pwc-archive/papers-with-abstracts huggingface dataset:
IoT Data Trust Evaluation via Machine Learning
Authors: Timothy Tadj, Reza Arablouei, Volkan Dedeoglu
Date: 2023-08-15 00:00:00
Tasks: Benchmarking, Time Series

Various approaches based on supervised or unsupervised machine learning (ML) have been proposed for evaluating IoT data trust. However, assessing their real-world efficacy is hard mainly due to the lack of related publicly-available datasets that can be used for benchmarking. Since obtaining such datasets is challenging, we propose a data synthesis method, called random walk infilling (RWI), to augment IoT time-series datasets by synthesizing untrustworthy data from existing trustworthy data. Thus, RWI enables us to create labeled datasets that can be used to develop and validate ML models for IoT data trust evaluation. We also extract new features from IoT time-series sensor data that effectively capture its auto-correlation as well as its cross-correlation with the data of the neighboring (peer) sensors. These features can be used to learn ML models for recognizing the trustworthiness of IoT sensor data. Equipped with our synthesized ground-truth-labeled datasets and informative correlation-based feature, we conduct extensive experiments to critically examine various approaches to evaluating IoT data trust via ML. The results reveal that commonly used ML-based approaches to IoT data trust evaluation, which rely on unsupervised cluster analysis to assign trust labels to unlabeled data, perform poorly. This poor performance can be attributed to the underlying unsubstantiated assumption that clustering provides reliable labels for data trust, a premise that is found to be untenable. The results also show that the ML models learned from datasets augmented via RWI while using the proposed features generalize well to unseen data and outperform existing related approaches. Moreover, we observe that a semi-supervised ML approach that requires only about 10% of the data labeled offers competitive performance while being practically more appealing compared to the fully-supervised approaches.
Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognition
Authors: Brandon Sheffield, Frank E. Bobe III, Bradley Marchand, Matthew S. Emigh
Date: 2023-08-12 00:00:00
Tasks: object-detection, Object Detection, Self-Supervised Learning

Synthetic Aperture Sonar (SAS) imaging has become a crucial technology for underwater exploration because of its unique ability to maintain resolution at increasing ranges, a characteristic absent in conventional sonar techniques. However, the effective application of deep learning to SAS data processing is often limited due to the scarcity of labeled data. To address this challenge, this paper proposes MoCo-SAS that leverages self-supervised learning (SSL) for SAS data processing, classification, and pattern recognition. The experimental results demonstrate that MoCo-SAS significantly outperforms traditional supervised learning methods, as evidenced by significant improvements observed in terms of the F1-score. These findings highlight the potential of SSL in advancing the state-of-the-art in SAS data processing, offering promising avenues for enhanced underwater object detection and classification.
Deep learning-based flow disaggregation for short-term hydropower plant operations
Authors: Duo Zhang
Date: 2023-08-11 00:00:00
Tasks: Decision Making, Deep Learning, Management, Time Series

High temporal resolution data plays a vital role in effective short-term hydropower plant operations. In the majority of the Norwegian hydropower system, inflow data is predominantly collected at daily resolutions through measurement installations. However, for enhanced precision in managerial decision-making within hydropower plants, hydrological data with intraday resolutions, such as hourly data, are often indispensable. To address this gap, time series disaggregation utilizing deep learning emerges as a promising tool. In this study, we propose a deep learning-based time series disaggregation model to derive hourly inflow data from daily inflow data for short-term hydropower plant operations. Our preliminary results demonstrate the applicability of our method, with scope for further improvements.
Autonomous Vehicles as a Sensor: Simulating Data Collection Process
Authors: Yunfei Zhang, Mario Ilic, Klaus Bogenberger
Date: 2023-08-09 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Management, State Estimation

