Ray-I Chang

Data mining for providing a personalized learning path in creativity: An application of decision trees

Customizing a learning environment to optimize personal learning has recently become a popular trend in e-learning. Because creativity has become an essential skill in the current e-learning epoch, this study aims to develop a personalized creativity learning system (PCLS) that is based on the data mining technique of decision trees to provide personalized learning paths for optimizing the performance of creativity. The PCLS includes a series of creativity tasks as well as a questionnaire regarding several key variables. Ninety-two college students were included in this study to examine the effectiveness of the PCLS. The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity. Moreover, the findings in this study shed light on what components should be accounted for when designing a personalized creativity learning system as well as how to integrate personalized learning and game-based learning into a creative learning program to maximize learner motivation and learning effects.

An effective and efficient traffic smoothing scheme for delivery of online VBR media streams

Traffic smoothing for delivery of online VBR media streams is one of the most important problems in designing multimedia systems. Given available client buffer and a window-sliding size, conventional approaches try to reduce bandwidth allocated in each window. However, they can not lead to the minimization of bandwidth allocated for transmitting the entire stream. Although a window-sliding approach was introduced previously to further reduce the bandwidth allocated, it was computational costly. In this paper, an effective and efficient online traffic-smoothing scheme is proposed. Different from the conventional static window-sliding approaches, this approach dynamically decides the suitable window-sliding size to online smooth the bursty traffic. Then, an aggressive workahead scheme is applied in transmitting the entire stream. By examining different media streams, the approach has a small bandwidth, high bandwidth utilization and small computation cost. Considering the online transmission of a Star War movie, our approach result is 13% less for the bandwidth and 4% less for the network idle rate than SLWIN(1). Comparing the number of window sliding, our approach is 75% less than SLWIN(1). The relations between the characteristic of the input traffic and the behavior of obtained scheduling results are discussed. Finally, an extension of the proposed approach to resolve the latency and quality tolerance applications is also introduced.

Deadline-modification-SCAN with maximum-scannable-groups for multimedia real-time disk scheduling

Real-time disk scheduling is important to multimedia systems support for digital audio and video. In these years, various approaches are presented to use the seek-optimizing scheme to improve the disk throughput of a real-time guaranteed schedule. However, as these conventional approaches apply SCAN only to the requests with the same deadline or within the same constant-sized group, their improvements are limited. In this paper, we introduce the DM-SCAN (deadline-modification-SCAN) algorithm with an idea of MSG (maximum-scannable-group). The proposed DM-SCAN method can apply SCAN to MSG iteratively by modifying request deadlines. We have implemented the DM-SCAN algorithm on UnixWare 2.01. Experiments show that DM-SCAN is significantly better than that of the best-known SCAN-EDF method in both the obtained disk throughput and the number of supported disk requests.

Niched ant colony optimization with colony guides for QoS multicast routing

Quality-of-service (QoS) multicast routing is essential to many network applications such as IPTV, Internet radio, multimedia broadcasting, and real-time telecommunication. Recently, the minimum-cost multicast tree with the delay-and-bandwidth constraint (MinC/DB) problem in QoS multicast routing has caused the most attention. As the MinC/DB problem can be reduced to the constrained Steiner tree problem which has been shown to be NP-complete, it is very unlikely that a polynomial time algorithm would exist. In this paper, we propose a niched ant colony optimization with colony guides (NACOg) algorithm to tackle the MinC/DB problem. The NACOg algorithm first deliberates a constrained tree traversal (CTT) strategy that guarantees the search to any feasible trees with respect to the QoS constraints. The proposed CTT strategy employs adaptive memory structure as contemplated in the tabu search and the strategy is more effective in constraint-handling than both the penalty-function and the produce-and-repair strategies which were broadly used in the literature. The evolutionary optimization of the NACOg algorithm is empowered by the search balance between diversification and intensification. The diversification search is practiced by the individual niche-colony with its own pheromone matrix, and the intensification search is facilitated by the learning scheme which refers to the experience obtained by the niche-colony guide and the entire-colony guide. The experimental results on 100 problem instances have shown that the proposed NACOg algorithm produces the least cost QoS multicast trees compared to those obtained by the Haghighat genetic algorithm and the well-known KPP heuristic.

Designing the ON-OFF CBR transmission schedule for jitter-free VBR media playback in real-time networks

This paper addresses the problem of designing the CBR transmission schedule (CTS) for jitter-free media playback. Comparing to the VBR transmission schedule (VTS), CTS is known to have advantages in low overhead and complexity for managing real-time communications. However, media data are usually compressed as VBR streams with noticeable bit-rate variabilities. Conventional CTS approaches usually require large buffer size or workahead to resolve this problem. In this paper, a linear-time CTS algorithm with the minimum system resources is proposed to transmit VBR media for jitter-free playback. Different from the conventional approaches, the obtained CTS is with the ON-OFF switch behavior. As the ATM transport mechanism used in current high-speed networks, it transmits fixed size cells in the ON period and zero cells in the OFF period. In 1993, Sohraby has shown that the general ON-OFF resource has a simple multiplexer queue length distribution function. It is very easy to do the call admission control (CAC) for real-time networks. In this paper, empirical studies to different VBR media streams are explored. The obtained results demonstrate that the proposed approach is effective and efficient.

