With the wide application of the Internet of Things (IoT), storing large amounts of IoT data and protecting data privacy has become a meaningful issue. In general, the access control mechanism is used to prevent illegal users from accessing private data. However, traditional data access control schemes face some non-ignorable problems, such as only supporting coarse-grained access control, the risk of centralization, and high trust issues. In this paper, an attribute-based data access control scheme using blockchain technology is proposed. To address these problems, attribute-based encryption (ABE) has become a promising solution for encrypted data access control. Firstly, we utilize blockchain technology to construct a decentralized access control scheme, which can grant data access with transparency and traceability. Furthermore, our scheme also guarantees the privacy of policies and attributes on the blockchain network. Secondly, we optimize an ABE scheme, which makes the size of system parameters smaller and improves the efficiency of algorithms. These optimizations enable our proposed scheme supports large attribute universe requirements in IoT environments. Thirdly, to prohibit attribute impersonation and attribute replay attacks, we design a challenge-response mechanism to verify the ownership of attributes. Finally, we evaluate the security and performance of the scheme. And comparisons with other related schemes show the advantages of our proposed scheme. Compared to existing schemes, our scheme has more comprehensive advantages, such as supporting a large universe, full security, expressive policy, and policy hiding.

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximuma posterioridecoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximuma posteriori(MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+’s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet’s strong learning ability and great robustness to various scenarios.

The annotation of significant events within soccer videos is a fundamental step in content-based soccer video retrieval. In this paper, we propose a soccer video annotation approach based on semantic matching with coarse time constraints, where video events and external text information (match reports) are synchronized using their semantic correspondence in the temporal sequence. Unlike the state-of-the-art soccer video analysis methods that assume that the time of an event's occurrence is given precisely by the external text information, this paper considers the problem of annotating soccer videos using match reports with coarse-grained time information. The contributions of the proposed approach are: 1) we propose a more generalized approach that synchronizes video events with text descriptions using high-level semantics with coarse time constraints, rather than assuming that the timestamp is given exactly in the text description; 2) the detection of event boundaries is improved by attack-defense transition analysis (ADTA); 3) a robust and fast center circle detection algorithm is proposed for the classification of soccer field zones and ADTA; and 4) unlike conventional audio-based whistle detection, we propose a novel Hough-transform-based algorithm from the perspective of image processing. This allows the game start time to be detected, and further helps the synchronization of video and text events. The experimental results conducted on a large number of soccer videos validate the effectiveness of the proposed approach.

The boom of Internet and multimedia technology leads to the explosion of multimedia information, especially image, which has created an urgent need of quickly retrieving similar and interested images from huge image collections. The content-based high-dimensional indexing mechanism holds the key to achieving this goal by efficiently organizing the content of images and storing them in computer memory. In the past decades, many important developments in high-dimensional image indexing technologies have occurred to cope with the ‘curse of dimensionality’. The high-dimensional indexing mechanisms can mainly be divided into three categories: tree-based index, hashing-based index, and visual words based inverted index. In this paper we review the technologies with respect to these three categories of mechanisms, and make several recommendations for future research issues.

This paper deals with the problem of city scale on-device mobile visual location recognition by fusing the inertial sensors and computer vision techniques. The main contributions are as follows: Firstly, we design an efficient vector quantization strategy by combining the Transform Coding (TC) and Residual Vector Quantization (RVQ). Our method can compress a visual descriptor into only several bytes while providing reasonable searching accuracy, which makes the managing of city scale image database directly on mobile devices come true. Secondly, we integrate the information from inertial sensors into the Vector of Locally Aggregated Descriptors (VLAD) generation and image similarity evaluation processes. Our method is not only fast enough for on-device implementation, but it also can improve the location recognition accuracy obviously. Thirdly, we also release a set of 1.295 million geo-tagged street view images with the information from inertial sensors, as well as a difficult set of query images. These resources can be used as a new benchmark to facilitate further research in the area. Experimental results prove the validity of the proposed methods for on-device mobile visual location recognition applications.

Computer Graphics is an important course for graduate students in computer major. Aiming at the present teaching situation of computer graphics course, combined with an the course characteristics, some educational reform measures, such as adjusting of teaching content, employing more examples in teaching, strengthening the combination of knowledge and practical ability, was proposed. The teaching practices show that these methods can arouse students' learning interest, make the students understand the course better, and obviously improve the quality of teaching.

Based on an analysis of the present teaching situation of Digital Logic course in higher school, this paper points out its existing problems, discusses some key issues and then proposes the ideas such as bringing modern design methodology into course teaching, conducting hierarchical textbooks construction, employing different teaching method and teaching content for different teaching objectives and intensifying the combination of textbook and engineering practical use, so as to improve teaching effectiveness, develop students' interest and enhance their comprehensive practical ability. As the coping strategy to deal with the existing problems of Digital Logic course in higher education, this method has already made certain achievements in both teaching and experiment.