pwc-archive/papers-with-abstracts huggingface dataset:
Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees
Authors: Yaman Kumar Singla, Sriram Krishna, Rajiv Ratn Shah, Changyou Chen
Date: 2021-11-17 00:00:00

Automated Scoring (AS), the natural language processing task of scoring essays and speeches in an educational testing setting, is growing in popularity and being deployed across contexts from government examinations to companies providing language proficiency services. However, existing systems either forgo human raters entirely, thus harming the reliability of the test, or score every response by both human and machine thereby increasing costs. We target the spectrum of possible solutions in between, making use of both humans and machines to provide a higher quality test while keeping costs reasonable to democratize access to AS. In this work, we propose a combination of the existing paradigms, sampling responses to be scored by humans intelligently. We propose reward sampling and observe significant gains in accuracy (19.80% increase on average) and quadratic weighted kappa (QWK) (25.60% on average) with a relatively small human budget (30% samples) using our proposed sampling. The accuracy increase observed using standard random and importance sampling baselines are 8.6% and 12.2% respectively. Furthermore, we demonstrate the system's model agnostic nature by measuring its performance on a variety of models currently deployed in an AS setting as well as pseudo models. Finally, we propose an algorithm to estimate the accuracy/QWK with statistical guarantees (Our code is available at https://git.io/J1IOy).
User Response and Sentiment Prediction for Automatic Dialogue Evaluation
Authors: Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis, Yang Liu, Dilek Hakkani-Tur
Date: 2021-11-16 00:00:00
Tasks: Dialogue Evaluation, Open-Domain Dialog

Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of open-domain dialog systems. In this work we propose to use the sentiment of the next user utterance for turn or dialog level evaluation. Specifically we propose three methods: one that predicts the next sentiment directly, and two others that predict the next user utterance using an utterance or a feedback generator model and then classify its sentiment. Experiments show our model outperforming existing automatic evaluation metrics on both written and spoken open-domain dialogue datasets.
Facilitating reflection in teletandem through automatically generated conversation metrics and playback video
Authors: Aparajita Dey-Plissonneau, Hyowon Lee, Michael Scriney, Alan F. Smeaton, Vincent Pradier, Hamza Riaz
Date: 2021-11-16 00:00:00

This pilot study focuses on a tool called L2L that allows second language (L2) learners to visualise and analyse their Zoom interactions with native speakers. L2L uses the Zoom transcript to automatically generate conversation metrics and its playback feature with timestamps allows students to replay any chosen portion of the conversation for post-session reflection and self-review. This exploratory study investigates a seven-week teletandem project, where undergraduate students from an Irish University learning French (B2) interacted with their peers from a French University learning English (B2+) via Zoom. The data collected from a survey (N=43) and semi-structured interviews (N=35) show that the quantitative conversation metrics and qualitative review of the synchronous content helped raise students' confidence levels while engaging with native speakers. Furthermore, it allowed them to set tangible goals to improve their participation, and be more aware of what, why and how they are learning.
Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records
Authors: Tanish Tyagi, Colin G. Magdamo, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi, Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T. Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das
Date: 2021-11-13 00:00:00

Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are diagnosed. Even when a diagnosis is made, it may not be entered as a structured International Classification of Diseases (ICD) diagnosis code in a patient's charts. Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors. Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data. We developed natural language processing (NLP) tools to identify patients with cognitive impairment and demonstrate that linguistic context enhances performance for the cognitive impairment classification task. We fine-tuned our attention based deep learning model, which can learn from complex language structures, and substantially improved accuracy (0.93) relative to a baseline NLP model (0.84). Further, we show that deep learning NLP can successfully identify dementia patients without dementia-related ICD codes or medications.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
Authors: Kyunghoon Hur, Jiyoung Lee, JungWoo Oh, Wesley Price, Young-Hak Kim, Edward Choi
Date: 2021-11-12 00:00:00
Tasks: Representation Learning

EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals. To overcome this problem, we introduceDescription-based Embedding,DescEmb, a code-agnostic representation learning framework forEHR. DescEmb takes advantage of the flexibil-ity of neural language understanding models toembed clinical events using their textual descrip-tions rather than directly mapping each event toa dedicated embedding. DescEmb outperformedtraditional code-based embedding in extensiveexperiments, especially in a zero-shot transfertask (one hospital to another), and was able totrain a single unified model for heterogeneousEHR datasets.
A Case Study of Vehicle Route Optimization
Authors: Veronika Lesch, Maximilian König, Samuel Kounev, Anthony Stein, Christian Krupitzer
Date: 2021-11-17 00:00:00

In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.
NLP based grievance redressal system for Indian Railways
Authors: Mukesh Rawat, Neha Kaushik
Date: 2021-11-17 00:00:00

