pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives
Authors: Dixon Domfeh, Arpita Chatterjee, Matthew Dixon
Date: 2022-05-09 00:00:00
Tasks: Clustering, Uncertainty Quantification

Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known empirically about the expected risk-premia of CAT bonds. The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset pricing approach with informative expected risk premia. Specifically, using a modified collective risk model -- Dirichlet Prior-Hierarchical Bayesian Collective Risk Model (DP-HBCRM) framework -- we model catastrophe risk via a model-based clustering approach. Interest rate risk is modeled as a CIR process under the Bayesian approach. As a consequence of casting CAT pricing models into our framework, we evaluate the price and expected risk premia of various CAT bond contracts corresponding to clustering of catastrophe risk profiles. Numerical experiments show how these clusters reveal how CAT bond prices and expected risk premia relate to claim frequency and loss severity.
Massive Enhanced Extracted Email Features Tailored for Cosine Distance
Authors: Farshad Barahimi
Date: 2022-05-10 00:00:00

In this paper, the process of converting the Enron email dataset (the version cited in the preprint) to thousands of features per email for a selected set of 2400 labelled emails is explained and evaluated. The final features are tailored for Cosine distance so that the Cosine distance invertly reflect the number of top indicative words of each email that are common between the two emails in an explainable normalized fashion. The labelling is based on the leaf folder name in the Enron email dataset (the version cited in the preprint) folders tree and the 2400 emails selected consist 300 emails for each of the 8 labels. The evaluation is based on the accuracy of a k nearest neighbours majority voting classification using Cosine distance. In addition to KNN majority voting classification accuracy and confusion matrix, some statistics for the process is reported. The KNN majority voting classification accuracy using Cosine distance is 76.75% which shows at least some level of success given the 8 labels involved. The result of conversion is 48557 features per selected email out of which exactly 40 features per email are non-zero. The result of conversion is a data set named MeeefTCD (Massive Enhanced Extracted Email Features Tailored for Cosine Distance) available at https://web.cs.dal.ca/~barahimi/data-sets/meeeftcd/ and on a github repository mentioned in this paper.
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
Authors: Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke
Date: 2022-05-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Methods for reinforcement learning for recommendation (RL4Rec) are increasingly receiving attention as they can quickly adapt to user feedback. A typical RL4Rec framework consists of (1) a state encoder to encode the state that stores the users' historical interactions, and (2) an RL method to take actions and observe rewards. Prior work compared four state encoders in an environment where user feedback is simulated based on real-world logged user data. An attention-based state encoder was found to be the optimal choice as it reached the highest performance. However, this finding is limited to the actor-critic method, four state encoders, and evaluation-simulators that do not debias logged user data. In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset. Importantly, our experimental results indicate that existing findings do not generalize to the debiased SOFA simulator generated from a different dataset and a Deep Q-Network (DQN)-based method when compared with more state encoders.
Selective Fairness in Recommendation via Prompts
Authors: Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He
Date: 2022-05-10 00:00:00
Tasks: Attribute, Fairness, Sequential Recommendation

Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness. The source codes are released in \url{https://github.com/wyqing20/PFRec}.
CODEC: Complex Document and Entity Collection
Authors: Iain Mackie, Paul Owoicho, Carlos Gemmell, Sophie Fischer, Sean MacAvaney, Jeffrey Dalton
Date: 2022-05-09 00:00:00
Tasks: Document Ranking, Re-Ranking, Retrieval

CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks?". CODEC includes 42 topics developed by researchers and a new focused web corpus with semantic annotations including entity links. This resource includes expert judgments on 17,509 documents and entities (416.9 per topic) from diverse automatic and interactive manual runs. The manual runs include 387 query reformulations, providing data for query performance prediction and automatic rewriting evaluation. CODEC includes analysis of state-of-the-art systems, including dense retrieval and neural re-ranking. The results show the topics are challenging with headroom for document and entity ranking improvement. Query expansion with entity information shows significant gains in document ranking, demonstrating the resource's value for evaluating and improving entity-oriented search. We also show that the manual query reformulations significantly improve document ranking and entity ranking performance. Overall, CODEC provides challenging research topics to support the development and evaluation of entity-centric search methods.
Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model
Authors: Dawit Kiros Redie, Abdulhakim Edao Sirko, Tensaie Melkamu Demissie, Semagn Sisay Teferi, Vimal Kumar Shrivastava, Om Prakash Verma & Tarun Kumar Sharma
Date: 2022-03-09 00:00:00
Proceeding: journal 2022 3
Tasks: Binary Classification, COVID-19 Diagnosis, Medical Diagnosis, Multi-class Classification

