bluuebunny/crossref_metadata_2025 huggingface dataset:
State Variable Effects in Graphical Event Models
Authors: Bhattacharjya Debarun , Subramanian Dharmashankar , Gao Tian
Published: July 2020

Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
Authors: Bai Junwen , Kong Shufeng , Gomes Carla
Published: July 2020

Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We propose a novel framework for multi-label classification, Multivariate Probit Variational AutoEncoder (MPVAE), that effectively learns latent embedding spaces as well as label correlations. MPVAE learns and aligns two probabilistic embedding spaces for labels and features respectively. The decoder of MPVAE takes in the samples from the embedding spaces and models the joint distribution of output targets under a Multivariate Probit model by learning a shared covariance matrix. We show that MPVAE outperforms the existing state-of-the-art methods on important computational sustainability applications as well as on other application domains, using public real-world datasets. MPVAE is further shown to remain robust under noisy settings. Lastly, we demonstrate the interpretability of the learned covariance by a case study on a bird observation dataset.
Bridging Cross-Tasks Gap for Cognitive Assessment via Fine-Grained Domain Adaptation
Authors: Zhang Yingwei , Chen Yiqiang , Yu Hanchao , Lv Zeping , Li Qing , Yang Xiaodong
Published: July 2020

Discriminating pathologic cognitive decline from the expected decline of normal aging is an important research topic for elderly care and health monitoring. However, most cognitive assessment methods only work when data distributions of the training set and testing set are consistent. Enabling existing cognitive assessment models to adapt to the data in new cognitive assessment tasks is a significant challenge. In this paper, we propose a novel domain adaptation method, namely the Fine-Grained Adaptation Random Forest (FAT), to bridge the cognitive assessment gap when the data distribution is changed. FAT is composed of two essential parts 1) information gain based model evaluation strategy (IGME) and 2) domain adaptation tree growing mechanism (DATG). IGME is used to evaluate every individual tree, and DATG is used to transfer the source model to the target domain. To evaluate the performance of FAT, we conduct experiments in real clinical environments. Experimental results demonstrate that FAT is significantly more accurate and efficient compared with other state-of-the-art methods.
WEFE: The Word Embeddings Fairness Evaluation Framework
Authors: Badilla Pablo , Bravo-Marquez Felipe , Pérez Jorge
Published: July 2020

Word embeddings are known to exhibit stereotypical biases towards gender, race, religion, among other criteria. Severa fairness metrics have been proposed in order to automatically quantify these biases. Although all metrics have a similar objective, the relationship between them is by no means clear. Two issues that prevent a clean comparison is that they operate with different inputs, and that their outputs are incompatible with each other. In this paper we propose WEFE, the word embeddings fairness evaluation framework, to encapsulate, evaluate and compare fairness metrics. Our framework needs a list of pre-trained embeddings and a set of fairness criteria, and it is based on checking correlations between fairness rankings induced by these criteria. We conduct a case study showing that rankings produced by existing fairness methods tend to correlate when measuring gender bias. This correlation is considerably less for other biases like race or religion. We also compare the fairness rankings with an embedding benchmark showing that there is no clear correlation between fairness and good performance in downstream tasks.
Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth
Authors: Yadav Amulya , Singh Roopali , Siapoutis Nikolas , Barman-Adhikari Anamika , Liang Yu
Published: July 2020

This paper presents CORTA, a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid addiction. Many rehabilitation centers treat opioid addiction in homeless youth by prescribing rehabilitation programs that are tailored to the underlying causes of addiction. To date, rehabilitation centers have relied on ad-hoc assessments and unprincipled heuristics to deliver rehabilitation programs to homeless youth suffering from opioid addiction, which greatly undermines the effectiveness of the delivered programs. CORTA addresses these challenges via three novel contributions. First, CORTA utilizes a first-of-its-kind real-world dataset collected from ~1400 homeless youth to build causal inference models which predict the likelihood of opioid addiction among these youth. Second, utilizing counterfactual predictions generated by our causal inference models, CORTA solves novel optimization formulations to assign appropriate rehabilitation programs to the correct set of homeless youth in order to minimize the expected number of homeless youth suffering from opioid addiction. Third, we provide a rigorous experimental analysis of CORTA along different dimensions, e.g., importance of causal modeling, importance of optimization, and impact of incorporating fairness considerations, etc. Our simulation results show that CORTA outperforms baselines by ~110% in minimizing the number of homeless youth suffering from opioid addiction.
Who Am I?: Towards Social Self-Awareness for Intelligent Agents
Authors: Subagdja Budhitama , Tay Han Yi , Tan Ah-Hwee
Published: July 2020

