Abstract In this paper, four types of fractional Fourier cosine and sine Laplace weighted convolutions are defined, and the corresponding fractional Fourier cosine and sine Laplace convolution theorems associated with the fractional cosine transform, fractional sine transform and Laplace transform are derived in detail. Furthermore, the relationship between the fractional Fourier cosine (sine) Laplace weighted convolutions and existing convolutions are studied, and Young's type theorem is also investigated. In addition, as an application for fractional Fourier cosine (sine)‐Laplace weighted convolution, the system of convolution‐type integral equations is considered, the computational complexity is analysed and the explicit solutions for these equations are obtained.

Identifying medical terms with specific meaning and information with semantic attribute is the prerequisite of conducting semantic analysis in medical field. However, the problem of medical data island restricts the development of entity recognition in a great extent. In addition to the ban of data sharing between different hospitals, different departments in the same hospital also can not exchange data due to privacy security concerns and ethical issues. To solve these problems, in the federated learning framework, the server trains a global model collaboratively through aggregating the encrypted or noised model parameters of the local participated clients without data leakage. In this paper, to well apply federated learning on biomedical named entity recognition (BioNER), we propose the federated adversarial learning (FAL) method with consideration of the training cost and model performance. FAL not only makes use of a modified structured pruning scheme to reduce the number of model parameters but also exploits an improved adversarial learning approach named protected fast gradient method (PFGM) to enhance the robustness and generalization of the model. In the experiment, we use the datasets of five departments in the same tumor hospital, such as gynecology department and gastric surgery department. Results show that the proposed FAL framework achieves expected effect with high efficiency.

Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored. Compared with images, attacking a video needs to consider not only spatial cues but also temporal cues. Moreover, to improve the imperceptibility as well as reduce the computation cost, perturbations should be added on as few frames as possible, i.e., adversarial perturbations are temporally sparse. This further motivates the propagation of perturbations, which denotes that perturbations added on the current frame can transfer to the next frames via their temporal interactions. Thus, no (or few) extra perturbations are needed for these frames to misclassify them. To this end, we propose the first white-box video attack method, which utilizes an l2,1-norm based optimization algorithm to compute the sparse adversarial perturbations for videos. We choose the action recognition as the targeted task, and networks with a CNN+RNN architecture as threat models to verify our method. Thanks to the propagation, we can compute perturbations on a shortened version video, and then adapt them to the long version video to fool DNNs. Experimental results on the UCF101 dataset demonstrate that even only one frame in a video is perturbed, the fooling rate can still reach 59.7%.

With the continuous growth of micro-blog services, Sina Weibo is increasingly found in the daily lives of ordinary Chinese individuals. More than one hundred million tweets are released in Sina Weibo everyday. By analyzing these mass data timely, media companies could learn how to generate buzz for new films, famous stars, or fashion shows more effectively. However, how to predict which topics will be the most popular search terms in Sina Weibo in realtime remains unknown. In this paper, we present a realtime hot topic prediction method in an online platform. Experiments are carried out on the platform to evaluate the proposed scheme. The results show that our model gets an average precision 44.32% and the median value is 45.83%. The proposed hot topic prediction method can predict the hot topics about 9.5 hours in average in advance.

Quality of Experience (QoE) support becomes more demanding due to the increasing popularity of multimedia services. However, QoE-based multimedia provisioning will impose a high pressure on server capacity and network bandwidth, which becomes one of the major concerns for both content providers and network operators. A common approach for efficient and scalable multimedia service provisioning in content distribution networks (CDN) is to replicate certain “hot” service or content from the original server to multiple replica servers, usually closer to end users. However, how to direct distributed user requests to an appropriate replica server to fundamentally improve the user perceived service quality largely remains unknown. In this paper, we present a user-oriented QoE-driven server selection scheme to solve the aforementioned problem. Experiments carried out on a real-world network have been performed to evaluate the proposed scheme, and the results show that the proposed scheme can improve QoE perceived by end users at least 24.6%.

Information-Centric Networking (ICN) is a new emerging concept, in which the principal paradigm shifts from the traditional end-to-end connection to the information-centric communication model. In ICN, information unit, such as content or service, is distributed in different sites or data centers to provide large-scale services. A common supporting approach for scalable service provisioning is deploying multiple replica servers throughout the network. Accordingly, an efficient and flexible scheme is needed to direct distributed requests to an appropriate replica server. In this paper, we first propose an incrementally deployable ICN architecture based on edge/core separation. And then, a practical anycast-based service provisioning scheme is presented with joint considerations of both servers contexts and the underlying network conditions. Extensive experiments have been performed to evaluate the proposed scheme. Experiment results show that efficient context-aware distributed service provisioning can be achieved.

Personality can be defined as a set of characteristics which makes a person unique. Psychological theory suggests that people's behavior is a reflection of personality. Therefore, it is feasible to predict personality through behavior. Conventional p

Personality can be defined as a set of characteristics which makes a person unique. The study of personality is of central importance in psychology. Conventional personality assessment is performed by self-report inventory, which costs much manual efforts and cannot be done in real time. To solve these problems, this research aims to measure the Big-Five personality from the usages of Sina Microblog objectively. By conducting a user study with 444 users, this paper proposes multi-task regression and incremental regression algorithms to predict the Big-Five personality from online behaviors. The results indicate that personality can be predicted with a high accuracy through online Microblog usage.

Information-Centric Network (ICN) aims to achieve the shift of communication model from host-centric to information-centric. In ICN, anycast plays an important role and is even regarded as a primary network primitive in some architectures such as DONA and SCAFFOLD. However, network layer anycast based on route-by-name in these architectures may severely worsen the problem of routing scalability as IP anycast does. On the other hand, the widely used application layer anycast based on lookup-by-name in current Internet exists many flaws such as inaccuracy of server selection and weak responsiveness to dynamic network conditions. Accordingly, how to achieve efficient and flexible anycast becomes a research challenge in future internet. In this paper, a novel anycast routing scheme based on ID/LOC separation is proposed. Then, a decentralized server selection algorithm based on Ant Colony Optimization (ACO) is further investigated. Experiments show that efficient and flexible anycast can be achieved to combine the advantages of route-by-name anycast and lookup-by-name anycast.