Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Topic": models, code, and papers

Learning enables adaptation in cooperation for multi-player stochastic games

Jul 29, 2020
Feng Huang, Ming Cao, Long Wang

Interactions among individuals in natural populations often occur in a dynamically changing environment. Understanding the role of environmental variation in population dynamics has long been a central topic in theoretical ecology and population biology. However, the key question of how individuals, in the middle of challenging social dilemmas (e.g., the "tragedy of the commons"), modulate their behaviors to adapt to the fluctuation of the environment has not yet been addressed satisfactorily. Utilizing evolutionary game theory and stochastic games, we develop a game-theoretical framework that incorporates the adaptive mechanism of reinforcement learning to investigate whether cooperative behaviors can evolve in the ever-changing group interaction environment. When the action choices of players are just slightly influenced by past reinforcements, we construct an analytical condition to determine whether cooperation can be favored over defection. Intuitively, this condition reveals why and how the environment can mediate cooperative dilemmas. Under our model architecture, we also compare this learning mechanism with two non-learning decision rules, and we find that learning significantly improves the propensity for cooperation in weak social dilemmas, and, in sharp contrast, hinders cooperation in strong social dilemmas. Our results suggest that in complex social-ecological dilemmas, learning enables the adaptation of individuals to varying environments.


  Access Paper or Ask Questions

Migration and Refugee Crisis: a Critical Analysis of Online Public Perception

Jul 20, 2020
Isa Inuwa-Dutse, Mark Liptrott, Ioannis Korkontzelos

The migration rate and the level of resentments towards migrants are an important issue in modern civilisation. The infamous EU refugee crisis caught many countries unprepared, leading to sporadic and rudimentary containment measures that, in turn, led to significant public discourse. Decades of offline data collected via traditional survey methods have been utilised earlier to understand public opinion to foster peaceful coexistence. Capturing and understanding online public opinion via social media is crucial towards a joint strategic regulation spanning safety, rights of migrants and cordial integration for economic prosperity. We present a analysis of opinions on migrants and refugees expressed by the users of a very popular social platform, Twitter. We analyse sentiment and the associated context of expressions in a vast collection of tweets related to the EU refugee crisis. Our study reveals a marginally higher proportion of negative sentiments vis-a-vis migrants and a large proportion of the negative sentiments is more reflected among the ordinary users. Users with many followers and non-governmental organisations (NGO) tend to tweet favourably about the topic, offsetting the distribution of negative sentiment. We opine that they can be encouraged to be more proactive in neutralising negative attitudes that may arise concerning similar incidences.

* 15 pages, 8 figures 

  Access Paper or Ask Questions

Text Recognition in Real Scenarios with a Few Labeled Samples

Jun 22, 2020
Jinghuang Lin, Zhanzhan Cheng, Fan Bai, Yi Niu, Shiliang Pu, Shuigeng Zhou

Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a huge number of synthetic text images to recognize unconstrained scene texts, and have achieved substantial progress. However, these methods are not quite applicable in many real-world scenarios where 1) high recognition accuracy is required, while 2) labeled samples are lacked. To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples). This is done by simultaneously learning each character's feature representation with an attention mechanism and establishing the corresponding character-level latent subspace with adversarial learning. Our approach can maximize the character-level confusion between the source domain and the target domain, thus achieves the sequence-level adaptation with even a small number of labeled samples in the target domain. Extensive experiments on various datasets show that our method significantly outperforms the finetuning scheme, and obtains comparable performance to the state-of-the-art STR methods.

* 8 pages, 6 figures 

  Access Paper or Ask Questions

A Photo-Based Mobile Crowdsourcing Framework for Event Reporting

May 03, 2020
Aymen Hamrouni, Hakim Ghazzai, Mounir Frikha, Yehia Massoud

Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the built-in cameras of mobile devices are becoming the most common way for visual logging techniques in our daily lives. MCS photo-based frameworks collect photos in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable. This inevitably leads to evolving picture streams which possibly contain misleading and redundant information that affects the task result. In order to overcome these issues, we develop, in this paper, a solution for selecting highly relevant data from an evolving picture stream and ensuring correct submission. The proposed photo-based MCS framework for event reporting incorporates (i) a deep learning model to eliminate false submissions and ensure photos credibility and (ii) an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Simulation results indicate that the implemented framework can effectively reduce false submissions and select a subset with high utility coverage with low redundancy ratio from the streaming data.

* 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Dallas, TX, USA, 2019, pp. 198-202 
* Published in 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) 

  Access Paper or Ask Questions

A Photo-Based Mobile Crowdsourcing Frameworkfor Event Reporting

Apr 28, 2020
Aymen Hamrouni, Hakim Ghazzai, Mounir Frikha, Yehia Massoud

Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the built-in cameras of mobile devices are becoming the most common way for visual logging techniques in our daily lives. MCS photo-based frameworks collect photos in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable. This inevitably leads to evolving picture streams which possibly contain misleading and redundant information that affects the task result. In order to overcome these issues, we develop, in this paper, a solution for selecting highly relevant data from an evolving picture stream and ensuring correct submission. The proposed photo-based MCS framework for event reporting incorporates (i) a deep learning model to eliminate false submissions and ensure photos credibility and (ii) an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Simulation results indicate that the implemented framework can effectively reduce false submissions and select a subset with high utility coverage with low redundancy ratio from the streaming data.

