pwc-archive/papers-with-abstracts huggingface dataset:
A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market
Authors: Jiayue Xu
Date: 2022-01-24 00:00:00
Tasks: Prediction

The price of carbon emission rights play a crucial role in carbon trading markets. Therefore, accurate prediction of the price is critical. Taking the Shanghai pilot market as an example, this paper attempted to design a carbon emission purchasing strategy for enterprises, and establish a carbon emission price prediction model to help them reduce the purchasing cost. To make predictions more precise, we built a hybrid deep learning model by embedding Generalized Autoregressive Conditional Heteroskedastic (GARCH) into the Gate Recurrent Unit (GRU) model, and compared the performance with those of other models. Then, based on the Iceberg Order Theory and the predicted price, we proposed the purchasing strategy of carbon emission rights. As a result, the prediction errors of the GARCH-GRU model with a 5-day sliding time window were the minimum values of all six models. And in the simulation, the purchasing strategy based on the GARCH-GRU model was executed with the least cost as well. The carbon emission purchasing strategy constructed by the hybrid deep learning method can accurately send out timing signals, and help enterprises reduce the purchasing cost of carbon emission permits.
EXSeQETIC: Expert System to Support the Implementation of eQETIC Model
Authors: Rogerio Rossi, Pollyana Notagiarcomo
Date: 2022-01-15 00:00:00
Tasks: Management

The digital educational solutions are increasingly used demanding high quality functionalities. In this sense, standards and models are made available by governments, associations, and researchers being most used in quality control and assessment sessions. The eQETIC model was built according to the approach of continuous process improvement favoring the quality management for development and maintenance of digital educational solutions. This article presents two expert systems to support the implementation of eQETIC model and demonstrates that such systems are able to support users during the model implementation. Developed according to two types of shells (SINTA/UFC and e2gLite/eXpertise2go), the systems were used by a professional who develops these type of solutions and showed positive results regarding the support offered by them in implementing the rules proposed by eQETIC model.
On Optimizing Shared-ride Mobility Services with Walking Legs
Authors: Zifan Wang, Michael F Hyland, Younghun Bahk, Navjyoth JS Sarma
Date: 2022-01-29 00:00:00

Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size, or some combination of these factors, compared to door-to-door (D2D) shared-ride services. This paper provides a review of shared-ride services with walking legs (SRSWL), particularly the studies in the literature that model the operational problem(s) associated with SRSWL. The paper describes the operational and societal benefits of SRSWL as well as compares the SRSWL to circuitous D2D shared-ride services, ride-hailing services, and fixed-route transit services, in terms of VKT and traveler walking distance. The paper then delineates the operational subproblems associated with the SRSWL and discusses their computational complexity. Additionally, the review classifies configurations of SRSWL based on flexibility in assigning travelers to pickup and drop-off locations. The paper also discusses four modelling challenge: short-distance person trips, drop-off location choice for a vehicle's last remaining passenger, allowing vehicles to wait for travelers at pickup locations, and simultaneously reducing VHT/VKT and improving customer service quality relative to D2D shared-ride services. The review paper concludes by discussing the most critical areas of future research related to SRSWL.
Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set Method
Authors: Luis Ángel Larios-Cárdenas, Frédéric Gibou
Date: 2022-01-22 00:00:00
Tasks: Dimensionality Reduction

We present an error-neural-modeling-based strategy for approximating two-dimensional curvature in the level-set method. Our main contribution is a redesigned hybrid solver [Larios-C\'ardenas and Gibou, J. Comput. Phys. (May 2022), 10.1016/j.jcp.2022.111291] that relies on numerical schemes to enable machine-learning operations on demand. In particular, our routine features double predicting to harness curvature symmetry invariance in favor of precision and stability. The core of this solver is a multilayer perceptron trained on circular- and sinusoidal-interface samples. Its role is to quantify the error in numerical curvature approximations and emit corrected estimates for select grid vertices along the free boundary. These corrections arise in response to preprocessed context level-set, curvature, and gradient data. To promote neural capacity, we have adopted sample negative-curvature normalization, reorientation, and reflection-based augmentation. In the same manner, our system incorporates dimensionality reduction, well-balancedness, and regularization to minimize outlying effects. Our training approach is likewise scalable across mesh sizes. For this purpose, we have introduced dimensionless parametrization and probabilistic subsampling during data production. Together, all these elements have improved the accuracy and efficiency of curvature calculations around under-resolved regions. In most experiments, our strategy has outperformed the numerical baseline at twice the number of redistancing steps while requiring only a fraction of the cost.
Accelerating Laue Depth Reconstruction Algorithm with CUDA
Authors: Ke Yue, Schwarz Nicholas, Tischler Jonathan Z
Date: 2022-01-20 00:00:00
Tasks: CPU, GPU

