pwc-archive/papers-with-abstracts huggingface dataset:
Extending a Physics-Based Constitutive Model using Genetic Programming
Authors: Gabriel Kronberger, Evgeniya Kabliman, Johannes Kronsteiner, Michael Kommenda
Date: 2021-08-03 00:00:00

In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions. A major drawback is the calibration of model parameters that depend on processing conditions. Currently, these parameters must be optimized to fit measured data since their relations to processing conditions (e.g. deformation temperature, strain rate) are not fully understood. We present a new approach that identifies the functional dependency of calibration parameters from processing conditions based on genetic programming. We propose two (explicit and implicit) methods to identify these dependencies and generate short interpretable expressions. The approach is used to extend a physics-based constitutive model for deformation processes. This constitutive model operates with internal material variables such as a dislocation density and contains a number of parameters, among them three calibration parameters. The derived expressions extend the constitutive model and replace the calibration parameters. Thus, interpolation between various processing parameters is enabled. Our results show that the implicit method is computationally more expensive than the explicit approach but also produces significantly better results.
An Interpretable Music Similarity Measure Based on Path Interestingness
Authors: Giovanni Gabbolini, Derek Bridge
Date: 2021-08-03 00:00:00

We introduce a novel and interpretable path-based music similarity measure. Our similarity measure assumes that items, such as songs and artists, and information about those items are represented in a knowledge graph. We find paths in the graph between a seed and a target item; we score those paths based on their interestingness; and we aggregate those scores to determine the similarity between the seed and the target. A distinguishing feature of our similarity measure is its interpretability. In particular, we can translate the most interesting paths into natural language, so that the causes of the similarity judgements can be readily understood by humans. We compare the accuracy of our similarity measure with other competitive path-based similarity baselines in two experimental settings and with four datasets. The results highlight the validity of our approach to music similarity, and demonstrate that path interestingness scores can be the basis of an accurate and interpretable similarity measure.
iART: A Search Engine for Art-Historical Images to Support Research in the Humanities
Authors: Matthias Springstein, Stefanie Schneider, Javad Rahnama, Eyke Hüllermeier, Hubertus Kohle, Ralph Ewerth
Date: 2021-08-03 00:00:00
Tasks: Clustering, Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision. The system integrates various machine learning techniques for keyword- and content-based image retrieval as well as category formation via clustering. An intuitive GUI supports users to define queries and explore results. By using a state-of-the-art cross-modal deep learning approach, it is possible to search for concepts that were not previously detected by trained classification models. Art-historical objects from large, openly licensed collections such as Amsterdam Rijksmuseum and Wikidata are made available to users.
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks
Authors: Chuang Liu, Shimin Yu, Ying Huang, Zi-Ke Zhang
Date: 2021-08-03 00:00:00
Tasks: Decision Making, Feature Engineering, Link Prediction, Network Embedding, Prediction, Recommendation Systems

Link and sign prediction in complex networks bring great help to decision-making and recommender systems, such as in predicting potential relationships or relative status levels. Many previous studies focused on designing the special algorithms to perform either link prediction or sign prediction. In this work, we propose an effective model integration algorithm consisting of network embedding, network feature engineering, and an integrated classifier, which can perform the link and sign prediction in the same framework. Network embedding can accurately represent the characteristics of topological structures and cooperate with the powerful network feature engineering and integrated classifier can achieve better prediction. Experiments on several datasets show that the proposed model can achieve state-of-the-art or competitive performance for both link and sign prediction in spite of its generality. Interestingly, we find that using only very low network embedding dimension can generate high prediction performance, which can significantly reduce the computational overhead during training and prediction. This study offers a powerful methodology for multi-task prediction in complex networks.
The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale
Authors: Michael Soprano, Kevin Roitero, David La Barbera, Davide Ceolin, Damiano Spina, Stefano Mizzaro, Gianluca Demartini
Date: 2021-08-03 00:00:00
Tasks: Informativeness, Misinformation

