Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

To seamlessly adapt to time-varying network bandwidths, Quality Scalable High-Efficiency Video Coding (QSHVC) is developed. However, its coding process is overwhelmingly complex, and this seriously limits its wide applications in realtime environments. Therefore, it is of great significance to study fast coding algorithms for QSHVC. In this paper, we propose a novel probability-based All-Zero Block (AZB) early termination algorithm for QSHVC. We observe that the generated residual coefficients follow the Laplace distribution if a CU is accurately predicted. Based on this observation, we derive the sum of squared differences-based AZB decision condition. Second, the probability of each coding mode and coding depth being chosen as the best ones are combined with AZBs to derive the probability-based early termination condition. The experimental results show that the proposed algorithm can improve the average coding speed by 74.95% with a 0.26% increase in BDBR.

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. Mining high utility itemsets in a database with$n$items generally results in a huge search space, composed of$2^{n}$itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance AlgorithM for Mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

Software reuse through Application Programming Interfaces (APIs) is a common practice in software development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of Web-based services. This paper proposes a service recommendation approach via extracting semantic relationship from natural language API descriptions and inferring. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.

In this paper, we propose a bottom-up audio attention model based on spatial audio cues for unsupervised event detecting in stereo audio surveillance. Firstly, the spatial audio parameter Interaural Level Difference (ILD) is extracted to calculate and represent the attention events, which are caused by rapid moving sound source. Then an environment adaptive normalization is used to assess the normalized attention level. Experimental results demonstrate that our proposed audio attention model is effective for audio surveillance event detection.

This paper proposes a bottom-up audio attention model based on spatial audio cues and sub band energy change for unsupervised event detection in stereo audio surveillance. Firstly, the spatial audio parameter Interaural Level Difference (ILD) is extracted to calculate and represent the attention events, which are caused by rapid moving sound source. Then the sub band energy change is computed to present the salient energy distribution change in frequency domain. At last, an environment adaptive normalization is used to assess the normalized attention level. Experimental results demonstrate that the proposed audio attention model is effective for audio surveillance event detection.

In video coding, intra coding plays an important role in both coding efficiency and error resilience. In this paper, a new intra coding method based on video epitomic analysis is proposed. Image epitome suitable for coding is extracted through video epitomic analysis, and is used to generate the prediction for each block. The mapping between the original image and image epitome as well as image epitome should be coded and transmitted to the decoder side. Due to the independence of adjacent reconstructed blocks, the proposed intra coding method can also be applied to improve error resilience of intra refresh. Experimental results show that, the proposed intra coding method can significantly improve the performance of intra coding by 0.7dB in terms of PSNR. The simulation results under packet loss environment show that the proposed intra refresh can out-perform random intra refresh (RIR) by up to 1dB, and better subjective quality can also be obtained.