Most of today's AI technologies are geared towards mastering specific tasks performance through learning from a huge volume of data. However, less attention has still been given to make the AI understand its own purposes or be responsible socially. In this paper, a new model of agent is presented with the capacity to represent itself as a distinct individual with identity, a mind of its own, unique experiences, and social lives. In this way, the agent can interact with its surroundings and other agents seamlessly and meaningfully. A practical framework for developing an agent architecture with this model of self and self-awareness is proposed allowing self to be ascribed to an existing intelligent agent architecture in general to enable its social ability, interactivity, and co-presence with others. Possible applications are discussed with some exemplifying cases based on an implementation of a conversational agent.
Discrete Biorthogonal Wavelet Transform Based Convolutional Neural Network for Atrial Fibrillation Diagnosis from Electrocardiogram
Authors: Xie Qingsong , Tu Shikui , Wang Guoxing , Lian Yong , Xu Lei
Published: July 2020

For the problem of early detection of atrial fibrillation (AF) from electrocardiogram (ECG), it is difficult to capture subject-invariant discriminative features from ECG signals, due to the high variation in ECG morphology across subjects and the

noise in ECG. In this paper, we propose an Discrete Biorthogonal Wavelet Transform (DBWT) Based Convolutional Neural Network (CNN) for AF detection, shortly called DBWT-AFNet. In DBWT-AFNet, rather than directly feeding ECG into CNN, DBWT is used to separate sub-signals in frequency band of heart beat from ECG, whose output is fed to CNN for AF diagnosis. Such sub-signals are better than the raw ECG for subject-invariant CNN representation learning because noisy information irrelevant to human beat has been largely filtered out. To strengthen the generalization ability of CNN to discover subject-invariant pattern in ECG, skip connection is exploited to propagate information well in neural network and channel attention is designed to adaptively highlight informative channel-wise features. Experiments show that the proposed DBWT-AFNet outperforms the state-of- the-art methods, especially for ECG segments classification across different subjects, where no data from testing subjects have been used in training.
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness
Authors: Yeom Samuel , Fredrikson Matt
Published: July 2020

We turn the definition of individual fairness on its head - rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness. This can facilitate the discussion on the fairness of a model, addressing the issue that it may be difficult to specify a priori a suitable metric. Our contributions are twofold:

First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted Lp metric. Our experiments show that adapting the minimal metrics of linear models to more complicated neural networks can lead to meaningful and interpretable fairness guarantees at little cost to utility.
PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction
Authors: Zhang Feng , Feng Ningxuan , Liu Yani , Yang Cheng , Zhai Jidong , Zhang Shuhao , He Bingsheng , Lin Jiazao , Du Xiaoyong
Published: July 2020

In big cities, there are plenty of parking spaces, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, and weekdays. PewLSTM has been successfully integrated into a real parking space reservation system, ThsParking, which is one of the top smart parking platforms in China. Based on 452,480real parking records in 683 days from 10 parking lots, PewLSTM yields 85.3% parking prediction accuracy, which is about 20% higher than the state-of-the-art parking behavior prediction method. The code and data can be obtained fromhttps://github.com/NingxuanFeng/PewLSTM.
Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems
Authors: Fang Boli , Jiang Miao , Cheng Pei-yi , Shen Jerry , Fang Yi
Published: July 2020

Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social decision problems across the world. Automated machine learning/deep learning(ML/DL) classification models, through quantitative modeling, have the potential to improve upon human decisions in a wide range of decision problems on social resource allocation such as Medicaid and Supplemental Nutrition Assistance Program(SNAP, commonly referred to as Food Stamps). However, given the limitations in ML/DL model design, these algorithms may fail to leverage various factors for decision making, resulting in improper decisions that allocate resources to individuals who may not be in the most need of such resource. In view of such an issue, we propose in this paper the strategy of fairgroups, based on the legal doctrine of disparate impact, to improve fairness in prediction outcomes. Experiments on various datasets demonstrate that our fairgroup construction method effectively boosts the fairness in automated decision making, while maintaining high prediction accuracy.
A Two-level Reinforcement Learning Algorithm for Ambiguous Mean-variance Portfolio Selection Problem
Authors: Huang Xin , Li Duan
Published: July 2020