Recent work has demonstrated the viability of using crowdsourcing as a tool for evaluating the truthfulness of public statements. Under certain conditions such as: (1) having a balanced set of workers with different backgrounds and cognitive abilities; (2) using an adequate set of mechanisms to control the quality of the collected data; and (3) using a coarse grained assessment scale, the crowd can provide reliable identification of fake news. However, fake news are a subtle matter: statements can be just biased ("cherrypicked"), imprecise, wrong, etc. and the unidimensional truth scale used in existing work cannot account for such differences. In this paper we propose a multidimensional notion of truthfulness and we ask the crowd workers to assess seven different dimensions of truthfulness selected based on existing literature: Correctness, Neutrality, Comprehensibility, Precision, Completeness, Speaker's Trustworthiness, and Informativeness. We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments. A comprehensive analysis of crowdsourced judgments shows that: (1) the crowdsourced assessments are reliable when compared to an expert-provided gold standard; (2) the proposed dimensions of truthfulness capture independent pieces of information; (3) the crowdsourcing task can be easily learned by the workers; and (4) the resulting assessments provide a useful basis for a more complete estimation of statement truthfulness.
Medical Literature Mining and Retrieval in a Conversational Setting
Authors: Souvik Das, Sougata Saha, Rohini K. Srihari
Date: 2021-07-23 00:00:00
Tasks: Information Retrieval, Literature Mining, Retrieval

The Covid-19 pandemic has caused a spur in the medical research literature. With new research advances in understanding the virus, there is a need for robust text mining tools which can process, extract and present answers from the literature in a concise and consumable way. With a DialoGPT based multi-turn conversation generation module, and BM-25 \& neural embeddings based ensemble information retrieval module, in this paper we present a conversational system, which can retrieve and answer coronavirus-related queries from the rich medical literature, and present it in a conversational setting with the user. We further perform experiments to compare neural embedding-based document retrieval and the traditional BM25 retrieval algorithm and report the results.
FEBR: Expert-Based Recommendation Framework for beneficial and personalized content
Authors: Mohamed Lechiakh, Alexandre Maurer
Date: 2021-07-17 00:00:00
Tasks: Recommendation Systems

So far, most research on recommender systems focused on maintaining long-term user engagement and satisfaction, by promoting relevant and personalized content. However, it is still very challenging to evaluate the quality and the reliability of this content. In this paper, we propose FEBR (Expert-Based Recommendation Framework), an apprenticeship learning framework to assess the quality of the recommended content on online platforms. The framework exploits the demonstrated trajectories of an expert (assumed to be reliable) in a recommendation evaluation environment, to recover an unknown utility function. This function is used to learn an optimal policy describing the expert's behavior, which is then used in the framework to provide high-quality and personalized recommendations. We evaluate the performance of our solution through a user interest simulation environment (using RecSim). We simulate interactions under the aforementioned expert policy for videos recommendation, and compare its efficiency with standard recommendation methods. The results show that our approach provides a significant gain in terms of content quality, evaluated by experts and watched by users, while maintaining almost the same watch time as the baseline approaches.
Inscriptis -- A Python-based HTML to text conversion library optimized for knowledge extraction from the Web
Authors: Albert Weichselbraun
Date: 2021-07-12 00:00:00

Inscriptis provides a library, command line client and Web service for converting HTML to plain text. Its development has been triggered by the need to obtain accurate text representations for knowledge extraction tasks that preserve the spatial alignment of text without drawing upon heavyweight, browser-based solutions such as Selenium. In contrast to related software packages, Inscriptis (i) provides a layout-aware conversion of HTML that more closely resembles the rendering obtained from standard Web browsers; and (ii) supports annotation rules, i.e., user-provided mappings that allow for annotating the extracted text based on structural and semantic information encoded in HTML tags and attributes. These unique features ensure that downstream knowledge extraction components can operate on accurate text representations, and may even use information on the semantics and structure of the original HTML document.
PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling
Authors: Xiaoxue Zang, Lijuan Liu, Maria Wang, Yang song, Hao Zhang, Jindong Chen
Date: 2021-07-06 00:00:00
Proceeding: ACL 2021 5
Tasks: Image Retrieval, Retrieval

We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in onlin emessaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context. In addition, for both tasks, we provide baseline models using the state-of-the-art models and report their benchmark performances. The best image retrieval model achieves 10.4% recall@1 (out of 1000 candidates) and the best photo intent prediction model achieves 58.1% F1 score, indicating that the dataset presents interesting yet challenging real-world problems. We are releasing PhotoChat to facilitate future research work among the community.
Real-time Spatio-temporal Action Localization via Learning Motion Representation
Authors: Yuanzhong Liu, Zhigang Tu, Liyu Lin, Xing Xie, and Qianqing Qin
Date: 2020-11-30 00:00:00
Proceeding: ACCV 2020 11
Tasks: Action Classification, Action Localization, Knowledge Distillation, Optical Flow Estimation, Spatio-Temporal Action Localization, Temporal Action Localization

