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"Recommendation": models, code, and papers

Learning Adversarial Markov Decision Processes with Delayed Feedback

Dec 29, 2020
Tal Lancewicki, Aviv Rosenberg, Yishay Mansour

Reinforcement learning typically assumes that the agent observes feedback from the environment immediately, but in many real-world applications (like recommendation systems) the feedback is observed in delay. Thus, we consider online learning in episodic Markov decision processes (MDPs) with unknown transitions, adversarially changing costs and unrestricted delayed feedback. That is, the costs and trajectory of episode $k$ are only available at the end of episode $k + d^k$, where the delays $d^k$ are neither identical nor bounded, and are chosen by an adversary. We present novel algorithms based on policy optimization that achieve near-optimal high-probability regret of $\widetilde O ( \sqrt{K} + \sqrt{D} )$ under full-information feedback, where $K$ is the number of episodes and $D = \sum_{k} d^k$ is the total delay. Under bandit feedback, we prove similar $\widetilde O ( \sqrt{K} + \sqrt{D} )$ regret assuming that the costs are stochastic, and $\widetilde O ( K^{2/3} + D^{2/3} )$ regret in the general case. To our knowledge, we are the first to consider the important setting of delayed feedback in adversarial MDPs.


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Adversarial Attack on Facial Recognition using Visible Light

Nov 25, 2020
Morgan Frearson, Kien Nguyen

The use of deep learning for human identification and object detection is becoming ever more prevalent in the surveillance industry. These systems have been trained to identify human body's or faces with a high degree of accuracy. However, there have been successful attempts to fool these systems with different techniques called adversarial attacks. This paper presents a final report for an adversarial attack using visible light on facial recognition systems. The relevance of this research is to exploit the physical downfalls of deep neural networks. This demonstration of weakness within these systems are in hopes that this research will be used in the future to improve the training models for object recognition. As results were gathered the project objectives were adjusted to fit the outcomes. Because of this the following paper initially explores an adversarial attack using infrared light before readjusting to a visible light attack. A research outline on infrared light and facial recognition are presented within. A detailed analyzation of the current findings and possible future recommendations of the project are presented. The challenges encountered are evaluated and a final solution is delivered. The projects final outcome exhibits the ability to effectively fool recognition systems using light.


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Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph

Oct 18, 2020
Jinta Weng, Ying Gao, Jing Qiu, Guozhu Ding, Huanqin Zheng

Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications. Using two crowdsourcing approaches, crowdsourcing task distribution and reverse captcha generation, to construct knowledge graph in the field of teaching system. Generating a complete hierarchical knowledge graph of the teaching domain by nodes of school, student, teacher, course, knowledge point and exercise type. The knowledge graph constructed in a crowdsourcing manner requires many users to participate collaboratively with fully consideration of teachers' guidance and users' mobilization issues. Based on the three subgraphs of knowledge graph, prominent teacher, student learning situation and suitable learning route could be visualized. Personalized exercises recommendation model is used to formulate the personalized exercise by algorithm based on the knowledge graph. Collaborative creation model is developed to realize the crowdsourcing construction mechanism. Though unfamiliarity with the learning mode of knowledge graph and learners' less attention to the knowledge structure, system based on Crowdsourcing Knowledge Graph can still get high acceptance around students and teachers

* 4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019 
* Number of references:15 Classification code:903.3 Information Retrieval and Use Conference code: 235759 

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On Multi-Session Website Fingerprinting over TLS Handshake

Sep 19, 2020
Aida Ramezani, Amirhossein Khajehpour, Mahdi Jafari Siavoshani

Analyzing users' Internet traffic data and activities has a certain impact on users' experiences in different ways, from maintaining the quality of service on the Internet and providing users with high-quality recommendation systems to anomaly detection and secure connection. Considering that the Internet is a complex network, we cannot disintegrate the packets for each activity. Therefore we have to have a model that can identify all the activities an Internet user does in a given period of time. In this paper, we propose a deep learning approach to generate a multi-label classifier that can predict the websites visited by a user in a certain period. This model works by extracting the server names appearing in chronological order in the TLSv1.2 and TLSv1.3 Client Hello packets. We compare the results on the test data with a simple fully-connected neural network developed for the same purpose to prove that using the time-sequential information improves the performance. For further evaluations, we test the model on a human-made dataset and a modified dataset to check the model's accuracy under different circumstances. Finally, our proposed model achieved an accuracy of 95% on the test dataset and above 90% on both the modified dataset and the human-made dataset.


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Improving the HardNet Descriptor

Jul 19, 2020
Milan Pultar

In the thesis we consider the problem of local feature descriptor learning for wide baseline stereo focusing on the HardNet descriptor, which is close to state-of-the-art. AMOS Patches dataset is introduced, which improves robustness to illumination and appearance changes. It is based on registered images from selected cameras from the AMOS dataset. We provide recommendations on the patch dataset creation process and evaluate HardNet trained on data of different modalities. We also introduce a dataset combination and reduction methods, that allow comparable performance on a significantly smaller dataset. HardNet8, consistently outperforming the original HardNet, benefits from the architectural choices made: connectivity pattern, final pooling, receptive field, CNN building blocks found by manual or automatic search algorithms -- DARTS. We show impact of overlooked hyperparameters such as batch size and length of training on the descriptor quality. PCA dimensionality reduction further boosts performance and also reduces memory footprint. Finally, the insights gained lead to two HardNet8 descriptors: one performing well on a variety of benchmarks -- HPatches, AMOS Patches and IMW Phototourism, the other is optimized for IMW Phototourism.

