Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech-based Data Exploration for Diabetes Management among Older Adults

Diabetes management is particularly challenging for older adults due the various factors that affect the blood glucose of a patient. Based on a requirements analysis, we developed a prototype that will allow older adults to interact with their own blood glucose data using speech. Through self-reflection and contextual clues, we aim to increase the awareness of the factors that may lead to instability in blood glucose levels among patients. *e-mail: vedangibhavsar123@gmail.com †e-mail: utkarshanehe@gmail.com ‡e-mail: adeetya.m@gmail.com §e-mail: srushti.sardeshmukh@gmail.com ¶e-mail: apjoshi@usfca.edu
Adaptive Social Planner to Accompany People in Real-life Dynamic Environments

Robots must develop the ability to socially navigate in uncontrolled urban environments to be able to be included in our daily lives. This paper presents a new robot navigation framework called the Adaptive Social Planner (ASP) and a robotic system, which includes the ASP. Our re-sults and previous work show that the ASP can adapt to different collaborative tasks involving humans and robots, such as independent robot navigation, human-robot accompaniment, a robot approaching people, robot navigation tasks that combine learning techniques, and human-drone interactions. Our approach in this paper focuses on demonstrating how the ASP can be customized to implement two new methods for group accompaniment: The Adaptive Social Plan-ner using a V-formation model to accompany Groups of people (ASP-VG) and the Adaptive Social Planner using a Side-by-side model to accompany Groups of people (ASP-SG). These two methods result in a robot accompanying groups of people by anticipating human and uncontrolled urban environment behaviors. Also, we develop four new robot skills to deal with unexpected human behaviors, such as rearrangement of the position of the companions inside the group, un-foreseen changes in the velocity of the robot companions, occlusions among group members, and changes in the direction toward destinations in the environment. Moreover, we develop di ff erent performance metrics, based on social distances, to evaluate the tasks of the robot. In addition, we present the guidelines followed in performing the real-life experiments with volunteers, including a human-robot speech interaction to help humans create a relationship with the robot to be genuinely involved in the mutual accompa-niment. Finally, we include an exhaustive validation of the methods by evaluating the behavior of the robot through
Study of Nonlinear Analysis and Chaos in Vibrations and Fluids

Chaos and turbulence are two important topics in nonlinear dynamics. In this study, two problems related to chaos and turbulence modelling are presented. They are the chaotic vibration phenomenon in high-dimensional partial differential equations and the emergence of the Navier-Stokes-alpha model for channel flows. The study of the chaotic vibration phenomenon in high-dimensional partial differential equations is explained from both the numerical and theoretical aspects. In the numerical perspective, we have studied the chaotic vibration phenomenon of the 2D wave equation through numerical simulations. Based on the finite-volume method, we have built our own solver “img2Foam” in the Computational Fluid Dynamics software OpenFOAM (Open source Field Operation and Manipulation). We have implemented several numerical simulations containing both chaotic and non-chaotic cases. As for the theoretical perspective, we give a rigorous proof for the chaotic vibration phenomenon of the 2D non-strictly hyperbolic equation. After introducing two linear operators, the initial system of the 2D non-strictly hyperbolic equation is converted into a system of two coupled first order equations. By using the method of characteristics, we have found the explicit solution formulas of the new system. We have also found a regime of the parameters when the chaotic vibration phenomenon occurs by applying the period-doubling bifurcation theorem. Numerical simulations are presented to validate the theoretical results. Inspired by the concept of the regular part of the weak attractor of the 3D NavierStokes equations, we concentrate on a restricted class of fluid flows to explore the transition from the Navier-Stokes equations to the Navier-Stokes-alpha model for channel flows. The Navier-Stokes equations have been widely used to describe the
Anchor-free Hand-detection with Lightweight Design for Smart Factories

Nowadays, good hand detection has been proven helpful for a smart assembly factory to improve work efficiency. Particularly, an assembly line for an Industry 4.0 factory needs to manufacture a diverse range of products, its assemblers must acquire knowledge of distinct assembly processes and inspection procedures. Therefore, hand detection via cameras has become a prevalent method of aiding the assembly process in smart factories. However, existing hand detection in a smart factory still relies on a powerful back-end server for image processing due to the limited computing power of a camera. To address this issue, we propose a “Lightweight Anchor-Free Hand-detection Model” (LAFHDM), and the resultant deep neural networks (DNNs) can directly fit into a smart camera to detect assemblers’ hand positions to verify the correctness of assembly steps. The proposal is also in accordance with the inevitable trends of edge computing and the Internet of Things. The experimental results show that the inference speed of the LAFHDM is at least 40 times faster, the accuracy can be 27.41% higher than that of previous models. Moreover, the inference speed of an edge camera is improved approximately three times.
LPV-Based Control Design with Guarantees: a Case Study for Automated Steering of Road Vehicles

