pwc-archive/papers-with-abstracts huggingface dataset:
QAGAN: Adversarial Approach To Learning Domain Invariant Language Features
Authors: Shubham Shrivastava, Kaiyue Wang
Date: 2022-06-24 00:00:00
Tasks: Data Augmentation, Question Answering

Training models that are robust to data domain shift has gained an increasing interest both in academia and industry. Question-Answering language models, being one of the typical problem in Natural Language Processing (NLP) research, has received much success with the advent of large transformer models. However, existing approaches mostly work under the assumption that data is drawn from same distribution during training and testing which is unrealistic and non-scalable in the wild. In this paper, we explore adversarial training approach towards learning domain-invariant features so that language models can generalize well to out-of-domain datasets. We also inspect various other ways to boost our model performance including data augmentation by paraphrasing sentences, conditioning end of answer span prediction on the start word, and carefully designed annealing function. Our initial results show that in combination with these methods, we are able to achieve $15.2\%$ improvement in EM score and $5.6\%$ boost in F1 score on out-of-domain validation dataset over the baseline. We also dissect our model outputs and visualize the model hidden-states by projecting them onto a lower-dimensional space, and discover that our specific adversarial training approach indeed encourages the model to learn domain invariant embedding and bring them closer in the multi-dimensional space.
Text and author-level political inference using heterogeneous knowledge representations
Authors: Samuel Caetano da Silva, Ivandre Paraboni
Date: 2022-06-24 00:00:00

The inference of politically-charged information from text data is a popular research topic in Natural Language Processing (NLP) at both text- and author-level. In recent years, studies of this kind have been implemented with the aid of representations from transformers such as BERT. Despite considerable success, however, we may ask whether results may be improved even further by combining transformed-based models with additional knowledge representations. To shed light on this issue, the present work describes a series of experiments to compare alternative model configurations for political inference from text in both English and Portuguese languages. Results suggest that certain text representations - in particular, the combined use of BERT pre-trained language models with a syntactic dependency model - may outperform the alternatives across multiple experimental settings, making a potentially strong case for further research in the use of heterogeneous text representations in these and possibly other NLP tasks.
Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech
Authors: Florian Lux, Julia Koch, Ngoc Thang Vu
Date: 2022-06-24 00:00:00
Tasks: text-to-speech, Text to Speech

The cloning of a speaker's voice using an untranscribed reference sample is one of the great advances of modern neural text-to-speech (TTS) methods. Approaches for mimicking the prosody of a transcribed reference audio have also been proposed recently. In this work, we bring these two tasks together for the first time through utterance level normalization in conjunction with an utterance level speaker embedding. We further introduce a lightweight aligner for extracting fine-grained prosodic features, that can be finetuned on individual samples within seconds. We show that it is possible to clone the voice of a speaker as well as the prosody of a spoken reference independently without any degradation in quality and high similarity to both original voice and prosody, as our objective evaluation and human study show. All of our code and trained models are available, alongside static and interactive demos.
MVP: Multi-task Supervised Pre-training for Natural Language Generation
Authors: Tianyi Tang, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen
Date: 2022-06-24 00:00:00
Tasks: Text Generation

Pre-trained language models (PLMs) have achieved remarkable success in natural language generation (NLG) tasks. Up to now, most NLG-oriented PLMs are pre-trained in an unsupervised manner using the large-scale general corpus. In the meanwhile, an increasing number of models pre-trained with labeled data (i.e. "supervised pre-training") showcase superior performance compared to unsupervised pre-trained models. Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation. We collect a large-scale natural language generation corpus, MVPCorpus, from $77$ datasets over $11$ diverse NLG tasks. Then we unify these examples into a general text-to-text format to pre-train the text generation model MVP in a supervised manner. For each task, we further pre-train specific soft prompts to stimulate the model's capacity to perform a specific task. Our MVP model can be seen as a practice that utilizes recent instruction tuning on relatively small PLMs. Extensive experiments have demonstrated the effectiveness and generality of our MVP model in a number of NLG tasks, which achieves state-of-the-art performance on $13$ out of $17$ datasets, outperforming BART by $9.3\%$ and Flan-T5 by $5.8\%$.
Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System
Authors: Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie, Yajing Sun
Date: 2022-06-24 00:00:00

