pwc-archive/papers-with-abstracts huggingface dataset:
Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model
Authors: Marko Niemelä, Mikaela von Bonsdorff, Sami Äyrämö, Tommi Kärkkäinen
Date: 2024-02-02 00:00:00
Tasks: feature selection, Medical Diagnosis

Memory disorders are a central factor in the decline of functioning and daily activities in elderly individuals. The confirmation of the illness, initiation of medication to slow its progression, and the commencement of occupational therapy aimed at maintaining and rehabilitating cognitive abilities require a medical diagnosis. The early identification of symptoms of memory disorders, especially the decline in cognitive abilities, plays a significant role in ensuring the well-being of populations. Features related to speech production are known to connect with the speaker's cognitive ability and changes. The lack of standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken language. Non-lexical but acoustic properties of spoken language have proven useful when fast, cost-effective, and scalable solutions are needed for the rapid diagnosis of a disease. The work presents an approach related to feature selection, allowing for the automatic selection of the essential features required for diagnosis from the Geneva minimalistic acoustic parameter set and relative speech pauses, intended for automatic paralinguistic and clinical speech analysis. These features are refined into word histogram features, in which machine learning classifiers are trained to classify control subjects and dementia patients from the Dementia Bank's Pitt audio database. The results show that achieving a 75% average classification accuracy with only twenty-five features with the separate ADReSS 2020 competition test data and the Leave-One-Subject-Out cross-validation of the entire competition data is possible. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.
SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis
Authors: Teysir Baoueb, Haocheng Liu, Mathieu Fontaine, Jonathan Le Roux, Gael Richard
Date: 2024-01-30 00:00:00
Tasks: Generative Adversarial Network, Speech Synthesis

Generative adversarial network (GAN) models can synthesize highquality audio signals while ensuring fast sample generation. However, they are difficult to train and are prone to several issues including mode collapse and divergence. In this paper, we introduce SpecDiff-GAN, a neural vocoder based on HiFi-GAN, which was initially devised for speech synthesis from mel spectrogram. In our model, the training stability is enhanced by means of a forward diffusion process which consists in injecting noise from a Gaussian distribution to both real and fake samples before inputting them to the discriminator. We further improve the model by exploiting a spectrally-shaped noise distribution with the aim to make the discriminator's task more challenging. We then show the merits of our proposed model for speech and music synthesis on several datasets. Our experiments confirm that our model compares favorably in audio quality and efficiency compared to several baselines.
Identifying False Content and Hate Speech in Sinhala YouTube Videos by Analyzing the Audio
Authors: W. A. K. M. Wickramaarachchi, Sameeri Sathsara Subasinghe, K. K. Rashani Tharushika Wijerathna, A. Sahashra Udani Athukorala, Lakmini Abeywardhana, A. Karunasena
Date: 2024-01-30 00:00:00
Tasks: Hate Speech Detection, Misinformation, text-classification, Text Classification, Text Summarization

YouTube faces a global crisis with the dissemination of false information and hate speech. To counter these issues, YouTube has implemented strict rules against uploading content that includes false information or promotes hate speech. While numerous studies have been conducted to reduce offensive English-language content, there's a significant lack of research on Sinhala content. This study aims to address the aforementioned gap by proposing a solution to minimize the spread of violence and misinformation in Sinhala YouTube videos. The approach involves developing a rating system that assesses whether a video contains false information by comparing the title and description with the audio content and evaluating whether the video includes hate speech. The methodology encompasses several steps, including audio extraction using the Pytube library, audio transcription via the fine-tuned Whisper model, hate speech detection employing the distilroberta-base model and a text classification LSTM model, and text summarization through the fine-tuned BART-Large- XSUM model. Notably, the Whisper model achieved a 48.99\% word error rate, while the distilroberta-base model demonstrated an F1 score of 0.856 and a recall value of 0.861 in comparison to the LSTM model, which exhibited signs of overfitting.
Calibration and Correctness of Language Models for Code
Authors: Claudio Spiess, David Gros, Kunal Suresh Pai, Michael Pradel, Md Rafiqul Islam Rabin, Amin Alipour, Susmit Jha, Prem Devanbu, Toufique Ahmed
Date: 2024-02-03 00:00:00
Tasks: Decision Making