This paper proposes a Linear Parameter Varying (LPV) based steering control design method, which contains data aided control elements, e.g., learning-based agents. The framework is based on a supervisory control structure, which contains a supervisor, a LPV controller and the data aided control element. The goal of this paper is to provide a safe steering control, with which the human steering intervention can be effectively imitated. Moreover, in the proposed framework the data aided control can be adapted to the actual requirements on driving style, without re-designing the LPV control. Thus, a general control structure with performance guarantees on path following constraints is provided, in which the data aided steering control element can be varied. The effectiveness of the proposed method through driver-in-the-loop scenarios is illustrated, in which different settings on the control system are analyzed.
Built-in battery-less sensors for the wireless temperature monitoring of undercarriages in connected industrial vehicles

Undercarriages for mining, construction, forestry, and agriculture machinery often operate in harsh environments (mud, water, dust), with an expected thermal gradient during operative conditions of about 20-30 °C. Overheating issues might reduce the lifespan of undercarriage mechanical parts, particularly the track rollers. The possibility to get the temperature on the central axis of the roller, as close as possible to its core, could therefore enable unprecedented data collection. A miniaturized, battery-less, wireless temperature sensor is proposed to be embedded within the track rollers with minimal geometry modifications. Despite the presence of many metal parts, the battery-less transponders enable robust communication with a minimal number of interrogating antennas, leading to the creation of a Connected Vehicle.
An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization*

In this paper we propose a randomized accelerated method for the minimization of a strongly convex function under linear constraints. The method is of Kaczmarz-type, i.e. it only uses a single linear equation in each iteration. To obtain acceleration we build on the fact that the Kaczmarz method is dual to a coordinate descent method. We use a recently proposed acceleration method for the randomized coordinate descent and transfer it to the primal space. This method inherits many of the attractive features of the accelerated coordinate descent method, including its worst-case convergence rates. Theoretical analysis of the convergence of the proposed method is given. Numerical experiments show that the proposed method is more efficient and faster than the existing methods for solving the same problem
Validation of ship intention model for maritime collision avoidance control using historical AIS data

This article tests the method for inferring and modeling ship intentions presented in [1] on real ship encounters gathered through the automatic identification system (AIS) that all larger ships are required to use. Empirical distributions on how early ships tend to perform avoidance maneuvers and how close they tend to come are evaluated. These are used by the intention model to identify when a ship’s behavior is outside normal behavior. Running the intention model on the historical ship encounters demonstrates that the intention model is able to correctly infer the intentions of ships in real collision encounters. The model is able to distinguish between different types of incompliant behavior such as a ship not giving way when it should, the wrong ship giving way, or a ship giving way in the wrong direction. Some improvement potentials are identified, mainly with respect to understanding when the situation starts.
Design and development of a serious game with physical interface for return to work in construction site

Helping workers return to work as soon as safe and functionally possible after a workplace injury is a subject of major interest in the context of occupational health. For this, we developed a serious game for construction industry aimed at training workers about the proper use of an electric pallet truck inside a simulated environment. The simulation platform is endowed with a physical control interface and three training missions were implemented. During the game experience, the player is also asked to answer to some questions about the possible risks and the related protection measures. The results of preliminary experiments about the usability of our training framework are here reported and discussed.
Bias Correction of Discounted Optimal Steady-State using Cost Modification

In the literature of Economic Model Predictive Control (EMPC) and undiscounted Optimal Control Problem (OCP), the optimal steady-state point is an equilibrium point with the minimum stage cost. If the Economic MPC is discounted, this property does not hold, and the optimal steady-state point is not the same as the one obtained from the undiscounted EMPC. Therefore the discounted steady-state point does not yield minimum stage cost and has a bias with respect to the undiscounted one. In this paper, we propose a computationally inexpensive cost modification in the discounted MPC that results in the undiscounted optimal steady-state point, i.e., the steady-state point that leads to the best stage cost. Moreover, we show that this modification does not affect the closed-loop system behavior. We illustrate the proposed method in a numerical example.
Lightning Sensitivity Assessment Based on Topographic Factors

Cloud-to-ground lightning flash has been shown to have a strong correlation with geography. In order to assess lightning risk in a specific environment from the perspective of topography, this paper divides Yunnan Province in China into two parts according to the topographic features, East and West, and conducts the study respectively. Firstly, the nonlinear functional relationships between ground flash density and topographic factors including elevation, slope, slope direction, topographic position index, and ground cover type are fitted, and then the entropy method is used to assign weights to the five terrain factors to finally obtain the lightning sensitivity assessment model. Finally, two typical areas are selected to verify the validity of the sensitivity model. The lightning sensitivity model has a high spatial resolution and can provide reference of lightning risk for transmission line construction in places without LLS site coverage.
Quantifying the Uncertainty in 3D CT Lung Cancer Images Classification