* The thesis was supervised by Dmytro Mishkin. Many pieces of advice came from Ji\v{r}\'i Matas 

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Handling Position Bias for Unbiased Learning to Rank in Hotels Search

Feb 28, 2020
Yinxiao Li

Nowadays, search ranking and recommendation systems rely on a lot of data to train machine learning models such as Learning-to-Rank (LTR) models to rank results for a given query, and implicit user feedbacks (e.g. click data) have become the dominant source of data collection due to its abundance and low cost, especially for major Internet companies. However, a drawback of this data collection approach is the data could be highly biased, and one of the most significant biases is the position bias, where users are biased towards clicking on higher ranked results. In this work, we will investigate the marginal importance of properly handling the position bias in an online test environment in Tripadvisor Hotels search. We propose an empirically effective method of handling the position bias that fully leverages the user action data. We take advantage of the fact that when user clicks a result, he has almost certainly observed all the results above, and the propensities of the results below the clicked result will be estimated by a simple but effective position bias model. The online A/B test results show that this method leads to an improved search ranking model.


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Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration

Feb 11, 2020
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri

The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generalizable models, has led to crucial insights into network initialization and has enabled computationally efficient inferencing. In order to realize the full potential of these pruning strategies, particularly when utilized in transfer learning scenarios, it is necessary to understand the behavior of winning tickets when they might overfit to the dataset characteristics. In supervised and semi-supervised learning, prediction calibration is a commonly adopted strategy to handle such inductive biases in models. In this paper, we study the impact of incorporating calibration strategies during model training on the quality of the resulting lottery tickets, using several evaluation metrics. More specifically, we incorporate a suite of calibration strategies to different combinations of architectures and datasets, and evaluate the fidelity of sub-networks retrained based on winning tickets. Furthermore, we report the generalization performance of tickets across distributional shifts, when the inductive biases are explicitly controlled using calibration mechanisms. Finally, we provide key insights and recommendations for obtaining reliable lottery tickets, which we demonstrate to achieve improved generalization.


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Should Artificial Intelligence Governance be Centralised? Design Lessons from History

Jan 10, 2020
Peter Cihon, Matthijs M. Maas, Luke Kemp

Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.

* A shorter version of the paper is to be published in the proceedings of AAAI/ACM AIES 2020 

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Landmark Map: An Extension of the Self-Organizing Map for a User-Intended Nonlinear Projection

Aug 20, 2019
Akinari Onishi

The self-organizing map (SOM) is an unsupervised artificial neural network that is widely used in, e.g., data mining and visualization. Supervised and semi-supervised learning methods have been proposed for the SOM. However, their teacher labels do not describe the relationship between the data and the location of nodes. This study proposes a landmark map (LAMA), which is an extension of the SOM that utilizes several landmarks, e.g., pairs of nodes and data points. LAMA is designed to obtain a user-intended nonlinear projection to achieve, e.g., the landmark-oriented data visualization. To reveal the learning properties of LAMA, the Zoo dataset from the UCI Machine Learning Repository and an artificial formant dataset were analyzed. The analysis results of the Zoo dataset indicated that LAMA could provide a new data view such as the landmark-centered data visualization. Furthermore, the artificial formant data analysis revealed that LAMA successfully provided the intended nonlinear projection associating articular movement with vertical and horizontal movement of a computer cursor. Potential applications of LAMA include data mining, recommendation systems, and human-computer interaction.


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A multimodal movie review corpus for fine-grained opinion mining

Feb 26, 2019
Alexandre Garcia, Slim Essid, Florence d'Alché-Buc, Chloé Clavel

In this paper, we introduce a set of opinion annotations for the POM movie review dataset, composed of 1000 videos. The annotation campaign is motivated by the development of a hierarchical opinion prediction framework allowing one to predict the different components of the opinions (e.g. polarity and aspect) and to identify the corresponding textual spans. The resulting annotations have been gathered at two granularity levels: a coarse one (opinionated span) and a finer one (span of opinion components). We introduce specific categories in order to make the annotation of opinions easier for movie reviews. For example, some categories allow the discovery of user recommendation and preference in movie reviews. We provide a quantitative analysis of the annotations and report the inter-annotator agreement under the different levels of granularity. We provide thus the first set of ground-truth annotations which can be used for the task of fine-grained multimodal opinion prediction. We provide an analysis of the data gathered through an inter-annotator study and show that a linear structured predictor learns meaningful features even for the prediction of scarce labels. Both the annotations and the baseline system will be made publicly available.


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