Miracle-dz/UniFAR-FTUs huggingface dataset:
CIERT: A Framework for Measuring Efficacy in Software-Based Simulation Training for Critical Incidents and Emergency Response

Software-based simulators are valuable tools for training individuals to effectively respond to critical incidents and emergencies. These events can range from natural disasters to terrorist attacks, requiring well-trained personnel capable of making quick decisions under high-pressure situations. Evaluating the efficacy of software-based simulators for critical incident and emergency response training requires a robust framework that can quantify training outcomes and provide meaningful insights. This paper introduces the Critical Incidents and Emergency Response Training framework, a comprehensive approach for quantifying the efficacy of software-based simulators used in this type of training. The proposed framework includes qualitative and quantitative measures to capture various aspects of training effectiveness, such as trainee performance, scenario realism, training duration, resource utilization, and skill acquisition. By establishing standardized metrics, the framework enables systematic evaluation of training interventions, identifies areas for improvement, and facilitates performance comparisons across simulators and training modules. This metric framework provides valuable guidance for researchers, practitioners, and policymakers involved in designing, developing, and evaluating software-based simulators for critical incident and emergency response training. It contributes to enhancing training programs, promoting the acquisition of essential skills, and improving preparedness and response capabilities in critical incidents and emergencies.
Experimental Demonstration of 4-level PAM for Sub-pixel Optical Camera Communications

As Internet of Things (IoT) technologies proliferate, a scarcity of radio frequency (RF) spectrum resources becomes apparent. One promising solution lies in Visible Light Communication (VLC), which offers a novel approach to wireless data transmission. Optical Camera Communications (OCC), a sub-field of VLC, demonstrates potential for outdoor wireless sensor networks applications, particularly through sub-pixel OCC, a technique allowing the re-use of existing camera equipment for communication. This study presents an experimental demonstration of a sub-pixel OCC link using 4-Pulse Amplitude Modulation (4-PAM), an approach striking a balance between spectral efficiency and receiver complexity. The paper outlines the system architecture, experimental setup, and comprehensive results, focusing on the potential of a sub-pixel OCC system with 4-PAM for a wide range of applications, from indoor localization to smart environments and IoT connectivity. The results demonstrate the system’s ability to accurately distinguish between different signal levels under varying light conditions, contributing to the exploration of OCC links and providing a viable blueprint for their application in various technological domains. This experimental work bridges the theoretical and practical aspects, thereby fostering the continuous development and expansion of VLC technology.
Classification of Inflammation of Unknown Origin patients based on RNA-seq and SomaScan data

A preliminary analysis was conducted on data acquired from RNA sequencing and SomaScan platforms, for the classification of patients with Inflammation of Unknown Origin. To this end, a multimodal data integration approach was designed, by combining the two platforms, in order to assess the potentiality of learning estimators, using the differentially expressed features from the independent profiling experiments of both platforms. The classification framing was the differentiation of Inflammation of Unknown Origin patients against a multitude of Systemic Autoinflammatory disease patients. Separate false discovery rate analyses were performed on each dataset to extract statistically significant features between the two designated sample groups. Genomic analysis managed higher overall classification metrics compared to proteomic analysis, averaging an ~19% increase overall metrics and classifiers, with a ~0.07% increase in standard error. The multimodal data integration approach achieved similar results to the individual platforms’ analyses. More specifically, it managed the same classification accuracy, sensitivity, and specificity scores as the best individual analysis, with the simple Logistic Regression estimator.Clinical Relevance— This study highlights the advantage of exploiting RNA sequencing data to identify potential Inflammation of Unknown Origin disease specific biomarkers, even against other Systemic Autoinflammatory diseases. These findings are further emphasized given the non-apparent clinical discrepancy between Inflammation of Unknown Origin and other Systemic Autoinflammatory diseases.
SAR Images Clustering Based on Modified Nonlinear Orthogonal Nonnegative Matrix Factorization (NMF)

This study presents the results of unsupervised learning (clustering) on Synthetic Aperture Radar (SAR) images. Fast and accurate clustering allows us to perform Automatic Target Recognition (ATR) in SAR images. Data can be automatically clustered based on similar properties. Nonnegative matrix factorization (NMF) is usually applied linearly. We employed a nonlinear NMF with orthogonal properties in this presentation. The outcomes of spectral clustering for subspaces and NMF matrix factorization are equivalent, as demonstrated by mathematical and empirical experiments. The results of kernel-based orthogonal in subspace clustering with updates in this method have been superior to those in other methods. We have been using the stationary target dataset to study our method's accuracy in acquisition and recognition.
Using Deep Learning Depth Maps to Improve Monocular SLAM