Lung cancer has caused the deaths of millions of people. Early diagnosis is proven vital to increase survival rate. This can be done using computed tomography screening, and when such a system is in place with a computer-aided diagnostic system and deep learning algorithms, it has been proven to be an effective approach. However, most existing systems do not quantify the uncertainty in the prediction. Radiologists should have complete confidence in the predictions during the screening. Therefore, to address this issue, we have developed an uncertainty-aware model framework not only to classify lung cancer nodules from 3D computed tomography images, but also to estimate the associated uncertainty in the prediction of the model. We have used the ResNet50 architecture as our base model and, through investigation with different loss functions and the inclusion of class weights, we concluded that the use of the focal loss function shows its superiority over the results of the benchmark model. Two approaches have been adopted to estimate the uncertainty during test time; Monte Carlo dropout and Test Time Augmentation, and from the multiple predictions obtained, three uncertainty metrics have been calculated; entropy, standard deviation, and Bhattacharyya coefficient. Our model achieves the best performance with the F1 score, 83.0 % using the dropout rate, p, 0.5 and Monte Carlo iterations, M, 10. We have also shown that using a threshold value can greatly improve the classification performance even more, but the amount of data to be referred needs to be taken into account.
SAS-UNet: Modified encoder-decoder network for the segmentation of obscenity in images

Obscene content on online platforms can be harmful to society in several ways. Firstly, it can negatively impact the mental health of individuals who view or are exposed to it, particularly children and vulnerable individuals. Secondly, it can lead to the spread of illegal content such as child pornography or revenge porn, which can cause significant harm to the victims involved. Lastly, it can create a toxic and unsafe online environment, where cyberbullying, harassment, and hate speech thrive, ultimately leading to social division and harm to the community. As a result, it is important to promote the responsible and respectful use of online platforms to prevent such harm. Therefore, it is important to segment such obscene regions present in images across social media platforms. To deal with the issue, we have proposed a modified encoder-decoder-based technique entitled sandglass-block encoder with attention skip and swin-based bottleneck (SAS-UNet) for the segmentation of obscenity in nude/obscene images. Our proposed method SASUNet incorporates the usage of a sandglass block in the encoder section of U-Net, a swin transformer in the bottleneck, and CBAM inclusion at the skip connection. The proposed model is trained and validated with the segmented obscene image (SOI) dataset. The collected obscene images were segmented precisely to build a binary mask of all the obscene images. The proposed SAS-UNet achieved a precision of 94.30%, 93.22% recall, 92.31% of mDC value, and 91.42% of mIOU value which outperformed the existing algorithms.
AI-assisted Cyber Security Exercise Content Generation: Modeling a Cyber Conflict

A cyber conflict can be defined as a cyberattack or a series of attacks that target the critical functions of a country. Such attacks can potentially wreak havoc on government and civilian infrastructure and disrupt critical systems, resulting in damage to the state and even loss of life. National bodies are usually expected to run cyber crisis exercises to prevent such attacks and prepare for their impact. Developing risk scenarios that are both relevant and up to date with the current threat landscape is a critical element in the success of any cyber exercise, especially a cyber conflict scenario.Our work explores the results of applying machine learning to unstructured information sources to generate structured cyber exercise content in preparation for or during a destructive cyber conflict. We collected a dataset of publicly available cyber security articles and used them to assess future threats and as a skeleton for new exercise scenarios. We utilize named-entity recognition to structure the information based on a novel ontology. With the help of graph comparison methodologies, we match the generated scenarios to known threat actors’ tactics, techniques, and procedures and enrich the final scenario accordingly, with the help of synthetic text generators following our novel artificial-intelligence-assisted cyber exercise framework (AiCEF). Our framework has been evaluated on its efficiency and speed and can produce structured cyber exercise scenarios in real time, provided with incident descriptions in raw text format or a set of keywords. By deep diving into a pool of pre-tagged incidents, AiCEF can build exercise content from scratch, assisting inexperienced exercise planners in generating a scenario quicker and achieving a level of quality similar to an experienced planner or subject matter expert.We have assessed our methodology for relevance and preparedness by applying it to a real cyber conflict use case to model two categories of crisis management exercise scenarios: pre-conflict and post-conflict initiation. Thus, we assess whether the generated scenarios match the attack trends and the news feeds that were not used in training the AiCEF and prove that we can provide targeted and customized awareness of upcoming incidents.
Multi-robot Motion Planning under MITL Specifications based on Time Petri Nets

