pwc-archive/papers-with-abstracts huggingface dataset:
Integrated multimodal artificial intelligence framework for healthcare applications
Authors: Luis R. Soenksen, Yu Ma, Cynthia Zeng, Leonard D. J. Boussioux, Kimberly Villalobos Carballo, Liangyuan Na, Holly M. Wiberg, Michael L. Li, Ignacio Fuentes, Dimitris Bertsimas
Date: 2022-02-25 00:00:00
Tasks: Time Series Analysis

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N=34,537 samples) containing 7,279 unique hospitalizations and 6,485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48-hour mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
Self-Supervised and Interpretable Anomaly Detection using Network Transformers
Authors: Daniel L. Marino, Chathurika S. Wickramasinghe, Craig Rieger, Milos Manic
Date: 2022-02-25 00:00:00
Tasks: Anomaly Detection

Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks. Machine Learning (ML) and Deep Neural Networks (DNNs) have been proposed in the past as a tool to identify anomalies in computer networks. Although detecting these anomalies provides an indication of an attack, just detecting an anomaly is not enough information for a user to understand the anomaly. The black-box nature of off-the-shelf ML models prevents extracting important information that is fundamental to isolate the source of the fault/attack and take corrective measures. In this paper, we introduce the Network Transformer (NeT), a DNN model for anomaly detection that incorporates the graph structure of the communication network in order to improve interpretability. The presented approach has the following advantages: 1) enhanced interpretability by incorporating the graph structure of computer networks; 2) provides a hierarchical set of features that enables analysis at different levels of granularity; 3) self-supervised training that does not require labeled data. The presented approach was tested by evaluating the successful detection of anomalies in an Industrial Control System (ICS). The presented approach successfully identified anomalies, the devices affected, and the specific connections causing the anomalies, providing a data-driven hierarchical approach to analyze the behavior of a cyber network.
Attacks and Faults Injection in Self-Driving Agents on the Carla Simulator -- Experience Report
Authors: Niccolò Piazzesi, Massimo Hong, Andrea Ceccarelli
Date: 2022-02-25 00:00:00
Tasks: Autonomous Driving

Machine Learning applications are acknowledged at the foundation of autonomous driving, because they are the enabling technology for most driving tasks. However, the inclusion of trained agents in automotive systems exposes the vehicle to novel attacks and faults, that can result in safety threats to the driv-ing tasks. In this paper we report our experimental campaign on the injection of adversarial attacks and software faults in a self-driving agent running in a driving simulator. We show that adversarial attacks and faults injected in the trained agent can lead to erroneous decisions and severely jeopardize safety. The paper shows a feasible and easily-reproducible approach based on open source simula-tor and tools, and the results clearly motivate the need of both protective measures and extensive testing campaigns.
OCR-IDL: OCR Annotations for Industry Document Library Dataset
Authors: Ali Furkan Biten, Rubèn Tito, Lluis Gomez, Ernest Valveny, Dimosthenis Karatzas
Date: 2022-02-25 00:00:00
Tasks: Optical Character Recognition (OCR)

Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in https://github.com/furkanbiten/idl_data.
Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators
Authors: Manu Lahariya, Craig Innes, Chris Develder, Subramanian Ramamoorthy
Date: 2022-02-25 00:00:00

Soft actuators offer a safe, adaptable approach to tasks like gentle grasping and dexterous manipulation. Creating accurate models to control such systems however is challenging due to the complex physics of deformable materials. Accurate Finite Element Method (FEM) models incur prohibitive computational complexity for closed-loop use. Using a differentiable simulator is an attractive alternative, but their applicability to soft actuators and deformable materials remains underexplored. This paper presents a framework that combines the advantages of both. We learn a differentiable model consisting of a material properties neural network and an analytical dynamics model of the remainder of the manipulation task. This physics-informed model is trained using data generated from FEM, and can be used for closed-loop control and inference. We evaluate our framework on a dielectric elastomer actuator (DEA) coin-pulling task. We simulate the task of using DEA to pull a coin along a surface with frictional contact, using FEM, and evaluate the physics-informed model for simulation, control, and inference. Our model attains < 5% simulation error compared to FEM, and we use it as the basis for an MPC controller that requires fewer iterations to converge than model-free actor-critic, PD, and heuristic policies.
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
Authors: Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Insup Lee, Julia Parish-Morris
Date: 2022-02-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging control tasks is that they often receive expert intervention that enables them to understand the high-level structure of the task before mastering low-level control actions. We propose a framework for leveraging expert intervention to solve long-horizon reinforcement learning tasks. We consider \emph{option templates}, which are specifications encoding a potential option that can be trained using reinforcement learning. We formulate expert intervention as allowing the agent to execute option templates before learning an implementation. This enables them to use an option, before committing costly resources to learning it. We evaluate our approach on three challenging reinforcement learning problems, showing that it outperforms state-of-the-art approaches by two orders of magnitude. Videos of trained agents and our code can be found at: https://sites.google.com/view/stickymittens
A Hardware-Aware System for Accelerating Deep Neural Network Optimization
Authors: Anthony Sarah, Daniel Cummings, Sharath Nittur Sridhar, Sairam Sundaresan, Maciej Szankin, Tristan Webb, J. Pablo Munoz
Date: 2022-02-25 00:00:00
Tasks: Bayesian Optimization, Evolutionary Algorithms, Neural Architecture Search

Recent advances in Neural Architecture Search (NAS) which extract specialized hardware-aware configurations (a.k.a. "sub-networks") from a hardware-agnostic "super-network" have become increasingly popular. While considerable effort has been employed towards improving the first stage, namely, the training of the super-network, the search for derivative high-performing sub-networks is still largely under-explored. For example, some recent network morphism techniques allow a super-network to be trained once and then have hardware-specific networks extracted from it as needed. These methods decouple the super-network training from the sub-network search and thus decrease the computational burden of specializing to different hardware platforms. We propose a comprehensive system that automatically and efficiently finds sub-networks from a pre-trained super-network that are optimized to different performance metrics and hardware configurations. By combining novel search tactics and algorithms with intelligent use of predictors, we significantly decrease the time needed to find optimal sub-networks from a given super-network. Further, our approach does not require the super-network to be refined for the target task a priori, thus allowing it to interface with any super-network. We demonstrate through extensive experiments that our system works seamlessly with existing state-of-the-art super-network training methods in multiple domains. Moreover, we show how novel search tactics paired with evolutionary algorithms can accelerate the search process for ResNet50, MobileNetV3 and Transformer while maintaining objective space Pareto front diversity and demonstrate an 8x faster search result than the state-of-the-art Bayesian optimization WeakNAS approach.
Learning English with Peppa Pig
Authors: Mitja Nikolaus, Afra Alishahi, Grzegorz Chrupała
Date: 2022-02-25 00:00:00
Tasks: Descriptive

Recent computational models of the acquisition of spoken language via grounding in perception exploit associations between the spoken and visual modalities and learn to represent speech and visual data in a joint vector space. A major unresolved issue from the point of ecological validity is the training data, typically consisting of images or videos paired with spoken descriptions of what is depicted. Such a setup guarantees an unrealistically strong correlation between speech and the visual data. In the real world the coupling between the linguistic and the visual modality is loose, and often confounded by correlations with non-semantic aspects of the speech signal. Here we address this shortcoming by using a dataset based on the children's cartoon Peppa Pig. We train a simple bi-modal architecture on the portion of the data consisting of dialog between characters, and evaluate on segments containing descriptive narrations. Despite the weak and confounded signal in this training data our model succeeds at learning aspects of the visual semantics of spoken language.
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
Authors: Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu
Date: 2022-02-25 00:00:00
Proceeding: ACL 2022 5
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results.
Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
Authors: Gabriel Spadon, Martha D. Ferreira, Amilcar Soares, Stan Matwin
Date: 2022-02-24 00:00:00

The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered. Such data comes from the oceans and their surface, such as Automatic Identification System (AIS) messages used for tracking vessels' trajectories. AIS messages are transmitted over radio or satellite at ideally periodic time intervals but vary irregularly over time. As such, this paper aims to model the AIS message transmission behavior through neural networks for forecasting upcoming AIS messages' content from multiple vessels, particularly in a simultaneous approach despite messages' temporal irregularities as outliers. We present a set of experiments comprising multiple algorithms for forecasting tasks with horizon sizes of varying lengths. Deep learning models (e.g., neural networks) revealed themselves to adequately preserve vessels' spatial awareness regardless of temporal irregularity. We show how convolutional layers, feed-forward networks, and recurrent neural networks can improve such tasks by working together. Experimenting with short, medium, and large-sized sequences of messages, our model achieved 36/37/38% of the Relative Percentage Difference - the lower, the better, whereas we observed 92/45/96% on the Elman's RNN, 51/52/40% on the GRU, and 129/98/61% on the LSTM. These results support our model as a driver for improving the prediction of vessel routes when analyzing multiple vessels of diverging types simultaneously under temporally noise data.
Path-Aware Graph Attention for HD Maps in Motion Prediction
Authors: Fang Da, Yu Zhang
Date: 2022-02-23 00:00:00
Tasks: Autonomous Driving, Graph Attention, Motion Forecasting, motion prediction

The success of motion prediction for autonomous driving relies on integration of information from the HD maps. As maps are naturally graph-structured, investigation on graph neural networks (GNNs) for encoding HD maps is burgeoning in recent years. However, unlike many other applications where GNNs have been straightforwardly deployed, HD maps are heterogeneous graphs where vertices (lanes) are connected by edges (lane-lane interaction relationships) of various nature, and most graph-based models are not designed to understand the variety of edge types which provide crucial cues for predicting how the agents would travel the lanes. To overcome this challenge, we propose Path-Aware Graph Attention, a novel attention architecture that infers the attention between two vertices by parsing the sequence of edges forming the paths that connect them. Our analysis illustrates how the proposed attention mechanism can facilitate learning in a didactic problem where existing graph networks like GCN struggle. By improving map encoding, the proposed model surpasses previous state of the art on the Argoverse Motion Forecasting dataset, and won the first place in the 2021 Argoverse Motion Forecasting Competition.
Query Expansion and Entity Weighting for Query Reformulation Retrieval in Voice Assistant Systems
Authors: Zhongkai Sun, Sixing Lu, Chengyuan Ma, Xiaohu Liu, Chenlei Guo
Date: 2022-02-22 00:00:00
Tasks: Retrieval

Voice assistants such as Alexa, Siri, and Google Assistant have become increasingly popular worldwide. However, linguistic variations, variability of speech patterns, ambient acoustic conditions, and other such factors are often correlated with the assistants misinterpreting the user's query. In order to provide better customer experience, retrieval based query reformulation (QR) systems are widely used to reformulate those misinterpreted user queries. Current QR systems typically focus on neural retrieval model training or direct entities retrieval for the reformulating. However, these methods rarely focus on query expansion and entity weighting simultaneously, which may limit the scope and accuracy of the query reformulation retrieval. In this work, we propose a novel Query Expansion and Entity Weighting method (QEEW), which leverages the relationships between entities in the entity catalog (consisting of users' queries, assistant's responses, and corresponding entities), to enhance the query reformulation performance. Experiments on Alexa annotated data demonstrate that QEEW improves all top precision metrics, particularly 6% improvement in top10 precision, compared with baselines not using query expansion and weighting; and more than 5% improvement in top10 precision compared with other baselines using query expansion and weighting.
An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload
Authors: Bujar Raufi, Luca Longo
Date: 2022-02-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), regression

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.
Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild
Authors: Han Yu, Akane Sano
Date: 2022-02-22 00:00:00
Tasks: Data Augmentation

Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since the stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle in developing accurate and generalized stress predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We first applied data augmentation techniques on the physiological and behavioral data to improve the robustness of supervised stress detection models. Using an auto-encoder with actively selected unlabeled sequences, we pre-trained the supervised model structure to leverage the information learned from unlabeled samples. Then, we developed a semi-supervised learning framework to leverage the unlabeled data sequences. We combined data augmentation techniques with consistency regularization, which enforces the consistency of prediction output based on augmented and original unlabeled data. We validated these methods using three wearable/mobile sensor datasets collected in the wild. Our results showed that combining the proposed methods improved stress classification performance by 7.7% to 13.8% on the evaluated datasets, compared to the baseline supervised learning models.
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals
Authors: Ravikiran Parameshwara, Soujanya Narayana, Murugappan Murugappan, Ramanathan Subramanian, Ibrahim Radwan, Roland Goecke
Date: 2022-02-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition, valid

While Parkinson's disease (PD) is typically characterized by motor disorder, there is evidence of diminished emotion perception in PD patients. This study examines the utility of affective Electroencephalography (EEG) signals to understand emotional differences between PD vs Healthy Controls (HC), and for automated PD detection. Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals. Our results reveal that PD patients comprehend arousal better than valence, and amongst emotion categories, \textit{fear}, \textit{disgust} and \textit{surprise} less accurately, and \textit{sadness} most accurately. Mislabeling analyses confirm confounds among opposite-valence emotions with PD data. Emotional EEG responses also achieve near-perfect PD vs HC recognition. {Cumulatively, our study demonstrates that (a) examining \textit{implicit} responses alone enables (i) discovery of valence-related impairments in PD patients, and (ii) differentiation of PD from HC, and (b) emotional EEG analysis is an ecologically-valid, effective, facile and sustainable tool for PD diagnosis vis-\'a-vis self reports, expert assessments and resting-state analysis.}