The Laue diffraction microscopy experiment uses the polychromatic Laue micro-diffraction technique to examine the structure of materials with sub-micron spatial resolution in all three dimensions. During this experiment, local crystallographic orientations, orientation gradients and strains are measured as properties which will be recorded in HDF5 image format. The recorded images will be processed with a depth reconstruction algorithm for future data analysis. But the current depth reconstruction algorithm consumes considerable processing time and might take up to 2 weeks for reconstructing data collected from one single experiment. To improve the depth reconstruction computation speed, we propose a scalable GPU program solution on the depth reconstruction problem in this paper. The test result shows that the running time would be 10 to 20 times faster than the prior CPU design for various size of input data.
Threshold Independent Evaluation of Sound Event Detection Scores
Authors: Janek Ebbers, Romain Serizel, Reinhold Haeb-Umbach
Date: 2022-01-31 00:00:00
Tasks: Event Detection, Sound Event Detection

Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detection (PSD)-receiver operating characteristic (ROC) and PSD score (PSDS) make an important step into the direction of an evaluation of SED systems which is independent from a certain decision threshold. This allows to obtain a more complete picture of the overall system behavior which is less biased by threshold tuning. Yet, the PSD-ROC is currently only approximated using a finite set of thresholds. The choice of the thresholds used in approximation, however, can have a severe impact on the resulting PSDS. In this paper we propose a method which allows for computing system performance on an evaluation set for all possible thresholds jointly, enabling accurate computation not only of the PSD-ROC and PSDS but also of other collar-based and intersection-based performance curves. It further allows to select the threshold which best fulfills the requirements of a given application. Source code is publicly available in our SED evaluation package sed_scores_eval.
PostGAN: A GAN-Based Post-Processor to Enhance the Quality of Coded Speech
Authors: Srikanth Korse, Nicola Pia, Kishan Gupta, Guillaume Fuchs
Date: 2022-01-31 00:00:00
Tasks: Decoder

The quality of speech coded by transform coding is affected by various artefacts especially when bitrates to quantize the frequency components become too low. In order to mitigate these coding artefacts and enhance the quality of coded speech, a post-processor that relies on a-priori information transmitted from the encoder is traditionally employed at the decoder side. In recent years, several data-driven post-postprocessors have been proposed which were shown to outperform traditional approaches. In this paper, we propose PostGAN, a GAN-based neural post-processor that operates in the sub-band domain and relies on the U-Net architecture and a learned affine transform. It has been tested on the recently standardized low-complexity, low-delay bluetooth codec (LC3) for wideband speech at the lowest bitrate (16 kbit/s). Subjective evaluations and objective scores show that the newly introduced post-processor surpasses previously published methods and can improve the quality of coded speech by around 20 MUSHRA points.
HGCN: Harmonic gated compensation network for speech enhancement
Authors: Tianrui Wang, Weibin Zhu, Yingying Gao, Junlan Feng, Shilei Zhang
Date: 2022-01-30 00:00:00
Tasks: Action Detection, Activity Detection, Speech Enhancement

Mask processing in the time-frequency (T-F) domain through the neural network has been one of the mainstreams for single-channel speech enhancement. However, it is hard for most models to handle the situation when harmonics are partially masked by noise. To tackle this challenge, we propose a harmonic gated compensation network (HGCN). We design a high-resolution harmonic integral spectrum to improve the accuracy of harmonic locations prediction. Then we add voice activity detection (VAD) and voiced region detection (VRD) to the convolutional recurrent network (CRN) to filter harmonic locations. Finally, the harmonic gating mechanism is used to guide the compensation model to adjust the coarse results from CRN to obtain the refinedly enhanced results. Our experiments show HGCN achieves substantial gain over a number of advanced approaches in the community.
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?
Authors: Huy Phan, Thi Ngoc Tho Nguyen, Philipp Koch, Alfred Mertins
Date: 2022-01-29 00:00:00
Tasks: Classification, Event Detection, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Polyphonic events are the main error source of audio event detection (AED) systems. In deep-learning context, the most common approach to deal with event overlaps is to treat the AED task as a multi-label classification problem. By doing this, we inherently consider multiple one-vs.-rest classification problems, which are jointly solved by a single (i.e. shared) network. In this work, to better handle polyphonic mixtures, we propose to frame the task as a multi-class classification problem by considering each possible label combination as one class. To circumvent the large number of arising classes due to combinatorial explosion, we divide the event categories into multiple groups and construct a multi-task problem in a divide-and-conquer fashion, where each of the tasks is a multi-class classification problem. A network architecture is then devised for multi-class multi-task modelling. The network is composed of a backbone subnet and multiple task-specific subnets. The task-specific subnets are designed to learn time-frequency and channel attention masks to extract features for the task at hand from the common feature maps learned by the backbone. Experiments on the TUT-SED-Synthetic-2016 with high degree of event overlap show that the proposed approach results in more favorable performance than the common multi-label approach.
Automatic Audio Captioning using Attention weighted Event based Embeddings
Authors: Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2022-01-28 00:00:00
Tasks: Audio captioning, Decoder, Event Detection, Transfer Learning

Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships. The limited samples in AAC datasets at present, has set up a trend to incorporate transfer learning with Audio Event Detection (AED) as a parent task. Towards this direction, in this paper, we propose an encoder-decoder architecture with light-weight (i.e. with lesser learnable parameters) Bi-LSTM recurrent layers for AAC and compare the performance of two state-of-the-art pre-trained AED models as embedding extractors. Our results show that an efficient AED based embedding extractor combined with temporal attention and augmentation techniques is able to surpass existing literature with computationally intensive architectures. Further, we provide evidence of the ability of the non-uniform attention weighted encoding generated as a part of our model to facilitate the decoder glance over specific sections of the audio while generating each token.
Impact of Naturalistic Field Acoustic Environments on Forensic Text-independent Speaker Verification System
Authors: Zhenyu Wang, John H. L. Hansen
Date: 2022-01-28 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

Audio analysis for forensic speaker verification offers unique challenges in system performance due in part to data collected in naturalistic field acoustic environments where location/scenario uncertainty is common in the forensic data collection process. Forensic speech data as potential evidence can be obtained in random naturalistic environments resulting in variable data quality. Speech samples may include variability due to vocal efforts such as yelling over 911 emergency calls, whereas others might be whisper or situational stressed voice in a field location or interview room. Such speech variability consists of intrinsic and extrinsic characteristics and makes forensic speaker verification a complicated and daunting task. Extrinsic properties include recording equipment such as microphone type and placement, ambient noise, room configuration including reverberation, and other environmental scenario-based issues. Some factors, such as noise and non-target speech, will impact the verification system performance by their mere presence. To investigate the impact of field acoustic environments, we performed a speaker verification study based on the CRSS-Forensic corpus with audio collected from 8 field locations including police interviews. This investigation includes an analysis of the impact of seven unseen acoustic environments on speaker verification system performance using an x-Vector system.
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study
Authors: Van Sy Mai, Richard J. La, Tao Zhang, Abdella Battou
Date: 2022-01-31 00:00:00
Tasks: Distributed Optimization

Providing differentiated services to meet the unique requirements of different use cases is a major goal of the fifth generation (5G) telecommunication networks and will be even more critical for future 6G systems. Fulfilling this goal requires the ability to assure quality of service (QoS) end to end (E2E), which remains a challenge. A key factor that makes E2E QoS assurance difficult in a telecommunication system is that access networks (ANs) and core networks (CNs) manage their resources autonomously. So far, few results have been available that can ensure E2E QoS over autonomously managed ANs and CNs. Existing techniques rely predominately on each subsystem to meet static local QoS budgets with no recourse in case any subsystem fails to meet its local budgets and, hence will have difficulty delivering E2E assurance. Moreover, most existing distributed optimization techniques that can be applied to assure E2E QoS over autonomous subsystems require the subsystems to exchange sensitive information such as their local decision variables. This paper presents a novel framework and a distributed algorithm that can enable ANs and CNs to autonomously "cooperate" with each other to dynamically negotiate their local QoS budgets and to collectively meet E2E QoS goals by sharing only their estimates of the global constraint functions, without disclosing their local decision variables. We prove that this new distributed algorithm converges to an optimal solution almost surely, and also present numerical results to demonstrate that the convergence occurs quickly even with measurement noise.
A Safe Control Architecture Based on a Model Predictive Control Supervisor for Autonomous Driving
Authors: Maryam Nezami, Georg Maennel, Hossam Seddik Abbas, Georg Schildbach
Date: 2022-01-31 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