Abstract. Most state-of-the-art spatio-temporal (S-T) action localization methods explicitly use optical flow as auxiliary motion information. Although the combination of optical flow and RGB significantly improves the performance, optical flow estimation brings a large amount of computational cost and the whole network is not end-to-end trainable. These shortcomings hinder the interactive fusion between motion information and RGB information, and greatly limit its real-world applications. In this paper, we exploit better ways to use motion information in a unified end-to-end trainable network architecture. First, we use knowledge distillation to enable the 3D-Convolutional branch to learn motion information from RGB inputs. Second, we propose a novel motion cue called short-range-motion (SRM) module to enhance the 2DConvolutional branch to learn RGB information and dynamic motion information. In this strategy, flow computation at test time is avoided. Finally, we apply our methods to learn powerful RGB-motion representations for action classification and localization. Experimental results show that our method significantly outperforms the state-of-the-arts on
dataset benchmarks J-HMDB-21 and UCF101-24 with an impressive improvement of ∼ 8% and ∼ 3%.
Semantic Enrichment of Pretrained Embedding Output for Unsupervised IR
Authors: Edmund Dervakos, Giorgos Filandrianos, Konstantinos Thomas, Alexios Mandalios, Chrysoula Zerva, Giorgos Stamou
Date: 2021-03-24 00:00:00
Proceeding: AAAI-MAKE 2021 3
Tasks: Information Retrieval, Navigate

The rapid growth of scientific literature in the biomedical and clinical domain has significantly com-
plicated the identification of information of interest by researchers as well as other practitioners. More
importantly, the rapid emergence of new topics and findings, often hinders the performance of super-
vised approaches, due to the lack of relevant annotated data. The global COVID-19 pandemic further
highlighted the need to query and navigate uncharted ground in the scientific literature in a prompt
and efficient way.
In this paper we investigate the potential of semantically enhancing deep transformer architectures
using SNOMED-CT in order to answer user queries in an unsupervised manner. Our proposed system
attempts to filter and re-rank documents related to a query that were initially retrieved using BERT
models. To achieve that, we enhance queries and documents with SNOMED-CT concepts and then im-
pose filters on concept co-occurrence between them. We evaluate this approach on OHSUMED dataset
and show competitive performance and we also present our approach for adapting such an approach to
full papers, such as kaggle’s CORD-19 full-text dataset challenge.
Escaping Arrow's Theorem: The Advantage-Standard Model
Authors: Wesley H. Holliday, Mikayla Kelley
Date: 2021-08-02 00:00:00
Tasks: model

There is an extensive literature in social choice theory studying the consequences of weakening the assumptions of Arrow's Impossibility Theorem. Much of this literature suggests that there is no escape from Arrow-style impossibility theorems, while remaining in an ordinal preference setting, unless one drastically violates the Independence of Irrelevant Alternatives (IIA). In this paper, we present a more positive outlook. We propose a model of comparing candidates in elections, which we call the Advantage-Standard (AS) model. The requirement that a collective choice rule (CCR) be representable by the AS model captures a key insight of IIA but is weaker than IIA; yet it is stronger than what is known in the literature as weak IIA (two profiles alike on $x,y$ cannot have opposite strict social preferences on $x$ and $y$). In addition to motivating violations of IIA, the AS model makes intelligible violations of another Arrovian assumption: the negative transitivity of the strict social preference relation $P$. While previous literature shows that only weakening IIA to weak IIA or only weakening negative transitivity of $P$ to acyclicity still leads to impossibility theorems, we show that jointly weakening IIA to AS representability and weakening negative transitivity of $P$ leads to no such impossibility theorems. Indeed, we show that several appealing CCRs are AS representable, including even transitive CCRs.
Learning a Neural Diff for Speech Models
Authors: Jonathan Macoskey, Grant P. Strimel, Ariya Rastrow
Date: 2021-08-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Model Compression, speech-recognition, Speech Recognition, Spoken Language Understanding