Traditional modeling on the mean-variance portfolio selection often assumes a full knowledge on statistics of assets' returns. It is, however, not always the case in real financial markets. This paper deals with an ambiguous mean-variance portfolio selection problem with a mixture model on the returns of risky assets, where the proportions of different component distributions are assumed to be unknown to the investor, but being constants (in any time instant). Taking into consideration the updates of proportions from future observations is essential to find an optimal policy with active learning feature, but makes the problem intractable when we adopt the classical methods. Using reinforcement learning, we derive an investment policy with a learning feature in a two-level framework. In the lower level, the time-decomposed approach (dynamic programming) is adopted to solve a family of scenario subcases where in each case the series of component distributions along multiple time periods is specified. At the upper level, a scenario-decomposed approach (progressive hedging algorithm) is applied in order to iteratively aggregate the scenario solutions from the lower layer based on the current knowledge on proportions, and this two-level solution framework is repeated in a manner of rolling horizon. We carry out experimental studies to illustrate the execution of our policy scheme.
IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data
Authors: Guo Ruocheng , Li Jundong , Li Yichuan , Candan K. Selçuk , Raglin Adrienne , Liu Huan
Published: July 2020

Networked observational data presents new opportunities for learning individual causal effects, which plays an indispensable role in decision making. Such data poses the challenge of confounding bias. Previous work presents two desiderata to handle confounding bias. On the treatment group level, we aim to balance the distributions of confounder representations. On the individual level, it is desirable to capture patterns of hidden confounders that predict treatment assignments. Existing methods show the potential of utilizing network information to handle confounding bias, but they only try to satisfy one of the two desiderata. This is because the two desiderata seem to contradict each other. When the two distributions of confounder representations are highly overlapped, then we confront the undiscriminating problem between the treated and the controlled. In this work, we formulate the two desiderata as a minimax game. We propose IGNITE that learns representations of confounders from networked observational data, which is trained by a minimax game to achieve the two desiderata. Experiments verify the efficacy of IGNITE on two datasets under various settings.
An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization
Authors: Lin Siyu , Beling Peter A.
Published: July 2020

In this article, we propose an end-to-end adaptive framework for optimal trade execution based on Proximal Policy Optimization (PPO). We use two methods to account for the time dependencies in the market data based on two different neural network architecture: 1) Long short-term memory (LSTM) networks, 2) Fully-connected networks (FCN) by stacking the most recent limit orderbook (LOB) information as model inputs. The proposed framework can make trade execution decisions based on level-2 limit order book (LOB) information such as bid/ask prices and volumes directly without manually designed attributes as in previous research. Furthermore, we use a sparse reward function, which gives the agent reward signals at the end of each episode as an indicator of its relative performances against the baseline model, rather than implementation shortfall (IS) or a shaped reward function. The experimental results have demonstrated advantages over IS and the shaped reward function in terms of performance and simplicity. The proposed framework has outperformed the industry commonly used baseline models such as TWAP, VWAP, and AC as well as several Deep Reinforcement Learning (DRL) models on most of the 14 US equities in our experiments.
Online Portfolio Selection with Cardinality Constraint and Transaction Costs based on Contextual Bandit
Authors: Zhu Mengying , Zheng Xiaolin , Wang Yan , Liang Qianqiao , Zhang Wenfang
Published: July 2020

Online portfolio selection (OLPS) is a fundamental and challenging problem in financial engineering, which faces two practical constraints during the real trading, i.e., cardinality constraint and non-zero transaction costs. In order to achieve greater feasibility in financial markets, in this paper, we propose a novel online portfolio selection method named LExp4.TCGP with theoretical guarantee of sublinear regret to address the OLPS problem with the two constraints. In addition, we incorporate side information into our method based on contextual bandit, which further improves the effectiveness of our method. Extensive experiments conducted on four representative real-world datasets demonstrate that our method significantly outperforms the state-of-the-art methods when cardinality constraint and non-zero transaction costs co-exist.
VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract)
Authors: Decroos Tom , Bransen Lotte , Van Haaren Jan , Davis Jesse
Published: July 2020

