With numerous connected devices and appliances, the smart home is one of the representative fields of Internet of Things (IoT). As the complexity of devices/appliances increase, numerous buttons (sometimes dozens) are designed on the remote controller in home spaces even if several of them are seldom used. A user may be confused with the controller even if he or she only intends to perform a simple operation. This confusion also leads to a higher probability of mal-operations. In addition, conventional methods of communication between remote controllers and connected devices, such as eXtensible Markup Language (XML) messages, are usually bandwidth-consumptive. To address these problems, an intelligent universal remote control system for home appliances named Point-n-Press is proposed. Point-n-Press addresses the directionality feature, which enables easy and intuitive control by pointing to the target device to display the target's control interface on the screen of the remote controller. By leveraging the state dependencies of home device/appliance operations, only functional buttons that are relevant to the current context are utilized. Two real prototypes are implemented to verify the feasibility of the proposed scheme. The evaluation results show that Point-n-Press is a useful and suitable control scheme for IoT-based smart homes.

Nowadays, recommender systems have become a necessity in various applications, especially in a large-scale online shop. In addition to the rating information provided by the users, social relationships of a user begin to be incorporated to further improve the performance of current recommender systems. Among several alternatives, matrix factorization is recognized as an effective technique to reduce data dimensionality and to capture significant latent relationships between users and items. Furthermore, recommender systems are used in an ever-changing commercial environment and usually operate on the large-scale data. Note that there are always new users, items and ratings as time advances, resulting in a rating matrix of increasing size. This poses a challenging problem because decomposing the entire matrix is costly. In this work, we thus propose an incremental scheme to directly update the rating matrix without the need to decompose the entire rating matrix. This helps to achieve better efficiency at the cost of some approximation errors. Experimental results show that our scheme has high efficiency as expected and significantly enhances the recommendation quality for cold-start users.

As low-cost depth cameras are released recently, motion data containing 3D coordinates of skeleton joints during a time period can be directly captured. Nevertheless, analyzing the motion data is usually a challenging problem and requires huge computation costs because of the high-dimensionality. Among several alternatives, the self-organizing map (SOM) is verified to be an effective technique to handle such motion data. Specifically, a captured motion sequence can be easily and precisely mapped to form an indexed motion string through the use of a trained SOM. However, the training process of the SOM is high computation complexity and is thus typically tedious. In view of this, we propose in this work to incorporate a hierarchical structure into the SOM technique. Generally speaking, our approach named as GQSOM (growing quadtree self-organizing map) helps to significantly reduce the required computation complexity while preserving the effectiveness of the SOM technique. Empirical studies using the WorkSU-10 exercise dataset show that our approach is both efficient and effective to perform indexing and retrieval tasks of motion data.

To ease the problem of data overloading, it is crucial to understand the user behavior when s/he interacts with online contents, or more specifically, a web page containing several entries for further exploration. We devise to estimate the relevance of an entry to the user goal by observing eye movements as implicit feedback. Specifically, this study proposes a framework that assumes eye movement measures can be used to infer a user's cognition. A rating task was conducted in which subjects were required to judge whether an image was relevant to a word. Results showed that the total fixation duration and the fixation count can be used to discriminate between the relevant and irrelevant conditions, in contrast, the first fixation duration cannot. In addition, the subjective rating and relevancy manipulation interacted on the total fixation duration. Converging evidence verifies the assumption we have proposed.

Due to the dynamic nature of data streams, a sliding window is used to generate synopses that approximate the most recent data within the retrospective horizon to answer queries or discover patterns. In this paper, we propose a dynamic scheme for wavelet synopses management in sensor networks. We define a data structure sliding dual tree, abbreviated as SDT, to generate dynamic synopses that adapts to the insertions and deletions in the most recent sliding window. By exploiting the properties of Haar wavelet transform, we develop several operations to incrementally maintain SDT over consecutive time windows in a time- and space-efficient manner. These operations directly operate on the transformed time-frequency domain without the need of storing/reconstructing the original data. As shown in our thorough analysis, our SDT-based approach greatly reduces the required resources for synopses generation and maximizes the storage utilization of wavelet synopses in terms of the window length and quality measures. We also show that the approximation error of the dynamic wavelet synopses, i.e., L2-norm error, can be incrementally updated. We also derive the bound of the overestimation of the approximation error due to the incremental thresholding scheme. Furthermore, the synopses can be used to answer various kinds of numerical queries such as point and distance queries. In addition, we show that our SDT can adapt to resource allocation to further enhance the overall storage utilization over time. As demonstrated by our experimental results, our proposed framework can outperform current techniques in both real and synthetic data.