Coronavirus disease, also known as COVID-19, is an infectious disease caused by SARS-CoV-2. It has a direct impact on the upper and lower respiratory tract and threatened the health of many people around the world. The latest statistics show that the number of people diagnosed with COVID-19 is growing exponentially. Diagnosing positive cases of COVID-19 is important for preventing further spread of the disease. Currently, Coronavirus is a serious threat to scientists, medical experts and researchers around the world from its detection to its treatment. It is currently detected using reverse transcription polymerase chain reaction (RT-PCR) analysis at the most test centers around the world. Yet, knowing the reliability of a deep learning based medical diagnosis is important for doctors to build confidence in the technology and improve treatment. The goal of this study is to develop a model that automatically identifies COVID-19 by using chest X-ray images. To achieve this, we modified the DarkCovidNet model which is based on a convolutional neural network (CNN) and plotted the experimental results for two scenarios: binary classification (COVID-19 versus No-findings) and multi-class classification (COVID-19 versus pneumonia versus No-findings). The model is trained on more than 10 thousand X-ray images and achieved an average accuracy of 99.53% and 94.18% for binary and multi-class classification, respectively. Therefore, the proposed method demonstrates the effectiveness of COVID-19 detection using X-ray images. Our model can be used to test the patient via cloud and also be used in situations where RT-PCR tests and other options aren't available.
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System
Authors: Mohamed Ridha Znaidi, Gaurav Gupta, Paul Bogdan
Date: 2022-04-24 00:00:00
Tasks: Federated Learning, Privacy Preserving

Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on various agents to perform multiple distributed training tasks. Considering the decentralized computing topology, privacy has become a first-class concern. Moreover, assuming limited information processing capability for the agents calls for a sophisticated \textit{privacy-preserving decentralization} that ensures efficient computation. Towards this end, we study the \textit{privacy-aware server to multi-agent assignment} problem subject to information processing constraints associated with each agent, while maintaining the privacy and assuring learning informative messages received by agents about a global terminal through the distributed private federated learning (DPFL) approach. To find a decentralized scheme for a two-agent system, we formulate an optimization problem that balances privacy and accuracy, taking into account the quality of compression constraints associated with each agent. We propose an iterative converging algorithm by alternating over self-consistent equations. We also numerically evaluate the proposed solution to show the privacy-prediction trade-off and demonstrate the efficacy of the novel approach in ensuring privacy in DL and FL.
Preliminary assessment of a cost-effective headphone calibration procedure for soundscape evaluations
Authors: Bhan Lam, Kenneth Ooi, Karn N. Watcharasupat, Zhen-Ting Ong, Yun-Ting Lau, Trevor Wong, Woon-Seng Gan
Date: 2022-05-10 00:00:00

The introduction of ISO 12913-2:2018 has provided a framework for standardized data collection and reporting procedures for soundscape practitioners. A strong emphasis was placed on the use of calibrated head and torso simulators (HATS) for binaural audio capture to obtain an accurate subjective impression and acoustic measure of the soundscape under evaluation. To auralise the binaural recordings as recorded or at set levels, the audio stimuli and the headphone setup are usually calibrated with a HATS. However, calibrated HATS are too financially prohibitive for most research teams, inevitably diminishing the availability of the soundscape standard. With the increasing availability of soundscape binaural recording datasets, and the importance of cross-cultural validation of the soundscape ISO standards, e.g.\ via the Soundscape Attributes Translation Project (SATP), it is imperative to assess the suitability of cost-effective headphone calibration methods to maximise availability without severely compromising on accuracy. Hence, this study objectively examines an open-circuit voltage (OCV) calibration method in comparison to a calibrated HATS on various soundcard and headphone combinations. Preliminary experiments found that calibration with the OCV method differed significantly from the reference binaural recordings in sound pressure levels, whereas negligible differences in levels were observed with the HATS calibration.
Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis
Authors: Zhenzi Weng, Zhijin Qin, Xiaoming Tao, Chengkang Pan, Guangyi Liu, Geoffrey Ye Li
Date: 2022-05-09 00:00:00
Tasks: Deep Learning, Semantic Communication, speech-recognition, Speech Recognition, Speech Synthesis

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST.
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer
Authors: Ruikang Zhong, Yuanwei Liu, Xidong Mu, Yue Chen, Xianbin Wang, Lajos Hanzo
Date: 2022-05-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.
Neuromimetic Linear Systems -- Resilience and Learning
Authors: Zexin Sun, John Baillieul
Date: 2022-05-10 00:00:00
Tasks: Combinatorial Optimization, Q-Learning, Quantization

Building on our recent work on {\em neuromimetic control theory}, new results on resilience and neuro-inspired quantization are reported. The term neuromimetic refers to the models having features that are characteristic of the neurobiology of biological motor control. As in previous work, the focus is on what we call {\em overcomplete} linear systems that are characterized by larger numbers of input and output channels than the dimensions of the state. The specific contributions of the present paper include a proposed {\em resilient} observer whose operation tolerates output channel intermittency and even complete dropouts. Tying these ideas together with our previous work on resilient stability, a resilient separation principle is established. We also propose a {\em principled quantization} in which control signals are encoded as simple discrete inputs which act collectively through the many channels of input that are the hallmarks of the overcomplete models. Aligned with the neuromimetic paradigm, an {\em emulation} problem is proposed and this in turn defines an optimal quantization problem. Several possible solutions are discussed including direct combinatorial optimization, a Hebbian-like iterative learning algorithm, and a deep Q-learning (DQN) approach. For the problems being considered, machine learning approaches to optimization provide valuable insights regarding comparisons between optimal and nearby suboptimal solutions. These are useful in understanding the kinds of resilience to intermittency and channel dropouts that were earlier demonstrated for continuous systems.
Understanding the Capability of PD Control for Uncertain Stochastic Systems
Authors: Cheng Zhao, Yanbin Zhang
Date: 2022-05-10 00:00:00

