pwc-archive/papers-with-abstracts huggingface dataset:
Framing Legitimacy in CSR: A Corpus of Chinese and American Petroleum Company CSR Reports and Preliminary Analysis
Authors: Jieyu Chen, Kathleen Ahrens, Chu-Ren Huang
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6

We examine how Chinese and American oil companies use the gain- and loss-framed BUILDING source domain to legitimize their business in Corporate Social Responsibility (CSR) reports. Gain and loss frames can create legitimacy because they can ethically position an issue. We will focus on oil companies in China and the U.S. because different socio-cultural contexts in these two countries can potentially lead to different legitimation strategies in CSR reports, which can shed light on differences in Chinese and American CSR. All of the oil companies in our data are on the Fortune 500 list (2020). The results showed that Chinese oil companies used BUILDING metaphors more frequently than American oil companies. The most frequent keyword in Chinese CSRs “build” highlights environmental achievements in compliance with governments’ policies. American CSRs often used the metaphorical verb “support” to show their alignment with environmental policies and the interests of different stakeholders. The BUILDING source domain was used more often as gain frames in both Chinese and American CSR reports to show how oil companies create benefits for different stakeholders.
MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain
Authors: Aleksandra Gabryszak, Philippe Thomas
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children. The data can be used to identify hurdles and improve travel planning for vulnerable passengers, as well as to monitor a perception of transportation businesses regarding the social inclusion of all passengers. The data is publicly available under: https://github.com/DFKI-NLP/sim3s-corpus
Detecting Violation of Human Rights via Social Media
Authors: Yash Pilankar, Rejwanul Haque, Mohammed Hasanuzzaman, Paul Stynes, Pramod Pathak
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6
Tasks: Relation

Social media is not just meant for entertainment, it provides platforms for sharing information, news, facts and events. In the digital age, activists and numerous users are seen to be vocal regarding human rights and their violations in social media. However, their voices do not often reach to the targeted audience and concerned human rights organization. In this work, we aimed at detecting factual posts in social media about violation of human rights in any part of the world. The end product of this research can be seen as an useful asset for different peacekeeping organizations who could exploit it to monitor real-time circumstances about any incident in relation to violation of human rights. We chose one of the popular micro-blogging websites, Twitter, for our investigation. We used supervised learning algorithms in order to build human rights violation identification (HRVI) models which are able to identify Tweets in relation to incidents of human right violation. For this, we had to manually create a data set, which is one of the contributions of this research. We found that our classification models that were trained on this gold-standard dataset performed excellently in classifying factual Tweets about human rights violation, achieving an accuracy of upto 93% on hold-out test set.
Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model
Authors: Lu Lu, Jinghang Gu, Chu-Ren Huang
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6
Tasks: Diversity

Inclusion, as one of the foundations in the diversity, equity, and inclusion initiative, concerns the degree of being treated as an ingroup member in a workplace. Despite of its importance in a corporate’s ecosystem, the inclusion strategies and its performance are not adequately addressed in corporate social responsibility (CSR) and CSR reporting. This study proposes a machine learning and big data-based model to examine inclusion through the use of stereotype content in actual language use. The distribution of the stereotype content in general corpora of a given society is utilized as a baseline, with which texts about corporate texts are compared. This study not only propose a model to identify and classify inclusion in language use, but also provides insights to measure and track progress by including inclusion in CSR reports as a strategy to build an inclusive corporate team.
Towards Classification of Legal Pharmaceutical Text using GAN-BERT
Authors: Tapan Auti, Rajdeep Sarkar, Bernardo Stearns, Atul Kr. Ojha, Arindam Paul, Michaela Comerford, Jay Megaro, John Mariano, Vall Herard, John P. McCrae
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6
Tasks: Classification, Sentence, Sentence Classification, Specificity, text-classification, Text Classification

