bluuebunny/crossref_metadata_2025 huggingface dataset:
DDL: Deep Dictionary Learning for Predictive Phenotyping
Authors: Fu Tianfan , Hoang Trong Nghia , Xiao Cao , Sun Jimeng
Published: July 2019

Predictive phenotyping is about accurately predicting what phenotypes will occur in the next clinical visit based on longitudinal Electronic Health Record (EHR) data. Several deep learning (DL) models have demonstrated great performance in predictive phenotyping. However, these DL-based phenotyping models requires access to a large amount of labeled data, which are often  expensive to acquire. To address this label-insufficient challenge, we propose a deep dictionary learning framework (DDL) for phenotyping, which utilizes unlabeled data as a complementary source of information to generate a better, more succinct data representation. With extensive experiments on multiple real-world EHR datasets, we demonstrated DDL can outperform the state of the art predictive phenotyping methods on a wide variety of clinical tasks that require patient phenotyping such as heart failure classification, mortality prediction, and sequential prediction. All empirical results consistently show that unlabeled data can indeed be used to generate better data representation, which helps improve DDL's phenotyping performance over existing baseline methods that only uses labeled data.
Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning
Authors: Ghosh Supriyo , Koh Jing Yu , Jaillet Patrick
Published: July 2019

In bike sharing systems (BSSs), the uncoordinated movements of customers using bikes lead to empty or congested stations, which causes a significant loss in customer demand. In order to reduce the lost demand, a wide variety of existing research has employed a fixed set of historical demand patterns to design efficient bike repositioning solutions. However, the progress remains slow in understanding the underlying uncertainties in demand and designing proactive robust bike repositioning solutions. To bridge this gap, we propose a dynamic bike repositioning approach based on a probabilistic satisficing method which uses the uncertain
demand parameters that are learnt from historical data. We develop a novel and computationally efficient mixed integer linear program for maximizing the probability of satisfying the uncertain demand so as to improve the overall customer satisfaction and efficiency of the system. Extensive experimental results from a simulation model built on a real-world bike sharing data set demonstrate that our approach is not only robust to uncertainties in customer demand, but also outperforms the existing state-of-the-art repositioning approaches in terms of reducing the expected lost demand.
mdfa: Multi-Differential Fairness Auditor for Black Box Classifiers
Authors: Gitiaux Xavier , Rangwala Huzefa
Published: July 2019

Machine learning algorithms are increasingly involved in sensitive decision-making processes with adversarial implications on individuals. This paper presents a new tool,  mdfa that identifies the characteristics of the victims of a classifier's discrimination. We measure discrimination as a violation of multi-differential fairness.  Multi-differential fairness is a guarantee that a black box classifier's outcomes do not leak information on the sensitive attributes of a small group of individuals. We reduce the problem of identifying worst-case violations to matching distributions and predicting where sensitive attributes and classifier's outcomes coincide. We apply mdfa to a recidivism risk assessment classifier widely used in the United States and demonstrate that for individuals with little criminal history, identified African-Americans are three-times more likely to be considered at high risk of violent recidivism than similar non-African-Americans.
CounterFactual Regression with Importance Sampling Weights
Authors: Hassanpour Negar , Greiner Russell
Published: July 2019

Perhaps the most pressing concern of a patient diagnosed with cancer is her life expectancy under various treatment options. For a binary-treatment case, this translates into estimating the difference between the outcomes (e.g., survival time) of the two available treatment options – i.e., her Individual Treatment Effect (ITE). This is especially challenging to estimate from observational data, as that data has selection bias: the treatment assigned to a patient depends on that patient's attributes. In this work, we borrow ideas from domain adaptation to address the distributional shift between the source (outcome of the administered treatment, appearing in the observed training data) and target (outcome of the alternative treatment) that exists due to selection bias. We propose a context-aware importance sampling re-weighing scheme, built on top of a representation learning module, for estimating ITEs. Empirical results on two publicly available benchmarks demonstrate that the proposed method significantly outperforms state-of-the-art.
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Authors: Hong Shenda , Xiao Cao , Ma Tengfei , Li Hongyan , Sun Jimeng
Published: July 2019

Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge. By extracting multilevel (beat-, rhythm- and frequency-level) domain knowledge features separately, MINA combines the medical knowledge and ECG data via a multilevel attention model, making the learned models highly interpretable. Our experiments showed MINA achieved PR-AUC 0.9436 (outperforming the best baseline by 5.51%) in real world ECG dataset. Finally, MINA also demonstrated robust performance and strong interpretability against signal distortion and noise contamination.
RDPD: Rich Data Helps Poor Data via Imitation
Authors: Hong Shenda , Xiao Cao , Hoang Trong Nghia , Ma Tengfei , Li Hongyan , Sun Jimeng
Published: July 2019

In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipments (e.g., intensive care units) often provides high-quality multi-modal data, which are acquired from multiple sensory devices and have rich-feature representations. On the other hand, an environment with poor observation equipment (e.g., at home) only provides low-quality, uni-modal data with poor-feature representations. To deploy a competitive model in a poor-data environment without requiring direct access to multi-modal data acquired from a rich-data environment, this paper develops and presents a knowledge distillation (KD) method (RDPD) to enhance a predictive model trained on poor data using knowledge distilled from a high-complexity model trained on rich, private data. We evaluated RDPD on three real-world datasets and shown that its distilled model consistently outperformed all baselines across all datasets, especially achieving the greatest performance improvement over a model trained only on low-quality data by 24.56% on PR-AUC and 12.21% on ROC-AUC, and over that of a state-of-the-art KD model by 5.91% on PR-AUC and 4.44% on ROC-AUC.
Systematic Conservation Planning for Sustainable Land-use Policies: A Constrained Partitioning Approach to Reserve Selection and Design.
Authors: Justeau-Allaire Dimitri , Vismara Philippe , Birnbaum Philippe , Lorca Xavier
Published: July 2019

Faced with natural habitat degradation, fragmentation, and destruction, it is a major challenge for environmental managers to implement sustainable land use policies promoting socioeconomic development and natural habitat conservation in a balanced way. Relying on artificial intelligence and operational research, reserve selection and design models can be of assistance. This paper introduces a partitioning approach based on Constraint Programming (CP) for the reserve selection and design problem, dealing with both coverage and complex spatial constraints. Moreover, it introduces the first CP formulation of the buffer zone constraint, which can be reused to compose more complex spatial constraints. This approach has been evaluated in a real-world dataset addressing the problem of forest fragmentation in New Caledonia, a biodiversity hotspot where managers are gaining interest in integrating these methods into their decisional processes. Through several scenarios, it showed expressiveness, flexibility, and ability to quickly find solutions to complex questions.
Truly Batch Apprenticeship Learning with Deep Successor Features
Authors: Lee Donghun , Srinivasan Srivatsan , Doshi-Velez Finale
Published: July 2019

We introduce a novel apprenticeship learning algorithm to learn an expert's underlying reward structure in off-policy model-free batch settings. Unlike existing methods that require hand-crafted features, on-policy evaluation, further data acquisition for evaluation policies or the knowledge of model dynamics, our algorithm requires only batch data (demonstrations) of the observed expert behavior.  Such settings are common in many real-world tasks---health care, finance, or industrial process control---where accurate simulators do not exist and additional data acquisition is costly.  We develop a transition-regularized imitation learning model to learn a rich feature representation and a near-expert initial policy that makes the subsequent batch inverse reinforcement learning process viable. We also introduce deep successor feature networks that perform off-policy evaluation to estimate feature expectations of candidate policies. Under the batch setting, our method achieves superior results on control benchmarks as well as a real clinical task of sepsis management in the Intensive Care Unit.
Preferences Single-Peaked on a Tree: Sampling and Tree Recognition
Authors: Sliwinski Jakub , Elkind Edith
Published: July 2019