This paper proposes the use of a neural network to generate depth maps from 2D frames, offering an alternative to depth cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate depth data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D SLAM.ORB-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between depth sensor depth maps in test datasets and the generated depth maps. The MiDaS neural network was used to generate estimated depth maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for testing.The generated depth maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM depth maps had a root mean square error (RMSE) or 0.24 m while the generated depth maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between depth maps causes this approach to fail in slow-moving scenes.
Robust Satisfaction of Joint Position and Velocity Bounds in Discrete-Time Acceleration Control of Robot Manipulators

This paper deals with the robust control of fully-actuated robots subject to joint position, velocity and acceleration bounds. Robotic systems are subject to disturbances, which may arise from modeling errors, sensor noises or communication delays. This work presents mathematical and computational tools to ensure the robust satisfaction of joint bounds in the control of robot manipulators. We consider a system subject to bounded additive disturbances on the control inputs, with constant joint position, velocity and acceleration bounds. We compute the robust viability kernel, which is the set of states such that, starting from any such state, it is possible to avoid violating the constraints in the future, despite the presence of disturbances. Then we develop an efficient algorithm to compute the range of feasible accelerations that allow the state to remain inside the robust viability kernel. Our derivation ensures the continuous-time robust satisfaction of the joint bounds, while considering the discrete-time nature of the control inputs. Tests are performed in simulation with a single joint and a 6-DOF robot manipulator, demonstrating the effectiveness of the proposed approach compared to other state-of-the-art methods.
A New Dataset and Parsing Model for Chinese Multiparty Dialogue Discourse Structure

With the advancement of Natural Language Pro-cessing (NLP) technology, discourse parsing in multi-party dia-logues has become increasingly significant ina pplications such as machine reading comprehension, machine translation, and text summarization. However, there remains a lack of discourse parsing datasets specifically designed f or Chinese multi-party dialogues, and the fusion of speaker information has not been fully investigated. This research, based on the script of the TV series “I Love My Family”, has constructed a multi-party dialogue discourse parsing dataset named DialogueDSA 1• In this study, we also propose a speaker-aware parsing model for multi-party dialogue discourse. Our method addresses the problem of representing excessively long texts in DialogueDSA by encoding each Elementary Discourse Unit individually, and further interactions are facilitated through the use of BiGRU, attention mechanisms, and Transformer. The effectiveness and robustness of the proposed method are empirically demonstrated on both the English Molweni and STAC datasets, as well as the Chinese DialogueDSA dataset.
Machine Learning Empowered Large RIS-assisted Near-field Communications

A large reconfigurable intelligent surface (LRIS) assisted wireless communication system is investigated in this paper. The increased aperture size and reconfigurable element number of LRIS bring new challenges, including limited incident beam coverage on LRIS, near-field signal propagation, and high beamforming complexity. Against these challenges, a two-step low-complexity beamforming approach is proposed, where a deep reinforcement learning (DRL) algorithm is invoked for determining the optimal beam direction, and a codebook based on the geometric channel state information is designed to map the direction to the beamforming matrixes. The proposed approach not only reduces the computational complexity, but also exploits the geometric channel of BS-LRIS to reduce the channel estimation complexity caused by LRIS. Simulation results indicate that the LRIS can further reduce power consumption compared to the small-size RIS. Meanwhile, the proposed joint codebook-DRL approach achieves a counterbalance compared to the sheer DRL algorithm with lower complexity.
Research on Classification Method of Transients in Nuclear Power Plant Based on Curve Similarity Comparison

The transient in the primary circuit of nuclear power plant is one of the important references to characterize the fatigue of the primary circuit materials. The key information of the transient data is the transient species, start time, and duration. The detailed data is stored in a special data recording computer in the form of time series data. Dynamic time warping (Dynamic Time Warping, DTW) algorithm is an intelligent algorithm which can compare the similarity of curves at different time scales. Some researchers have used the designed transient data and virtual transient to verify the availability of the algorithm for transient classification. According to the characteristics of transient data classification, the DTW algorithm is optimized and modified to realize the similarity calculation between real transient data, and the similarity calculation results are given. The real transient data of M310 unit is used to test the algorithm, and the results show that the optimized DTW algorithm has good applicability, and the classification results can meet the daily use needs of transient engineers in power plants.
Closing the Loop for Patients with Chronic Diseases - from Problems to a Solution Architecture