* 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Dallas, TX, USA, 2019, pp. 198-202 
* Published in 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) 

  Access Paper or Ask Questions

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Dec 23, 2019
Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, Saber Fallah

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

* 23 pages, 3 figures, Accepted in IEEE Transactions on Intelligent Transportation Systems 

  Access Paper or Ask Questions

What Will Your Child Look Like? DNA-Net: Age and Gender Aware Kin Face Synthesizer

Nov 16, 2019
Pengyu Gao, Siyu Xia, Joseph Robinson, Junkang Zhang, Chao Xia, Ming Shao, Yun Fu

Visual kinship recognition aims to identify blood relatives from facial images. Its practical application-- like in law-enforcement, video surveillance, automatic family album management, and more-- has motivated many researchers to put forth effort on the topic as of recent. In this paper, we focus on a new view of visual kinship technology: kin-based face generation. Specifically, we propose a two-stage kin-face generation model to predict the appearance of a child given a pair of parents. The first stage includes a deep generative adversarial autoencoder conditioned on ages and genders to map between facial appearance and high-level features. The second stage is our proposed DNA-Net, which serves as a transformation between the deep and genetic features based on a random selection process to fuse genes of a parent pair to form the genes of a child. We demonstrate the effectiveness of the proposed method quantitatively and qualitatively: quantitatively, pre-trained models and human subjects perform kinship verification on the generated images of children; qualitatively, we show photo-realistic face images of children that closely resemble the given pair of parents. In the end, experiments validate that the proposed model synthesizes convincing kin-faces using both subjective and objective standards.


  Access Paper or Ask Questions

GarmNet: Improving Global with Local Perception for Robotic Laundry Folding

Jun 30, 2019
Daniel Fernandes Gomes, Shan Luo, Luis F. Teixeira

Developing autonomous assistants to help with domestic tasks is a vital topic in robotics research. Among these tasks, garment folding is one of them that is still far from being achieved mainly due to the large number of possible configurations that a crumpled piece of clothing may exhibit. Research has been done on either estimating the pose of the garment as a whole or detecting the landmarks for grasping separately. However, such works constrain the capability of the robots to perceive the states of the garment by limiting the representations for one single task. In this paper, we propose a novel end-to-end deep learning model named GarmNet that is able to simultaneously localize the garment and detect landmarks for grasping. The localization of the garment represents the global information for recognising the category of the garment, whereas the detection of landmarks can facilitate subsequent grasping actions. We train and evaluate our proposed GarmNet model using the CloPeMa Garment dataset that contains 3,330 images of different garment types in different poses. The experiments show that the inclusion of landmark detection (GarmNet-B) can largely improve the garment localization, with an error rate of 24.7% lower. Solutions as ours are important for robotics applications, as these offer scalable to many classes, memory and processing efficient solutions.

* 13 pages, 5 figures, published in the 20th Towards Autonomous Robotic Systems Conference 

  Access Paper or Ask Questions

One-Way Prototypical Networks

Jun 03, 2019
Anna Kruspe

Few-shot models have become a popular topic of research in the past years. They offer the possibility to determine class belongings for unseen examples using just a handful of examples for each class. Such models are trained on a wide range of classes and their respective examples, learning a decision metric in the process. Types of few-shot models include matching networks and prototypical networks. We show a new way of training prototypical few-shot models for just a single class. These models have the ability to predict the likelihood of an unseen query belonging to a group of examples without any given counterexamples. The difficulty here lies in the fact that no relative distance to other classes can be calculated via softmax. We solve this problem by introducing a "null class" centered around zero, and enforcing centering with batch normalization. Trained on the commonly used Omniglot data set, we obtain a classification accuracy of .98 on the matched test set, and of .8 on unmatched MNIST data. On the more complex MiniImageNet data set, test accuracy is .8. In addition, we propose a novel Gaussian layer for distance calculation in a prototypical network, which takes the support examples' distribution rather than just their centroid into account. This extension shows promising results when a higher number of support examples is available.


  Access Paper or Ask Questions

An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model

Apr 29, 2019
Oluwatobi Olabiyi, Anish Khazane, Alan Salimov, Erik T. Mueller

In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator. We also explore two approaches to accomplish the conditional discriminator: (1) phredGAN_a, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) phredGAN_d, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance. To demonstrate the superior performance of phredGAN over the persona Seq2Seq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends. Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation. We also explore the trade-offs from using either variant of phredGAN on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).

* NAACL NeuralGen Workshop 2019. arXiv admin note: substantial text overlap with arXiv:1905.01998 

  Access Paper or Ask Questions

<<
503
504
505
506
507
508
509
510
511
512
513
514
515
>>