As the computer technology advances, data acquisition, processing and visualization techniques have a tremendous impact on medical imaging. On the other hand, however, the interpretation of medical images is still almost performed by radiologists nowadays. Developments in artificial intelligence and image processing show that computer-aided diagnosis emerges with increasingly high potential. In this paper, we develop an intelligent approach to perform image segmentation and thus to discover region of interest (ROI) for diagnosis purposes through the use of self-organizing map (SOM) techniques. Specifically, we propose a two-stage SOM approach which can precisely identify dominant color components and thus segment a medical image into several smaller pieces. In addition, with a proper merging step conducted iteratively, one or more ROIs in a medical image can usually be identified. Empirical studies show that our approach is effective at processing various types of medical images. Moreover, the feasibility of our approach is also evaluated by the illustration of image semantics.

Web caching and Web prefetching are two important techniques used to reduce the noticeable response time perceived by users. Note that by integrating Web caching and Web prefetching, these two techniques can complement each other since the Web caching technique exploits the temporal locality, whereas Web prefetching technique utilizes the spatial locality of Web objects. However, without circumspect design, the integration of these two techniques might cause significant performance degradation to each other. In view of this, we propose in this paper an innovative cache replacement algorithm, which not only considers the caching effect in the Web environment, but also evaluates the prefetching rules provided by various prefetching schemes. Specifically, we formulate a normalized profit function to evaluate the profit from caching an object (i.e., either a nonimplied object or an implied object according to some prefetching rule). Based on the normalized profit function devised, we devise an innovative Web cache replacement algorithm, referred to as Algorithm IWCP (standing for the Integration of Web Caching and Prefetching). Using an event-driven simulation, we evaluate the performance of Algorithm IWCP under several circumstances. The experimental results show that Algorithm IWCP consistently outperforms the companion schemes in various performance metrics.

For data stream applications, both approximation and adaptability are important issues for effective mining. We explore in this paper a fundamental problem that how the limited resources, e.g., memory space and computation power, can be well utilized to produce accurate estimates. Two important features for tracking mined patterns with properly utilized resources are examined. The first issue is temporal granularity which refers to the phenomenon that as time advances, people are more interested in recent events, meaning that more resources can be utilized to explore more recent data with finer granularities. Second, with the mining task of discovering frequent temporal patterns, more resources are expected to be allocated to the processing of those borderline patterns whose statistics, e.g., occurrence frequencies, are close to the specified threshold so as to have proper frequent itemset identification. This feature is called mining with support count granularity. Consequently, algorithm RAM-DS (Resource-Aware Mining for Data Streams) is designed to not only reduce the memory required for data storage but also retain good approximation of target time series. Experimental results have shown that the memory required for storing significant wavelet coefficients is very small and the quality of approximation is stable when performing incremental data updates, indicating that algorithm RAM-DS is feasible and suitable for adaptive mining in data streams.

In this paper a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of items with that of others. The process of mining substitution rules can be decomposed into two procedures. The first identifies concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second is substitution rule generation. Two concrete itemsets X and Y form a substitution rule, denoted by X /spl utri/ Y to mean that X is a substitute for Y if and only if X and Y are negatively correlated and the negative association rule X /spl rarr/ Y~ exists. We derive theoretical properties for the model of substitution rule mining. Then, in light of these properties, the SRM algorithm (substitution rule mining) is designed and implemented to discover substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of the SRM algorithm. It is shown that SRM produces substitution rules of very high quality.