As the amount of recorded TV content is increasing rapidly, people need active and interactive browsing methods. In this paper, we use both text information from closed captions and visual information from video frames to generate links to enable users to easily explore not only the original video content but also augmented information from the Web. This solution especially shows its superiority when the video content cannot be fully represented by closed captions. A prototype system was implemented and some experiments were carried out to prove its effectiveness and efficiency.

Providing students with useful feedback on faulty programs can effectively help students fix programs. Spectrum-Based Fault Location (SBFL), which is a widely studied and lightweight technique, can automatically generate a suspicious value of statement ranking to help users find potential faults in a program. However, the performance of SBFL on student programs is not satisfactory, to improve the accuracy of SBFL in student programs, we propose a novel Multi-Correct Programs based Fault Localization (MCPFL) approach. Specifically, We first collected the correct programs submitted by students on the OJ system according to the programming problem numbers and removed the highly similar correct programs based on code similarity, and then stored them together with the faulty program to be located to construct a set of programs. Afterward, we analyzed the suspiciousness of the term in the faulty program through the Term Frequency-Inverse Document Frequency (TF-IDF). Finally, we designed a formula to calculate the weight of suspiciousness for program statements based on the number of input variables in the statement and weighted it to the spectrum-based fault localization formula. To evaluate the effectiveness of MCPFL, we conducted empirical studies on six student program datasets collected in our OJ system, and the results showed that MCPFL can effectively improve the traditional SBFL methods. In particular, on the EXAM metric, our approach improves by an average of 27.51% on the Dstar formula.

Accurate channel estimation is necessary before we can demodulate orthogonal frequency division multiplexing (OFDM) signals since the radio channel is frequency-selective and time-varying for wideband mobile communication systems. For pilot-symbol-aided channel estimation, pilot sequences are inserted periodically into the data stream enabling coherent detection at receiver. The control signal information can be embedded in pilot sequences and transmitted implicitly in OFDM systems to save the bandwidth. In order to estimate the channel and control signal jointly at the receiver, we propose a novel noise subspace based method in this paper. The proposed method is developed from the DFT-based channel estimator. If the hypothesized sequence coincides with the transmitted pilot sequence, the last part of the channel impulse response (CIR) estimate is only contributed by Gaussian noise and its average power is expected to be the minimum among all possible hypothesized sequences. Simulation results show that the proposed method works well in any of the channels even if integer carrier frequency offset (CFO) is considered.