The Influence of Culture on Attitudes Towards Humorous Advertising

open access articl

Computer and Mathematical Modeling: Translational Research and Economics in Clinical Diagnostics

The computer and modeling approach has begun to be used extensively in clinical intelligence diagnosis, we have refined the necessary techniques related to intelligence medicine, and we have performed economics-directional analysis of models and structures of artificial intelligence in the translational medicine sense.At the same time, the development of clinical diagnostic techniques is also the result of constant innovation, and we propose the necessary strategy for a cross-disciplinary approach to clinical diagnostics and computer and mathematical modeling, with the authors reporting in conjunction with the results of the study

Computed Web Learning Software Design with a Medical Psychological Perspective: Depression as an Example and Economic Analysis

We have tried to use computer technology in teaching and designing the necessary knowledge points for the diagnosis, treatment, and prevention of depression. We have also used computer platforms to elucidate this model as an economics product and carry out the necessary investigation and study of the market prospects, and we have proposed innovative points in solving the problem based on basic knowledge in medical psychology, and we have reported the results in conjunction with the results of the study

Layered Controllable Video Generation

We introduce layered controllable video generation, where we, without any supervision, decompose the initial frame of a video into foreground and background layers, with which the user can control the video generation process by simply manipulating the foreground mask. The key challenges are the unsupervised foreground-background separation, which is ambiguous, and ability to anticipate user manipulations with access to only raw video sequences. We address these challenges by proposing a two-stage learning procedure. In the first stage, with the rich set of losses and dynamic foreground size prior, we learn how to separate the frame into foreground and background layers and, conditioned on these layers, how to generate the next frame using VQ-VAE generator. In the second stage, we fine-tune this network to anticipate edits to the mask, by fitting (parameterized) control to the mask from future frame. We demonstrate the effectiveness of this learning and the more granular control mechanism, while illustrating state-of-the-art performance on two benchmark datasets. We provide a video abstract as well as some video results on https://gabriel-huang.github.io/layered_controllable_video_generationComment: This paper has been accepted to ECCV 2022 as an Oral pape

FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems

Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, the federated learning (FL) framework allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically evaluated how robust FL-based NIDSs are against existing privacy attacks under existing defenses. To address this issue, we propose two privacy evaluation metrics designed for FL-based NIDSs, including (1) privacy score that evaluates the similarity between the original and recovered traffic features using reconstruction attacks, and (2) evasion rate against NIDSs using Generative Adversarial Network-based adversarial attack with the reconstructed benign traffic. We conduct experiments to show that existing defenses provide little protection that the corresponding adversarial traffic can even evade the SOTA NIDS Kitsune. To defend against such attacks and build a more robust FL-based NIDS, we further propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. It achieves both high utility by minimizing the gradient distance and strong privacy protection by maximizing the input distance. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection with up to 7 times higher privacy score, while maintaining model accuracy loss within 3% under optimal parameter combination.Comment: 14 pages, 9 figures, submitted to TIF

Unsupervised Cross-lingual Image Captioning

Most recent image captioning works are conducted in English as the majority of image-caption datasets are in English. However, there are a large amount of non-native English speakers worldwide. Generating image captions in different languages is worth exploring. In this paper, we present a novel unsupervised method to generate image captions without using any caption corpus. Our method relies on 1) a cross-lingual auto-encoding, which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality. By leveraging cross-lingual auto-encoding, cross-modal feature mapping, and adversarial learning, our method can learn an image captioner to generate captions in different languages. We verify the effectiveness of our proposed method on the Chinese image caption generation. The comparisons against several baseline methods demonstrate the effectiveness of our approach.Comment: 8 page

Towards Understanding Third-party Library Dependency in C/C++ Ecosystem

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++package manager.Comment: ASE 202

Pheromones emitted by both female and male moths regulate coordination between the sexes for Agriphila aeneociliella (Lepidoptera: Crambidae).

peer reviewedThe complex and efficient sex pheromone communication system in insects is essential for reproduction and for reproductive isolation of species. In moths, sex pheromone communication starts with male attraction to compounds emitted by females; only a few species act in the reverse. However, how the pheromones that are emitted by both sexes co-regulate and coordinate mate finding and mating remains unknown. Here, we identified both the male and female pheromones of Eastern Grass Veneer moth, Agriphila aeneociliella (Lepidoptera: Crambidae), and demonstrated their efficiency in manipulating behavioral responses of the opposite sex. Combining data from analysis of gas chromatography-electroantennogram detection, gas chromatography-mass spectrometry, and olfactory behavior assays, the female pheromone of A. aeneociliella was identified as (Z,Z,Z)-9,12,15-octadecatrienal and (Z)-9-hexadecenyl acetate, while the male pheromone was determined to be 1-nonanal. Both the 2 individual components of the female pheromone and their binary mixture were significantly attractive to males, and the 1-nonanal male pheromone induced strong electrophysiological responses in females and induced attraction of females in a Y-tube olfactory test. Depending on the concentration of 1-nonanal, its addition to the binary mixture of the female pheromone either enhanced (10-3 or 10-2  μg/μL) or reduced (1 μg/μL) the aphrodisiac effect of the mixture on males. In wind-tunnel bioassays, different concentrations of pheromones, including the binary mixture of female pheromone and the mixture of male and female pheromones, had significant effects on male behavior. Our findings suggested that the blend of both female and male pheromones plays a significant role in the sexual communication system in some moths