Urban traffic state estimation is pivotal in furnishing precise and reliable insights into traffic flow characteristics, thereby enabling efficient traffic management. Traditional traffic estimation methodologies have predominantly hinged on labor-intensive and costly techniques such as loop detectors and floating car data. Nevertheless, the relentless progression in autonomous driving technology has catalyzed an increasing interest in capitalizing on the extensive potential of on-board sensor data, giving rise to a novel concept known as "Autonomous Vehicles as a Sensor" (AVaaS). This paper innovatively refines the AVaaS concept by simulating the data collection process. We take real-world sensor attributes into account and employ more accurate estimation techniques based on the on-board sensor data. Such data can facilitate the estimation of high-resolution, link-level traffic states and, more extensively, online cluster- and network-level traffic states. We substantiate the viability of the AVaaS concept through a case study conducted using a real-world traffic simulation in Ingolstadt, Germany. The results attest to the ability of AVaaS in estimating both microscopic (link-level) and macroscopic (cluster- and network-level) traffic states, thereby highlighting the immense potential of the AVaaS concept in effecting precise and reliable traffic state estimation and also further applications.
Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management
Authors: Yiwen Xu, Dengfeng Liu, Liangtao Huang, Zhiquan Lin, Tiesong Zhao, Sam Kwong
Date: 2023-08-08 00:00:00
Tasks: All, energy management, Management

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-invasive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things (IoT).
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis
Authors: Saiful Islam, Pitambar Khanra, Johan Nakuci, Sarah F. Muldoon, Takamitsu Watanabe, Naoki Masuda
Date: 2023-08-23 00:00:00
Tasks: Clustering, EEG, Electroencephalogram (EEG), Time Series

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
What it takes to solve the Origin(s) of Life: An integrated review of techniques
Authors: OoLEN, Silke Asche, Carla Bautista, David Boulesteix, Alexandre Champagne-Ruel, Cole Mathis, Omer Markovitch, Zhen Peng, Alyssa Adams, Avinash Vicholous Dass, Arnaud Buch, Eloi Camprubi, Enrico Sandro Colizzi, Stephanie Colón-Santos, Hannah Dromiack, Valentina Erastova, Amanda Garcia, Ghjuvan Grimaud, Aaron Halpern, Stuart A Harrison, Seán F. Jordan, Tony Z Jia, Amit Kahana, Artemy Kolchinsky, Odin Moron-Garcia, Ryo Mizuuchi, Jingbo Nan, Yuliia Orlova, Ben K. D. Pearce, Klaus Paschek, Martina Preiner, Silvana Pinna, Eduardo Rodríguez-Román, Loraine Schwander, Siddhant Sharma, Harrison B. Smith, Andrey Vieira, Joana C. Xavier
Date: 2023-08-22 00:00:00
Tasks: Philosophy

Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, mathematics and philosophy. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence can vary dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of tools and techniques rather than positing a unifying view on how life emerges. We review the common tools and techniques that have been used significantly in OoL studies in recent years. In particular, we aim to identify which information is most relevant for comparing and integrating the results of experimental analyses into mathematical and computational models. This review aims to provide a baseline expectation and understanding of technical aspects of origins research, rather than being a primer on any particular topic. As such, it spans broadly -- from analytical chemistry to mathematical models -- and highlights areas of future work that will benefit from a multidisciplinary approach to tackling the mystery of life's origin. Ultimately, we hope to empower a new generation of OoL scientists by reviewing how they can investigate life's origin, rather than dictating how to think about the problem.
Optimal Robust Reinsurance with Multiple Insurers
Authors: Emma Kroell, Sebastian Jaimungal, Silvana M. Pesenti
Date: 2023-08-22 00:00:00

We study a reinsurer who faces multiple sources of model uncertainty. The reinsurer offers contracts to $n$ insurers whose claims follow compound Poisson processes representing both idiosyncratic and systemic sources of loss. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an entropy penalty. Insurers meanwhile seek to maximise their expected utility without ambiguity. We solve this continuous-time Stackelberg game for general reinsurance contracts and find that the reinsurer prices under a distortion of the barycentre of the insurers' models. We apply our results to proportional reinsurance and excess-of-loss reinsurance contracts, and illustrate the solutions numerically. Furthermore, we solve the related problem where the reinsurer maximises, still under ambiguity, their expected utility and compare the solutions.
Investigating Short-Term Dynamics in Green Bond Markets
Authors: Lorenzo Mercuri, Andrea Perchiazzo, Edit Rroji
Date: 2023-08-23 00:00:00

The paper investigates the effect of the label green in bond markets from the lens of the trading activity. The idea is that jumps in the dynamics of returns have a specific memory nature that can be well represented through a self-exciting process. Specifically, using Hawkes processes where the intensity is described through a continuous time moving average model, we study the high-frequency dynamics of bond prices. We also introduce a bivariate extension of the model that deals with the cross-effect of upward and downward price movements. Empirical results suggest that differences emerge if we consider periods with relevant interest rate announcements, especially in the case of an issuer operating in the energy market.
Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASH
Authors: Nghia T. Nguyen, Long Luu, Phuong L. Vo, Thi Thanh Sang Nguyen, Cuong T. Do, Ngoc-thanh Nguyen
Date: 2023-07-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL), Scheduling

Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user's quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, content-centric networks,... usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluating. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.
Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion
Authors: Yerong Li, Roxana Girju
Date: 2023-08-18 00:00:00
Tasks: Classification, Relation, Relation Classification, Relation Extraction

Relation Extraction (RE) is a pivotal task in automatically extracting structured information from unstructured text. In this paper, we present a multi-faceted approach that integrates representative examples and through co-set expansion. The primary goal of our method is to enhance relation classification accuracy and mitigating confusion between contrastive classes. Our approach begins by seeding each relationship class with representative examples. Subsequently, our co-set expansion algorithm enriches training objectives by incorporating similarity measures between target pairs and representative pairs from the target class. Moreover, the co-set expansion process involves a class ranking procedure that takes into account exemplars from contrastive classes. Contextual details encompassing relation mentions are harnessed via context-free Hearst patterns to ascertain contextual similarity. Empirical evaluation demonstrates the efficacy of our co-set expansion approach, resulting in a significant enhancement of relation classification performance. Our method achieves an observed margin of at least 1 percent improvement in accuracy in most settings, on top of existing fine-tuning approaches. To further refine our approach, we conduct an in-depth analysis that focuses on tuning contrastive examples. This strategic selection and tuning effectively reduce confusion between classes sharing similarities, leading to a more precise classification process. Experimental results underscore the effectiveness of our proposed framework for relation extraction. The synergy between co-set expansion and context-aware prompt tuning substantially contributes to improved classification accuracy. Furthermore, the reduction in confusion between contrastive classes through contrastive examples tuning validates the robustness and reliability of our method.
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Authors: Xintao Wang, Qianwen Yang, Yongting Qiu, Jiaqing Liang, Qianyu He, Zhouhong Gu, Yanghua Xiao, Wei Wang
Date: 2023-08-17 00:00:00
Tasks: Retrieval, World Knowledge

Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting LLMs with external knowledge sources, the integration of knowledge bases (KBs) remains understudied and faces several challenges. In this paper, we introduce KnowledGPT, a comprehensive framework to bridge LLMs with various knowledge bases, facilitating both the retrieval and storage of knowledge. The retrieval process employs the program of thought prompting, which generates search language for KBs in code format with pre-defined functions for KB operations. Besides retrieval, KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands. With extensive experiments, we show that by integrating LLMs with KBs, KnowledGPT properly answers a broader range of questions requiring world knowledge compared with vanilla LLMs, utilizing both knowledge existing in widely-known KBs and extracted into personalized KBs.
A Double Modulation Wave CBPWM Strategy Providing Neutral-Point Voltage Oscillation Elimination and CMV Reduction for Three-Level NPC Inverters
Authors: Peng Liu; Shanxu Duan; Chuan Yao; Changsong Chen
Date: 2017-06-05 00:00:00
Proceeding: IEEE 2017 6

