pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Authors: Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick
Date: 2021-07-08 00:00:00
Tasks: Sensitivity

Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks. Prior specification is, however, relatively difficult for such models, given that their flexibility implies that the consequences of prior choices are often relatively opaque. Moreover, these choices can have a substantial effect on posterior inferences. Thus, considerations of robustness need to go hand in hand with nonparametric modeling. In the current paper, we tackle this challenge by exploiting the fact that variational Bayesian methods, in addition to having computational advantages in fitting complex nonparametric models, also yield sensitivities with respect to parametric and nonparametric aspects of Bayesian models. In particular, we demonstrate how to assess the sensitivity of conclusions to the choice of concentration parameter and stick-breaking distribution for inferences under Dirichlet process mixtures and related mixture models. We provide both theoretical and empirical support for our variational approach to Bayesian sensitivity analysis.
Graph Neural Pre-training for Enhancing Recommendations using Side Information
Authors: Zaiqiao Meng, Siwei Liu, Craig Macdonald, Iadh Ounis
Date: 2021-07-08 00:00:00
Tasks: Entity Embeddings, Recommendation Systems

Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important modelling dimension. While many existing approaches focus on the integration scheme to incorporate entity side information -- by combining the recommendation loss function with an extra side information-aware loss -- in this paper, we propose instead a novel pre-training scheme for leveraging the side information. In particular, we first pre-train a representation model using the side information of the entities, and then fine-tune it using an existing general representation-based recommendation model. Specifically, we propose two pre-training models, named GCN-P and COM-P, by considering the entities and their relations constructed from side information as two different types of graphs respectively, to pre-train entity embeddings. For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks. For the COM-P model, two multi-relational graphs are constructed to pre-train the entity representations by using the Composition-based Graph Convolutional Networks. An extensive evaluation of our pre-training models fine-tuned under four general representation-based recommender models, i.e. MF, NCF, NGCF and LightGCN, shows that effectively pre-training embeddings with both the user's and item's side information can significantly improve these original models in terms of both effectiveness and stability.
ICTCAS-UCAS-TAL Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2021
Authors: Yuanhang Zhang, Susan Liang, Shuang Yang, Xiao Liu, Zhongqin Wu, Shiguang Shan
Date: 2021-06-01 00:00:00
Proceeding: The ActivityNet Large-Scale Activity Recognition Challenge Workshop, CVPR 2021 6
Tasks: Active Speaker Detection, Audio-Visual Active Speaker Detection

This report presents a brief description of our method for the AVA Active Speaker Detection (ASD) task at ActivityNet
Challenge 2021. Our solution, the Extended Unified Context Network (Extended UniCon) is based on a novel Unified
Context Network (UniCon) designed for robust ASD, which combines multiple types of contextual information to optimize all candidates jointly. We propose a few changes to the original UniCon in terms of audio features, temporal modeling architecture, and loss function design. Together, our best model ensemble sets a new state-of-the-art at 93.4% mAP on the AVA-ActiveSpeaker test set without any form of pretraining, and currently ranks first on the ActivityNet challenge leaderboard.
The Price of Diversity
Authors: Hari Bandi, Dimitris Bertsimas
Date: 2021-07-03 00:00:00
Tasks: Diversity

Systemic bias with respect to gender, race and ethnicity, often unconscious, is prevalent in datasets involving choices among individuals. Consequently, society has found it challenging to alleviate bias and achieve diversity in a way that maintains meritocracy in such settings. We propose (a) a novel optimization approach based on optimally flipping outcome labels and training classification models simultaneously to discover changes to be made in the selection process so as to achieve diversity without significantly affecting meritocracy, and (b) a novel implementation tool employing optimal classification trees to provide insights on which attributes of individuals lead to flipping of their labels, and to help make changes in the current selection processes in a manner understandable by human decision makers. We present case studies on three real-world datasets consisting of parole, admissions to the bar and lending decisions, and demonstrate that the price of diversity is low and sometimes negative, that is we can modify our selection processes in a way that enhances diversity without affecting meritocracy significantly, and sometimes improving it.
A Decision Support System for Safer Airplane Landings: Predicting Runway Conditions Using XGBoost and Explainable AI
Authors: Alise Danielle Midtfjord, Riccardo De Bin, Arne Bang Huseby
Date: 2021-07-01 00:00:00
Tasks: Friction

The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to identify slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. The XGBoost models are combined with SHAP approximations to provide a reliable decision support system for airport operators, which can contribute to safer and more economic operations of airport runways. To evaluate the performance of the prediction models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy. Published version: https://doi.org/10.1016/j.coldregions.2022.103556.
CVEH: A Dynamic Framework To Profile Vehicle Movements To Mitigate Hit And Run Cases Using Crowdsourcing
Authors: Attiq ur Rehman, Asad Waqar Malik, Anis Ur Rahman, Sohail Iqbal, Ghalib Ahmed Tahir
Date: 2021-06-28 00:00:00
Tasks: Management

In developed countries like the USA, Germany, and the UK, the security forces used highly sophisticated equipment, fast vehicles, drones, and helicopters to catch offenders' vehicles. Whereas, in developing countries with limited resources such schemes cannot be utilized due to management cost and other constraints. In this paper, we proposed a framework called CVEH that enables developing countries to profile the offender vehicle movements through crowdsourcing technique and act as an early warning system to the law forcing agencies. It also engages citizens to play their role in improving security conditions. The proposed CVEH framework allows Vehicle-to-Infrastructure (V2I) communication to monitor the movement of the offender's vehicle and shared its information with the Command and Control (CC) centre. The CC centre projects the path and engages nearly located law enforcement agencies. CVEH is developed and evaluated on android smartphones. Simulations conducted for this study exhibit the effectiveness of our framework.
Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction
Authors: Byungsoo Kim, Hangyeol Yu, Dongmin Shin, Youngduck Choi
Date: 2021-06-28 00:00:00
Tasks: Prediction, Transfer Learning

The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which brings challenge in taking machine learning approaches for academic performance prediction. To this end, inspired by the recent advancement of pre-training method in natural language processing community, we propose DPA, a transfer learning framework with Discriminative Pre-training tasks for Academic performance prediction. DPA pre-trains two models, a generator and a discriminator, and fine-tunes the discriminator on academic performance prediction. In DPA's pre-training phase, a sequence of interactions where some tokens are masked is provided to the generator which is trained to reconstruct the original sequence. Then, the discriminator takes an interaction sequence where the masked tokens are replaced by the generator's outputs, and is trained to predict the originalities of all tokens in the sequence. Compared to the previous state-of-the-art generative pre-training method, DPA is more sample efficient, leading to fast convergence to lower academic performance prediction error. We conduct extensive experimental studies on a real-world dataset obtained from a multi-platform ITS application and show that DPA outperforms the previous state-of-the-art generative pre-training method with a reduction of 4.05% in mean absolute error and more robust to increased label-scarcity.
Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects
Authors: Mahmoud Nasr, Md. Milon Islam, Shady Shehata, Fakhri Karray, Yuri Quintana
Date: 2021-06-24 00:00:00

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.
Medical Profile Model: Scientific and Practical Applications in Healthcare
Authors: Pavel Blinov, Vladimir Kokh
Date: 2021-06-21 00:00:00
Tasks: Medical Diagnosis, Representation Learning, Temporal Sequences