This paper proposes a high-level path planning strategy under Time Petri net (TPN) formalism for a multiagent system, which is subject to Metric Interval Temporal Logic (MITL) specifications. The work aims to design a scalable model with respect to the number of agents, as the MITL formula requires multiple agents to ensure similar tasks. The obtained model is denoted Composed Time Petri net and it couples two TPN representations assigned to the motion of the agents, respectively to the MITL specification. The planning approach is based on model-checking methods and the results are evaluated on a case study applied in robotics industry.
Smart IoT platform for Big Data analysis in predictive maintenance

A smart IoT platform for big data analysis in predictive maintenance can help achieve goals of predictive maintenance by collection, processing, and analysis of data from various sensors. This platform can use machine learning algorithms and other advanced techniques to identify patterns and trends that can be used to predict when equipment is likely to fail, allowing maintenance teams to take proactive action to prevent those failures from occurring.
3P4W Grid Interactive PV-BES System With Seamless Transition Capability

For seamless transition of 3P4W (three phase four-wire) grid interactive solar photovoltaic (PV) system, a suboptimal finite impulse response (FIR) filter with approximation based on infinite impulse response filter (IIR) is employed here. The need for power quality improvement is essential and in order to guarantee an improved PQ, the grid currents are analyzed in covenant with the IEEE-519 standard. The power transfer proficiency with suboptimal filter utilization reduced intermittency associated with renewable sources along with improved power quality. Moreover, with the rise in the usage of distributed energy resources involving renewable energy sources, the need for power quality improvement is paramount. The need for improving PQ, stems from the fact that an uninterrupted supply is essential to the critical loads along with an improved power quality. Therefore, during operating modes such as grid linkage/interruption, the utilization of an efficient control is mandatory. Moreover, the behavior is required to be in agreement with the IEEE-519 and IEEE-1547 standards and in off-grid mode, a voltage controller is utilized for the power transfer. Thus, with adequate performance during the weak grid conditions, corroboration of satisfactory system behavior is obtained.
Accurate Real-Time Joint Torque Estimation for Dynamic Prediction of Human Locomotion

Inverse dynamics is a common tool for determining human joint torques during walking. The traditional approaches rely on ground reaction force and kinematics measurements prior to analysis. A novel real-time hybrid method is proposed in this work by integrating a neural network and dynamic model that only requires kinematic data. An end-to-end neural network for direct joint torque estimation is also developed based on kinematic data. The neural networks are trained on a variety of walking conditions, including starting and stopping, sudden speed changes, and asymmetrical walking. The hybrid model is first tested in a detailed dynamic gait simulation (OpenSim) which results in root mean square errors less than 5 N.m and a correlation coefficient of greater than 0.95 for all the joints. Experiments demonstrate that the end-to-end model on average outperforms the hybrid model across the whole test when compared to the gold standard approach which requires both kinetic and kinematic information. The two torque estimators are also tested on one participant wearing a lower limb exoskeleton. In this case, the hybrid model <italic>(R <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula> 0.84)</italic> has significantly better performance than the end-to-end neural network <italic>(R <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula> 0.59)</italic>. This indicates that the hybrid model is better applicable to scenarios which differ from the training data.
On robust stability analysis of interval time delay systems using delayed controllers

The focus of this research is to explore the robust stability of interval time delay systems that utilize delayed controllers. Specifically, the study assumes the use of a Proportional-Integral-Retarded (PIR) controller in conjunction with a first-order plus dead-time (FOPDT) model of the time delay system. The parameters of the system, including the gain, time constant, and time delay, are subject to interval uncertainties, which are a type of structured uncertainties for the plant model. To investigate the robust stability of the system, the study derives necessary and sufficient conditions, accounting for the presence of uncertainties. To facilitate the assessment of robust stability, the study also introduces a frequency range and robust stability checking function. Ultimately, the study aims to demonstrate the accuracy of its findings through the presentation of two numerical examples.
The concept of text processing in an ontological approach to spatio-temporal social network analysis

The task of analyzing the spatio-temporal properties of social network objects and predicting behavioral characteristics based on them requires the development of new methods and approaches to managing knowledge extracted from heterogeneous information sources. This study analyses the most popular systems for processing textual arrays and forming ontologies to integrate heterogeneous information resources and shows that existing technologies do not allow structural reflection of multi-thematic knowledge due to the multiplicity and disorderliness of their semantic properties, as well as the uncertainty of their descriptions. To integrate heterogeneous data and ensure their semantic interoperability, a multilevel ontology is proposed, based on the concept of text processing, which focuses on the possibility of synchronizing text content from different social networks with information stored in ontologies.