There is growing evidence that mobile health (mHealth) applications can assist patients with chronic conditions. However, most mHealth apps are isolated from healthcare professional (HCP) workflows and IT infrastructure. The resulting fragmentation of digital support in healthcare calls for integrating architectures. They would benefit patients, HCPs, product managers, and software developers. Our analysis of existing architectures has revealed valuable architectural elements, but none of the analyzed architectures provided sufficient integration for the chronically ill. Therefore, we propose an architecture for integrated mHealth solutions. We followed a design science research approach and performed all activities of the DSRM Process Model. By forming a closed control loop and engaging HCPs, the architecture is designed to improve patient adherence to treatment, health literacy, and recall of recommendations and information. The resulting Closing-the-Loop Architecture (LoopArt) deploys three software agents: a Health Literacy Agent, an Adherence Agent, and a Conversational Agent. For demonstration purposes, the Health Literacy Agent was implemented for obese patients as an integrated system consisting of a mHealth app and a collaboration tool as part of the electronic medical record (EMR).
Angular-domain Secret Key Generation for RIS-aided mmWave MIMO systems

This paper investigates a physical layer key generation (PLKG) scheme for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input multipleoutput (MIMO) systems. Unlike traditional PLKG which relies on channel state information (CSI), we exploit the virtual angles of departure (AoDs). To accurately estimate these angles, we propose a redundant transforming matrix-based compressive sampling matching pursuit (RTMCoSa) method. We then derive the secret key rate (SKR) of the RIS-aided mmWave system. Simulation results demonstrate that the RTMCoSa method outperforms existing orthogonal matching pursuit (OMP) methods in channel probing for angle information. What is more, the proposed key generation scheme surpasses traditional CSI-based methods in SKR when the SNR is low. And the SKR of our method maintains robust when the SNR decreases.
Neural Network Prediction of Ocean Wave Behavior Using Frequency Domain Mapping

As ocean levels rise, and global weather events become more extreme, it is more important than ever to understand, visualize, and predict ocean wave behavior. Such predictions, especially if done with low latency, are an important component of emergency preparation. For example, the ability to predict where a tsunami will hit is key to safeguarding coastal communities, ships, fishermen, and offshore platforms. In recent years, key advances have been made in the application of neural networks toward weather related predictions. One neural network architecture, the Fourier Neural Operator (FNO), has shown great promise in image to image mapping where the images represent the solution space of a physical system. In the current research, we use an FNO to predict ocean wave behavior in near real-time. We generated FNO training data by solving by solving the shallow water equations using traditional numerical methods. Although this method is a 2.5D solution to a 3D problem, we believe it is an important initial step to showing whether a more complex and intensive approach is warranted. Using any arbitrarily-shaped ocean basin topography as the input, we tasked the FNO with predicting the resulting wave height for a pre-determined time step. Using only 1,050 training points of $128\times 128$ resolution, the FNO performed well on validation inputs of resolution $1024\times 1024$. When tasked with predicting the tsunami wave front on just a single ocean basin topography, the FNO learned exceptionally well, with error in the wave height on the order of 3.88E-7.
Empirical Study and Signal Intensity Prediction for Cellular Vehicle-to-Everything (C-V2X)

The development of autonomous driving has led to the proposal of vehicle-to-everything (V2X) to improve the reliability of autonomous driving systems through information sharing among vehicles and infrastructure. However, the high-speed mobility of autonomous vehicles and the dynamic surrounding environment make the V2X network unstable and unreliable. To address this issue, empirically studying the characteristics and predicting the signal intensity of the V2X network is crucial, which can provide more information for further optimizing communication strategies and enhancing driving safety. In this paper, we collect the real-world vehicle-to-infrastructure (V2I) performance under different driving scenarios and build the quantitative relationship between several environmental factors and the V2I performance. We also develop deep learning models to predict the V2I signal intensity based on external environmental conditions. Experimental results in real-world data demonstrate the effectiveness of our model on classification and regression tasks compared to other models. Our study aims to enhance the applications of V2X on autonomous driving and improve driving safety and traffic efficiency.
Implementation of the YOLOv3 Method in Zebra Cross Detection Systems as Early Warning for Drivers

Early warning is an effort to prevent us from doing something that could be dangerous. Pedestrians and drivers need to be aware of their surroundings to minimize accidents. A common accident is when a driver hits a pedestrian who is crossing the road, even though the pedestrian is on the zebra cross. The cause of the accident is usually the driver's lack of concentration. Therefore, an object detection system can help reduce accidents. The YOLOv3 method can detect objects quickly and can be customized to produce models that have accuracy and detection performance according to user needs. This study aims to implement the YOLOv3 method in a zebra cross detection system as an early warning for drivers. This study has two classes of objects, people and zebra cross. Tests were conducted with four variations of train and test ratio, namely 70:30, 80:20, 85:15 and 90:10. There are several variations of maximum batches from 500 to 5000. The results show that the YOLOv3 method can recognize objects well in models with a train and test ratio of 85:15 and max batches of 4500. The implemented detection system can provide visual and audible warnings when it detects zebra cross and who's walking across them.
Application of Unified Medical Language System (UMLS) to Standardize Pediatric Drug Data