This paper proposes a double modulation wave carrier-based pulse width modulation (CBPWM) strategy for the three-level neutral-point-clamped (NPC) inverter. This modulation strategy not only overcomes one of the main problems of the NPC inverter, which is the lowfrequency voltage oscillation on the neutral point, but also reduces the common-mode voltage (CMV) generated by the inverter. Through the rigorous theoretical derivation, the unified duty cycles of each phase are derived, and the double modulation waves can be easily obtained according to different optimization objectives. However, in the previous double wave CBPWM work, the modulation strategy could not reduce the CMV, whose amplitude is still the same as that of the conventional CBPWM strategy. By rearranging the switching sequence, the vectors corresponding to the large CMV amplitude have been eliminated, and the amplitude of CMV can be reduced by half. Considering the occurrence of neutral-point voltage perturbations, a voltage controller is adopted to recover from the imbalance. Finally, the performance of the proposed modulation technique is verified by the experimental results.
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning
Authors: Andrea Esuli
Date: 2022-06-22 00:00:00
Proceeding: IEEE Access 2022 6
Tasks: Active Learning, Classification, text-classification, Text Classification

We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification. The application uses machine learning to continuously fit automatic classification models that are in turn used to actively support its users with classification suggestions. The key requirement we have established for the development of ICS is to give its users total freedom of action: they can at any time modify any classification schema and any label assignment, possibly reusing any relevant information from previous activities. We investigate how this requirement challenges the typical scenarios faced in machine learning research, which instead give no active role to humans or place them into very constrained roles, e.g., on-demand labeling in active learning processes, and always assume some degree of batch processing of data. We satisfy the “total freedom” requirement by designing an unobtrusive machine learning model, i.e., the machine learning component of ICS acts as an unobtrusive observer of the users, that never interrupts them, continuously adapts and updates its models in response to their actions, and it is always available to perform automatic classifications. Our efficient implementation of the unobtrusive machine learning model combines various machine learning methods and technologies, such as hash-based feature mapping, random indexing, online learning, active learning, and asynchronous processing.
FARMUR: Fair Adversarial Retraining to Mitigate Unfairness in Robustness
Authors: Seyed Ali Mousavi, Hamid Mousavi2, Masoud Daneshtalab
Date: 2023-07-01 00:00:00
Proceeding: ADBIS 2023 2023 7
Tasks: Decision Making, Fairness

Deep Neural Networks (DNNs) have been deployed in safetycritical real-world applications, including automated decision-making systems. There are often concerns about two aspects of these systems: the fairness of the predictions and their robustness against adversarial attacks. In recent years, extensive studies have been devoted to addressing these issues independently through adversarial training and unfairness mitigation techniques. To consider fairness and robustness simultaneously, the robustness-bias concept is introduced, which means an attacker can more easily target a particular sub-partition in the dataset. However, there is no unified and mathematical definition for measuring fairness in the robustness of DNNs independent of the type of adversarial attacks. In this paper, we first provide a unified, precise, and mathematical theory and measurement for fairness in robustness independent of adversarial
attacks for a DNN model. Finally, we proposed a fair adversarial retraining method (FARMUR) to mitigate unfairness in robustness that retrains the DNN models based on vulnerable and robust sub-partitions. In particular, FARMUR leverages different objective functions for vulnerable and robust sub-partitions to retrain the DNN. Experimental results demonstrate the effectiveness of FARMUR in mitigating the unfairness in robustness during adversarial training without significantly degrading robustness. FARMUR improves fairness in robustness by 19.18% with only 2.22% reduction in robustness in comparison with adversarial training on the UTKFace dataset, which is partitioned based on race attributes.