual feature and reveal the relationship between our aesthetic features and the aesthetic perceptions of human beings.

In this paper we introduce our haiku generator, which, in contrast to other systems, is not restricted to limited classic vocabulary sets and preserves a classic style without becoming too random and abstract because it performs a semantic integrity check using the Internet. Moreover, it is able to analyze blog entry input and, by using nouns and adjectives for web-mining, to stay on topic and still preserve kigo, traditional seasonal words used in Japanese poetry. The haiku generator utilizes grammar templates automatically generated from poems written by Japanese poets and a lexicon of 2,473 kigo words from an online haiku repository. In addition to generating haiku poems, it can output them vocally together with related sound effects and images retrieved from the WWW. Our experiments demonstrate that the proposed system generates high-quality haikus and that using content-related input and multimedia-rich output is effective for increasing users' satisfaction. We have performed impression evaluation experiments and confirmed that our method is especially useful for generating haikus with higher depth and sound-sharpness, which are two very important categories in professional evaluation of Japanese poetry. Next, haikus generated using the proposed method were evaluated by blog authors and blog readers and again, the proposed method outperformed the baseline. We also measured how the presence or absence of multimedia output influenced the evaluation. While using both vocal output and an image achieved higher scores than text alone, there were cases in which some combinations of effects were evaluated higher than all the effects used together. With our original approach to generating poetry, we wish to show the importance of new media and possibilities that are arising from the utilization of the "wisdom of (web-)crowds" in order to achieve higher standards for AI-generated art.

An important aspect of music perception in humans is the ability to segment streams of musical events into structural units such as motifs and phrases.A promising approach to the computational modeling of music segmentation employs the statistical and information-theoretic properties of musical data, based on the hypothesis that these properties can (at least partly) account for music segmentation in humans. Prior work has shown that in particular the information content of music events, as estimated from a generative probabilistic model of those events, is a good indicator for segment boundaries.In this paper we demonstrate that, remarkably, a substantial increase in segmentation accuracy can be obtained by not using information content estimates directly, but rather in a bootstrapping fashion. More specifically, we use information content estimates computed from a generative model of the data as a target for a feed-forward neural network that is trained to estimate the information content directly from the data. We hypothesize that the improved segmentation accuracy of this bootstrapping approach may be evidence that the generative model provides noisy estimates of the information content, which are smoothed by the feed-forward neural network, yielding more accurate information content estimates.

Artistic creation is often based on the concept of blending. Linguistic creativity is no exception, as demonstrated for instance by the importance of metaphors in poetry. Blending can also be used to evoke a secondary concept while playing with an already given piece of language, either with the intention of making the secondary concept well perceivable to the reader, or instead, to subtly evoke something additional. Current language technology can do a lot in this connection, and automated language creativity can be useful in cases where input or target are to change continuously, making human production not feasible. In this work we present a system that takes existing well-known expressions and innovates them by bringing in a novel concept coming from evolving news. The technology is composed of several steps concerned with the selection of the sortable concepts and the production of novel expressions, largely relying on state of the art corpus-based methods. Proposed applications include: i) producing catchy news headlines by "parasitically" exploiting well known successful expressions and adapting them to the news at hand; ii) generating adaptive slogans that allude to news of the day and give life to the concept evoked by the slogan; iii) providing artists with an application for boosting their creativity.

Recommendation systems play an important role in E-Commerce. However, their potential usefulness in real world applications is greatly limited by the availability of historical rating records from the customers. This paper presents a novel method to tackle the problem of data sparseness in user ratings with rich and timely domain information from social media. We first extract multiple side information for products from their relevant social media contents. Next, we convert the information into weighted topic-item ratings and inject them into an extended latent factor based recommendation model in an optimized approach. Our evaluation on two real world datasets demonstrates the superiority of our method over state-of-the-art methods.

