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Android Security using NLP Techniques: A Review
[article]
2021
arXiv
pre-print
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The availability of such useful textual data together with the advancement in Natural Language Processing (NLP) that is used to process and understand textual data has encouraged researchers to investigate ...
Especially, security solutions based on NLP have accelerated in the last 5 years and proven to be useful. ...
Acknowledgements This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK-118E141). The authors would like to thank TUBITAK for its support. ...
arXiv:2107.03072v1
fatcat:yo3qc3w5jbhu7kb6db76hq2d2e
Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training
[article]
2021
arXiv
pre-print
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In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. ...
, which leads to a low data utilization and hinders model training from having abundant supervision. ...
For neural networks, this approximation can be performed with K sets of back-propagations. ...
arXiv:2106.10835v1
fatcat:342m3stdfjhjjconnc4ny4lfva
Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?
2021
Cryptography
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Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed ...
method, the electronic component localization and detection network (ECLAD-Net). ...
For example, resistors can be identified by their resistance values and ICs can be identified by their model and part numbers. Second, processing should be performed extremely fast. ...
doi:10.3390/cryptography5010009
doaj:534eed76eda14d8caed0b4c33922eb97
fatcat:g7qtlb3ljfhkriz6ajxjl3o4u4
Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial Network
[article]
2021
arXiv
pre-print
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Our model can integrate the most specific details at different sub-regions of the image. We distinctively concentrate on the relevant words in the natural language description. ...
Considering that, we propose Bangla Attentional Generative Adversarial Network (AttnGAN) that allows intensified, multi-stage processing for high-resolution Bangla text-to-image generation. ...
The attentional generative network and the deep attentional multi-modal similarity model are two distinct components of our proposed Bangla Attentional Generative Adversarial Network (AttnGAN).
A. ...
arXiv:2109.11749v1
fatcat:vezzdd6dyzd4lleltsk5ix2ho4
Interdisciplinarity and Clinical Legal Education: how synergies can improve access to rights in prison
2018
International Journal of Clinical Legal Education
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This article discusses the resistance experienced by the clinical legal education movement in Italy due to a widespread legal positivist approach which views law as a self-contained technical subject, and ...
</p><p>The choice that the newly-born Italian CLE movement now faces is the option to either become a new socio-legal epistemology of law in action and a social change-maker, or to ascribe to a simple ...
The situation here described is a fundamental example of the engagement of a network of numerous wardens and partnerships aimed at establishing collaboration among public and private people and institutions ...
doi:10.19164/ijcle.v25i1.699
fatcat:bmyvnbnpj5egfgvkcvvtef4c5i
Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning
2020
IEEE Access
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Traditional approaches include collaborative filtering methods [1] - [3] , which use similar preferences among similar users to discover users' potential preferences for items, and are vulnerable to ...
A Probabilistic Model of Hybrid Deep Collaborative Filtering (PHD) [17] : it proposes a probabilistic model that combines a stacked denoising autoencoder and a convolutional neural network together with ...
how to deal with sparsity data more effectively and build a simplified and reasonable recommendation framework in the future. Jiangnan University, Wuxi, Jiangsu, China. ...
doi:10.1109/access.2020.3038770
fatcat:uiiwv4qjvbcazn46o6et6cfguq
MirrorGAN: Learning Text-To-Image Generation by Redescription
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing semantic consistency between the text description ...
GLAM has a cascaded architecture for generating target images from coarse to fine scales, leveraging both local word attention and global sentence attention to progressively enhance the diversity and semantic ...
, and IH-180100002. ...
doi:10.1109/cvpr.2019.00160
dblp:conf/cvpr/QiaoZXT19
fatcat:hgu6fp3vp5e35aqzfcfcu6ygzi
MirrorGAN: Learning Text-to-image Generation by Redescription
[article]
2019
arXiv
pre-print
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Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing semantic consistency between the text description ...
GLAM has a cascaded architecture for generating target images from coarse to fine scales, leveraging both local word attention and global sentence attention to progressively enhance the diversity and semantic ...
, and IH-180100002. ...
arXiv:1903.05854v1
fatcat:5t4ro5nxsngmvihpgk6rz7d74y
Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks
2020
Discrete Dynamics in Nature and Society
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Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training ...
Adversarial Networks (AttenGAN). ...
[26] proposed a global-to-local collaborative attention module that uses word attention and global sentence attention to enhance the consistency of generated images and semantics. ...
doi:10.1155/2020/6452536
fatcat:vbfnzey7n5g5bebh6ym2jmmi5a
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs
[article]
2021
arXiv
pre-print
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It is known that deep neural networks, trained for the classification of a non-sensitive target attribute, can reveal some sensitive attributes of their input data; through features of different granularity ...
We take a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data into the classifier's outputs for the target attribute, at inference time ...
Moreover, collecting outputs can help a server to monitor and enhance its decisions and the provided service. ...
arXiv:2105.12049v2
fatcat:ffsok2y7ajgnvawqyevpif36mi
SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs
[article]
2021
arXiv
pre-print
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Simple visualizations exploit the temporal structure in narratives so domain experts can quickly spot trends, identify key features, and note anomalies over hundreds of arcs and millions of data points ...
The joint optimization over both the corpus and model solves the generalization problem. ...
As a group, they offer a good trade-off between fast training, minimal overhead and good performance that can be enhanced with careful feature engineering and custom training. ...
arXiv:2110.09454v1
fatcat:2lmkzp3suvegjizvxyrieppiza
Essay: A 'Safety Model' Perspective Can Aid Diagnosis, Prevention, and Restoration after Criminal Justice Harms
2018
Social Science Research Network
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Familiarity with an emerging Safety Model can improve practice, and may fuel transformation. ...
in advance, and explicitly." 129 They can enhance public trust in the law and system legitimacy by showing that they care about getting things right. ...
Fears of liability augmentation, whether actual or used simply to cloak inertia and inchoate discomfort with novelty, can be a destructive inhibiting force. ...
doi:10.2139/ssrn.3171542
fatcat:k5zrbstnjrg7bewej6x2lhuf3a
DeepC2: AI-powered Covert Botnet Command and Control on OSNs
[article]
2021
arXiv
pre-print
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By leveraging neural networks, bots can find botmasters by avatars, which are converted into feature vectors and built into bots. ...
Experiments on Twitter show that command-embedded contents can be generated efficiently, and bots can find botmasters and obtain commands accurately. ...
Attacking the C&C in this way is not recommended. 3) Train a Decoder: As adversaries have access to vectors and neural network models, structure and implementation, adversaries can attempt to recover and ...
arXiv:2009.07707v6
fatcat:bm4c353lnvghbickormlbvldlu
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
[article]
2020
arXiv
pre-print
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., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items. ...
For example, the generative adversarial neural network, which include a generator and a discriminator, can produce samples with similar distribution toward real instances by training two operator simultaneously ...
[122] focus on the user-item relationships and use an attention module that can visualize the model and enhance the model performance with capturing the significant patterns. Chen et al. ...
arXiv:2004.03774v2
fatcat:q7mehir7hbbzpemw3q5fkby5ty
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
[article]
2020
arXiv
pre-print
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Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. ...
Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. ...
[156] proposed a novel model called ternary adversarial networks with self-supervision (TANSS). ...
arXiv:2006.08159v1
fatcat:g4467zmutndglmy35n3eyfwxku
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