The current grievance redressal system has a dedicated 24X7 Twitter Cell, wherein the human experts take actions and respond to the tweets of customers addressed to Ministry of Railways. It is done quite promptly by the human experts. It is understood that the software plugin to process the tweets addressed towards Ministry of Railways can not match the human expertise. Still, efforts can be done to build a software plugin which can ease the human effort. This project aims at building a software plug-in to minimize the human effort involved in analysis of tweets addressed to Indian Railways and aid in existing complaints redressal system by identifying the complaints from the tweets. It is understood that it is not possible to match human promptness in terms of handling the tweets, still we can try to reduce the human efforts by working on the following objectives: 1.
Reinforcement Learning for UA V Attitude Control
Authors: William Koch, Renato Mancuso, Richard West, Azer Bestavros
Date: 2019-02-13 00:00:00
Proceeding: ACM Transactions on Cyber-Physical Systems 2019 2
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Abstract—Autopilot systems are typically composed of an
“inner loop” providing stability and control, while an “outer
loop” is responsible for mission-level objectives, e.g. way-point
navigation. Autopilot systems for UA Vs are predominately im-
plemented using Proportional, Integral Derivative (PID) control
systems, which have demonstrated exceptional performance in
stable environments. However more sophisticated control is
required to operate in unpredictable, and harsh environments.
Intelligent flight control systems is an active area of research
addressing limitations of PID control most recently through the
use of reinforcement learning (RL) which has had success in
other applications such as robotics. However previous work has
focused primarily on using RL at the mission-level controller.
In this work, we investigate the performance and accuracy of
the inner control loop providing attitude control when using
intelligent flight control systems trained with the state-of-the-
art RL algorithms, Deep Deterministic Gradient Policy (DDGP),
Trust Region Policy Optimization (TRPO) and Proximal Policy
Optimization (PPO). To investigate these unknowns we first
developed an open-source high-fidelity simulation environment to
train a flight controller attitude control of a quadrotor through
RL. We then use our environment to compare their performance
to that of a PID controller to identify if using RL is appropriate
in high-precision, time-critical flight control.
Building Extraction from Remote Sensing Images with Sparse Token Transformers
Authors: Keyan Chen, Zhengxia Zou, Zhenwei Shi
Date: 2021-11-05 00:00:00
Proceeding: Remote Sens. 2021 11
Tasks: Building change detection for remote sensing images, Extracting Buildings In Remote Sensing Images

Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of “sparse” feature vectors in their feature space by introducing a new module called “sparse token sampler”. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.
Social movie recommender system based on deep autoencoder network using Twitter data
Authors: Hossein Tahmasebi1, Reza Ravanmehr1, Rezvan Mohamadrezaei1
Date: 2020-10-07 00:00:00
Proceeding: 16/06 2020 10
Tasks: Collaborative Filtering, Decision Making, Recommendation Systems

Recommender systems attempt to provide effective suggestions to each user based on their interests and behaviors. These recommendations usually match the personal user preferences and assist them in the decision-making process. With the ever-expanding growth of information on the web, online education systems, e-commerce, and, eventually, the emergence of social networks, the necessity of developing such systems is unavoidable. Collaborative filtering and content-based filtering are among the most important techniques used in recommender systems. Meanwhile, with the significant advances in deep learning in recent years, the use of this technology has been widely observed in recommender systems. In this study, a hybrid social recommender system utilizing a deep autoencoder network is introduced. The proposed approach employs collaborative and content-based filtering, as well as users’ social influence. The social influence of each user is calculated based on his/her social characteristics and behaviors on Twitter. For the evaluation purpose, the required datasets have been collected from MovieTweetings and Open Movie Database. The evaluation results show that the accuracy and effectiveness of the proposed approach have been improved compared to the other state-of-the-art methods.
Shortening the runtime using larger time steps for the simulation of marine ecosystem models
Authors: Markus Pfeil, Thomas Slawig
Date: 2021-11-17 00:00:00

The reduction of computational costs for marine ecosystem models is important for the investigation and detection of the relevant biogeochemical processes because such models are computationally expensive. In order to lower these computational costs by means of larger time steps we investigated the accuracy of steady annual cycles (i.e., an annual periodic solution) calculated with different time steps. We compared the accuracy for a hierarchy of biogeochemical models showing an increasing complexity and computed the steady annual cycles with offline simulations that are based on the transport matrix approach. For each of these biogeochemical models, we obtained practically the same solution even though larger time steps. This indicates that larger time steps shortened the runtime with an acceptable loss of accuracy.
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial Fibrillation
Authors: Nahuel Costa, Luciano Sanchez
Date: 2021-03-09 00:00:00
Proceeding: IEEE Access 2021 3
Tasks: Time Series, Time Series Analysis