Many e-commerce systems allow users to express their opinions towards products through user reviews systems. The user generated reviews not only help other users to gain a more insightful view of the products, but also help online businesses to make targeted improvements on the products or services. Besides, they compose the key component of various personalized recommender systems. However, the existence of spam user accounts in the review systems introduce unfavourable disturbances into personalized recommendation by promoting or degrading targeted items intentionally through fraudulent reviews. Previous shilling attack detection algorithms usually deal with a specific kind of attacking strategy, and are exhausted to handle with the continuously emerging new cheating methods. In this work, we propose to conduct shilling attack detection for more informed recommendation by fraudulent action propagation on the reviews themselves, without caring about the specific underlying cheating strategy, which allows us a unified and flexible framework to detect the spam users.

Image location prediction is to estimate the geolocation where an image is taken. Social image contains heterogeneous contents, which makes image location prediction nontrivial. Moreover, it is observed that image content patterns and location preferences correlate hierarchically. Traditional image location prediction methods mainly adopt a single-level architecture, which is not directly adaptable to the hierarchical correlation. In this paper, we propose a geographically hierarchical bi-modal deep belief network model (GH-BDBN), which is a compositional learning architecture that integrates multi-modal deep learning model with non-parametric hierarchical prior model. GH-BDBN learns a joint representation capturing the correlations among different types of image content using a bi-modal DBN, with a geographically hierarchical prior over the joint representation to model the hierarchical correlation between image content and location. Experimental results demonstrate the superiority of our model for image location prediction.

Community detection is of crucial importance in understanding structures of complex networks. In many real-world networks, communities naturally overlap since a node usually has multiple community memberships. One popular technique to cope with overlapping community detection is Matrix Factorization (MF). However, existing MF-based models have ignored the fact that besides neighbors, "local non-neighbors" (e.g., my friend's friend but not my direct friend) are helpful when discovering communities. In this paper, we propose a Locality-based Non-negative Matrix Factorization (LNMF) model to refine a preference-based model by incorporating locality into learning objective. We define a subgraph called "k-degree local network" to set a boundary between local non-neighbors and other non-neighbors. By discriminately treating these two class of non-neighbors, our model is able to capture the process of community formation. We propose a fast sampling strategy within the stochastic gradient descent based learning algorithm. We compare our LNMF model with several baseline methods on various real-world networks, including large ones with ground-truth communities. Results show that our model outperforms state-of-the-art approaches.

In this work, we investigate the bidirectional mutual interactions (BMI) between users' activities and user-user relationships on social networking sites. We analyze and study the fundamental mechanism that drives the characteristics and dynamics of BMI is the underlying social influence. We make an attempt at a unified probabilistic approach, called joint activity and relation (JAR), for modeling and predicting users' activities and user-user relationships simultaneously in a single coherent framework. Instead of incorporating social influence in an ad hoc manner, we show that social influence can be captured quantitatively. Based on JAR, we learn social influence between users and users' personal preferences for both user activity prediction and user-user relation discovery through statistical inference. To address the challenges of the introduced multiple layers of hidden variables in JAR, we propose a new learning algorithm based on expectation maximization (EM) and we further propose a powerful and efficient generalization of the EM based algorithm for model fitting.We show that JAR exploits mutual interactions and benefits, by taking advantage of the learned social influence and users' personal preferences, for enhanced user activity prediction and user-user relation discovery. We further experiment with real world dataset to verify the claimed advantages achieving substantial performance gains.

Semantic relatedness (SR) measures form the algorithmic foundation of intelligent technologies in domains ranging from artificial intelligence to human-computer interaction. Although SR has been researched for decades, this work has focused on developing general SR measures rooted in graph and text mining algorithms that perform reasonably well for many different types of concepts. This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts. Using the domain of geography as a case study, we show that domain-specific SR — and even geography-specific signals alone (e.g. distance, containment) without sophisticated graph or text mining algorithms — significantly outperform the SR state-of-the-art for geographic concepts. In addition to substantially improving SR measures for geospatial technologies, an area that is rapidly increasing in importance, this work also unlocks an important new direction for SR research: SR measures that incorporate domain-specific customizations to increase accuracy.