As more speech processing applications execute locally on edge devices, a set of resource constraints must be considered. In this work we address one of these constraints, namely over-the-network data budgets for transferring models from server to device. We present neural update approaches for release of subsequent speech model generations abiding by a data budget. We detail two architecture-agnostic methods which learn compact representations for transmission to devices. We experimentally validate our techniques with results on two tasks (automatic speech recognition and spoken language understanding) on open source data sets by demonstrating when applied in succession, our budgeted updates outperform comparable model compression baselines by significant margins.
Amortized Neural Networks for Low-Latency Speech Recognition
Authors: Jonathan Macoskey, Grant P. Strimel, Jinru Su, Ariya Rastrow
Date: 2021-08-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We introduce Amortized Neural Networks (AmNets), a compute cost- and latency-aware network architecture particularly well-suited for sequence modeling tasks. We apply AmNets to the Recurrent Neural Network Transducer (RNN-T) to reduce compute cost and latency for an automatic speech recognition (ASR) task. The AmNets RNN-T architecture enables the network to dynamically switch between encoder branches on a frame-by-frame basis. Branches are constructed with variable levels of compute cost and model capacity. Here, we achieve variable compute for two well-known candidate techniques: one using sparse pruning and the other using matrix factorization. Frame-by-frame switching is determined by an arbitrator network that requires negligible compute overhead. We present results using both architectures on LibriSpeech data and show that our proposed architecture can reduce inference cost by up to 45\% and latency to nearly real-time without incurring a loss in accuracy.
A Game-Theoretic Framework for Controlled Islanding in the Presence of Adversaries
Authors: Luyao Niu, Dinuka Sahabandu, Andrew Clark, Radha Poovendran
Date: 2021-08-03 00:00:00

Controlled islanding effectively mitigates cascading failures by partitioning the power system into a set of disjoint islands. In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adversary. We formulate the interaction between the grid operator and adversary using a game-theoretic framework. The grid operator first computes a controlled islanding strategy, along with the power generation for the post-islanding system to guarantee stability. The adversary observes the strategies of the grid operator. The adversary then identifies critical substations of the power system to compromise and trips the transmission lines that are connected with compromised substations. For our game formulation, we propose a double oracle algorithm based approach that solves the best response for each player. We show that the best responses for the grid operator and adversary can be formulated as mixed integer linear programs. In addition, the best response of the adversary is equivalent to a submodular maximization problem under a cardinality constraint, which can be approximated up to a $(1-\frac{1}{e})$ optimality bound in polynomial time. We compare the proposed approach with a baseline where the grid operator computes an islanding strategy by minimizing the power flow disruption without considering the possible response from the adversary. We evaluate both approaches using IEEE 9-bus, 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus power system case study data. Our proposed approach achieves better performance than the baseline in about $44\%$ of test cases, and on average it incurs about 12.27 MW less power flow disruption.
Distributed System Identification for Linear Stochastic Systems with Binary Sensors
Authors: Kewei Fu, Han-Fu Chen, Wenxiao Zhao
Date: 2021-08-03 00:00:00
Tasks: Distributed Optimization

The problem of distributed identification of linear stochastic system with unknown coefficients over time-varying networks is considered. For estimating the unknown coefficients, each agent in the network can only access the input and the binary-valued output of the local system. Compared with the existing works on distributed optimization and estimation, the binary-valued local output observation considered in the paper makes the problem challenging. By assuming that the agent in the network can communicate with its adjacent neighbours, a stochastic approximation based distributed identification algorithm is proposed, and the consensus and convergence of the estimates are established. Finally, a numerical example is given showing that the simulation results are consistent with the theoretical analysis.
Optimal Management of the Peak Power Penalty for Smart Grids Using MPC-based Reinforcement Learning
Authors: WenQi Cai, Hossein N. Esfahani, Arash B. Kordabad, Sébastien Gros
Date: 2021-08-03 00:00:00
Tasks: Management, Model Predictive Control, Reinforcement Learning (RL)

The cost of the power distribution infrastructures is driven by the peak power encountered in the system. Therefore, the distribution network operators consider billing consumers behind a common transformer in the function of their peak demand and leave it to the consumers to manage their collective costs. This management problem is, however, not trivial. In this paper, we consider a multi-agent residential smart grid system, where each agent has local renewable energy production and energy storage, and all agents are connected to a local transformer. The objective is to develop an optimal policy that minimizes the economic cost consisting of both the spot-market cost for each consumer and their collective peak-power cost. We propose to use a parametric Model Predictive Control (MPC)-scheme to approximate the optimal policy. The optimality of this policy is limited by its finite horizon and inaccurate forecasts of the local power production-consumption. A Deterministic Policy Gradient (DPG) method is deployed to adjust the MPC parameters and improve the policy. Our simulations show that the proposed MPC-based Reinforcement Learning (RL) method can effectively decrease the long-term economic cost for this smart grid problem.