Collaboration of Pre-trained Models Makes Better Few-shot Learner

Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regimes. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. Based on this point, we question if the large-scale pre-training can alleviate the few-shot data deficiency and also assist the representation learning by the pre-learned knowledge. In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning. Our CoMo includes: CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, and DALL-E's language-generative knowledge. Specifically, CoMo works in two aspects: few-shot data expansion and diverse knowledge ensemble. For one, we generate synthetic images via zero-shot DALL-E to enrich the few-shot training data without any manpower. For the other, we introduce a learnable Multi-Knowledge Adapter (MK-Adapter) to adaptively blend the predictions from CLIP and DINO. By such collaboration, CoMo can fully unleash the potential of different pre-training methods and unify them to perform state-of-the-art for few-shot classification. We conduct extensive experiments on 11 datasets to demonstrate the superiority and generalization ability of our approach.Comment: 10 pages, 6 figure

Knowledge, Attitudes, and Social Responsiveness Toward Corona Virus Disease 2019 (COVID-19) Among Chinese Medical Students—Thoughts on Medical Education

Purpose: To assess knowledge, attitudes, and social responsiveness toward COVID-19 among Chinese medical students.Methods: Self-administered questionnaires were used to collect data from 889 medical students in three well-known Chinese medical universities. The questionnaire was comprised of three domains which consisted of demographic characteristic collection, seven items for knowledge, and eight items for attitudes and social responsiveness toward COVID-19. Data from different universities were lumped together and were divided into different groups to compare the differences, including (1) students at the clinical learning stage (Group A) or those at the basic-medicine stage (Group B) and (2) students who have graduated and worked (Group C) or those newly enrolled (Group D).Results: Medical students at group B had a weaker knowledge toward COVID-19 than did students at group A, especially in the question of clinical manifestations (p < 0.001). The percentage of totally correct answers of COVID-19 knowledge in group C was higher than that in Group D (p < 0.001). There were significant differences between groups C and D in the attitudes and social responsiveness toward COVID-19. Surprisingly, we found that the idea of newly enrolled medical students could be easily affected by interventions.Conclusions: In light of this information, medical education should pay attention not only to the cultivation of professional knowledge and clinical skills but also to the positive interventions to better the comprehensive qualities including communicative abilities and empathy

Internet Head Enterprises of the Monopoly Media Reports Analysis

This paper takes the monopoly of relevant domestic Internet enterprises as a case to climb the market share analysis of Tencent, Meituan, Sina Weibo and other Internet enterprises through network crawler technology, as well as the social influence as a sample framework analysis and classification by data comparison and desktop research. Research found that the Internet rapid popularization of digital economy development now, Internet companies in steady economic growth at the same time also strengthen the Internet head enterprise market monopoly, Internet head companies abuse market position forced or restrict trading, bundling, monopoly pricing and a series of monopoly behavior also have negative impact on market efficiency[1]

Internet Head Enterprises of the Monopoly Media Reports Analysis

This paper takes the monopoly of relevant domestic Internet enterprises as a case to climb the market share analysis of Tencent, Meituan, Sina Weibo and other Internet enterprises through network crawler technology, as well as the social influence as a sample framework analysis and classification by data comparison and desktop research. Research found that the Internet rapid popularization of digital economy development now, Internet companies in steady economic growth at the same time also strengthen the Internet head enterprise market monopoly, Internet head companies abuse market position forced or restrict trading, bundling, monopoly pricing and a series of monopoly behavior also have negative impact on market efficiency[1]

Evolutionary Computation for Sparse Synthesis Optimization of CCAAs: An Enhanced Whale Optimization Algorithm Method

Concentric circular antenna arrays (CCAAs) can obtain better performance than other antenna arrays. However, high overhead and excessive sidelobes still make its application difficult. In this paper, we consider the sparse synthesis optimization of CCAAs. Specifically, we aim to turn off a specific number of antennas while reducing the sidelobe of CCAAs. First, we formulate an optimization problem and present the solution space. Then, we propose a novel evolutionary method for solving the optimization problem. Our proposed method introduces hybrid solution initialization, hybrid crossover method, and hybrid update methods. Simulation results show the effectiveness of the proposed algorithm and the proposed improvement factors

An SMDP-Based service function allocation scheme for mobile edge clouds

© 2018 IEEE. With the increasing global mobile traffic, there is a trend to deploy network services at mobile edge clouds. Benefiting from the techniques of Network Function Virtualization and Software-Defined Networking, service function chains are enabled to compose a series of required network functions dynamically. As a consequence, most of common-used and IT-based mobile network services can be deployed at MEC cloud networks under the 5G context, remarkably reducing user latency and network traffic. However, as resources in cloud networks are limited, it is challenging to promote the system utilization with guaranteed user experience. Thus, in this paper, we formulate the allocation problem of service functions in MECs as an Semi-Markov Decision Process model and present a value iteration algorithm to find the optimized solution, aiming to increase request acceptance rate. Additionally, we discuss the parameter settings of the proposed scheme under different cases to find higher rewards

MTF: mitigating link flooding attacks in delay tolerant networks

© 2018 IEEE. The link flooding attack (LFA) is a new type of distributed denial-of-service (DDoS) attack emerged in recent years. Several defense mechanisms have been proposed in TCP/IP networks. However, due to the connectionless nature of Delay Tolerant Networks (DTN), the efficiency of these mechanisms is degraded facing the LFA in DTN. Thus, in this paper, we propose a new scheme named Macro Traffic Filtering (MTF), to defend the LFA in DTN efficiently. With the real prototype implementations and the long-term emulations, the preliminary results show that compared to the undifferentiated interception and the TE-based interplay scheme, MTF achieves significantly higher attack traffic hit ratio, lower collateral damage and higher cost to the attackers

SAT-GRD: An ID/Loc split network architecture interconnecting satellite and ground networks

Since the satellite network plays an irreplaceable role in many fields, how to interconnect it with the ground network has received an unprecedented attention. However, with much more requirements imposed to the current terrestrial network, many serious problems caused by the IP dual-role exposed. In this context, their direct interconnection seems not the most appropriate way. Thus, in this paper, SAT-GRD, an incrementally deployable ID/Loc split network architecture is proposed, aiming to integrate the satellite and ground networks efficiently. Specifically, SAT-GRD separates the identity of both the host and network from the location. Then, it isolates the host from the network, and further divides the whole network into core and edge networks. These make SAT-GRD much more flexible and scalable to achieve heterogeneous network convergence and avoid problems resulting from the overloaded semantics of IP addresses. In addition, much work has been done to implement the proof-of-concept prototype of SAT-GRD, and experimental results prove its feasibility