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Android Security using NLP Techniques: A Review [article]

Sevil Sen, Burcu Can
2021 arXiv   pre-print
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]

Tao Chen, Haochen Shi, Liyuan Liu, Siliang Tang, Jian Shao, Zhigang Chen, Yueting Zhuang
2021 arXiv   pre-print
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?

Mukhil Azhagan Mallaiyan Sathiaseelan, Olivia P. Paradis, Shayan Taheri, Navid Asadizanjani
2021 Cryptography  
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]

Md Aminul Haque Palash, Md Abdullah Al Nasim, Aditi Dhali, Faria Afrin
2021 arXiv   pre-print
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

Cecilia Blengino
2018 International Journal of Clinical Legal Education  
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

Hongbin Xia, Jingjing Li, Yuan Liu
2020 IEEE Access  
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

Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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]

Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
2019 arXiv   pre-print
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

Linyan Li, Yu Sun, Fuyuan Hu, Tao Zhou, Xuefeng Xi, Jinchang Ren, Longzhuang Li
2020 Discrete Dynamics in Nature and Society  
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]

Mohammad Malekzadeh and Anastasia Borovykh and Deniz Gündüz
2021 arXiv   pre-print
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]

Jon Chun
2021 arXiv   pre-print
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

James M Doyle
2018 Social Science Research Network  
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]

Zhi Wang, Chaoge Liu, Xiang Cui, Jiaxi Liu, Di Wu, Jie Yin
2021 arXiv   pre-print
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]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
., 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]

Yang Wang
2020 arXiv   pre-print
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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