Optimization of stored VBR video transmission on a CBR channel

This paper presents a linear-time method to optimize stored VBR video transmission on CBR channel. Given the transmission bandwidth, we have presented a linear-time method to minimize both the client buffer size and playback work-ahead. In this paper, the service connection time is minimized to maximize the network utilization for the given transmission bandwidth and client buffer. The required work- ahead is also minimized for the minimum response time. The proposed scheme can be extended easily to transmit the VBR video with minimum rate variability. Experiments of many well-known benchmark videos indicate that the proposed method can obtain better (or at least comparable) resource utilization and requires less memory buffer than conventional approaches. By considering the transmission of MPEG-1 video Advertisements with the same work-ahead time, our obtained results shows better network utilization than that of D-BIND. When compares by transmitting the long MPEG- 1 movie Star War, our approach uses smaller memory buffer than that of the combination of MVBA and D-BIND to achieve the same network utilization.

Effective distributed service architecture for ubiquitous video surveillance

Video surveillance systems are playing an important role to protect lives and assets of individuals, enterprises and governments. Due to the prevalence of wired and wireless access to Internet, it would be a trend to integrate present isolated video surveillance systems by applying distributed computing environment and to further gestate diversified multimedia intelligent surveillance (MIS) applications in ubiquity. In this paper, we propose a distributed and secure architecture for ubiquitous video surveillance (UVS) services over Internet and error-prone wireless networks with scalability, ubiquity and privacy. As cloud computing, users consume UVS related resources as a service and do not need to own the physical infrastructure, platform, or software. To protect the service privacy, preserve the service scalability and provide reliable UVS video streaming for end users, we apply the AES security mechanism, multicast overlay network and forward error correction (FEC), respectively. Different value-added services can be created and added to this architecture without introducing much traffic load and degrading service quality. Besides, we construct an experimental test-bed for UVS system with three kinds of services to detect fire and fall-incident features and record the captured video at the same time. Experimental results showed that the proposed distributed service architecture is effective and numbers of services on different multicast islands were successfully connected without influencing the playback quality. The average sending rate and the receiving rates of these services are quite similar, and the surveillance video is smoothly played.

Unsupervised query-based learning of neural networks using selective-attention and self-regulation

Query-based learning (QBL) has been introduced for training a supervised network model with additional queried samples. Experiments demonstrated that the classification accuracy is further increased. Although QBL has been successfully applied to supervised neural networks, it is not suitable for unsupervised learning models without external supervisors. In this paper, an unsupervised QBL (UQBL) algorithm using selective-attention and self-regulation is proposed. Applying the selective-attention, we can ask the network to respond to its goal-directed behavior with self-focus. Since there is no supervisor to verify the self-focus, a compromise is then made to environment-focus with self-regulation. In this paper, we introduce UQBL1 and UQBL2 as two versions of UQBL; both of them can provide fast convergence. Our experiments indicate that the proposed methods are more insensitive to network initialization. They have better generalization performance and can be a significant reduction in their training size.

FOS: A Funnel-Based Approach for Optimal Online Traffic Smoothing of Live Video

Traffic smoothing is an efficient means to reduce the bandwidth requirement for transmitting a variable-bit-rate video stream. Several traffic-smoothing algorithms have been presented to offline compute the transmission schedule for a prerecorded video. For live video applications, Sen present a sliding-window algorithm, referred to as SLWIN(k), to online compute the transmission schedule on the fly. SLWIN(k) looks ahead W video frames to compute the transmission schedule for the next k frametimes, where klesw. Note that W is upper bounded by the initial delay of the transmission. The time complexity of SLWIN(k) is O(W*N/k) for an N frame live video. In this paper, we present an O(N) online traffic-smoothing algorithm and two variants, denoted as FOS, FOS1 and FOS2, respectively. Note that O(N) is a trivial lower bound of the time complexity of the traffic-smoothing problem. Thus, the proposed algorithm is optimal. We compare the performance of our algorithms with SLWIN(k) based on several benchmark video clips. Experiment results show that FOS2, which adopts the aggressive workahead heuristic, further reduces the bandwidth requirement and better utilizes the client buffer for real-time interactive applications in which the initial delays are small

Force directed self-organizing map and its application to VLSI cell placement

A self-organizing map is described. It is called the force directed self-organizing map (FDSOM), and can be used in VLSI cell placement with various constraints on connection and dimension, such that the total wire length and area of the resulting placement are minimized. This procedure combines ideas from force-directed relaxation and the self-organization algorithm proposed by Kohonen. It is particularly suited for such a self-organization problem when those (input) sample vectors are not easily available. A proof of convergence for this model is given. With this property, it can be used in CAM or any other computational task. The experimental results obtained are quite encouraging. All these processes are convergent in a reasonable number of iterations. It is found that the proposed approach is competitive with state-of-the-art algorithms, and uses fewer nodes and connection weights.<<ETX>>