Pharmaceutical text classification is an important area of research for commercial and research institutions working in the pharmaceutical domain. Addressing this task is challenging due to the need of expert verified labelled data which can be expensive and time consuming to obtain. Towards this end, we leverage predictive coding methods for the task as they have been shown to generalise well for sentence classification. Specifically, we utilise GAN-BERT architecture to classify pharmaceutical texts. To capture the domain specificity, we propose to utilise the BioBERT model as our BERT model in the GAN-BERT framework. We conduct extensive evaluation to show the efficacy of our approach over baselines on multiple metrics.
The Performance Impact of Combining Agent Factorization with Different Learning Algorithms for Multiagent Coordination
Authors: Andreas Kallinteris, Stavros Orfanoudakis, Georgios Chalkiadakis
Date: 2022-09-09 00:00:00
Proceeding: SETN 2022 9
Tasks: Management, Policy Gradient Methods

Factorizing a multiagent system refers to partitioning the state-
action space to individual agents and defining the interactions be-
tween those agents. This so-called agent factorization is of much im-
portance in real-world industrial settings, and is a process that can
have significant performance implications. In this work, we explore
if the performance impact of agent factorization is different when
using different learning algorithms in multiagent coordination set-
tings. We evaluated six different agent factorization instances—or
agent definitions—in the warehouse traffic management domain,
comparing the performance of (mainly) two learning algorithms
suitable for learning coordinated multiagent policies: the Evolu-
tionary Strategies (ES), and a genetic algorithm (CCEA) previously
used in this setting. Our results demonstrate that different learning
algorithms are affected in different ways by alternative agent defi-
nitions. Given this, we can deduce that many important multiagent
coordination problems can potentially be solved by an appropriate
agent factorization in conjunction with an appropriate choice of
a learning algorithm. Moreover, our work shows that ES is an ef-
fective learning algorithm for the warehouse traffic management
domain; while, interestingly, celebrated policy gradient methods
do not fare well in this complex real-world problem setting.
Stock price prediction using Generative Adversarial Networks
Authors: HungChun Lin, Chen Chen, Gaofeng Huang, Amir Jafari
Date: 2021-04-02 00:00:00
Proceeding: Journal of Computer Science 2021 4
Tasks: Fraud Detection, Generative Adversarial Network, Management, Portfolio Optimization, Prediction, Stock Market Prediction, Stock Prediction, Stock Price Prediction, Time Series Analysis

Deep learning is an exciting topic. It has been utilized in many areas owing to its strong potential. For example, it has been widely used in the financial area which is vital to the society, such as high-frequency trading, portfolio optimization, fraud detection and risk management. Stock market prediction is one of the most popular and valuable areas in finance. In this paper, it proposes a stock prediction model using Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU) used as a generator that inputs historical stock price and generates future stock price and Convolutional Neural Network (CNN) as a discriminator to discriminate between the real stock price and generated stock price. Different from the traditional methods, which limited the forecasting on one-step-ahead only, by contrast, using the deep learning algorithm is possible to conduct the multi-step ahead prediction more accurately. In this study, it chose the Apple Inc. stock closing price as the target price, with features such as S&P 500 index, NASDAQ Composite index, U.S. Dollar index, etc. In addition, FinBert has been utilized to generate a news sentiment index for Apple Inc. as an additional predicting feature. Finally, this paper compares the proposed GAN model results with the baseline model.
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Authors: Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister
Date: 2021-12-21 00:00:00
Tasks: Anomaly Detection, Clustering, Deep Clustering, Image Clustering

We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike object-centered image clustering, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods. We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings. The weight defines the importance of instances (i.e., patch embeddings) in the bag, which may highlight defective regions. We compute weights in an unsupervised way or in a semi-supervised way when labeled normal data is available. Extensive experimental studies show the effectiveness of the proposed clustering framework along with a novel distance function upon exist-ing multiple instance or deep clustering frameworks. Over-all, our framework achieves 0.451 and 0.674 normalized mutual information scores on MVTec object and texture categories and further improve with a few labeled normal data (0.577, 0.669), far exceeding the baselines (0.244, 0.273) or state-of-the-art deep clustering methods (0.176, 0.277).
A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos
Authors: Anil Batra, Shreyank N Gowda, Frank Keller, Laura Sevilla-Lara
Date: 2022-09-30 00:00:00
Tasks: Dense Video Captioning, Segmentation

Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Procedure Segmentation and Summarization (PSS). In this paper, we take a closer look at PSS and propose three fundamental improvements over current methods. The segmentation task is critical, as generating a correct summary requires each step of the procedure to be correctly identified. However, current segmentation metrics often overestimate the segmentation quality because they do not consider the temporal order of segments. In our first contribution, we propose a new segmentation metric that takes into account the order of segments, giving a more reliable measure of the accuracy of a given predicted segmentation. Current PSS methods are typically trained by proposing segments, matching them with the ground truth and computing a loss. However, much like segmentation metrics, existing matching algorithms do not consider the temporal order of the mapping between candidate segments and the ground truth. In our second contribution, we propose a matching algorithm that constrains the temporal order of segment mapping, and is also differentiable. Lastly, we introduce multi-modal feature training for PSS, which further improves segmentation. We evaluate our approach on two instructional video datasets (YouCook2 and Tasty) and observe an improvement over the state-of-the-art of $\sim7\%$ and $\sim2.5\%$ for procedure segmentation and summarization, respectively.
Modeling dispersive silver in the electrodynamic lattice-Boltzmann method using complex-conjugate pole-residue pairs
Authors: Cael Warner, Loïc Markley, Kenneth J. Chau
Date: 2022-09-30 00:00:00

The polarization density of a broadband electrodynamic lattice-Boltzmann method (ELBM) is generalized to represent frequency-dispersion of materials interacting with electromagnetic waves. The frequency-dependent refractive index and extinction coefficient are modeled using complex-conjugate pole-residue pairs in an auxiliary-differential-equation (ADE). Electric and magnetic fields are evaluated on a single lattice, ensuring a stable numerical solution up to the Nyquist limit. The electric and magnetic fields from the ELBM are compared with the electric and magnetic fields from the finite-difference-time-domain (FDTD) method. Accurate transmittance of a 100 nm silver slab is extracted from the transmitted power spectrum of a broadband Dirac-delta wave-function for photon energies ranging from 0.125-5 eV. Given this capability, the ELBM with an ADE is an accurate and computationally efficient method for modeling broadband frequency-dispersion of materials.
Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple Sizes
Authors: Yahia Zakaria, Magda Fayek, Mayada Hadhoud
Date: 2022-09-29 00:00:00
Tasks: Diversity, Sokoban

A level generator is a tool that generates game levels from noise. Training a generator without a dataset suffers from feedback sparsity, since it is unlikely to generate a playable level via random exploration. A common solution is shaped rewards, which guides the generator to achieve subgoals towards level playability, but they consume effort to design and require game-specific domain knowledge. This paper proposes a novel approach to train generators without datasets or shaped rewards by learning at multiple level sizes starting from small sizes and up to the desired sizes. The denser feedback at small sizes negates the need for shaped rewards. Additionally, the generators learn to build levels at various sizes, including sizes they were not trained for. We apply our approach to train recurrent auto-regressive generative flow networks (GFlowNets) for controllable level generation. We also adapt diversity sampling to be compatible with GFlowNets. The results show that our generators create diverse playable levels at various sizes for Sokoban, Zelda, and Danger Dave. When compared with controllable reinforcement learning level generators for Sokoban, the results show that our generators achieve better controllability and competitive diversity, while being 9x faster at training and level generation.
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Authors: Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan
Date: 2022-09-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we propose a general framework that unifies model-based and model-free RL, and an Admissible Bellman Characterization (ABC) class that subsumes nearly all Markov Decision Process (MDP) models in the literature for tractable RL. We propose a novel estimation function with decomposable structural properties for optimization-based exploration and the functional eluder dimension as a complexity measure of the ABC class. Under our framework, a new sample-efficient algorithm namely OPtimization-based ExploRation with Approximation (OPERA) is proposed, achieving regret bounds that match or improve over the best-known results for a variety of MDP models. In particular, for MDPs with low Witness rank, under a slightly stronger assumption, OPERA improves the state-of-the-art sample complexity results by a factor of $dH$. Our framework provides a generic interface to design and analyze new RL models and algorithms.
Beyond Bayes-optimality: meta-learning what you know you don't know
Authors: Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein, Elliot Catt, Kevin Li, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, Marcus Hutter, Christopher Summerfield, Shane Legg, Pedro Ortega
Date: 2022-09-30 00:00:00
Tasks: Decision Making, Meta-Learning, Sensitivity

Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rather than an explicit modeling assumption. Bayes-optimal agents are risk-neutral, since they solely attune to the expected return, and ambiguity-neutral, since they act in new situations as if the uncertainty were known. This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge. Humans are also known to be averse to ambiguity and sensitive to risk in ways that aren't Bayes-optimal, indicating that such sensitivity can confer advantages, especially in safety-critical situations. How can we extend the meta-learning protocol to generate risk- and ambiguity-sensitive agents? The goal of this work is to fill this gap in the literature by showing that risk- and ambiguity-sensitivity also emerge as the result of an optimization problem using modified meta-training algorithms, which manipulate the experience-generation process of the learner. We empirically test our proposed meta-training algorithms on agents exposed to foundational classes of decision-making experiments and demonstrate that they become sensitive to risk and ambiguity.
Protein structure generation via folding diffusion
Authors: Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini
Date: 2022-09-30 00:00:00
Tasks: Denoising, Protein Structure Prediction

The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientation of the constituent amino acid residues, and generate new structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins biologically twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release the first open-source codebase and trained models for protein structure diffusion.
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Authors: Hung Nghiep Tran, Atsuhiro Takasu
Date: 2022-09-30 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Tensor Decomposition, World Knowledge

Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensor-decomposition-based models, such as ComplEx, provide a good trade-off between efficiency and expressiveness, that is crucial because of the large size of real world knowledge graphs. The recent multi-partition embedding interaction (MEI) model subsumes these models by using the block term tensor format and provides a systematic solution for the trade-off. However, MEI has several drawbacks, some of which carried from its subsumed tensor-decomposition-based models. In this paper, we address these drawbacks and introduce the Multi-partition Embedding Interaction iMproved beyond block term format (MEIM) model, with independent core tensor for ensemble effects and soft orthogonality for max-rank mapping, in addition to multi-partition embedding. MEIM improves expressiveness while still being highly efficient, helping it to outperform strong baselines and achieve state-of-the-art results on difficult link prediction benchmarks using fairly small embedding sizes. The source code is released at https://github.com/tranhungnghiep/MEIM-KGE.
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Authors: Mahdi Morafah, Saeed Vahidian, Chen Chen, Mubarak Shah, Bill Lin
Date: 2022-09-30 00:00:00
Tasks: Federated Learning

Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope with the statistical heterogeneity, previous works incorporated a proximal term in local optimization or modified the model aggregation scheme at the server side or advocated clustered federated learning approaches where the central server groups agent population into clusters with jointly trainable data distributions to take the advantage of a certain level of personalization. While effective, they lack a deep elaboration on what kind of data heterogeneity and how the data heterogeneity impacts the accuracy performance of the participating clients. In contrast to many of the prior federated learning approaches, we demonstrate not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for the FL participants. Our observations are intuitive: (1) Dissimilar labels of clients (label skew) are not necessarily considered data heterogeneity, and (2) the principal angle between the agents' data subspaces spanned by their corresponding principal vectors of data is a better estimate of the data heterogeneity. Our code is available at https://github.com/MMorafah/FL-SC-NIID.
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
Authors: Rindranirina Ramamonjison, Haley Li, Timothy T. Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang
Date: 2022-09-30 00:00:00

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains.