Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Misperception on Emergence of Risk in Platoon of Autonomous Vehicles

With the advent of advanced perception algorithms, achieving long-term autonomy in vehicle platooning has become a possibility. In this paper, we propose a framework to evaluate the risk of misperception resulting from noisy observations from the environment. Each vehicle relies on a perception unit to understand its surroundings and estimate the positions and velocities of other vehicles. We employ the Expected Shortfall (Average Value-at-Risk) measure to evaluate the risk of collision between pairs of vehicles and the risk of violating traffic laws for each vehicle under possible misperceptions. Obtaining an explicit expression for the risk measure allows us to investigate potential trade-offs between overall misperception-induced risks and network architecture. Using our framework, we demonstrate how misperception of highway traffic signs can cause phenomena similar to tailgating and quantify its impact on the risk of such events. Our results can also be used to identify vehicles that are highly susceptible to misperception and to enhance the platoon's robustness concerning overall risk measures in the presence of misperception. We validate our theoretical findings through extensive simulations.
Gasoline Controlled Auto-Ignition with Learning-Based Uncertainty Using Stochastic Model Predictive Control

The internal combustion engine faces a severe energy conservation and emission reduction challenge. In this regard, low-temperature combustion technology is a profitable solution, allowing for pollutant emission reduction while improving engine efficiency. However, the process is complex, and the cycles are mutually coupled, making it a huge challenge to stabilize the entire process behavior. Also, the model mismatch and the inherent stochasticity of the process bring considerable difficulties to the application of control technology. In this work, we propose a deep learning-based generative model to learn the distribution of system uncertainties. The uncertainty information is considered in the model predictive control (MPC) strategy design. We adopt the disturbance-affine stochastic MPC (sMPC) and transform the chance-constrained MPC problem into some tractable optimization problems. The results show better closed-loop performance with smaller output variance, given the prior knowledge of uncertainty realization from the proposed generative model.
Neuralecho: Hybrid of Full-Band and Sub-Band Recurrent Neural Network For Acoustic Echo Cancellation and Speech Enhancement

This paper presents a hybrid of full-band and sub-band recurrent neural network (RNN) model, named NeuralEcho, to jointly solve echo and noise suppression. The full-band model part processes the signal’s entire frequency bands as a whole, while the sub-band model part divides the features into sub-bands and processes each sub-band separately. This approach allows the model to capture both the fine-grained local details of the sub-band processing and the global context of the full-band processing. The single-channel model is then generalized to accommodate a range of input channel numbers. Experimental results show that the hybrid model outperforms the conventional full-band models in terms of objective speech quality metrics and speech recognition accuracy. This suggests that the hybrid approach of full-band and sub-band processing can be a promising direction for future research in the field of speech enhancement.
An Adaptive Distributed Observer for A Class of Discrete-Time Uncertain Linear Systems over Acyclic Digraphs

This paper proposes an adaptive distributed observer for a class of discrete-time uncertain linear leader systems. The leader system is assumed to be neutrally stable with unknown parameters in the system matrix. Such a leader system can produce multi-tone sinusoidal signals with unknown frequencies, magnitudes, and phases. Under the assumption that the digraph of the communication network is a spanning tree with the leader system as the root, the proposed adaptive distributed observer is shown to be capable of estimating over the communication network not only the leader's state, but also the unknown parameters of the leader's system matrix.
Traditional Chinese Medicine Formula Classification Using Large Language Models

Objective: In this study, we aim to investigate the utilization of large language models (LLMs) for traditional Chinese medicine (TCM) formula classification by fine-tuning the LLMs and prompt template. Methods: We refined and cleaned the data from the Coding Rules for Chinese Medicinal Formulas and Their Codes [1], the Chinese National Medical Insurance Catalog for Proprietary Chinese Medicines [2], and Textbooks of Formulas of Chinese Medicine [3] to address the standardization of TCM formula information, and finally we extracted 2308 TCM formula data as a dataset in this study. We designed a prompt template for the TCM formula classification task and randomly divided the formula dataset into three subsets: a training set (2000 formulas), a test set (208 formulas), and a validation set (100 formulas). We fine-tuned the open-source LLMs such as ChatGLM-6b and ChatGLM2-6b. Finally, we evaluate all selected LLMs in our study: ChatGLM-6b (original), ChatGLM2-6b (original), ChatGLM-130b, InternLM-20b, ChatGPT, ChatGLM-6b (fine-tuned), and ChatGLM2-6b (fine-tuned). Results: The results showed that ChatGLM2-6b (fine-tuned) and ChatGLM-6b (fine-tuned) achieved the highest accuracy rates of 71% and 70% on the validation set, respectively. The accuracy rates of other models were ChatGLM-130b 58%, ChatGPT 53%, InternLM-20b 52%, ChatGLM2-6b (original) 41%, and ChatGLM-6b (original) 23%. Conclusion: LLMs achieved an impressive 71% accuracy in the formula classification task in our study. This was achieved through fine-tuning and the utilization of prompt templates. And provided a novel option for the utilization of LLMs in the field of TCM.
Scalable Machine Learning Model for Highway CCTV Feed Real-Time Car Accident and Damage Detection