Despite the fact that objectively assessing the impact of the individual actions performed by soccer players during games is a crucial task, most traditional metrics have substantial shortcomings. First, many metrics only consider rare actions like shots and goals which account for less than 2% of all on-the-ball actions. Second, they fail to account for the context in which the actions occurred. This work summarizes several important contributions. First, we describe a language for representing individual player actions on the pitch. This language unifies several existing formats which greatly simplifies automated analysis and this language is becoming widely used in the soccer analytics community. Second, we describe our framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. This framework enables giving a broad overview of a player's performance, including quantifying a player's total offensive and defensive contributions to their team. Third, we provide illustrative use cases that highlight the working and benefits of our framework.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract)
Authors: Chen Zhenpeng , Shen Sheng , Hu Ziniu , Lu Xuan , Mei Qiaozhu , Liu Xuanzhe
Published: July 2020

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification.
Channel-Level Variable Quantization Network for Deep Image Compression
Authors: Zhong Zhisheng , Akutsu Hiroaki , Aizawa Kiyoharu
Published: July 2020

Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework was proposed, and many deep neural network-based methods achieved great success in image compression. However, almost all convolutional neural network-based methods treat channel-wise feature maps equally, reducing the flexibility in handling different types of information. In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels. Specifically, we propose a variable quantization controller. It consists of two key components: the channel importance module, which can dynamically learn the importance of channels during training, and the splitting-merging module, which can allocate different bitrates for different channels. We also formulate the quantizer into a Gaussian mixture model manner. Quantitative and qualitative experiments verify the effectiveness of the proposed model and demonstrate that our method achieves superior performance and can produce much better visual reconstructions.
Playing Atari with Six Neurons (Extended Abstract)
Authors: Cuccu Giuseppe , Togelius Julian , Cudré-Mauroux Philippe
Published: July 2020

Deep reinforcement learning applied to vision-based problems like Atari games maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting useful information and making decisions based on it. By separating image processing from decision-making, one could better understand the complexity of each task, as well as potentially find smaller policy representations that are easier for humans to understand and may generalize better. To this end, we propose a new method for learning policies and compact state representations separately but simultaneously for policy approximation in reinforcement learning. State representations are generated by an encoder based on two novel algorithms: Increasing Dictionary Vector Quantization makes the encoder capable of growing its dictionary size over time, to address new observations; and Direct Residuals Sparse Coding encodes observations by aiming for highest information inclusion. We test our system on a selection of Atari games using tiny neural networks of only 6 to 18 neurons (depending on the game's controls). These are still capable of achieving results comparable---and occasionally superior---to state-of-the-art techniques which use two orders of magnitude more neurons.
On the Splitting Property for Epistemic Logic Programs (Extended Abstract)
Authors: Cabalar Pedro , Fandinno Jorge , Fariñas del Cerro Luis
Published: July 2020

Epistemic logic programs constitute an extension of the stable model semantics to deal with new constructs called "subjective literals." Informally speaking, a subjective literal allows checking whether some objective literal is true in all or some stable models. However, its associated semantics has proved to be non-trivial, since the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. In this paper, we review an extension of the well-known splitting property for logic programs to the epistemic case. This "epistemic splitting property" is defined as a general condition that can be checked on any arbitrary epistemic semantics. Its satisfaction has desirable consequences both in the representation of conformant planning problems and in the encoding of the so-called subjective constraints.
Statistical Learning with a Nuisance Component (Extended Abstract)
Authors: Foster Dylan J. , Syrgkanis Vasilis
Published: July 2020

We provide excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate a target parameter depends on an unknown parameter that must be estimated from data (a "nuisance parameter"). We analyze a two-stage sample splitting meta-algorithm that takes as input two arbitrary estimation algorithms: one for the target parameter and one for the nuisance parameter. We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the nuisance estimation error on the excess risk bound achieved by the meta-algorithm is of second order. Our theorem is agnostic to the particular algorithms used for the target and nuisance and only makes an assumption on their individual performance. This enables the use of a plethora of existing results from statistical learning and machine learning literature to give new guarantees for learning with a nuisance component. Moreover, by focusing on excess risk rather than parameter estimation, we can give guarantees under weaker assumptions than in previous works and accommodate the case where the target parameter belongs to a complex nonparametric class. We characterize conditions on the metric entropy such that oracle rates---rates of the same order as if we knew the nuisance parameter---are achieved. We also analyze the rates achieved by specific estimation algorithms such as variance-penalized empirical risk minimization, neural network estimation and sparse high-dimensional linear model estimation. We highlight the applicability of our results in four settings of central importance in the literature: 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.
Learning URI Selection Criteria to Improve the Crawling of Linked Open Data (Extended Abstract)
Authors: Huang Hai , Gandon Fabien
Published: July 2020