In voting theory, impossibility results and computational hardness results are often circumvented by recognising that voters' preferences are not arbitrary, but lie within a restricted domain. Uncovering the structure of the underlying domain often provides useful insights about the nature of the alternative space, and may be helpful in identifying a collective choice. Preferences single-peaked on a tree are an example of a relatively broad domain that nonetheless exhibits several desirable properties. We consider the setting where voters' preferences are independently sampled from rankings that are single-peaked on a given tree, and study the problem of reliably identifying the tree that generated the observed votes. We test our algorithm empirically; to this end, we develop an algorithm to uniformly sample preferences that are single-peaked on a given tree.
Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
Authors: Li Jinning , Xue Yexiang
Published: July 2019

We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies
Authors: Masood Muhammad , Doshi-Velez Finale
Published: July 2019

Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain expert to efficiently explore the space of reasonable solutions.  Unfortunately, existing approaches that quantify uncertainty over policies are not ultimately relevant to finding policies with qualitatively distinct behaviors.  In this work, we formalize the difference between policies as a difference between the distribution of trajectories induced by each policy, which encourages diversity with respect to both state visitation and action choices.  We derive a gradient-based optimization technique that can be combined with existing policy gradient methods to now identify diverse collections of well-performing policies.  We demonstrate our approach on benchmarks and a healthcare task.
KitcheNette: Predicting and Ranking Food Ingredient Pairings using Siamese Neural Network
Authors: Park Donghyeon , Kim Keonwoo , Park Yonggyu , Shin Jungwoon , Kang Jaewoo
Published: July 2019

As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers. In this work, we propose KitcheNette which is a model that predicts food ingredient pairing scores and recommends optimal ingredient pairings. KitcheNette employs Siamese neural networks and is trained on our annotated dataset containing 300K scores of pairings generated from numerous ingredients in food recipes. As the results demonstrate, our model not only outperforms other baseline models, but also can recommend complementary food pairings and discover novel ingredient pairings.
MNN: Multimodal Attentional Neural Networks for Diagnosis Prediction
Authors: Qiao Zhi , Wu Xian , Ge Shen , Fan Wei
Published: July 2019

Diagnosis prediction plays a key role in clinical decision supporting process, which attracted extensive research attention recently. Existing studies mainly utilize discrete medical codes (e.g., the ICD codes and procedure codes) as the primary features in prediction. However, in real clinical settings, such medical codes could be either incomplete or erroneous. For example, missed diagnosis will neglect some codes which should be included, mis-diagnosis will generate incorrect medical codes. To increase the robustness towards noisy data, we introduce textual clinical notes in addition to medical codes. Combining information from both sides will lead to improved understanding towards clinical health conditions. To accommodate both the textual notes and discrete medical codes in the same framework, we propose Multimodal Attentional Neural Networks (MNN), which integrates multi-modal data in a collaborative manner. Experimental results on real world EHR datasets demonstrate the advantages of MNN in terms of both robustness and accuracy.
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance
Authors: Ruan Wenjie , Wu Min , Sun Youcheng , Huang Xiaowei , Kroening Daniel , Kwiatkowska Marta
Published: July 2019

Deployment of deep neural networks (DNNs) in safety-critical systems requires provable guarantees for their correct behaviours. We compute the maximal radius of a safe norm ball around a given input, within which there are no adversarial examples for a trained DNN. We define global robustness as an expectation of the maximal safe radius over a test dataset, and develop an algorithm to approximate the global robustness measure by iteratively computing its lower and upper bounds. Our algorithm is the first efficient method for the Hamming (L0) distance, and we hypothesise that this norm is a good proxy for a certain class of physical attacks. The algorithm is anytime, i.e., it returns intermediate bounds and robustness estimates that are gradually, but strictly, improved as the computation proceeds; tensor-based, i.e., the computation is conducted over a set of inputs simultaneously to enable efficient GPU computation; and has provable guarantees, i.e., both the bounds and the robustness estimates can converge to their optimal values. Finally, we demonstrate the utility of our approach by applying the algorithm to a set of challenging problems.
Pre-training of Graph Augmented Transformers for Medication Recommendation
Authors: Shang Junyuan , Ma Tengfei , Xiao Cao , Sun Jimeng
Published: July 2019

Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal electronic health records (EHRs) from a small number of patients with multiple visits ignoring a large number of patients with a single visit (selection bias). Moreover, important hierarchical knowledge such as diagnosis hierarchy is not leveraged in the representation learning process. Despite the success of deep learning techniques in computational phenotyping, most previous approaches have two limitations: task-oriented representation and ignoring hierarchies of medical codes.
To address these challenges, we propose G-BERT, a new model to combine the power of Graph Neural Networks (GNNs) and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation. We use GNNs to represent the internal hierarchical structures of medical codes. Then we integrate the GNN representation into a transformer-based visit encoder and pre-train it on EHR data from patients only with a single visit. The pre-trained visit encoder and representation are then fine-tuned for downstream predictive tasks on longitudinal EHRs from patients with multiple visits. G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.
Three-quarter Sibling Regression for Denoising Observational Data
Authors: Shankar Shiv , Sheldon Daniel , Sun Tao , Pickering John , Dietterich Thomas G.
Published: July 2019

Many ecological studies and conservation policies are based on field observations of species, which can be affected by systematic variability introduced by the observation process. A recently introduced causal modeling technique called 'half-sibling regression' can detect and correct for systematic errors in measurements of multiple independent random variables. However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes. We present a technique called 'three-quarter sibling regression' to partially overcome this limitation. It can filter the effect of systematic noise when the latent variables have observed common causes. We provide theoretical justification of this approach, demonstrate its effectiveness on synthetic data, and show that it reduces systematic detection variability due to moon brightness in moth surveys.
Daytime Sleepiness Level Prediction Using Respiratory Information
Authors: Shinoda Kazuhiko , Yoshii Masahiko , Yamaguchi Hayato , Kaji Hirotaka
Published: July 2019

Daytime sleepiness is not only the cause of productivity decline and accidents, but also an important metric of health risks. Despite its importance, the long-term quantitative analysis of sleepiness in daily living has hardly been done due to time and effort required for the continuous tracking of sleepiness. Although a number of sleepiness detection technologies have been proposed, most of them focused only on driver’s drowsiness. In this paper, we present the first step towards the continuous sleepiness tracking in daily living situations. We explore a methodology for predicting subjective sleepiness levels utilizing respiration and acceleration data obtained from a novel wearable sensor. A class imbalance handling technique and hidden Markov model are combined with supervised classifiers to overcome the difficulties in learning from an imbalanced and time series dataset. We evaluate the performance of our models through a comprehensive experiment.
Simultaneous Prediction Intervals for Patient-Specific Survival Curves
Authors: Sokota Samuel , D'Orazio Ryan , Javed Khurram , Haider Humza , Greiner Russell
Published: July 2019

Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models -- known as individual survival distributions (ISDs) -- produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results. Furthermore, we introduce both a modification to the existing method and a novel method for estimating simultaneous prediction intervals and show that they offer competitive performance. It is worth emphasizing that these methods are not limited to survival analysis and can be applied in any context in which sampling the distribution of interest is tractable. Code is available at https://github.com/ssokota/spie.
Controllable Neural Story Plot Generation via Reward Shaping
Authors: Tambwekar Pradyumna , Dhuliawala Murtaza , Martin Lara J. , Mehta Animesh , Harrison Brent , Riedl Mark O.
Published: July 2019

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model toward a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.
Model-Free Model Reconciliation
Authors: Sreedharan Sarath , Hernandez Alberto Olmo , Mishra Aditya Prasad , Kambhampati Subbarao
Published: July 2019