This study investigates the potential advantages of employing computer vision algorithms to enhance real-time accident detection and response on highways using CCTV feed. Traditional techniques rely on retrospective data, which can decrease response times and precision. Computer vision algorithms have the potential to enhance detection speed and precision, resulting in quicker emergency response and monitoring of traffic flow. The primary objective of this study is to identify the advantages of utilising computer vision algorithms and the data gathered through them to enhance road safety measures and reduce the occurrence of accidents. This study is anticipated to result in quicker emergency response times, the identification of areas where statistically more accidents are likely to occur, and the use of collected data for research purposes, which can lead to enhanced road safety measures. Using computer vision algorithms for accident detection and response has the potential to reduce the human and monetary costs associated with traffic accidents.
Development of an Automated Information System for the IT Clinic

Nowadays, modern universities organise units in which students of IT can obtain additional practical skills in IT during extracurricular time. Most often, their work is organized according to the clinic principle and, accordingly, requires various types of accounting and analytics. This paper deals with the development of an automated information system for the needs of IT clinic of Admiral Makarov State University of Maritime and Inland Shipping.
Least-Squares Composite Learning Backstepping Control with High-Order Tuners

Transient performance improvement in adaptive backstepping control is beneficial for the stability and robustness of control systems. In addition, parameter convergence in classical adaptive control is dependent on a stringent condition named persistent excitation (PE). This paper proposes a least squares-based composite learning backstepping control (LS-CLBC) strategy with high-order tuners for strict-feedback uncertain nonlinear systems such that exponential stability with parameter convergence is achieved under interval excitation (IE) or even partial IE that is strictly weaker than PE. In the LS-CLBC, the storage and forgetting of online historical data are determined by the exciting strength of a novel excitation matrix consisting of only active regressor channels, such that excitation information of regressor channels is exploited more effectively and efficiently to achieve parameter estimation. The learning rate is adjusted online based on LS and integrated into a high-order tuner to obtain the high-order time derivatives of parameter estimates. The closed-loop system is proven exponentially stable. Simulation results have demonstrated the superiority of the proposed approach.
Introducing an Open-Source Python Toolkit for Machine Learning Research in Physiological Signal based Affective Computing

In the realm of physiological-based affective computing, significant progress has been witnessed in machine learning over the last two decades. Nevertheless, the lack of consistency in measurement tools and data organization across diverse datasets poses a challenge when integrating new datasets for algorithm testing, research, and result comparison across multiple datasets. Despite the expansion of artificial intelligence-driven affective computing, a notable gap remains in the form of a comprehensive toolkit tailored for both machine learning researchers and psychologists who are new to the field of machine learning. In response to these challenges, we introduce a Python toolkit designed to fulfill two key roles: establishing a standardized benchmark for affective computing datasets and offering an all-encompassing toolkit for machine learning in physiological signal based affective computing. This toolkit encompasses vital components essential to the machine learning process, encompassing tasks like dataset integration and interpretation, signal preprocessing, feature derivation, post-processing, classification models, and evaluation metrics. Our proposed toolkit is designed for working with seven publicly available datasets, embracing five different modalities and incorporating twenty diverse machine learning models spanning from conventional options like the support vector machine (SVM) to cutting-edge deep learning models. To the best of our knowledge, the proposed toolkit stands as the pioneering initiative for creating a standardized dataset benchmarking system and a comprehensive solution tailored for machine learning applications in affective computing. The open-source codebase for the proposed toolkit is accessible via https://github.com/rjfang/pyAffeCT.
Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics.
Machine Learning Models for Prediction of Joint Infections Following Hip Replacement Surgery