Persuasive strategy recognition task requires the system to recognize the adopted strategy of the persuader according to the conversation. However, previous methods mainly focus on the contextual information, little is known about incorporating the psychological feedback, i.e. emotion of the persuadee, to predict the strategy. In this paper, we propose a Cross-channel Feedback memOry Network (CFO-Net) to leverage the emotional feedback to iteratively measure the potential benefits of strategies and incorporate them into the contextual-aware dialogue information. Specifically, CFO-Net designs a feedback memory module, including strategy pool and feedback pool, to obtain emotion-aware strategy representation. The strategy pool aims to store historical strategies and the feedback pool is to obtain updated strategy weight based on feedback emotional information. Furthermore, a cross-channel fusion predictor is developed to make a mutual interaction between the emotion-aware strategy representation and the contextual-aware dialogue information for strategy recognition. Experimental results on \textsc{PersuasionForGood} confirm that the proposed model CFO-Net is effective to improve the performance on M-F1 from 61.74 to 65.41.
Evolution of Activation Functions for Deep Learning-Based Image Classification
Authors: Raz Lapid, Moshe Sipper
Date: 2022-06-24 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

Activation functions (AFs) play a pivotal role in the performance of neural networks. The Rectified Linear Unit (ReLU) is currently the most commonly used AF. Several replacements to ReLU have been suggested but improvements have proven inconsistent. Some AFs exhibit better performance for specific tasks, but it is hard to know a priori how to select the appropriate one(s). Studying both standard fully connected neural networks (FCNs) and convolutional neural networks (CNNs), we propose a novel, three-population, coevolutionary algorithm to evolve AFs, and compare it to four other methods, both evolutionary and non-evolutionary. Tested on four datasets -- MNIST, FashionMNIST, KMNIST, and USPS -- coevolution proves to be a performant algorithm for finding good AFs and AF architectures.
On the Limitations of Elo: Real-World Games, are Transitive, not Additive
Authors: Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel
Date: 2022-06-21 00:00:00
Tasks: Starcraft, Starcraft II

Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans.
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank
Authors: Harrie Oosterhuis
Date: 2022-06-24 00:00:00
Tasks: counterfactual, Learning-To-Rank

Click-based learning to rank (LTR) tackles the mismatch between click frequencies on items and their actual relevance. The approach of previous work has been to assume a model of click behavior and to subsequently introduce a method for unbiasedly estimating preferences under that assumed model. The success of this approach is evident in that unbiased methods have been found for an increasing number of behavior models and types of bias. This work aims to uncover the implicit limitations of the high-level prevalent approach in the counterfactual LTR field. Thus, in contrast with limitations that follow from explicit assumptions, our aim is to recognize limitations that the field is currently unaware of. We do this by inverting the existing approach: we start by capturing existing methods in generic terms, and subsequently, from these generic descriptions we derive the click behavior for which these methods can be unbiased. Our inverted approach reveals that there are indeed implicit limitations to the counterfactual LTR approach: we find counterfactual estimation can only produce unbiased methods for click behavior based on affine transformations. In addition, we also recognize previously undiscussed limitations of click-modelling and pairwise approaches to click-based LTR. Our findings reveal that it is impossible for existing approaches to provide unbiasedness guarantees for all plausible click behavior models.
Unsupervised Learning Algorithms for Keyword Extraction in an Undergraduate Thesis
Authors: Fred Torres-Cruz, Edelfre Flores, William E. Arcaya, Irenio L. Chagua, Marga I. Ingaluque
Date: 2022-06-23 00:00:00
Tasks: Keyword Extraction, Management

The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communication technologies, such as the platform for integrated research and academic work with responsibility (PILAR), which records information about research projects, such as titles, summaries, and keywords in their various modalities, have gained relevance and importance in the management of these. We proved algorithms with these records of research projects that have been analysed in this study, and predictions were made for each of the nine (09) models of unsupervised machine learning algorithms that were implemented for each of the 7430 records from the dataset. The most efficient way of extracting keywords for this dataset was the TF-IDF method, obtaining 72% accuracy and [0.4786, SD 0.0501] in average extraction time for each thesis file processed by this model.
GreenMicro: Identifying Microservices from Use Cases in Greenfield Development
Authors: Deepali Bajaj1, Anita Goel2, S. C. Gupta3
Date: 2022-06-13 00:00:00
Proceeding: IEEE 2022 6