Machine learning models are widely used, but can also often be wrong. Users would benefit from a reliable indication of whether a given output from a given model should be trusted, so a rational decision can be made whether to use the output or not. For example, outputs can be associated with a confidence measure; if this confidence measure is strongly associated with likelihood of correctness, then the model is said to be well-calibrated. A well-calibrated confidence measure can serve as a basis for rational, graduated decision-making on how much review and care is needed when using generated code. Calibration has so far been studied in mostly non-generative (e.g. classification) settings, especially in software engineering. However, generated code can quite often be wrong: Given generated code, developers must decide whether to use directly, use after varying intensity of careful review, or discard model-generated code. Thus, calibration is vital in generative settings. We make several contributions. We develop a framework for evaluating the calibration of code-generating models. We consider several tasks, correctness criteria, datasets, and approaches, and find that, by and large, generative code models we test are not well-calibrated out of the box. We then show how calibration can be improved using standard methods, such as Platt scaling. Since Platt scaling relies on the prior availability of correctness data, we evaluate the applicability and generalizability of Platt scaling in software engineering, discuss settings where it has good potential for practical use, and settings where it does not. Our contributions will lead to better-calibrated decision-making in the current use of code generated by language models, and offers a framework for future research to further improve calibration methods for generative models in software engineering.
A minimal model of cognition based on oscillatory and current-based reinforcement processes
Authors: Linnéa Gyllingberg, Yu Tian, David J. T. Sumpter
Date: 2024-02-04 00:00:00

Building mathematical models of brains is difficult because of the sheer complexity of the problem. One potential starting point is through basal cognition, which gives abstract representation of a range of organisms without central nervous systems, including fungi, slime moulds and bacteria. We propose one such model, demonstrating how a combination of oscillatory and current-based reinforcement processes can be used to couple resources in an efficient manner, mimicking the way these organisms function. A key ingredient in our model, not found in previous basal cognition models, is that we explicitly model oscillations in the number of particles (i.e. the nutrients, chemical signals or similar, which make up the biological system) and the flow of these particles within the modelled organisms. Using this approach, we find that our model builds efficient solutions, provided the environmental oscillations are sufficiently out of phase. We further demonstrate that amplitude differences can promote efficient solutions and that the system is robust to frequency differences. In the context of these findings, we discuss connections between our model and basal cognition in biological systems and slime moulds, in particular, how oscillations might contribute to self-organised problem-solving by these organisms.
Timed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning
Authors: Hao Zhu, Kefan Jin, Rui Gao, Jialin Wang, C. -J. Richard Shi
Date: 2024-02-05 00:00:00
Tasks: Collision Avoidance, Trajectory Planning

Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination. In this article, we address autonomous trajectory planning problems, which aims at developing innovative solutions to enhance the adaptability and robustness of unmanned systems in navigating complex and dynamic environments. We first introduce the variable splitting (VS) method as a constrained optimization method to reimagine the renowned Timed-Elastic-Band (TEB) algorithm, resulting in a novel collision avoidance approach named Timed-Elastic-Band based variable splitting (TEB-VS). The proposed TEB-VS demonstrates superior navigation stability, while maintaining nearly identical resource consumption to TEB. We then analyze the convergence of the proposed TEB-VS method. To evaluate the effectiveness and efficiency of TEB-VS, extensive experiments have been conducted using TurtleBot2 in both simulated environments and real-world datasets.
ALIVE: A Low-Cost Interactive Vaccine Storage Environment Module ensuring easy portability and remote tracking of operational logistics to the last mile
Authors: Arkadeep Datta, Arani Mukhopadhyay, Amitava Datta, Ranjan Ganguly
Date: 2024-02-05 00:00:00

The COVID-19 pandemic has profoundly reshaped our lives, prompting a search for solutions to its far-reaching effects. Vaccines emerged as a beacon of hope, yet reaching remote areas faces last-mile hurdles and cost issues due to loss of vaccine potency due to poor temperature regulation of the storage units and unanticipated vaccine wastage en route, a common occurrence in conventional vaccine transportation methods. We introduce ALIVE, a low-cost Interactive Vaccine Storage Environment module. ALIVE provides an off-grid, self-sufficient solution for vaccine storage and transport, enabled by active cooling technology. ALIVE's innovation lies in its integration with the Internet of Things (IoT), allowing real-time monitoring and control. This IoT-enabled Application Programming Interface (API) features a data acquisition and environment parameter control system, managing oversight and decision-making. ALIVE's compact, lightweight design makes it adaptable to various logistical scenarios, while its versatility enables it to maintain both time-invariant and time-dependent thermophysical and spatial parameters. Operationalized through a PID algorithm, ALIVE ensures precise temperature control within the vaccine chamber. Its dynamic features, such as remote actuation and data sharing, demonstrate its adaptability and potential applications. Despite the frugal nature of development, the system promises significant benefits, including reduced vaccine loss and remote monitoring advantages. Collaborations with healthcare partners seek to further enhance ALIVE's readiness and expand its impact. ALIVE revolutionizes vaccine logistics, offering scalable, cost-effective solutions for bridging accessibility gaps in challenging distribution scenarios. Its adaptability positions it for widespread application, from last-mile vaccine delivery to environment-controlled supply chains and beyond.
Competitive Equilibrium in Microgrids With Dynamic Loads
Authors: Zeinab Salehi, Yijun Chen, Ian R. Petersen, Elizabeth L. Ratnam, Guodong Shi
Date: 2024-02-05 00:00:00