Periprosthetic joint infection (PJI) is a rare but serious complication following total hip replacement surgery. Personalized risk prediction and risk factor management can allow effective presurgical interventions and improved surgical outcomes. In this study, we implemented a data driven approach to develop PJI risk prediction models using large scale data from the electronic health records (EHR) at a large tertiary care hospital. Dataset comprised a total of 22,350 hip replacement surgeries with 283 (1.3%) PJI events within the 1-year window following surgery. We implemented four different models (classic lasso, relaxed lasso, gradient boosting model (GBM) and neural networks) and used 10-fold cross-validation to calculate measures of model performance. The relaxed lasso model using the Cox model structure outperformed the other models with a concordance of 0.793. Our analysis indicates large scale EHR data and machine learning models provide increased accuracy in prediction of joint infections in hip replacement patients.
Stress-ViT: In-Ear Plethysmography for Mental Stress Classification with Vision Transformer

Stressful situations have a significant impact on individuals’ well-being, necessitating prompt stress diagnosis to mitigate potential long-term harm. This paper introduces an innovative in-ear photoplethysmography (PPG) device for detecting mental stress. The experiment was conducted to collect raw PPG and tri-axis acceleration signals from 15 participants through stress induction protocols, including the Stroop-color-word interference test and mental arithmetic calculations. Preprocessing techniques, such as bandpass filtering and motion artifact cancellation, were employed to address physiological and motion-related artifacts. The Short-Time Fourier Transform (STFT) was utilized to convert the filtered PPG signals into spectrograms, representing time-frequency images. Leveraging the Vision Transformer (ViT) model, renowned for image classification, the PPG signals were classified into distinct stress levels using fixed-size overlapping patches. Comprehensive experiments were conducted to compare the proposed ViT classifier with other pretrained CNN methods, demonstrating its superior performance with an average accuracy of 91.98%, recall of 91.97%, and precision of 92.02%. Integrating the in-ear PPG sensor with the ViT model proves to be a promising approach for diagnosing mental stress. The proposed in-ear PPG with high accuracy and a convenient design make it suitable for everyday stress assessment, thereby enhancing its potential as a valuable tool.
Hacia una mejor comprensión de la eco- innovación como motor de la competitividad sostenible

espanolEn los ultimos anos el termino “eco-innovacion” es cada vez mas utilizado en la gestion y la politica medioambiental, aunque en diversos contextos y con diferentes connotaciones, lo que podria reducir su valor practico. En este articulo se pretende explorar la estructura subyacente del concepto de eco-innovacion, avanzando en la cuantificacion de un marco conceptual cualitativo previo EnglishIn recent years, the term “eco-innovation” has been increasingly used in environmental management and policy, although in diverse contexts and with different underlying connotations that may eventually reduce its practical value. This article aims to explore the underlying structure of the eco-innovation concept, through a quantitative analysis of a previously developed qualitative framework
Improved Approximation Algorithms for Sorting Unsigned Genomes by Reversals

Comparing genomes in terms of gene order is a classical combinatorial optimization problem in computational biology, which asks for a least number of genome rearrangement operations to transform one genome into the other. The problems of sorting genomes by reversals have been extensively studied over the past decades. Computing the reversal distance are NP-hard when the input genomes are unsigned. A general method to devise an approximation algorithm involves computing a proper alternating cycle decomposition of its breakpoint graph, which becomes the bottleneck for computing the corresponding rearrangement distances and prohibits to obtain approximation factors better than 1.375 in polynomial time. In this paper, we devise FPT approximation algorithms for the problems of sorting genomes by reversals, the approximation factor are improved to 4/3 + ε. The algorithm exploits a new randomized algorithm to decompose the breakpoint graph, which succeeds with a high probability $1 - \frac{1}{{{e^{O(n)}}}}$ according to Chernoff Bound. The time complexity of the algorithm is $O\left( {{2^{{d^{\ast}}}} \cdot {n^{O\left( {\frac{1}{\varepsilon }} \right)}}} \right)$, where n is the length of each genome and d* represents the optimal reversal distance.
Should Environmental Rights be a Constitutional Right in Malaysia?

