pwc-archive/papers-with-abstracts huggingface dataset:
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots
Authors: Shaokang Cai, Dezhi Han, Zibin Zheng, Dun Li, NoelCrespi
Date: 2022-07-13 00:00:00
Tasks: Chatbot, Few-Shot Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The rapid development of artificial intelligence (AI) technology has enabled large-scale AI applications to land in the market and practice. However, while AI technology has brought many conveniences to people in the productization process, it has also exposed many security issues. Especially, attacks against online learning vulnerabilities of chatbots occur frequently. Therefore, this paper proposes a semantics censorship chatbot system based on reinforcement learning, which is mainly composed of two parts: the Offensive semantics censorship model and the semantics purification model. Offensive semantics review can combine the context of user input sentences to detect the rapid evolution of Offensive semantics and respond to Offensive semantics responses. The semantics purification model For the case of chatting robot models, it has been contaminated by large numbers of offensive semantics, by strengthening the offensive reply learned by the learning algorithm, rather than rolling back to the early versions. In addition, by integrating a once-through learning approach, the speed of semantics purification is accelerated while reducing the impact on the quality of replies. The experimental results show that our proposed approach reduces the probability of the chat model generating offensive replies and that the integration of the few-shot learning algorithm improves the training speed rapidly while effectively slowing down the decline in BLEU values.
Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes
Authors: Jiahao Chen, Zitao Liu, Weiqi Luo
Date: 2022-07-13 00:00:00
Tasks: Math

In this paper, we investigate the opportunities of automating the judgment process in online one-on-one math classes. We build a Wide & Deep framework to learn fine-grained predictive representations from a limited amount of noisy classroom conversation data that perform better student judgments. We conducted experiments on the task of predicting students' levels of mastery of example questions and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.
Emotion detection of social data: APIs comparative study
Authors: Bilal Abu-Salih, Mohammad Alhabashneh, Dengya Zhu, Albara Awajan, Yazan Alshamaileh, Bashar Al-Shboul, Mohammad Alshraideh
Date: 2022-07-08 00:00:00

The development of emotion detection technology has emerged as a highly valuable possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of a large number of start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparison to social data. This study compares eight technologies; IBM Watson NLU, ParallelDots, Symanto-Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and NLP Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores that they delivered as well as the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
Authors: Hanyin Wang, Meghan R. Hutch, Yikuan Li, Adrienne S. Kline, Sebastian Otero, Leena B. Mithal, Emily S. Miller, Andrew Naidech, Yuan Luo
Date: 2022-07-05 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification

Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions of COVID-19 vaccines. We finetuned a deep learning classifier using a state-of-the-art model, XLNet, to detect each tweet's sentiment automatically. We employed validated methods to extract the users' race or ethnicity, gender, age, and geographical locations from user profiles. Incorporating multiple data sources, we assessed the sentiment patterns among subpopulations and juxtaposed them against vaccine uptake data to unravel their interactive patterns. 11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users over two years were analyzed. The finetuned model for sentiment classification yielded an accuracy of 0.92 on testing set. Users from various demographic groups demonstrated distinct patterns in sentiments towards COVID-19 vaccines. User sentiments became more positive over time, upon which we observed subsequent upswing in the population-level vaccine uptake. Surrounding dates where positive sentiments crest, we detected encouraging news or events regarding vaccine development and distribution. Positive sentiments in pregnancy-related tweets demonstrated a delayed pattern compared with trends in general population, with postponed vaccine uptake trends. Distinctive patterns across subpopulations suggest the need of tailored strategies. Global news and events profoundly involved in shaping users' thoughts on social media. Populations with additional concerns, such as pregnancy, demonstrated more substantial hesitancy since lack of timely recommendations. Feature analysis revealed hesitancies of various subpopulations stemmed from clinical trial logics, risks and complications, and urgency of scientific evidence.
Multilingual Disinformation Detection for Digital Advertising
Authors: Zofia Trstanova, Nadir El Manouzi, Maryline Chen, Andre L. V. da Cunha, Sergei Ivanov
Date: 2022-07-04 00:00:00

In today's world, the presence of online disinformation and propaganda is more widespread than ever. Independent publishers are funded mostly via digital advertising, which is unfortunately also the case for those publishing disinformation content. The question of how to remove such publishers from advertising inventory has long been ignored, despite the negative impact on the open internet. In this work, we make the first step towards quickly detecting and red-flagging websites that potentially manipulate the public with disinformation. We build a machine learning model based on multilingual text embeddings that first determines whether the page mentions a topic of interest, then estimates the likelihood of the content being malicious, creating a shortlist of publishers that will be reviewed by human experts. Our system empowers internal teams to proactively, rather than defensively, blacklist unsafe content, thus protecting the reputation of the advertisement provider.
Estimating value at risk: LSTM vs. GARCH
Authors: Weronika Ormaniec, Marcin Pitera, Sajad Safarveisi, Thorsten Schmidt
Date: 2022-07-21 00:00:00
Tasks: Time Series, Time Series Analysis

Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score.
On minimax density estimation via measure transport
Authors: Sven Wang, Youssef Marzouk
Date: 2022-07-20 00:00:00
Tasks: Density Estimation

We study the convergence properties, in Hellinger and related distances, of nonparametric density estimators based on measure transport. These estimators represent the measure of interest as the pushforward of a chosen reference distribution under a transport map, where the map is chosen via a maximum likelihood objective (equivalently, minimizing an empirical Kullback-Leibler loss) or a penalized version thereof. We establish concentration inequalities for a general class of penalized measure transport estimators, by combining techniques from M-estimation with analytical properties of the transport-based density representation. We then demonstrate the implications of our theory for the case of triangular Knothe-Rosenblatt (KR) transports on the $d$-dimensional unit cube, and show that both penalized and unpenalized versions of such estimators achieve minimax optimal convergence rates over H\"older classes of densities. Specifically, we establish optimal rates for unpenalized nonparametric maximum likelihood estimation over bounded H\"older-type balls, and then for certain Sobolev-penalized estimators and sieved wavelet estimators.
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
Authors: Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, Nina Vasan
Date: 2022-07-20 00:00:00
Tasks: Causal Inference, Ethics, Recommendation Systems

Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.
Synthesizing Light Field Video from Monocular Video
Authors: Shrisudhan Govindarajan, Prasan Shedligeri, Sarah, Kaushik Mitra
Date: 2022-07-21 00:00:00
Tasks: Self-Supervised Learning, Video Reconstruction

The hardware challenges associated with light-field(LF) imaging has made it difficult for consumers to access its benefits like applications in post-capture focus and aperture control. Learning-based techniques which solve the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have significantly reduced the requirement for complex hardware. LF video reconstruction from sparse views poses a special challenge as acquiring ground-truth for training these models is hard. Hence, we propose a self-supervised learning-based algorithm for LF video reconstruction from monocular videos. We use self-supervised geometric, photometric and temporal consistency constraints inspired from a recent self-supervised technique for LF video reconstruction from stereo video. Additionally, we propose three key techniques that are relevant to our monocular video input. We propose an explicit disocclusion handling technique that encourages the network to inpaint disoccluded regions in a LF frame, using information from adjacent input temporal frames. This is crucial for a self-supervised technique as a single input frame does not contain any information about the disoccluded regions. We also propose an adaptive low-rank representation that provides a significant boost in performance by tailoring the representation to each input scene. Finally, we also propose a novel refinement block that is able to exploit the available LF image data using supervised learning to further refine the reconstruction quality. Our qualitative and quantitative analysis demonstrates the significance of each of the proposed building blocks and also the superior results compared to previous state-of-the-art monocular LF reconstruction techniques. We further validate our algorithm by reconstructing LF videos from monocular videos acquired using a commercial GoPro camera.
Accurate clinical and biomedical Named entity recognition at scale
Authors: Kocaman, Veysel;  Talby, David
Date: 2022-07-19 00:00:00
Proceeding: Software Impacts 2022 7
Tasks: Clinical Concept Extraction, De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We introduce an agile, production-grade clinical and biomedical Named entity recognition (NER) algorithm based on a modified BiLSTM-CNN-Char DL architecture built on top of Apache Spark. Our NER implementation establishes new state-of-the-art accuracy on 7 of 8 well-known biomedical NER benchmarks and 3 clinical concept extraction challenges: 2010 i2b2/VA clinical concept extraction, 2014 n2c2 de-identification, and 2018 n2c2 medication extraction. Moreover, clinical NER models trained using this implementation outperform the accuracy of commercial entity extraction solutions, AWS Medical Comprehend and Google Cloud Healthcare API by a large margin (8.9% and 6.7% respectively), without using memory-intensive language models.
Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset
Authors: L. J. Muhammad, Ebrahem A. Algehyne, Sani Sharif Usman, Abdulkadir Ahmad, Chinmay Chakraborty & I. A. Mohammed
Date: 2020-11-27 00:00:00
Proceeding: Springer Nature Singapore Pte Ltd 2020 2020 11
Tasks: Epidemiology, Prognosis, Specificity