Designing agents capable of explaining complex sequential decisions remains a significant open problem in human-AI interaction. Recently, there has been a lot of interest in developing approaches for generating such explanations for various decision-making paradigms. One such approach has been the idea of explanation as model-reconciliation. The framework hypothesizes that one of the common reasons for a user's confusion could be the mismatch between the user's model of the agent's task model and the model used by the agent to generate the decisions. While this is a general framework, most works that have been explicitly built on this explanatory philosophy have focused on classical planning settings where the model of user's knowledge is available in a declarative form. Our goal in this paper is to adapt the model reconciliation approach to a more general planning paradigm and discuss how such methods could be used when user models are no longer explicitly available. Specifically, we present a simple and easy to learn labeling model that can help an explainer decide what information could help achieve model reconciliation between the user and the agent with in the context of planning with MDPs.
Bidirectional Active Learning with Gold-Instance-Based Human Training
Authors: Tang Feilong
Published: July 2019

Active learning was proposed to improve learning performance and reduce labeling cost. However, traditional relabeling-based schemes seriously limit the ability of active learning because human may repeatedly make similar mistakes, without improving their expertise. In this paper, we propose a Bidirectional Active Learning with human Training (BALT) model that can enhance human related expertise during labeling and improve relabelingquality accordingly. We quantitatively capture how gold instances can be used to both estimate labelers? previous performance and improve their future correctness ratio. Then, we propose the backward relabeling scheme that actively selects the most likely incorrectly labeled instances for relabeling. Experimental results on three real datasets demonstrate that our BALT algorithm significantly outperforms representative related proposals.
Group-Fairness in Influence Maximization
Authors: Tsang Alan , Wilder Bryan , Rice Eric , Tambe Milind , Zick Yair
Published: July 2019

Influence maximization is a widely used model for information dissemination in social networks. Recent work has employed such interventions across a wide range of social problems, spanning public health, substance abuse, and international development (to name a few examples). A critical but understudied question is whether the benefits of such interventions are fairly distributed across different groups in the population; e.g., avoiding discrimination with respect to sensitive attributes such as race or gender. Drawing on legal and game-theoretic concepts, we introduce formal definitions of fairness in influence maximization. We provide an algorithmic framework to find solutions which satisfy fairness constraints, and in the process improve the state of the art for general multi-objective submodular maximization problems. Experimental results on real data from an HIV prevention intervention for homeless youth show that standard influence maximization techniques oftentimes neglect smaller groups which contribute less to overall utility, resulting in a disparity which our proposed algorithms substantially reduce.
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving
Authors: Wachi Akifumi
Published: July 2019

We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent. Rule-based algorithms are required in safety-critical applications for them to work properly in a wide range of situations. Hence, every effort is made to find failure scenarios during the development phase. However, as the software becomes complicated, finding failure cases becomes difficult. Especially in multi-agent domains, such as autonomous driving environments, it is much harder to find useful failure scenarios that help us improve the algorithm. We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails. We demonstrate the effectiveness of our proposed method using a simple environment and autonomous driving simulator.
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
Authors: Wang Xiao , Wang Siyue , Chen Pin-Yu , Wang Yanzhi , Kulis Brian , Lin Xue , Chin Sang
Published: July 2019

Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification or stealthy reprogramming. Among different defense proposals, stochastic network defenses such as random neuron activation pruning or random perturbation to layer inputs are shown to be promising for attack mitigation. However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e.g., large drop in test accuracy.This paper is motivated by pursuing for a better trade-off between adversarial robustness and test accuracy for stochastic network defenses. We propose Defense Efficiency Score (DES), a comprehensive metric that measures the gain in unsuccessful attack attempts at the cost of drop in test accuracy of any defense. To achieve a better DES, we propose hierarchical random switching (HRS), which protects neural networks through a novel randomization scheme. A HRS-protected model contains several blocks of randomly switching channels to prevent adversaries from exploiting fixed model structures and parameters for their malicious purposes. Extensive experiments show that HRS is superior in defending against state-of-the-art white-box and adaptive adversarial misclassification attacks. We also demonstrate the effectiveness of HRS in defending adversarial reprogramming, which is the first defense against adversarial programs. Moreover, in most settings the average DES of HRS is at least 5X higher than current stochastic network defenses, validating its significantly improved robustness-accuracy trade-off.
Who Should Pay the Cost: A Game-theoretic Model for Government Subsidized Investments to Improve National Cybersecurity
Authors: Wang Xinrun , An Bo , Chan Hau
Published: July 2019