A Linked Data crawler performs a selection to focus on collecting linked RDF (including RDFa) data on the Web. From the perspectives of throughput and coverage, given a newly discovered and targeted URI, the key issue of Linked Data crawlers is to decide whether this URI is likely to dereference into an RDF data source and therefore it is worth downloading the representation it points to. Current solutions adopt heuristic rules to filter irrelevant URIs. But when the heuristics are too restrictive this hampers the coverage of crawling. In this paper, we propose and compare approaches to learn strategies for crawling Linked Data on the Web by predicting whether a newly discovered URI will lead to an RDF data source or not. We detail the features used in predicting the relevance and the methods we evaluated including a promising adaptation of FTRL-proximal online learning algorithm. We compare several options through extensive experiments including existing crawlers as baseline methods to evaluate their efficiency.
Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)
Authors: Kenny Eoin M. , Ruelle Elodie , Geoghegan Anne , Shalloo Laurence , O'Leary Micheál , O'Donovan Michael , Temraz Mohammed , Keane Mark T.
Published: July 2020

Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance.
Specializing Word Embeddings (for Parsing) by Information Bottleneck (Extended Abstract)
Authors: Li Xiang Lisa , Eisner Jason
Published: July 2020

Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser. We compress each word embedding to either a discrete tag or a continuous vector. In the discrete version, our automatically compressed tags form an alternative tag set: we show experimentally that our tags capture most of the information in traditional POS tag annotations, but our tag sequences can be parsed more accurately at the same level of tag granularity. In the continuous version, we show experimentally that moderately compressing the word embeddings by our method yields a more accurate parser in 8 of 9 languages, unlike simple dimensionality reduction.
Disentangled Feature Learning Network for Vehicle Re-Identification
Authors: Bai Yan , Lou Yihang , Dai Yongxing , Liu Jun , Chen Ziqian , Duan Ling-Yu
Published: July 2020

Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against different orientations of the same vehicle. However, these two characteristics are hard to be encapsulated into a single feature representation simultaneously with unified supervision. Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales. The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets.
Supporting Historical Photo Identification with Face Recognition and Crowdsourced Human Expertise (Extended Abstract)
Authors: Mohanty Vikram , Thames David , Mehta Sneha , Luther Kurt
Published: July 2020

Identifying people in historical photographs is important for interpreting material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65). Millions of these portraits survive, but only 10-20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits.
EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings
Authors: Calabrese Agostina , Bevilacqua Michele , Navigli Roberto
Published: July 2020

The problem of grounding language in vision is increasingly attracting scholarly efforts. As of now, however, most of the approaches have been limited to word embeddings, which are not capable of handling polysemous words. This is mainly due to the limited coverage of the available semantically-annotated datasets, hence forcing research to rely on alternative technologies (i.e., image search engines). To address this issue, we introduce EViLBERT, an approach which is able to perform image classification over an open set of concepts, both concrete and non-concrete. Our approach is based on the recently introduced Vision-Language Pretraining (VLP) model, and builds upon a manually-annotated dataset of concept-image pairs. We use our technique to clean up the image-to-concept mapping that is provided within a multilingual knowledge base, resulting in over 258,000 images associated with 42,500 concepts.

We show that our VLP-based model can be used to create multimodal sense embeddings starting from our automatically-created dataset. In turn, we also show that these multimodal embeddings improve the performance of a Word Sense Disambiguation architecture over a strong unimodal baseline. We release code, dataset and embeddings at http://babelpic.org.
A Survey on Computational Propaganda Detection
Authors: Martino Giovanni Da San , Cresci Stefano , Barrón-Cedeño Alberto , Yu Seunghak , Pietro Roberto Di , Nakov Preslav
Published: July 2020

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue.

In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
Authors: Gao Shen , Chen Xiuying , Ren Zhaochun , Zhao Dongyan , Yan Rui
Published: July 2020

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.

This research topic has started to attract the attention of a large community of researchers, and it is nowadays counted as one of the most promising research areas.