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.
Preterm Birth Prediction of Pregnant Women in Post Conization Period Using Machine Learning Techniques
Authors: Mian Ahmed Jamiul Bari, Mohammad Imtiaz Faisal, Mahmud Hasan, Labiba Islam, Md. Sabbir Hossain & Sifat Momen
Date: 2022-07-08 00:00:00
Proceeding: 11th Computer Science On-line Conference 2022 7
Tasks: BIG-bench Machine Learning

Pregnant women who underwent excisional surgeries (conization) for cervical intraepithelial neoplasia (CIN) display high risks of preterm birth. It is crucial to predict the risks of preterm birth amongst women in their post conization periods as this has severe consequences in terms of the cost as well as the health of the mother and the baby. This paper presents a preterm birth prediction system using machine learning approaches which will allow to evaluate the risk of a preterm birth. The dataset used in this work consisted of longitudinal cervical length (CL) of different gestational periods from 725 pregnant women undergoing surveillance programs in three clinics at London University Hospitals. Several machine learning algorithms were applied to make the prediction. Model based on decision tree achieved the highest accuracy (99.3%) on the test dataset.
The Unscented Transform Controller: a new model predictive control law for highly nonlinear systems
Authors: Anna Clarke, Per Olof Gutman
Date: 2022-07-21 00:00:00
Tasks: Model Predictive Control

The Unscented Transform which is the basis of the Unscented Kalman Filter, UKF, is used here to develop a novel predictive controller for non-linear plants, called the Unscented Transform Controller, UTC. The UTC can be seen as the dual of the UKF, the same way as the LQG regulator and the Kalman Filter are related. The UTC is demonstrated on the control of complex maneuvers in free fall of a virtual skydiver the model of which was verified in wind tunnel and free fall experiments.
Detection and Mitigation of Corrupted Information in Distributed Model Predictive Control Based on Resource Allocation
Authors: Rafael Accácio Nogueira, Romain Bourdais, Hervé Guéguen
Date: 2022-07-21 00:00:00
Tasks: Model Predictive Control

In distributed predictive control structures, communication among agents is required to achieve a consensus and approach an optimal global behavior. Such negotiation mechanisms are sensitive to attacks on these exchanges. This paper proposes a monitoring scheme that detects and mitigates these attacks' effects in a resource allocation framework. The performance of the proposed method is illustrated through simulations of the temperature control of multiple rooms under power scarcity.
Perspectives on distribution network flexible and curtailable resource activation and needs assessment
Authors: Md Umar Hashmi, Arpan Koirala, Hakan Ergun, Dirk Van Hertem
Date: 2022-07-21 00:00:00

{A curtailable and flexible resource activation framework for solving distribution network (DN) voltage and thermal congestions is used to quantify three important aspects with respect to modelling low voltage networks.} This framework utilizes the network states in the absence of such flexible or curtailable resources as the input for calculating flexibility activation signal (FAS). The FAS has some similarities with optimal power flow duals {associated with power balance constraint}. FAS due to drooping design, {incentivize corrective flexibility activation} prior to any network limit violations. {The nonlinear resource dispatch optimal power flow (RDOPF) utilizes FAS for the activation of flexible and curtailable resources. Solving the OPF problem for a large system is computationally intensive, and second-order cone (SOC) relaxation is often applied in the literature.} {First,} we highlight the multi-objective nature of SOC relaxed RDOPF. A Pareto front tuning mechanism {is proposed for choosing loss penalty factor} while reducing the optimality gap of the SOC relaxed RDOPF. {Secondly, we} present a methodology for evaluating temporal and locational flexibility needs assessment of a DN, which DSO's can utilize for flexibility planning {in operational timescales and procurement in the flexibility market}. {Lastly, we} quantify the impact of reactive power flexibility for a DN with varying load power factors. {Numerical simulations indicate that the presence of reactive flexibility reduces the active power flexibility needs by 50\% for the test feeder with 0.8 aggregated load power factor.}
Adversary Detection and Resilient Control for Multi-Agent Systems
Authors: Aquib Mustafa, Dimitra Panagou
Date: 2022-07-21 00:00:00

This paper presents an adversary detection mechanism and a resilient control framework for multi-agent systems under spatiotemporal constraints. Safety in multi-agent systems is typically addressed under the assumption that all agents collaborate to ensure the forward invariance of a desired safe set. This work analyzes agent behaviors based on certain behavior metrics, and designs a proactive adversary detection mechanism based on the notion of the critical region for the system operation. In particular, the presented detection mechanism not only identifies adversarial agents, but also ensures all-time safety for intact agents. Then, based on the analysis and detection results, a resilient QP-based controller is presented to ensure safety and liveness constraints for intact agents. Simulation results validate the efficacy of the presented theoretical contributions.