pwc-archive/papers-with-abstracts huggingface dataset:
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
Authors: Benoît Choffin, Fabrice Popineau, Yolaine Bourda, Jill-Jênn Vie
Date: 2019-05-14 00:00:00
Tasks: Knowledge Tracing, Memorization, Scheduling

Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just a generic scheduler. However, the applicability of such adaptive schedulers seems to be limited to pure memorization, e.g. flashcards or foreign language learning. In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills. To this end, we choose to rely on a student model for inferring knowledge state and memory dynamics on any skill or combination of skills. We argue that no knowledge tracing model takes both memory decay and multiple skill tagging into account for predicting student performance. As a consequence, we propose a new student learning and forgetting model suited to our research problem: DAS3H builds on the additive factor models and includes a representation of the temporal distribution of past practice on the skills involved by an item. In particular, DAS3H allows the learning and forgetting curves to differ from one skill to another. Finally, we provide empirical evidence on three real-world educational datasets that DAS3H outperforms other state-of-the-art EDM models. These results suggest that incorporating both item-skill relationships and forgetting effect improves over student models that consider one or the other.
Modelling urban networks using Variational Autoencoders
Authors: Kira Kempinska, Roberto Murcio
Date: 2019-05-14 00:00:00

A long-standing question for urban and regional planners pertains to the ability to describe urban patterns quantitatively. Cities' transport infrastructure, particularly street networks, provides an invaluable source of information about the urban patterns generated by peoples' movements and their interactions. With the increasing availability of street network datasets and the advancements in deep learning methods, we are presented with an unprecedented opportunity to push the frontiers of urban modelling towards more data-driven and accurate models of urban forms. In this study, we present our initial work on applying deep generative models to urban street network data to create spatially explicit urban models. We based our work on Variational Autoencoders (VAEs) which are deep generative models that have recently gained their popularity due to the ability to generate realistic images. Initial results show that VAEs are capable of capturing key high-level urban network metrics using low-dimensional vectors and generating new urban forms of complexity matching the cities captured in the street network data.
Correlating neural and symbolic representations of language
Authors: Grzegorz Chrupała, Afra Alishahi
Date: 2019-05-14 00:00:00
Proceeding: ACL 2019 7

Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then apply our methods to correlate neural representations of English sentences with their constituency parse trees.
Knowledge-based multi-level aggregation for decision aid in the machining industry
Authors: Mathieu Ritou, Farouk Belkadi, Zakaria Yahouni, Catherine Da Cunha, Florent Laroche, Benoit Furet
Date: 2019-05-14 00:00:00
Tasks: Decision Making, Management

In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry. Decision Making; Machining; Knowledge based system
Data Markets to support AI for All: Pricing, Valuation and Governance
Authors: Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan
Date: 2019-05-14 00:00:00
Tasks: All

We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we explain how to perform valuation of data in absolute terms (i.e just by itself), or relatively (i.e in comparison to multiple datasets) or in conditional terms (i.e valuating new data given currently existing data).
Visual Analytics of Anomalous User Behaviors: A Survey
Authors: Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao
Date: 2019-05-14 00:00:00
Tasks: Anomaly Detection, Survey

The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behavior-related data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss the findings and potential research directions.
Streetscape augmentation using generative adversarial networks: insights related to health and wellbeing
Authors: Jasper S. Wijnands, Kerry A. Nice, Jason Thompson, Haifeng Zhao, Mark Stevenson
Date: 2019-05-14 00:00:00
Tasks: Style Transfer

Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Australian population health survey (n=34,000) was used to identify the spatial distribution of health and wellbeing outcomes, including general health and social capital. For each outcome, the most and least desirable locations formed two domains. Streetscape design was sampled using around 80,000 Google Street View images per domain. Generative adversarial networks translated these images from one domain to the other, preserving the main structure of the input image, but transforming the `style' from locations where self-reported health was bad to locations where it was good. These translations indicate that areas in Melbourne with good general health are characterised by sufficient green space and compactness of the urban environment, whilst streetscape imagery related to high social capital contained more and wider footpaths, fewer fences and more grass. Beyond identifying relationships, the method is a first step towards computer-generated design interventions that have the potential to improve population health and wellbeing.
Classification of Perceived Human Stress using Physiological Signals
Authors: Aamir Arsalan, Muhammad Majid, Syed Muhammad Anwar, Ulas Bagci
Date: 2019-05-13 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Photoplethysmography (PPG)