In this paper, we consider microgrids that interconnect prosumers with distributed energy resources and dynamic loads. Prosumers are connected through the microgrid to trade energy and gain profit while respecting the network constraints. We establish a local energy market by defining a competitive equilibrium which balances energy and satisfies voltage constraints within the microgrid for all time. Using duality theory, we prove that under some convexity assumptions, a competitive equilibrium is equivalent to a social welfare maximization solution. Additionally, we show that a competitive equilibrium is equivalent to a Nash equilibrium of a standard game. In general, the energy price for each prosumer is different, leading to the concept of locational prices. We investigate a case under which all prosumers have the same locational prices. Additionally, we show that under some assumptions on the resource supply and network topology, locational prices decay to zero after a period of time, implying the available supply will be more than the demand required to stabilize the system. Finally, two numerical examples are provided to validate the results, one of which is a direct application of our results on electric vehicle charging control.
Modelling and cooling power control of a TES-backed-up vapour-compression refrigeration system
Authors: D. Rodríguez, G. Bejarano, M. Vargas, J. M. Lemos, M. G. Ortega
Date: 2024-02-04 00:00:00
Tasks: Scheduling

This work addresses the modelling, power control, and optimization of a thermal energy storage (TES) system combined with a vapour-compression refrigeration facility based on phase change materials (PCM). Given a novel design of a PCM-based TES tank and its interconnection with an existing refrigeration system, the joint dynamic modelling is first studied, exploring the different time scales that coexist at the interconnected system. Diverse operating modes are defined, according to the intended use of the TES tank as a cold-energy buffer to decouple cooling demand and production, whereas the static characteristic and power limits are calculated and show the high coupling between the main cooling powers involved (TES charging/discharging power, and direct power production at the evaporator). In this light, a decoupling control strategy is proposed, where the low-level controllers are simply PI regulators and the refrigerant/secondary mass flows are considered as virtual manipulated variables, applying a feedforward-based cascade strategy. The control performance is evaluated through a thorough simulation that includes all operating modes, where the reference tracking is shown to be fast and reliable enough to address high-level scheduling strategies, where the references on the main cooling powers are intended to be imposed considering economic and efficiency criteria.
Efficient simulation strategy for PCM-based cold-energy storage systems
Authors: G. Bejarano, M. Vargas, M. G. Ortega, F. Castaño, J. E. Normey-Rico
Date: 2024-02-04 00:00:00
Tasks: energy management, Management

This paper proposes a computationally efficient simulation strategy for cold thermal energy storage (TES) systems based on phase change material (PCM). Taking as a starting point the recent design of a TES system based on PCM, designed to complement a vapour-compression refrigeration plant, the new highly efficient modelling strategy is described and its performance is compared against the pre-existing one. The need for a new computationally efficient approach comes from the fact that, in the near future, such a TES model is intended to be used in combination with the model of the own mother refrigeration plant, in order to address efficient, long-term energy management strategies, where computation time will become a major issue. Comparative simulations show that the proposed computationally efficient strategy reduces the simulation time to a small fraction of the original figure (from around 1/30th till around 1/120th, depending on the particular choice of the main sampling interval), at the expense of affordable inaccuracy in terms of the PCM charge ratio.
Enhancing Optimal Microgrid Planning with Adaptive BESS Degradation Costs and PV Asset Management: An Iterative Post-Optimization Correction Framework
Authors: Hassan Zahid Butt, Xingpeng Li
Date: 2024-02-03 00:00:00
Tasks: Asset Management, Scheduling

The transition to renewable energy has positioned photovoltaic (PV) systems and battery energy storage systems (BESS) as essential assets in microgrids, particularly for remote installations. However, traditional planning models often neglect dynamic degradation costs or rely on complex or non-linear approaches, limiting their scalability and practical applicability. This paper introduces a microgrid planning model that integrates adaptive degradation cost modeling to enable accurate, efficient, and scalable long-term resource allocation. The proposed model employs the iterative post-optimization correction (IPOC) framework, solving a sequence of mixed-integer linear programming problems. Each iteration refines BESS degradation costs based on observed depth-of-discharge profiles and incorporates PV degradation costs to ensure realistic asset performance assessments. Sensitivity analysis of PV and BESS capital costs further underscores the model's robustness under varying economic conditions, with the IPOC framework achieving up to ~1% additional cost savings for the given test system compared to static approaches. The results demonstrate that by iteratively adjusting degradation penalties based on actual usage, the methodology optimizes BESS performance, ensures precise resource allocation, resolves issues of under- or overutilization, enhances system reliability, and facilitates scalable, sustainable microgrid planning.
A Discrete-time Dynamical Model for Optimal Dispatching and Rebalancing of Autonomous Mobility-on-Demand Systems
Authors: Ali Aalipour, Alireza Khani
Date: 2024-02-03 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Scheduling