In this article, we focus on the global stabilizability problem for a class of second order uncertain stochastic control systems, where both the drift term and the diffusion term are nonlinear functions of the state variables and the control variables. We will show that the widely applied proportional-derivative(PD) control in engineering practice has the ability to globally stabilize such systems in the mean square sense, provided that the upper bounds of the partial derivatives of the nonlinear functions satisfy a certain algebraic inequality. It will also be proved that the stabilizing PD parameters can only be selected from a two dimensional bounded convex set, which is a significant difference from the existing literature on PD controlled uncertain stochastic systems. Moreover, a particular polynomial on these bounds is introduced, which can be used to determine under what conditions the system is not stabilizable by the PD control, and thus demonstrating the fundamental limitations of PD control.
Characterization of electric consumers through an automated clustering pipeline
Authors: Francesca Soldan, Alberto Maldarella, Gabriele Paludetto, Enea Bionda, Federico Belloni, Samuele Grillo
Date: 2022-05-10 00:00:00
Tasks: Clustering, Load Forecasting, Time Series, Time Series Analysis

Clustering analysis of daily load profiles represents an effective technique to classify and aggregate electric users based on their actual consumption patterns. Among other purposes, it may be exploited as a preliminary stage for load forecasting, which is applied in the same way to consumers in the same cluster. Several clustering algorithms have been proposed and developed in the literature, and the choice of the most appropriate set of clustering parameters is crucial for ensuring reliable results. In this paper, an automated service, suited for repeated clustering analysis, is presented. The pipeline is able to process a generic time series dataset and is easily adjustable to test other clustering input parameters; therefore, it may be utilized to find the best set of parameters with the specific dataset. Moreover, it facilitates repeated characterization on real-time load profiles with the aim of detecting sudden changes of consumers behaviors and variable external conditions, which influence the real power forecasting activity on a short temporal scale.
Comparision of Traditional and Fuzzy Failure Mode and Effects Analysis for Smart Grid Electrical Distribution Systems
Authors: Shravan Kumar Akula, Hossein Salehfar, Shayan Behzadirafi
Date: 2022-05-10 00:00:00

Reliability Assessment is an indispensable technology for identifying, interpreting, and lessening the potential failures in safety-critical systems like smart grids. Failure modes and effects analysis (FMEA) is one of the well documented techniques for risk analysis to study the impact of failure modes on safety critical systems like smart grid. In traditional FMEA failure modes are prioritized based on a numeric assessment known as risk priority number. Risk priority number (RPN) is defined as the product of three risk factors namely severity (S), occurrence (O), and detection (D). These risk factors are generally attained by extensive team efforts and judgments which can lead to errors and inconsistencies. To address the shortcomings of the traditional FMEA, a fuzzy-based FMEA approach is proposed to generate reliable risk priority rankings. In this study, traditional and fuzzy-based FMEA risk priority rankings for smart grid electrical distribution systems are compared and recommendations are made based on the analysis. Results prove the efficiency of the proposed fuzzy-FMEA method.
InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks
Authors: Srijith Balakrishnan, Beatrice Cassottana
Date: 2022-05-10 00:00:00
Tasks: Model Predictive Control

Integrated simulation models are emerging as an alternative for analyzing large-scale interdependent infrastructure networks due to their modeling advantages over traditional interdependency models. This paper presents an open-source integrated simulation package for the asset-level analysis of interdependent infrastructure systems. The simulation platform, named 'InfraRisk' and developed in Python, can simulate disaster-induced infrastructure failures and subsequent post-disaster restoration in interconnected water-, power-, and road networks. InfraRisk consists of an infrastructure module, a hazard module, a recovery module, a simulation module, and a resilience quantification module. The infrastructure module integrates existing infrastructure network packages (wntr for water networks, pandapower for power systems, and a static traffic assignment model for transportation networks) through an interface that facilitates the network-level simulation of interdependent failures. The hazard module generates infrastructure component failure sequences based on various disaster characteristics. The recovery module determines repair sequences and assigns repair crews based on predefined heuristics-based recovery strategies or model predictive control (MPC) based optimization. Based on the schedule, the simulation module implements the network-wide simulation of the consequences of the disaster impacts and the recovery actions. The resilience quantification module offers system-level and consumer-level metrics to quantify both the risks and resilience of the integrated infrastructure networks against disaster events. InfraRisk provides a virtual platform for decision-makers to experiment and develop region-specific pre-disaster and post-disaster policies to enhance the overall resilience of interdependent urban infrastructure networks.