Microservices architecture is a new paradigm for developing a software system as a
collection of independent services that communicate through lightweight protocols. In greenfield
development, identifying the microservices is not a trivial task, as there is no legacy code lying around and
no old development to start with. Thus, identification of microservices from requirements becomes an
important decision during the analysis and design phase. Use cases play a vital role in the requirements
analysis modeling phases in a model-driven software engineering process. It is a technique of capturing
high-level user functions and scope of the system. In this paper, we propose GreenMicro, an automatic
microservice identification technique that utilizes the use cases model and the database entities. Both
features are the artifacts of analysis and design phase that depict complete functionality of an overall
system. In essence, a collection of related use cases indicates a bounded context of the system that can be
grouped in a suitable way as microservices. Therefore, our approach GreenMicro clusters close-knit use
cases to recover meaningful microservices. We investigate and validate our approach on an in-house
proprietary web application and three sample benchmark applications. We have mapped our approach to
state-of-the-art software quality assessment attributes and have presented the results. Preliminary results are
motivating and the proposed methodology works as anticipated in producing functionally cohesive and
loosely coupled microservice candidate recommendations. Our approach enables the system architects to
identify microservice candidates at an early analysis and design phase of development.
Data-driven reduced order models using invariant foliations, manifolds and autoencoders
Authors: Robert Szalai
Date: 2022-06-24 00:00:00

This paper explores how to identify a reduced order model (ROM) from a physical system. A ROM captures an invariant subset of the observed dynamics. We find that there are four ways a physical system can be related to a mathematical model: invariant foliations, invariant manifolds, autoencoders and equation-free models. Identification of invariant manifolds and equation-free models require closed-loop manipulation of the system. Invariant foliations and autoencoders can also use off-line data. Only invariant foliations and invariant manifolds can identify ROMs, the rest identify complete models. Therefore, the common case of identifying a ROM from existing data can only be achieved using invariant foliations. Finding an invariant foliation requires approximating high-dimensional functions. For function approximation, we use polynomials with compressed tensor coefficients, whose complexity increases linearly with increasing dimensions. An invariant manifold can also be found as the fixed leaf of a foliation. This only requires us to resolve the foliation in a small neighbourhood of the invariant manifold, which greatly simplifies the process. Combining an invariant foliation with the corresponding invariant manifold provides an accurate ROM. We analyse the ROM in case of a focus type equilibrium, typical in mechanical systems. The nonlinear coordinate system defined by the invariant foliation or the invariant manifold distorts instantaneous frequencies and damping ratios, which we correct. Through examples we illustrate the calculation of invariant foliations and manifolds, and at the same time show that Koopman eigenfunctions and autoencoders fail to capture accurate ROMs under the same conditions.
Collective Spatial Keyword Querying
Authors: Xin Cao1, Gao Cong
Date: 2011-06-12 00:00:00
Proceeding: 2011 2011 6

With the proliferation of geo-positioning and geo-tagging, spatial
web objects that possess both a geographical location and a textual
description are gaining in prevalence, and spatial keyword queries
that exploit both location and textual description are gaining in
prominence. However, the queries studied so far generally focus
on finding individual objects that each satisfy a query rather than
finding groups of objects where the objects in a group collectively
satisfy a query.
We define the problem of retrieving a group of spatial web objects such that the group’s keywords cover the query’s keywords
and such that objects are nearest to the query location and have the
lowest inter-object distances. Specifically, we study two variants
of this problem, both of which are NP-complete. We devise exact
solutions as well as approximate solutions with provable approximation bounds to the problems. We present empirical studies that
offer insight into the efficiency and accuracy of the solutions.
Liquidity Risks in Lending Protocols: Evidence from Aave Protocol
Authors: Xiaotong Sun, Charalampos Stasinakis, Georgios Sermpinis
Date: 2022-06-23 00:00:00

Lending Protocols (LPs), as blockchain-based lending systems, allow any agents to borrow and lend cryptocurrencies. However, liquidity risks could occur, especially when salient loans are initiated by a particular group of borrowers. This paper proposes measurements of liquidity risks, focusing on both available liquidity and market concentration in LPs. By using Aave as a case study, we find that liquidity risks are highly volatile and show complex effects on Aave, and liquidity in Aave may affect across on-chain lending market. Compared to new users, regular users that repeatedly borrow cryptocurrencies may negatively affect Aave protocol, implying that user loyalty is a double-edged sword for LPs.
Navigating Incommensurability Between Ethnomethodology, Conversation Analysis, and Artificial Intelligence
Authors: Stuart Reeves
Date: 2022-06-19 00:00:00

Like many research communities, ethnomethodologists and conversation analysts have begun to get caught up -- yet again -- in the pervasive spectacle of surging interests in Artificial Intelligence (AI). Inspired by discussions amongst a growing network of researchers in ethnomethodology (EM) and conversation analysis (CA) traditions who nurse such interests, I started thinking about what things EM and the more EM end of conversation analysis might be doing about, for, or even with, fields of AI research. So, this piece is about the disciplinary and conceptual questions that might be encountered, and -- in my view -- may need addressing for engagements with AI research and its affiliates. Although I'm mostly concerned with things to be aware of as well as outright dangers, later on we can think about some opportunities. And throughout I will keep using 'we' to talk about EM&CA researchers; but this really is for convenience only -- I don't wish to ventriloquise for our complex research communities. All of the following should be read as emanating from my particular research history, standpoint etc., and treated (hopefully) as an invitation for further discussion amongst EM and CA researchers turning to technology and AI specifically.
Optimization of Geometric Constellation Shaping for Wiener Phase Noise Channels with Varying Channel Parameters
Authors: Andrej Rode, Laurent Schmalen
Date: 2022-06-24 00:00:00

We present a novel method to investigate the effects of varying channel parameters on geometrically shaped constellations for communication systems employing the blind phase search algorithm. We show that introduced asymmetries significantly improve performance if adapted to changing channel parameters.
Deep-Learning-Aided Distributed Clock Synchronization for Wireless Networks
Authors: Emeka Abakasanga, Nir Shlezinger, Ron Dabora
Date: 2022-06-24 00:00:00
Tasks: Deep Learning

The proliferation of wireless communications networks over the past decades, combined with the scarcity of the wireless spectrum, have motivated a significant effort towards increasing the throughput of wireless networks. One of the major factors which limits the throughput in wireless communications networks is the accuracy of the time synchronization between the nodes in the network, as a higher throughput requires higher synchronization accuracy. Existing time synchronization schemes, and particularly, methods based on pulse-coupled oscillators (PCOs), which are the focus of the current work, have the advantage of simple implementation and achieve high accuracy when the nodes are closely located, yet tend to achieve poor synchronization performance for distant nodes. In this study, we propose a robust PCO-based time synchronization algorithm which retains the simple structure of existing approaches while operating reliably and converging quickly for both distant and closely located nodes. This is achieved by augmenting PCO-based synchronization with deep learning tools that are trainable in a distributed manner, thus allowing the nodes to train their neural network component of the synchronization algorithm without requiring additional exchange of information or central coordination. The numerical results show that our proposed deep learning-aided scheme is notably robust to propagation delays resulting from deployments over large areas, and to relative clock frequency offsets. It is also shown that the proposed approach rapidly attains full (i.e., clock frequency and phase) synchronization for all nodes in the wireless network, while the classic model-based implementation does not.
Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach
Authors: Xia Jiang, Jian Zhang, Xiaoyu Shi, Jian Cheng
Date: 2022-06-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework to implement adaptive control for an electric platoon composed of CAVs and human-driven vehicles (HDVs) at a signalized intersection. Firstly, a Markov Decision Process (MDP) model is proposed to describe the decision process of the mixed platoon. Novel state representation and reward function are designed for the model to consider the behavior of the whole platoon. Secondly, in order to deal with the delayed reward, an Augmented Random Search (ARS) algorithm is proposed. The control policy learned by the agent can guide the longitudinal motion of the CAV, which serves as the leader of the platoon. Finally, a series of simulations are carried out in simulation suite SUMO. Compared with several state-of-the-art (SOTA) reinforcement learning approaches, the proposed method can obtain a higher reward. Meanwhile, the simulation results demonstrate the effectiveness of the delay reward, which is designed to outperform distributed reward mechanism} Compared with normal car-following behavior, the sensitivity analysis reveals that the energy can be saved to different extends (39.27%-82.51%) by adjusting the relative importance of the optimization goal. On the premise that travel delay is not sacrificed, the proposed control method can save up to 53.64% electric energy.