Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check whether neural image captioning systems can be mislead to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.Comment: Accepted by 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). Hongge Chen and Huan Zhang contribute equally to this wor

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack and significantly outperforms existing black-box attacks via substitute models.Comment: Accepted by 10th ACM Workshop on Artificial Intelligence and Security (AISEC) with the 24th ACM Conference on Computer and Communications Security (CCS

Using Peer-to-Peer Technology for Knowledge Sharing in Communities of Practices

Communities of Practices (CoPs) are informal structures within organizations that bind people together through informal relationships and the sharing of expertise and experience. As such, they are effective tools for the creation and sharing of organizational knowledge, and, increasingly, organizations are adopting them as part of their knowledge management strategies. In this paper, we examine the knowledge sharing characteristics and roles of CoPs and develop a peer-to-peer knowledge sharing architecture that matches the behavioral characteristics of the members of the CoPs. We also propose a peer-to-peer knowledge sharing tool called KTella that enables members of CoPs to voluntarily share and retrieve knowledge more effectively

EFFECTS OF BACKPACK LOADS ON NECK-TRUNK MUSCLE ACTIVATION AMONG OFFICE WORKERS

The main purposes of this study were to investigate the effect of weight carriage on necktrunk muscle activation during standing and walking among office workers and to compare electromyography activation between healthy and symptomatic office workers. Twenty-one participants were recruited. Three load trials (0%, 10%, and 15% BW) and two conditions (standing and walking) were encountered. Repeated measure ANOVA was used to test main effect of load and condition on kinetic data. There was a significant condition*load interaction on right trapezius. Significantly increasing activation of right abdominis was found as carrying 15% BW. There was a significant decrease on activation of left erector spinae while carrying 10% BW. Considering to electromyography data, we suggest the backpack load under 10% BW was suitable for office workers

Coregulation of transcription factors and microRNAs in human transcriptional regulatory network

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the post-transcriptional level. Recent studies have suggested that miRNAs and transcription factors are primary metazoan gene regulators; however, the crosstalk between them still remains unclear.</p> <p>Methods</p> <p>We proposed a novel model utilizing functional annotation information to identify significant coregulation between transcriptional and post-transcriptional layers. Based on this model, function-enriched coregulation relationships were discovered and combined into different kinds of functional coregulation networks.</p> <p>Results</p> <p>We found that miRNAs may engage in a wider diversity of biological processes by coordinating with transcription factors, and this kind of cross-layer coregulation may have higher specificity than intra-layer coregulation. In addition, the coregulation networks reveal several types of network motifs, including feed-forward loops and massive upstream crosstalk. Finally, the expression patterns of these coregulation pairs in normal and tumour tissues were analyzed. Different coregulation types show unique expression correlation trends. More importantly, the disruption of coregulation may be associated with cancers.</p> <p>Conclusion</p> <p>Our findings elucidate the combinatorial and cooperative properties of transcription factors and miRNAs regulation, and we proposes that the coordinated regulation may play an important role in many biological processes.</p

On the Adversarial Robustness of Vision Transformers

Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision. This work provides the first and comprehensive study on the robustness of vision transformers (ViTs) against adversarial perturbations. Tested on various white-box and transfer attack settings, we find that ViTs possess better adversarial robustness when compared with convolutional neural networks (CNNs). This observation also holds for certified robustness. We summarize the following main observations contributing to the improved robustness of ViTs: 1) Features learned by ViTs contain less low-level information and are more generalizable, which contributes to superior robustness against adversarial perturbations. 2) Introducing convolutional or tokens-to-token blocks for learning low-level features in ViTs can improve classification accuracy but at the cost of adversarial robustness. 3) Increasing the proportion of transformers in the model structure (when the model consists of both transformer and CNN blocks) leads to better robustness. But for a pure transformer model, simply increasing the size or adding layers cannot guarantee a similar effect. 4) Pre-training on larger datasets does not significantly improve adversarial robustness though it is critical for training ViTs. 5) Adversarial training is also applicable to ViT for training robust models. Furthermore, feature visualization and frequency analysis are conducted for explanation. The results show that ViTs are less sensitive to high-frequency perturbations than CNNs and there is a high correlation between how well the model learns low-level features and its robustness against different frequency-based perturbations

Improved conversion efficiency of Ag2S quantum dot-sensitized solar cells based on TiO2 nanotubes with a ZnO recombination barrier layer

We improve the conversion efficiency of Ag2S quantum dot (QD)-sensitized TiO2 nanotube-array electrodes by chemically depositing ZnO recombination barrier layer on plain TiO2 nanotube-array electrodes. The optical properties, structural properties, compositional analysis, and photoelectrochemistry properties of prepared electrodes have been investigated. It is found that for the prepared electrodes, with increasing the cycles of Ag2S deposition, the photocurrent density and the conversion efficiency increase. In addition, as compared to the Ag2S QD-sensitized TiO2 nanotube-array electrode without the ZnO layers, the conversion efficiency of the electrode with the ZnO layers increases significantly due to the formation of efficient recombination layer between the TiO2 nanotube array and electrolyte

Crosstalk between transcription factors and microRNAs in human protein interaction network

<p>Abstract</p> <p>Background</p> <p>Gene regulatory networks control the global gene expression and the dynamics of protein output in living cells. In multicellular organisms, transcription factors and microRNAs are the major families of gene regulators. Recent studies have suggested that these two kinds of regulators share similar regulatory logics and participate in cooperative activities in the gene regulatory network; however, their combinational regulatory effects and preferences on the protein interaction network remain unclear.</p> <p>Methods</p> <p>In this study, we constructed a global human gene regulatory network comprising both transcriptional and post-transcriptional regulatory relationships, and integrated the protein interactome into this network. We then screened the integrated network for four types of regulatory motifs: single-regulation, co-regulation, crosstalk, and independent, and investigated their topological properties in the protein interaction network.</p> <p>Results</p> <p>Among the four types of network motifs, the crosstalk was found to have the most enriched protein-protein interactions in their downstream regulatory targets. The topological properties of these motifs also revealed that they target crucial proteins in the protein interaction network and may serve important roles of biological functions.</p> <p>Conclusions</p> <p>Altogether, these results reveal the combinatorial regulatory patterns of transcription factors and microRNAs on the protein interactome, and provide further evidence to suggest the connection between gene regulatory network and protein interaction network.</p

4-(5-Oxo-5H-1,2,4-dithia­zol-3-yl)phenyl 4-methyl­benzene­sulfonate

In the mol­ecular structure of the title compound, C15H11NO4S3, the 1,2,4-dithia­zolone and central benzene rings are approximately coplanar, making a dihedral angle of 3.08 (7)°. The central benzene ring and the 4-methyl­benzene ring subtend a dihedral angle of 57.47 (8)°. In the crystal, π–π stacking occurs between the central benzene ring and the 1,2,4-dithia­zolone ring of adjacent mol­ecules, which are aligned almost parallel, the centroid–centroid distance being 3.555 (7) Å