The paper researches the problem of representation learning for electronic health records. We present the patient histories as temporal sequences of diseases for which embeddings are learned in an unsupervised setup with a transformer-based neural network model. Additionally the embedding space includes demographic parameters which allow the creation of generalized patient profiles and successful transfer of medical knowledge to other domains. The training of such a medical profile model has been performed on a dataset of more than one million patients. Detailed model analysis and its comparison with the state-of-the-art method show its clear advantage in the diagnosis prediction task. Further, we show two applications based on the developed profile model. First, a novel Harbinger Disease Discovery method allowing to reveal disease associated hypotheses and potentially are beneficial in the design of epidemiological studies. Second, the patient embeddings extracted from the profile model applied to the insurance scoring task allow significant improvement in the performance metrics.
Meeting the SDGs : Enabling the Goals by Cooperation with Crowd using a Conversational AI Platform
Authors: J. Haqbeen, T. Ito, S. Sahab, R. Hadfi, T. Sato, S. Okuhara
Date: 2021-06-09 00:00:00

In this paper, we report about a large-scale online discussion with 1099 citizens on the Afghanistan Sustainable Development Goals.
Multi-Task Learning for Audio Visual Active Speaker Detection
Authors: Yuanhang Zhang, Jingyun Xiao, Shuang Yang, Shiguang Shan
Date: 2019-06-01 00:00:00
Proceeding: The ActivityNet Large-Scale Activity Recognition Challenge Workshop, CVPR 2019 6
Tasks: Active Speaker Detection, Audio-Visual Active Speaker Detection, Lipreading, Multi-Task Learning, Video Synchronization

This report describes the approach underlying our submission to the active speaker detection task (task B-2) of ActivityNet Challenge 2019. We introduce a new audio-visual model which builds upon a 3D-ResNet18 visual model pretrained for lipreading and a VGG-M acoustic model pretrained for audio-to-video synchronization. The model is trained with two losses in a multi-task learning fashion: a contrastive loss to enforce matching between audio and video features for active speakers, and a regular crossentropy loss to obtain speaker / non-speaker labels. This model obtains 84.0% mAP on the validation set of AVAActiveSpeaker. Experimental results showcase the pretrained embeddings' abilities to transfer across tasks and data formats, as well as the advantage of the proposed multi-task learning strategy.
Network and Sequence-Based Prediction of Protein-Protein Interactions
Authors: Leonardo Martini, Adriano Fazzone, Luca Becchetti
Date: 2021-07-08 00:00:00
Tasks: Prediction

Background:Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error prone.Many computational methods have been proposed to identify candidate interacting pairs. When accurate, they can serve as an inexpensive, preliminary filtering stage, to be followed by downstream experimental validation. Among such methods, sequence-based ones are very promising.Results:We present, a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare our Framework with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets.Conclusion:We shown that topological plus sequence-based computational methods can effectively predict the entire human interactome compared with methods that leverage only one source of biological information.
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer
Authors: Zongyang Du, Berrak Sisman, Kun Zhou, Haizhou Li
Date: 2021-07-08 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, Style Transfer, Voice Conversion

Traditional voice conversion(VC) has been focused on speaker identity conversion for speech with a neutral expression. We note that emotional expression plays an essential role in daily communication, and the emotional style of speech can be speaker-dependent. In this paper, we study the technique to jointly convert the speaker identity and speaker-dependent emotional style, that is called expressive voice conversion. We propose a StarGAN-based framework to learn a many-to-many mapping across different speakers, that takes into account speaker-dependent emotional style without the need for parallel data. To achieve this, we condition the generator on emotional style encoding derived from a pre-trained speech emotion recognition(SER) model. The experiments validate the effectiveness of our proposed framework in both objective and subjective evaluations. To our best knowledge, this is the first study on expressive voice conversion.
Heavily Augmented Sound Event Detection utilizing Weak Predictions
Authors: Hyeonuk Nam, Byeong-Yun Ko, Gyeong-Tae Lee, Seong-Hu Kim, Won-Ho Jung, Sang-Min Choi, Yong-Hwa Park
Date: 2021-07-08 00:00:00
Tasks: Data Augmentation, Event Detection, Sound Event Detection