In this work we propose a semi-supervised framework to visually assess the progression of time series. To this end, we present a recurrent version of the VAE to exploit the generative properties that lead it to learn in an unsupervised way a continuous compressed representation of the data. We introduce a classifier in the VAE training process to control the regulation of the latent space, allowing the network to learn latent variables that set the basis for creating an explainable evaluation of the data. We use the proposed framework to address the diagnosis of Atrial Fibrillation (AF) first validating it with simulated data with known properties and subsequently testing it with intracardiac data obtained from pacemakers and defibrillator systems.
Information dynamics of price and liquidity around the 2017 Bitcoin markets crash
Authors: Vaiva Vasiliauskaite, Fabrizio Lillo, Nino Antulov-Fantulin
Date: 2021-11-17 00:00:00

We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying components of predictability, memory, and synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models we argue that some results could relate to intra-market and inter-market regime shifts, and changes in direction of information flow between different market observables.
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks
Authors: Zhenbo Cheng, Xingguang Liu, Leilei Zhang, Hangcheng Meng, Qin Li, Xiao Gang
Date: 2021-10-19 00:00:00
Tasks: Attribute, Diversity, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When an individual's behavior has rational characteristics, this may lead to irrational collective actions for the group. A wide range of organisms from animals to humans often evolve the social attribute of cooperation to meet this challenge. Therefore, cooperation among individuals is of great significance for allowing social organisms to adapt to changes in the natural environment. Based on multi-agent reinforcement learning, we propose a new learning strategy for achieving coordination by incorporating a learning rate that can balance exploration and exploitation. We demonstrate that agents that use the simple strategy improve a relatively collective return in a decision task called the intertemporal social dilemma, where the conflict between the individual and the group is particularly sharp. We also explore the effects of the diversity of learning rates on the population of reinforcement learning agents and show that agents trained in heterogeneous populations develop particularly coordinated policies relative to those trained in homogeneous populations.
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Authors: Yuanfei Wang, Fangwei Zhong, Jing Xu, Yizhou Wang
Date: 2021-10-15 00:00:00
Proceeding: NeurIPS 2021 12

Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents decide "when" and with "whom" to share their intentions. With the information observed, inferred, and received, the agents decide their sub-goals and reach a consensus among the team. In the end, the low-level executors independently take primitive actions to accomplish the sub-goals. We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multi-sensor target coverage. The experiments show that the proposed model not only outperforms the state-of-the-art methods on reward and communication efficiency, but also shows good generalization across different scales of the environment.
Impact of subsidy on profitability of residential photovoltaics, battery and inverter in Flanders
Authors: Md Umar Hashmi, Mohammed Meraj Alam
Date: 2021-11-17 00:00:00

The sustained future growth of renewable energy in the distribution network is governed by its financial viability. The recent subsidies in Flanders for photovoltaic (PV) and battery are used to calculate the payback period on prosumer investment in residential PV, battery and inverter installations. Realistic scenario of PV, battery and inverter are created based on the market catalogue of one manufacturer per product. Using these scenarios, one-year simulations for a residential prosumer are conducted in a rolling horizon. Luminus, an energy service provider in Limburg, time-of-use and feed-in-tariff contract rates are utilized. The numerical evaluation indicates that the policies prefer new PV installations. The subsidy comparison for 2021 to 2024 is also conducted. Oversizing PV compared to inverter size increases profitability. Batteries performing only arbitrage may not attract much investment, as the payback period is high. This shows the need to maximize the value addition of prosumer investment on residential batteries by participating in multiple goals.
Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC
Authors: Yan Liu, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, George K. Karagiannidis
Date: 2021-11-17 00:00:00

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.
Convex Optimization for Fuel Cell Hybrid Trains: Speed, Energy Management System, and Battery Thermals
Authors: Rabee Jibrin, Stuart Hillmansen, Clive Roberts
Date: 2021-11-17 00:00:00
Tasks: energy management, Management

We optimize the operation of a fuel cell hybrid train using convex optimization. The main objective is to minimize hydrogen fuel consumption for a target journey time while considering battery thermal constraints. The state trajectories: train speed, energy management system, and battery temperature, are all optimized concurrently within a single optimization problem. A novel thermal model is proposed in order to include battery temperature yet maintain formulation convexity. Simulations show fuel savings and better thermal management when temperature is optimized concurrently with the other states rather than sequentially -- separately afterwards. The fuel reduction is caused by reduced cooling effort which is motivated by the formulation's awareness of active cooling energy consumption. The benefit is more pronounced for warmer ambient temperatures that require more cooling.