In general, text summarization algorithms aim at using a plain text document as input and then output a summary.

However, in real-world applications, most of the data is not in a plain text format.

Instead, there is much manifold information to be summarized, such as the summary for a web page based on a query in the search engine, extreme long document (e.g. academic paper), dialog history and so on.

In this paper, we focus on the survey of these new summarization tasks and approaches in the real-world application.
Planning Algorithms for Zero-Sum Games with Exponential Action Spaces: A Unifying Perspective
Authors: Lelis Levi H. S.
Published: July 2020

In this paper we review several planning algorithms developed for zero-sum games with exponential action spaces, i.e., spaces that grow exponentially with the number of game components that can act simultaneously at a given game state. As an example, real-time strategy games have exponential action spaces because the number of actions available grows exponentially with the number of units controlled by the player. We also present a unifying perspective in which several existing algorithms can be described as an instantiation of a variant of NaiveMCTS. In addition to describing several existing planning algorithms for exponential action spaces, we show that other instantiations of this variant of NaiveMCTS represent novel and promising algorithms to be studied in future works.
Reasoning About Inconsistent Formulas
Authors: Marques-Silva Joao , Mencía Carlos
Published: July 2020

The analysis of inconsistent formulas finds an ever-increasing range of applications, that include axiom pinpointing in description logics, fault localization in software, model-based diagnosis, optimization problems, but also explainability of machine learning models. This paper overviews approaches for analyzing inconsistent formulas, focusing on finding and enumerating explanations of and corrections for inconsistency, but also on solving optimization problems modeled as inconsistent formulas.
BDI Agent Architectures: A Survey
Authors: Silva Lavindra de , Meneguzzi Felipe , Logan Brian
Published: July 2020

The BDI model forms the basis of much of the research on symbolic models of agency and agent-oriented software engineering. While many variants of the basic BDI model have been proposed in the literature, there has been no systematic review of research on BDI agent architectures in over 10 years. In this paper, we survey the main approaches to each component of the BDI architecture, how these have been realised in agent programming languages, and discuss the trade-offs inherent in each approach.
The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation
Authors: Pasini Tommaso
Published: July 2020

Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context. It lies at the base of Natural Language Processing as it provides semantic information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets.

In this work, we first introduce the reader to the task of WSD through a short historical digression and then take the stock of the advancements to alleviate the knowledge acquisition bottleneck problem. In that, we survey the literature on manual, semi-automatic and automatic approaches to create English and multilingual corpora tagged with sense annotations and present a clear overview over supervised models for WSD. Finally, we provide our view over the future directions that we foresee for the field.
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
Authors: Burceanu Elena , Leordeanu Marius
Published: July 2020

We formulate object segmentation in video as a spectral graph clustering problem in space and time, in which nodes are pixels and their relations form local neighbourhoods. We claim that the strongest cluster in this pixel-level graph represents the salient object segmentation. We compute the main cluster using a novel and fast 3D filtering technique that finds the spectral clustering solution, namely the principal eigenvector of the graph's adjacency matrix, without building the matrix explicitly - which would be intractable. Our method is based on the power iteration which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume. This allows us to avoid creating the matrix and have a fast parallel implementation on GPU. We show that our method is much faster than classical power iteration applied directly on the adjacency matrix. Different from other works, ours is dedicated to preserving object consistency in space and time at the level of pixels.

In experiments, we obtain consistent improvement over the top state of the art methods on DAVIS-2016 dataset. We also achieve top results on the well-known SegTrackv2 dataset.
Fair Division: The Computer Scientist’s Perspective
Authors: Walsh Toby
Published: July 2020

I survey recent progress on a classic and challenging problem in social choice: the fair division of indivisible items. I discuss how a computational perspective has provided interesting insights into and understanding of how to divide items fairly and efficiently. This has involved bringing to bear tools such as those used in knowledge representation, computational complexity, approximation methods, game theory, online analysis and communication complexity.
Analogy Between Concepts (Extended Abstract)
Authors: Barbot Nelly , Miclet Laurent , Prade Henri
Published: July 2020

Analogical proportions are statements of the form “x is to y as z is to t”, where x, y, z, t are items of the same nature, or not. In this paper, we more particularly consider “relational proportions” of the form “object A has the same relationship with attribute a as object B with attribute b”. We provide a formal definition for relational proportions, and investigate how they can be extracted from a formal context, in the setting of formal concept analysis.
Predicting Strategic Behavior from Free Text (Extended Abstract)
Authors: Ben-Porat Omer , Kuchy Lital , Hirsch Sharon , Elad Guy , Reichart Roi , Tennenholtz Moshe
Published: July 2020

The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game? We initiate research on this question by providing preliminary positive results.
Rational Closure For All Description Logics (Extended Abstract)
Authors: Bonatti Piero A.
Published: July 2020

Many modern applications of description logics (DLs, for short), such as biomedical ontologies and semantic web policies, provide compelling motivations for extending DLs with an overriding mechanism analogous to the homonymous feature of object-oriented programming. Rational closure (RC) is one of the candidate semantics for such extensions, and one of the most intensively studied. So far, however, it has been limited to strict fragments of SROIQ(D) – the logic on which OWL2 is founded. In this paper we prove that RC cannot be extended to logics that do not satisfy the disjoint model union property, including SROIQ(D). Then we introduce a refinement of RC called stable rational closure that overcomes the dependency on the disjoint model union property. Our results show that stable RC is a natural extension of RC. However, its positive features come at a price: stable RC re-introduces one of the undesirable features of other nonmonotonic logics, namely, deductive closures may not exist and may not be unique.
Variable Elimination in Binary CSPs (Extended Abstract)
Authors: Cooper Martin C. , El Mouelhi Achref , Terrioux Cyril
Published: July 2020

We investigate rules which allow variable elimination in binary CSP (constraint satisfaction problem) instances while conserving satisfiability. We propose new rules and compare them, both theoretically and experimentally. We give optimised algorithms to apply these rules and show that each defines a novel tractable class. Using our variable-elimination rules in preprocessing allowed us to solve more benchmark problems than without.
Algorithms for Estimating the Partition Function of Restricted Boltzmann Machines (Extended Abstract)
Authors: Krause Oswin , Fischer Asja , Igel Christian
Published: July 2020

Estimating the normalization constants (partition functions) of energy-based probabilistic models (Markov random fields) with a high accuracy is required for measuring performance, monitoring the training progress of adaptive models, and conducting likelihood ratio tests. We devised a unifying theoretical framework for algorithms for estimating the partition function, including Annealed Importance Sampling (AIS) and Bennett's Acceptance Ratio method (BAR). The unification reveals conceptual similarities of and differences between different approaches and suggests new algorithms. The framework is based on a generalized form of Crooks' equality, which links the expectation over a distribution of samples generated by a transition operator to the expectation over the distribution induced by the reversed operator.

Different ways of sampling, such as parallel

tempering and path sampling, are covered by the framework.

We performed experiments in which we estimated the partition function of restricted Boltzmann

machines (RBMs) and Ising models. We found that BAR using parallel

tempering worked well with a small number of bridging distributions,

while path sampling based AIS performed best with many bridging

distributions. The normalization constant is measured w.r.t.~a reference distribution, and the choice of this distribution turned out to be very important in our experiments.

Overall, BAR gave the best empirical results, outperforming AIS.
Point at the Triple: Generation of Text Summaries from Knowledge Base Triples (Extended Abstract)
Authors: Vougiouklis Pavlos , Maddalena Eddy , Hare Jonathon , Simperl Elena
Published: July 2020

We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.
A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)
Authors: Leeuwenberg Artuur , Moens Marie-Francine
Published: July 2020

Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues.

Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
Formulas Free From Inconsistency: An Atom-Centric Characterization in Priest's Minimally Inconsistent LP (Extended Abstract)
Authors: Mu Kedian
Published: July 2020

As one of fundamental properties to characterize inconsistency measures for knowledge bases, the property of free formula independence captures well the intuition that free formulas are independent of the amount of inconsistency in a knowledge base for cases where inconsistency is characterized in terms of minimal inconsistent subsets. But it has been argued that not all the free formulas are independent of inconsistency in some other contexts of inconsistency characterization. In this paper, we propose a notion of Bi-free formula to describe formulas that are free from inconsistency in both syntactic characterization and paraconsistent models in the framework of Priest's minimally inconsistent LP. Then we propose the property of Bi-free formula independence, which is more suitable for characterizing the role of formulas free from inconsistency in measuring inconsistency from both syntactic and semantic perspectives.