This paper presents a novel, safe control architecture (SCA) for controlling an important class of systems: safety-critical systems. Ensuring the safety of control decisions has always been a challenge in automatic control. The proposed SCA aims to address this challenge by using a Model Predictive Controller (MPC) that acts as a supervisor for the operating controller, in the sense that the MPC constantly checks the safety of the control inputs generated by the operating controller and intervenes if the control input is predicted to lead to a hazardous situation in the foreseeable future invariably. Then an appropriate backup scheme can be activated, e.g., a degraded control mechanism, the transfer of the system to a safe state, or a warning signal issued to a human supervisor. For a proof of concept, the proposed SCA is applied to an autonomous driving scenario, where it is illustrated and compared in different obstacle avoidance scenarios. A major challenge of the SCA lies in the mismatch between the MPC prediction model and the real system, for which possible remedies are explored.
FastIoT -- A framework and holistic approach for rapid development of IIoT systems
Authors: Tilman Klaeger, Konstantin Merker
Date: 2022-01-31 00:00:00
Tasks: Edge-computing, Miscellaneous

While lots of research has been conducted on the architecture of Industrial Internet of Things (IIoT) systems, concepts of structuring their development processes are missing. Therefore, we propose a holistic approach supporting organizations in rapid development of IIoT systems. It includes the structuring of the development process into multiple projects sharing project conventions. Utilizing a single configurable build script for all projects, our goal is to make the integration of code from various projects into broader IIoT systems easy and the systems decomposable with minimal effort.
Implementation of an Elastic Reconfigurable Optical Add/Drop Multiplexer based on Subcarriers for Application in Optical Multichannel Networks
Authors: Faranak Khosravi, Mehdi Tarhani, Shivani Kurle, Mehdi Shadaram
Date: 2022-01-31 00:00:00

We designed a Reconfigurable Optical Add/Drop Multiplexer (ROADM) based on a subcarrier add/drop node in an optical communication system that is suitable for all kinds of optical multiplexing signals. To achieve this goal, at first, we designed an optical comb generator based on a dual-drive Mach Zehnder. The new ROADM setup is validated by a 100 Gb/s 4-subcarrier. In the final step, we checked the performance of the system in terms of the bit error rate (BER) versus optical signal-to-noise ratio (OSNR) to verify the add/drop operation had been successfully performed at 10-9 and is suitable to apply in an all-optical multiplexing technique.
Lyapunov Conditions for Input-to-State Stability of Hybrid Systems with Memory
Authors: Wei Ren, Junlin Xiong
Date: 2022-01-30 00:00:00

This paper studies input-to-state stability for hybrid systems with memory, which models hybrid dynamics affected by time delays. Using both Lyapunov-Razumikhin functions and Lyapunov-Krasovskii functionals, Lyapunov-based sufficient conditions are established for input-to-state stability. In addition, further extensions and relaxations are proposed for special cases, such as the stable flow/jump cases and the cases that Lyapunov functions do not decrease strictly during flow/jumps. Finally, two examples are used to illustrate the developed results.
Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks
Authors: Lai Wei, Ryan Mccloy, Jie Bao
Date: 2022-01-30 00:00:00
Tasks: Chemical Process

Driven by the flexible manufacturing trend in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems (e.g., using process models with parametric uncertainties) with adaptable performance. The proposed adaptive control approach incorporates into the control loop an adaptive neural network embedded contraction-based controller (to ensure convergence to time-varying references) and an online parameter identification module coupled with reference generation (to ensure modelled parameters converge those of the physical system). The integrated learning and control approach involves training a state and parameter dependent neural network to learn a contraction metric parameterized by the uncertain parameter and a differential feedback gain. This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online. As uncertain parameter estimates converge to the corresponding physical values, offset-free tracking, simultaneously with improved convergence rates, can be achieved, resulting in a flexible, efficient and less conservative approach to the reference tracking control of uncertain nonlinear processes. An illustrative example is included to demonstrate the overall approach. An illustrative example is included to demonstrate the overall approach.
Electrolyte Flow Rate Control for Vanadium Redox Flow Batteries using the Linear Parameter Varying Framework
Authors: Ryan Mccloy, Yifeng Li, Jie Bao, Maria Skyllas-Kazacos
Date: 2022-01-30 00:00:00

In this article, an electrolyte flow rate control approach is developed for an all-vanadium redox flow battery (VRB) system based on the linear parameter varying (LPV) framework. The electrolyte flow rate is regulated to provide a trade-off between stack voltage efficiency and pumping energy losses, so as to achieve optimal battery energy efficiency. The nonlinear process model is embedded in a linear parameter varying state-space description and a set of state feedback controllers are designed to handle fluctuations in current during both charging and discharging. Simulation studies have been conducted under different operating conditions to demonstrate the performance of the proposed approach. This control approach was further implemented on a laboratory scale VRB system.