The performances of Sound Event Detection (SED) systems are greatly limited by the difficulty in generating large strongly labeled dataset. In this work, we used two main approaches to overcome the lack of strongly labeled data. First, we applied heavy data augmentation on input features. Data augmentation methods used include not only conventional methods used in speech/audio domains but also our proposed method named FilterAugment. Second, we propose two methods to utilize weak predictions to enhance weakly supervised SED performance. As a result, we obtained the best PSDS1 of 0.4336 and best PSDS2 of 0.8161 on the DESED real validation dataset. This work is submitted to DCASE 2021 Task4 and is ranked on the 3rd place. Code availa-ble: https://github.com/frednam93/FilterAugSED.
Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation
Authors: Parag Narkhede, Shashi Poddar, Rahee Walambe, George Ghinea, Ketan Kotecha
Date: 2021-07-08 00:00:00
Tasks: Sensor Fusion

Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filters gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.
Cyber Insurance Against Cyberattacks on Electric Vehicle Charging Stations
Authors: Samrat Acharya, Robert Mieth, Charalambos Konstantinou, Ramesh Karri, Yury Dvorkin
Date: 2021-07-08 00:00:00

Cyberattacks in the energy sector are commonplace. Load-altering cyberattacks launched via the manipulations of high-wattage appliances and assets are particularly alarming, as they are not continuously monitored by electric power utilities. Public Electric Vehicle Charging Stations (EVCSs) are among such high-wattage assets. Even EVCSs monitored by the electric power utilities and protected by state-of-the-art defense mechanisms are vulnerable to cyberattacks. Such cyberattacks cause financial losses to the EVCSs. In this paper, we propose cyber insurance for EVCSs to hedge the economic loss due to such cyberattacks and develop a data-driven cyber insurance design model for public EVCSs. Under mild modeling assumptions, we derive an optimal cyber insurance premium. Then, we ensure the robustness of this optimal premium and investigate the risk of insuring the EVCSs using a suitable risk assessment metric (Conditional Value-at-Risk). A case study with data from EVCSs in Manhattan, New York illustrates our results. Our results demonstrate that risk assessment is crucial for designing insurance premiums. Furthermore, the premium increases in proportion to the loss coverage offered for the EVCSs. This work informs the stakeholders involved in the roll-out and operation of public EVCSs about the benefits of cyber insurance and suggests that insurance premiums can be reduced by deploying state-of-the-art defense mechanisms.
A Comparison of Data-Driven Techniques for Power Grid Parameter Estimation
Authors: Subhash Lakshminarayana, Saurav Sthapit, Carsten Maple
Date: 2021-07-08 00:00:00
Tasks: parameter estimation

Power grid parameter estimation involves the estimation of unknown parameters, such as inertia and damping coefficients, using observed dynamics. In this work, we present a comparison of data-driven algorithms for the power grid parameter estimation problem. First, we propose a new algorithm to solve the parameter estimation problem based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses linear regression to infer the parameters that best describe the observed data. We then compare its performance against two benchmark algorithms, namely, the unscented Kalman filter (UKF) approach and the physics-informed neural networks (PINN) approach. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN and UKF algorithms in being able to accurately estimate the power grid parameters over a wide range of system parameters (including high and low inertia systems). Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation.
High Force Density Multi-Stage Electrohydrodynamic Jets Using Folded Laser Microfabricated Electrodes
Authors: Daniel S. Drew, Sean Follmer
Date: 2021-07-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The electrohydrodynamic (EHD) force produced by ions ejected from a corona plasma is a solid state, silent mechanism for accelerating air, useful for applications ranging from electronics cooling to flying microrobots. This paper presents the theoretical motivation and the first implementation of a multi-stage, highly miniaturized EHD device, which can provide both improved absolute power output and power density as compared to single-stage devices. A laser microfabricated, folded electrode design reduces component count and assembly time. Data from one, two, and three-stage devices demonstrates a near linear scaling of output force with stage count, indicating inter-stage ducting successfully reduces losses. Device lifetime is assessed to validate the use of stainless-steel emission electrodes. Areal thrust, force density, and volumetric power density for the three-stage device are among the highest ever measured from an EHD actuator.