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Intra Block Copy (IBC) cannot work well for blocks with symmetry or mirror characteristic, because it is based on the translational block matching technique. In order to solve this problem, in this paper, an adequately designed IBC-Mirror mode is proposed for Versatile Video Coding (VVC), which extends our previous work. The newly proposed IBC-Mirror mode consists of two sub-modes, including IBC-Mirror Advanced Motion Vector Prediction (AMVP) mode and IBC-Mirror Merge mode. In IBC-Mirror AMVP mode, various Motion Estimation (ME) methods are applied, including previously proposed spatial search, and newly designed methods, i.e., Hash-based, Cache-based, Predictor-based and History-based IBC-Mirror search. As for IBC-Mirror Merge mode, Motion Information, composed of Block Vector (BV) and mirror flipping information, is added to the merge list to improve coding efficiency. Compared to the traditional IBC mode in VTM-10.0, the latest test model, the proposed mode can achieve 1~2% BD-rate saving. Compared to our previous work, methods in this paper can further achieve 0.5% coding gains and reduce 4% coding time. Higher coding efficiency and lower complexity show the superiority of extensions and improvements in this paper.

It is well-known that high-frequency information (e.g. textures, edges) is significant for single image super-resolution (SISR). However, Existing of deep Convolutional Neural Network (CNN) based methods directly model mapping function from low resolution (LR) to high resolution (HR), and they treat high-frequency and low-frequency information equally during feature extraction. Therefore, the high-frequency learning mode can not be sufficiently attentive, resulting in inaccurate representation of some local details. In this study, we aim to build potential frequencies' relations and handle high-frequency and low-frequency information differentially. Specifically, we propose a novel Frequency Separation Network (FSN) for image super-resolution (SR). In FSN, a new Octave Convolution (OC) is adopted, which uses four operations to perform information update and frequency communication between high frequency and low frequency features. In addition, global and hierarchical feature fusion are employeed to learn elaborate and comprehensive feature representations, in order to further benefit the quality of final image reconstruction. Extensive experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our method.

Nowadays, the Industrial Internet of Things (IIoT) has emerged as a promising paradigm for the future network, which intends to connect all kinds of objects, including smart home equipment and intelligent drive devices. Despite the fact that IIoT routing has already attracted a lot of attention from both academia and industry, current researches on IIoT routing cannot effectively solve the mobility problem in a self-adaptive and self-organized manner, because the number of IIoT devices connected is extremely large, and mobility is a very important feature of these IIoT devices. In this paper, a novel routing mechanism based on probability calculation and segment routing is proposed to solve the mobility and scalability problem in IIoT routing. On the one hand, probability calculation can address the extremely unbalanced load condition caused by mobility by forwarding packets to different routings with a certain probability. On the other hand, segment routing enables scalability and flexibility for packet forwarding, and it can also be used to bypass the over-loaded links, thus to achieve load balance. Finally, the simulation results indicate that the proposed mechanism not only solves the mobility problem effectively but also achieves a better performance in many aspects.

Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction.

With the rapid development of the tourist industry, an increasing number of passengers book flight tickets through online travel agencies. When searching for a flight ticket online, users are overwhelmed by the choice on offer. Even though flight ticket recommendation has been widely investigated, the current approaches are unable to recommend flight tickets that meet an individual's preference efficiently because of the severe cold-start problem. This paper provides strategies to address the cold-start problem for flight ticket recommendation. We conduct an exploratory study over the real-world flight ticket recommendation scenario and classify the cold-start problem of flight ticket recommendation into two categories, namely route cold-start and user cold-start. We propose methods based on route similarity and social relationships between passengers to improve user models. Finally, we map an enhanced user preference model and flight features to latent factor spaces to generate the recommendation results. The experimental results on a real-world data set demonstrate the effectiveness of the proposed approaches.

In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.

Coauthorship prediction is challenging yet important for academic collaboration and novel research topics discovery. The challenges lie in the dynamics of social or organizational relationships, changing preferences of suitable collaborators, and the evolution of research interests or topics. However, most current approaches and systems developed so far are mainly based on past coauthorships from a static viewpoint and do not capture the above evolving characteristics in coauthoring. Accordingly, this paper proposes a time-aware approach to capture the evolving coauthorships from online academic databases in terms of capturing the dynamics of social relationships and research interests. In particular, in order to understand the underlying factors influencing researchers to make choices of coauthors, we incorporate choice modeling based on utility theory. More specifically, our model conducts a series of pairwise choices over a poset induced by a utility function so as to learn the preference over all candidate coauthors. To complete the model inference, a gradient-based algorithm is devised to efficiently learn the model parameters for large-scale data. Finally, extensive experiments conducted on a real-world dataset show that our approach consistently outperforms other state-of-the-art methods.

Nowadays, most data analytical applications comprise of multiple tasks, which can be represented as workflow in nature. Some of data analytical applications, the data requests arrived continuously, such as fraud detection application and order application. In general, such workflow applications have a rigid requirement in relation to response time. When running the analytical workflow in a cloud platform, one of the critical questions which arise is how to provision resources so that the monetary cost can be reduced while guaranteeing system throughput. In this paper, we use queueing network theory to address this challenge. First, we present the performance analytic model for the elastic analytical workflows based on queueing network theory. Then, we design a resource provision strategy to determine the number of virtual machines for hosting components of the applications with throughput guarantee. Both real experiments and simulation experiments using the real workload traces data show that our proposed approach provides a simple yet powerful solution to provision resources for analytical workflows under dynamic workload conditions.

In the above-named article, the funding information was incorrect. In addition, there was an error in the author affiliation that is corrected here.