Pediatric Electronic Health Records (EHRs) contain drug/medication data. Despite the importance of standardizing drug data to identify drug class information and enable interoperability between computer systems, sometimes no biomedical vocabulary is used, and therefore the data is not standardized. This paper employed UMLS vocabularies to standardize Cincinnati Children’s Hospital Medical Center (CCHMC) EHR drug data to use it build models for pediatric mental health trajectories. We present an approach that identifies a UMLS Concept Unique Identifier (CUI) for a drug for the scenarios when the RxNorm code is available, and also for when it is not available. We employed an offline UMLS database to identify UMLS CUI for a drug when the RxNorm code was available. We used UMLS API and a fuzzy string matching approach to determine UMLS CUI for a drug when the RxNorm code was not available. Our early results demonstrate that drug data can be standardized using the UMLS vocabulary and this approach is helpful for anomaly detection.
Neural Adjusted Min-Sum Decoding for LDPC Codes

In this work, we propose a neural adjusted min-sum (NAMS) decoder for low-density parity-check (LDPC) codes. In particular, we improve the traditional normalized min-sum (NMS) decoder by introducing a selection mechanism to adjust the check-node update step, where either the min-sum rule or the belief propagation (BP) rule is selected. Besides, we unfold the modified decoder into a model-driven neural network, where layer-dependent trainable parameters are introduced as weights in the Tanner graph and optimized by gradient descent-based methods during network training. Simulation results demonstrate that the proposed NAMS decoder is able to provide superior error-correction performance as compared to the neural NMS decoder, with only slightly increased computational complexity. Moreover, in certain circumstances, the proposed NAMS decoder even outperforms the neural BP decoder, with much lower computational complexity.
Deep Learning Based Cross Frequency Channel Reconstruction and Modeling

Wireless channel modeling is widely considered as foundation of wireless communication system design. Sufficient and diverse channel data provides strong support for wireless channel characterization and modeling. However, channel data from real measurement is usually limited considering complexity of channel measurements for different scenarios and frequency bands. In this work, a deep learning-based cross-frequency channel generation and modeling framework is proposed. Without requiring a traditional parametric channel model, the proposed framework can generate realistic cross-frequency channels by employing generative adversarial networks. Based on vehicular channel measurement data, cross-frequency reconstruction performance of the proposed framework is validated by comparing characteristics of measured and reconstructed channels. It is also found that channel non-stationary characteristics can be well embodied in the reconstructed channels.
Symmetry-Based Modeling and Hybrid Orientation-Force Control of Wearable Cutaneous Haptic Device

We propose novel symmetry-based modeling and hybrid orientation-force control frameworks for cutaneous haptic device (CHD) to generate precise three degree-of-freedom (DoF) contact force on the fingertip robustly against user variability. The CHD hardware is designed in a form of an underactuated cable-driven parallel mechanism, with springs placed along the tendon to stabilize the pose. We analyze the kinematics of the CHD and propose a pose estimator by exploiting the symmetrical nature of the mechanism. We then devise a hybrid orientation-force controller to track the direction and magnitude of the desired contact force simultaneously in a feedback manner for control accuracy and robustness. We also adopt a tension regulator to mitigate friction effect during the actuation. Experimental validation and demonstration show the efficacy of the CHD with our proposed estimation and control framework.
ARGO 2.0: a Hybrid NLP/ML Framework for Diagnosis Standardization*

A relevant problem in medicine is the standardization of the diagnosis associated with a clinical case. Although diagnosis formulation is an intrinsically subjective and uncertain process, its standardization may take benefit from digital solutions automating the routines at the basis of such a decision. In this work, we propose ARGO 2.0: a framework for the development of decision support systems for diagnosis formulation. The framework can read free-text reports and store their clinically relevant information as personalized electronic Case Report Forms. A hybrid strategy, exploiting the synergy of Natural Language Processing and Machine Learning techniques, is used to automatically suggest a diagnosis in a standardized fashion. ARGO 2.0 has been designed to be template-independent and easily tailored to specific medical fields. We here demonstrate its feasibility in hemo lympho-pathology, by detailing its implementation, object of an ongoing validation campaign in a standing medical institute. ARGO 2.0 achieved an average Accuracy of 95.07%, an average precision of 94.85%, an average Recall of 96.31% and a F-Score of 95.32% onto the test set, outperforming both its embedded components, based on Natural Language Processing and Machine Learning.