Copyright protection of depth image-based rendering (DIBR) videos has raised significant concerns due to their increasing popularity. Zero-watermarking, emerging as a powerful tool to protect the copyright of DIBR 3D videos, mainly relies on traditional feature extraction methods, thus necessitating improvements in robustness against complex geometric attacks and its ability to strike a balance between robustness and distinguishability. This paper presents a novel zero-watermarking scheme based on cross-modality feature fusion within a contrastive learning framework. Our approach integrates complementary information from 2D frames and depth maps using a cross-modality attention feature fusion mechanism to obtain discriminative features. Moreover, our features achieve a better trade-off between robustness and distinguishability by leveraging a designed contrastive learning strategy with an adversarial distortion simulator. Experimental results demonstrate our remarkable performance by reducing the false negative rates to around 0.2% when the false positive rate is equal to 0.5%, which is superior to the state-of-the-art zero-watermarking methods. • Use contrastive learning to balance watermarking robustness and distinguishability. • Employ an adversarial distortion simulator to enhance robustness against various attacks. • Design cross-modality fusion mechanism to achieve better feature representation.

Low response time is the requirement for high performance computing applications. The network latency is an important influence factor on response time. Busy poll socket (BPS) is a new mechanism that can reduce network latency by introducing extra energy cost to busily poll RX queue. Unlike hardware dependent solutions such as RDMA, busy poll socket can improve performance without specialized hardware and transplanting applications. BPS now becomes mature and is supported by many off-the-shelf network interface cards. In this paper, we compare the mechanisms of BPS and NAPI. We make a comprehensive analysis about both BPS and NAPI mechanisms. We analyze which factors would influence latencies in BPS and NAPI modes respectively. We find out that BPS does not definitely outperform NAPI, and the reasons are explored in detail. Our findings can provide suggestions on the future deployment of BPS based applications.

Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.

We present a storage-efficient and computationally fast method for rapid navigation/browsing through large image repositories and for content-based image retrieval. In the developed system, multiple resolution, orientation achromatic, and opponent chromatic channels are sequentially encoded by a maximal information sensory encoding model, which conveniently and effectively indexes the images into a binary tree data structure. Content-based image retrieval, database navigation, and image browsing are done very efficiently and rapidly by manipulating the n-bit binary keys in the binary tree data structure. We present experimental results to demonstrate the effectiveness of our method.