Autonomous vehicles are rapidly evolving and will soon enable the application of large-scale mobility-on-demand (MoD) systems. Managing the fleets of available vehicles, commonly known as "rebalancing," is crucial to ensure that vehicles are distributed properly to meet customer demands. This paper presents an optimal control approach to optimize vehicle scheduling and rebalancing in an autonomous mobility-on-demand (AMoD) system. We use graph theory to model a city partitioned into virtual zones. Zones represent small areas of the city where vehicles can stop and pick up/drop off customers, whereas links denote corridors of the city along which autonomous vehicles can move. They are considered vertices and edges in the graph. Vehicles employed in the AMoD scheme are autonomous, and rebalancing can be executed by dispatching available empty vehicles to areas undersupplied. Rebalancing is performed on the graph's vertices, i.e., between city areas. We propose a linear, discrete-time model of an AMoD system using a transformed network. After acquiring the model, the desired number of rebalancing vehicles for the AMoD model is derived through an optimization problem. Moreover, the well-posedness of the model is illustrated. To leverage the proposed model, we implemented the model predictive control (MPC) framework to find the optimal rebalancing and scheduling policy. We show the MPC's effectiveness and how the MPC framework can be implemented in real-time for a real-world case study. The numerical results show that the MPC with a linear cost function and linear reference, which it tracks, is effective, outperforming other MPC-based and state-of-the-art algorithms across all evaluation criteria.
Outlier Accommodation for GNSS Precise Point Positioning using Risk-Averse State Estimation
Authors: Wang Hu, Jean-Bernard Uwineza, Jay A. Farrell
Date: 2024-02-02 00:00:00
Tasks: State Estimation

Reliable and precise absolute positioning is necessary in the realm of Connected Automated Vehicles (CAV). Global Navigation Satellite Systems (GNSS) provides the foundation for absolute positioning. Recently enhanced Precise Point Positioning (PPP) technology now offers corrections for GNSS on a global scale, with the potential to achieve accuracy suitable for real-time CAV applications. However, in obstructed sky conditions, GNSS signals are often affected by outliers; therefore, addressing outliers is crucial. In GNSS applications, there are many more measurements available than are required to meet the specification. Therefore, selecting measurements to avoid outliers is of interest. The recently developed Risk-Averse Performance-Specified (RAPS) state estimation optimally selects measurements to minimize outlier risk while meeting a positive semi-definite constraint on performance; at present, the existing solution methods are not suitable for real-time computation and have not been demonstrated using challenging real-world data or in Real-time PPP (RT-PPP) applications. This article makes contributions in a few directions. First, it uses a diagonal performance specification, which reduces computational costs relative to the positive semi-definite constraint. Second, this article considers GNSS RT-PPP applications. Third, the experiments use real-world GNSS data collected in challenging environments. The RT-PPP experimental results show that among the compared methods: all achieve comparable performance in open-sky conditions, and all exceed the Society of Automotive Engineers (SAE) specification; however, in challenging environments, the diagonal RAPS approach shows improvement of 6-19% over traditional methods. Throughout, RAPS achieves the lowest estimation risk.
Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
Authors: Shu Li, Jingxuan Yang, Honglin He, Yi Zhang, Jianming Hu, Shuo Feng
Date: 2024-02-02 00:00:00
Tasks: Autonomous Vehicles

Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible for a specific AV is severely limited by tight constraints on testing budgets and time. With the restrictions imposed by strictly restricted numbers of tests, existing testing methods often lead to significant uncertainty or difficulty to quantifying evaluation results. In this paper, we formulate this problem for the first time the "few-shot testing" (FST) problem and propose a systematic framework to address this challenge. To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity. Specifically, under the guidance of better generalization ability of the testing scenario set on AVs, we dynamically adjust this set and the contribution of each testing scenario to the evaluation result based on coverage, leveraging the prior information of surrogate models (SMs). With certain hypotheses on SMs, a theoretical upper bound of evaluation error is established to verify the sufficiency of evaluation accuracy within the given limited number of tests. The experiment results on cut-in scenarios demonstrate a notable reduction in evaluation error and variance of our method compared to conventional testing methods, especially for situations with a strict limit on the number of scenarios.