Due to the recent cyber attacks, cybersecurity is becoming more critical in modern society. A single attack (e.g., WannaCry ransomware attack) can cause as much as $4 billion in damage. However, the cybersecurity investment by companies is far from satisfactory. Therefore, governments (e.g., in the UK) launch grants and subsidies to help companies to boost their cybersecurity to create a safer national cyber environment. The allocation problem is hard due to limited subsidies and the interdependence between self-interested companies and the presence of a strategic cyber attacker. To tackle the government's allocation problem, we introduce a Stackelberg game-theoretic model where the government first commits to an allocation and the companies/users and attacker simultaneously determine their protection and attack (pure or mixed) strategies, respectively. For the pure-strategy case, while there may not be a feasible allocation in general, we prove that computing an optimal allocation is NP-hard and propose a linear reverse convex program when the attacker can attack all users. For the mixed-strategy case, we show that there is a polynomial time algorithm to find an optimal allocation when the attacker has a single-attack capability. We then provide a heuristic algorithm, based on best-response-gradient dynamics, to find an effective allocation in the general setting. Experimentally, we show that our heuristic is effective and outperforms other baselines on synthetic and real data.
Automatic Grassland Degradation Estimation Using Deep Learning
Authors: Yan Xiyu , Jiang Yong , Chen Shuai , He Zihao , Li Chunmei , Xia Shu-Tao , Dai Tao , Dong Shuo , Zheng Feng
Published: July 2019

Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage.  2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.
Balanced Ranking with Diversity Constraints
Authors: Yang Ke , Gkatzelis Vasilis , Stoyanovich Julia
Published: July 2019

Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the over-all representativeness of the selected set. An unintended consequence of these constraints, however, is reduced in-group fairness: the selected candidates from a given group may not be the best ones, and this unfairness may not be well-balanced across groups. In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes. We then introduce additional constraints, aimed at balancing the in-group fairness across groups, and formalize the induced optimization problems as integer linear programs. Using these programs, we conduct an experimental evaluation with real datasets, and quantify the feasible trade-offs between balance and overall performance in the presence of diversity constraints.
A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data
Authors: Zhang Mingyang , Li Tong , Shi Hongzhi , Li Yong , Hui Pan
Published: July 2019

Urban anomalies such as abnormal flow of crowds and traffic accidents could result in loss of life or property if not handled properly. Detecting urban anomalies at the early stage is important to minimize the adverse effects. However, urban anomaly detection is difficult due to two challenges: a) the criteria of urban anomalies varies with different locations and time; b) urban anomalies of different types may show different signs. In this paper, we propose a decomposing approach to address these two challenges. Specifically, we decompose urban dynamics into the normal component and the abnormal component. The normal component is merely decided by spatiotemporal features, while the abnormal component is caused by anomalous events. Then, we extract spatiotemporal features and estimate the normal component accordingly. At last, we derive the abnormal component to identify anomalies. We evaluate our method using both real-world and synthetic datasets. The results show our method can detect meaningful events and outperforms state-of-the-art anomaly detecting methods by a large margin.
Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields
Authors: Zhang Yue , Ramesh Arti
Published: July 2019

Statistical relational models such as Markov logic networks (MLNs) and hinge-loss Markov random fields (HL-MRFs) are specified using templated weighted first-order logic clauses, leading to the creation of complex, yet easy to encode models that effectively combine uncertainty and logic. Learning the structure of these models from data reduces the human effort of identifying the right structures. In this work, we present an asynchronous deep reinforcement learning algorithm to automatically learn HL-MRF clause structures. Our algorithm possesses the ability to learn semantically meaningful structures that appeal to human intuition and understanding, while simultaneously being able to learn structures from data, thus learning structures that have both the desirable qualities of interpretability and good prediction performance. The asynchronous nature of our algorithm further provides the ability to learn diverse structures via exploration, while remaining scalable. We demonstrate the ability of the models to learn semantically meaningful structures that also achieve better prediction performance when compared with a greedy search algorithm, a path-based algorithm, and manually defined clauses on two computational social science applications: i) modeling recovery in alcohol use disorder, and ii) detecting bullying.
K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Authors: Zhou Yuxi , Hong Shenda , Shang Junyuan , Wu Meng , Wang Qingyun , Li Hongyan , Xie Junqing
Published: July 2019

Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to high error rates of computerized interpretation. One study found that only about 66% of AF were correctly recognized from noisy ECGs. This is in part due to insufficient training data, class skewness, as well as semantical ambiguities caused by noisy segments in an ECG record. In this paper, we propose a K-margin-based Residual-Convolution-Recurrent neural network (K-margin-based RCR-net) for AF detection from noisy ECGs. In detail, a skewness-driven dynamic augmentation method is employed to handle the problems of data inadequacy and class imbalance. A novel RCR-net is proposed to automatically extract both long-term rhythm-level and local heartbeat-level characters. Finally, we present a K-margin-based diagnosis model to automatically focus on the most important parts of an ECG record and handle noise by naturally exploiting expected consistency among the segments associated for each record. The experimental results demonstrate that the proposed method with 0.8125 F1NAOP score outperforms all state-of-the-art deep learning methods for AF detection task by 6.8%.
Aggregating Incomplete Pairwise Preferences by Weight
Authors: Terzopoulou Zoi , Endriss Ulle
Published: July 2019

We develop a model for the aggregation of preferences that do not need to be either complete or transitive. Our focus is on the normative characterisation of aggregation rules under which each agent has a weight that depends only on the size of her ballot, i.e., on the number of pairs of alternatives for which she chooses to report a relative ranking. We show that for rules that satisfy a restricted form of majoritarianism these weights in fact must be constant, while for rules that are invariant under agents with compatible preferences forming pre-election pacts it must be the case that an agent's weight is inversely proportional to the size of her ballot.
LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning
Authors: Camacho Alberto , Toro Icarte Rodrigo , Klassen Toryn Q. , Valenzano Richard , McIlraith Sheila A.
Published: July 2019

In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many interactions with the environment to learn from sparse rewards, and it can be challenging to specify reward functions that reflect complex reward-worthy behavior. We propose using reward machines (RMs), which are automata-based representations that expose reward function structure, as a normal form representation for reward functions. We show how specifications of reward in various formal languages, including LTL and other regular languages, can be automatically translated into RMs, easing the burden of complex reward function specification. We then show how the exposed structure of the reward function can be exploited by tailored q-learning algorithms and automated reward shaping techniques in order to improve the sample efficiency of reinforcement learning methods. Experiments show that these RM-tailored techniques significantly outperform state-of-the-art (deep) RL algorithms, solving problems that otherwise cannot reasonably be solved by existing approaches.
Playgol: Learning Programs Through Play
Authors: Cropper Andrew
Published: July 2019

Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of user-supplied build tasks and initial background knowledge (BK). Before solving the build tasks, the learner enters an unsupervised playing stage where it creates its own play tasks to solve, tries to solve them, and saves any solutions (programs) to the BK. After the playing stage is finished, the learner enters the supervised building stage where it tries to solve the build tasks and can reuse solutions learnt whilst playing. The idea is that playing allows the learner to discover reusable general programs on its own which can then help solve the build tasks. We claim that playing can improve learning performance. We show that playing can reduce the textual complexity of target concepts which in turn reduces the sample complexity of a learner. We implement our idea in Playgol, a new inductive logic programming system. We experimentally test our claim on two domains: robot planning and real-world string transformations. Our experimental results suggest that playing can substantially improve learning performance.
Learning Relational Representations with Auto-encoding Logic Programs
Authors: Dumancic Sebastijan , Guns Tias , Meert Wannes , Blockeel Hendrik
Published: July 2019