In recent times, there has been international recognition of the fact that environmental rights are human rights. Despite being a State Party to many international conventions that proclaim environmental rights as human rights, Malaysia has yet to explicitly recognise this in any of her domestic legislation, much less the Federal Constitution. This article argues that given the inter-generational nature of environmental rights, there is immediate necessity for Malaysia to boldly and expressly proclaim environmental rights as a human right by including environmental rights as a fundamental liberty in the Federal Constitution. For the purpose of this research, a qualitative research method is adopted. The data collection method is document analysis consisting of both primary and secondary sources such as the Federal Constitution, international instruments, textbooks, journal articles, published law reports, and case law. The research found that the time is ripe for Malaysia to memorialise her international obligations by declaring environmental rights as a separate and distinct human right in the Federal Constitution. Environmental rights deserve to be en-shrined and preserved for time immemorial in the supreme law of the land, because only then will there be longevity and continuity in the quest to conserve and preserve the environment not only for the foreseeable future, but for the future generations yet unborn that stand to inherit the Earth.
Advanced Signal Processing Techniques for Demagnetization Detection in PM Generators at Variable Speed

In this paper, demagnetization detection in permanent magnet generators is analyzed using advanced signal processing techniques. The t-f decomposition of the current signal is calculated using the min-norm method. Three signal processing techniques are investigated which are the adaptive demodulation, the time-varying resampling and the envelope analysis. The merits of these methods are thoroughly compared against the commonly adopted short-time Fourier transform analysis. Moreover, traditional signal processing techniques may not be suitable for fault detection when the machine operates at variable speed. Under such condition, interference between the fundamental frequency of the machine and the fault harmonic can lead to false alarms. Simulation test and experimental results demonstrate effective performance and feasibility of the proposed methods for demagnetization detection of permanent generators operating under non-stationary conditions.
Identifying People with Mild Cognitive Impairment at Risk of Developing Dementia using Speech Analysis

Mild Cognitive Impairment (MCI) is the intermediate stage between ageing and possible dementia. $50 \%$ of people with MCI may progress to dementia (prodromal Alzheimer’s Disease (pr-AD)). Identifying those at risk is a challenging but important task that can help with anxiety and treatment. Currently, clinicians wait for severe signs of impairment to emerge, however, recent studies have shown promising automatic speech-based approaches. This paper works on a unique dataset containing 50 healthy controls (HCs) and 50 MCI (of which 22 are pr-AD and 28 are non-progressed (nonP)). The recordings are of people speaking with a virtual agent. A number of acoustic, text and contextual features were extracted and used for training classifiers. The best result was achieved with an $F_{1}$-scores of $\mathbf{81.2 \%}$ for the detection of MCI versus HC, and $\mathbf{75} \%$ for MCI(pr-AD) versus MCI(nonP). The best $F_{1}$-score of $\mathbf{66.9 \%}$ was achieved for the detection of MCI (pr-AD) versus MCI (nonP) versus HC.
CMOS Leaky Integrate-and-Fire Neurons with Local Dynamic Biasing and Inhibitory Self-Oscillation

This paper presents a CMOS-based neuromorphic circuit that can simulate self-oscillatory firing behaviors. Based on the leaky integrate-and-fire neuron model, a dynamic bias controller and an excitation integrator are added for controlling inhibitory connection between neurons as well as switching the operation mode with an integrated excitatory control signal. The proposed neuron circuit replicates extracellular fluidic biasing of membrane potential that oscillates at low frequency on top of the temporal integration of the action potential firing at high frequency. The proposed neuron can also replicate the synaptic fatigue by implementing inhibition declining over time, which leads the bias to oscillate. The proposed neuron model was implemented with 250nm CMOS process at 2.5V supply voltage. Each neuron consumes 46.16pJ energy/spike from leaky integration. Simulation results verified that a pair of proposed neuron cells can generate an oscillatory rhythm, where the bias waveform oscillates with the output spikes generated based on the bias level change. The proposed neuron circuit model can be utilized to reproduce more complicated oscillatory network behaviors of biological neurons.
A Unified Approach to Solve the Dynamic Consensus on the Average, Maximum, and Median Values with Linear Convergence

This manuscript proposes novel distributed algorithms for solving the dynamic consensus problem in discrete-time multi-agent systems on three different objective functions: the average, the maximum, and the median. In this problem, each agent has access to an external time-varying scalar signal and aims to estimate and track a function of all the signals by exploiting only local communications with other agents. By recasting the problem as an online distributed optimization problem, the proposed algorithms are derived based on the distributed implementation of the alternating direction method of multipliers (ADMM) and are thus amenable to a unified analysis technique. A major contribution is that of proving linear convergence of these ADMM-based algorithms for the specific dynamic consensus problems of interest, for which current results could only guarantee sub-linear convergence. In particular, the tracking error is shown to converge within a bound, whereas the steady-state error is zero. Numerical simulations corroborate the theoretical findings, empirically show the robustness of the proposed algorithms to re-initialization errors, and compare their performance with that of state-of-the-art algorithms.