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of $28$ participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
Authors: Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao
Date: 2019-05-13 00:00:00
Tasks: Generative Adversarial Network

Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Therefore, reliable and accurate closure models for unresolved physics remains an important requirement for many computational physics problems, e.g., turbulence simulation. Recently, several researchers have adopted generative adversarial networks (GANs), a novel paradigm of training machine learning models, to generate solutions of PDEs-governed complex systems without having to numerically solve these PDEs. However, GANs are known to be difficult in training and likely to converge to local minima, where the generated samples do not capture the true statistics of the training data. In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs. We show that such a statistical regularization leads to better performance compared to standard GANs, measured by (1) the constrained model's ability to more faithfully emulate certain physical properties of the system and (2) the significantly reduced (by up to 80%) training time to reach the solution. We exemplify this approach on the Rayleigh-Benard convection, a turbulent flow system that is an idealized model of the Earth's atmosphere. With the growth of high-fidelity simulation databases of physical systems, this work suggests great potential for being an alternative to the explicit modeling of closures or parameterizations for unresolved physics, which are known to be a major source of uncertainty in simulating multi-scale physical systems, e.g., turbulence or Earth's climate.
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Authors: Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
Date: 2019-05-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Denoising, Language Modeling, Language Modelling, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Text to speech (TTS) and automatic speech recognition (ASR) are two dual tasks in speech processing and both achieve impressive performance thanks to the recent advance in deep learning and large amount of aligned speech and text data. However, the lack of aligned data poses a major practical problem for TTS and ASR on low-resource languages. In this paper, by leveraging the dual nature of the two tasks, we propose an almost unsupervised learning method that only leverages few hundreds of paired data and extra unpaired data for TTS and ASR. Our method consists of the following components: (1) a denoising auto-encoder, which reconstructs speech and text sequences respectively to develop the capability of language modeling both in speech and text domain; (2) dual transformation, where the TTS model transforms the text $y$ into speech $\hat{x}$, and the ASR model leverages the transformed pair $(\hat{x},y)$ for training, and vice versa, to boost the accuracy of the two tasks; (3) bidirectional sequence modeling, which addresses error propagation especially in the long speech and text sequence when training with few paired data; (4) a unified model structure, which combines all the above components for TTS and ASR based on Transformer model. Our method achieves 99.84% in terms of word level intelligible rate and 2.68 MOS for TTS, and 11.7% PER for ASR on LJSpeech dataset, by leveraging only 200 paired speech and text data (about 20 minutes audio), together with extra unpaired speech and text data.
Identifying collaborators in large codebases
Authors: Waren Long, Vadim Markovtsev, Hugo Mougard, Egor Bulychev, Jan Hula
Date: 2019-05-07 00:00:00
Tasks: Clustering

The way developers collaborate inside and particularly across teams often escapes management's attention, despite a formal organization with designated teams being defined. Observability of the actual, organically formed engineering structure provides decision makers invaluable additional tools to manage their talent pool. To identify existing inter and intra-team interactions - and suggest relevant opportunities for suitable collaborations - this paper studies contributors' commit activity, usage of programming languages, and code identifier topics by embedding and clustering them. We evaluate our findings collaborating with the GitLab organization, analyzing 117 of their open source projects. We show that we are able to restore their engineering organization in broad strokes, and also reveal hidden coding collaborations as well as justify in-house technical decisions.
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
Authors: Yashar Kiarashinejad, Sajjad Abdollahramezani, Mohammadreza Zandehshahvar, Omid Hemmatyar, Ali Adibi
Date: 2019-05-07 00:00:00
Tasks: Dimensionality Reduction

In this paper, we present a deep learning-based (DL-based) algorithm, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures. This approach is based on using the dimensionality reduction (DR) technique to significantly reduce the dimensionality of a generic EM wave-matter interaction problem without imposing significant error. Such an approach implicitly provides useful information about the role of different features (or design parameters such as geometry) of the nanostructure in its response functionality. To demonstrate the practical capabilities of this DL-based technique, we apply it to a reconfigurable optical metadevice enabling dual-band and triple-band optical absorption in the telecommunication window. Combination of the proposed approach with existing commercialized full-wave simulation tools offers a powerful toolkit to extract basic mechanisms of wave-matter interaction in complex EM devices and facilitate the design and optimization of nanostructures for a large range of applications including imaging, spectroscopy, and signal processing. It is worth to mention that the demonstrated approach is general and can be used in a large range of problems as long as enough training data can be provided.
Uncertainty quantification of molecular property prediction using Bayesian neural network models
Authors: Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Date: 2018-11-19 00:00:00
Tasks: Molecular Property Prediction, Property Prediction, Uncertainty Quantification