Deep learning methods capable of handling relational data have proliferated over the past years. In contrast to traditional relational learning methods that leverage first-order logic for representing such data, these methods aim at re-representing symbolic relational data in Euclidean space. They offer better scalability, but can only approximate rich relational structures and are less flexible in terms of reasoning. This paper introduces a novel framework for relational representation learning that combines the best of both worlds. This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the the original and latent representation is done by means of logic programs instead of neural networks. We show how learning can be cast as a constraint optimisation problem for which existing solvers can be used. The use of logic as a representation language makes the proposed framework more accurate (as the representation is exact, rather than approximate), more flexible, and more interpretable than deep learning methods. We experimentally show that these latent representations are indeed beneficial in relational learning tasks.
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Authors: Dumancic Sebastijan , Garcia-Duran Alberto , Niepert Mathias
Published: July 2019

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches such as Statistical relational learning, recent methods in (deep) representation learning have shown promising results for specialised tasks such as knowledge base completion. These approaches, also known as distributional, abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare distributional and symbolic relational learning approaches on various standard relational classification and knowledge base completion tasks. Furthermore, we analyse the properties of the datasets and relate them to the performance of the methods in the comparison. The results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.
Learning Hierarchical Symbolic Representations to Support Interactive Task Learning and Knowledge Transfer
Authors: Kirk James R. , Laird John E.
Published: July 2019

Interactive Task Learning (ITL) focuses on learning the definition of tasks through online natural language instruction in real time. Learning the correct grounded meaning of the instructions is difficult due to ambiguous words, lack of common ground, and the presence of distractors in the environment and the agent’s knowledge. We present a learning strategy embodied in an ITL agent that interactively learns in one shot the meaning of task concepts for 40 games and puzzles in ambiguous scenarios. Our approach learns hierarchical symbolic representations of task knowledge rather than learning a mapping directly from perceptual representations. These representations enable the agent to transfer and compose knowledge, analyze and debug multiple interpretations, and communicate efficiently with the teacher to resolve ambiguity. We evaluate the efficiency of the learning by examining the number of words required to teach tasks across cases of no transfer, positive transfer, and interference from prior tasks. Our results show that the agent can correctly generalize, disambiguate, and transfer concepts within variations in language descriptions and world representations of the same task, and across variations in different tasks.
EL Embeddings: Geometric Construction of Models for the Description Logic EL++
Authors: Kulmanov Maxat , Liu-Wei Wang , Yan Yuan , Hoehndorf Robert
Published: July 2019

An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic ??⁺⁺ that are also models of the TBox. To find such embeddings, we define an optimization problem that characterizes the model-theoretic semantics of the operators in ??⁺⁺ within ℝⁿ, thereby solving the problem of finding an interpretation function for an ??⁺⁺ theory given a particular domain Δ. Our approach is mainly relevant to large ??⁺⁺ theories and knowledge bases such as the ontologies and knowledge graphs used in the life sciences. We demonstrate that our method can be used for improved prediction of protein--protein interactions when compared to semantic similarity measures or knowledge graph embeddings.
How Well Do Machines Perform on IQ tests: a Comparison Study on a Large-Scale Dataset
Authors: Liu Yusen , He Fangyuan , Zhang Haodi , Rao Guozheng , Feng Zhiyong , Zhou Yi
Published: July 2019

AI benchmarking becomes an increasingly important task. As suggested by many researchers, Intelligence Quotient (IQ) tests, which is widely regarded as one of the predominant benchmarks for measuring human intelligence, raises an interesting challenge for AI systems. For better solving IQ tests automatedly by machines, one needs to use, combine and advance many areas in AI including knowledge representation and reasoning, machine learning, natural language processing and image understanding. Also, automated IQ tests provides an ideal testbed for integrating symbolic and sub-symbolic approaches as both are found useful here. Hence, we argue that IQ tests, although not suitable for testing machine intelligence, provides an excellent benchmark for the current development of AI research. Nevertheless, most existing IQ test datasets are not comprehensive enough for this purpose. As a result, the conclusions obtained are not representative. To address this issue, we create IQ10k, a large-scale dataset that contains more than 10,000 IQ test questions. We also conduct a comparison study on IQ10k with a number of state-of-the-art approaches.