In chemistry, deep neural network models have been increasingly utilized in a
variety of applications such as molecular property predictions, novel molecule
designs, and planning chemical reactions. Despite the rapid increase in the use
of state-of-the-art models and algorithms, deep neural network models often
produce poor predictions in real applications because model performance is
highly dependent on the quality of training data. In the field of molecular
analysis, data are mostly obtained from either complicated chemical experiments
or approximate mathematical equations, and then quality of data may be
questioned.In this paper, we quantify uncertainties of prediction using
Bayesian neural networks in molecular property predictions. We estimate both
model-driven and data-driven uncertainties, demonstrating the usefulness of
uncertainty quantification as both a quality checker and a confidence indicator
with the three experiments. Our results manifest that uncertainty
quantification is necessary for more reliable molecular applications and
Bayesian neural network models can be a practical approach.
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
Authors: Farzad Eskandanian, Bamshad Mobasher
Date: 2018-09-29 00:00:00
Tasks: Change Point Detection, Recommendation Systems, Sequential Recommendation

Recommender systems help users find relevant items of interest based on the
past preferences of those users. In many domains, however, the tastes and
preferences of users change over time due to a variety of factors and
recommender systems should capture these dynamics in user preferences in order
to remain tuned to the most current interests of users. In this work we present
a recommendation framework based on Hidden Markov Models (HMM) which takes into
account the dynamics of user preferences. We propose a HMM-based approach to
change point detection in the sequence of user interactions which reflect
significant changes in preference according to the sequential behavior of all
the users in the data. The proposed framework leverages the identified change
points to generate recommendations in two ways. In one approach change points
are used to create a sequence-aware non-negative matrix factorization model to
generate recommendations that are aligned with the current tastes of user. In
the second approach the HMM is used directly to generate recommendations taking
into account the identified change points. These models are evaluated in terms
of accuracy of change point detection and also the effectiveness of
recommendations using a real music streaming dataset.
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
Authors: Zhengping Luo, Shangqing Zhao, Zhuo Lu, Jie Xu, Yalin E. Sagduyu
Date: 2019-05-04 00:00:00
Tasks: BIG-bench Machine Learning

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a fusion center. However, existing studies usually assume network or attackers as passive entities, e.g., assuming the prior knowledge of attacks is known or fixed. In practice, attackers can actively adopt arbitrary behaviors and avoid pre-assumed patterns or assumptions used by defense strategies. In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. We propose a generic algorithm to create malicious sensing data using this surrogate model. Our real-world experiments show that the LEB attack is effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the proposed LEB attack and existing defenses, we introduce a non-invasive method named as influence-limiting defense, which can coexist with existing defenses to defend against LEB attack or other similar attacks. We show that this defense is highly effective and reduces the overall disruption ratio of LEB attack by up to 80%.
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks
Authors: Yifei Shen, Yuanming Shi, Jun Zhang, Khaled B. Letaief
Date: 2018-11-17 00:00:00
Tasks: Transfer Learning

Effective resource allocation plays a pivotal role for performance
optimization in wireless networks. Unfortunately, typical resource allocation
problems are mixed-integer nonlinear programming (MINLP) problems, which are
NP-hard. Machine learning based methods recently emerge as a disruptive way to
obtain near-optimal performance for MINLP problems with affordable
computational complexity. However, they suffer from severe performance
deterioration when the network parameters change, which commonly happens in
practice and can be characterized as the task mismatch issue. In this paper, we
propose a transfer learning method via self-imitation, to address this issue
for effective resource allocation in wireless networks. It is based on a
general "learning to optimize" framework for solving MINLP problems. A unique
advantage of the proposed method is that it can tackle the task mismatch issue
with a few additional unlabeled training samples, which is especially important
when transferring to large-size problems. Numerical experiments demonstrate
that with much less training time, the proposed method achieves comparable
performance with the model trained from scratch with sufficient amount of
labeled samples. To the best of our knowledge, this is the first work that
applies transfer learning for resource allocation in wireless networks.
Anomaly Detection with Robust Deep Autoencoders
Authors: Chong Zhou, Randy C. Paffenroth
Date: 2017-08-13 00:00:00
Proceeding: KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 8
Tasks: Anomaly Detection, Denoising

Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.