Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault diagnosis of rolling bearing using a transfer ensemble deep reinforcement learning method

The reliable operation of rolling bearings is related to machinery safety. However, fault signals encountered in practical engineering applications are often characterized by high-dimensionality, complexity, and volume, which restricts the application of deep neural networks in fault diagnosis. Additionally, conventional diagnostic methods are limited by their reliance on manual feature extraction and a significant quantity of labeled samples, which can be time-consuming and resource-intensive. To address these limitations and improve the performance of fault diagnosis in the absence of labeled samples, an intelligent diagnostic agent (TERL-Agent) that combines transfer learning, ensemble learning and reinforcement learning is proposed. Firstly, an intelligent diagnostic agent is constructed by ensemble learning, which combines multiple reinforcement learning agents based on the Deep Q Network structure and has interactive learning capability to learn and classify fault data in the source domain environment. Secondly, transfer learning is used to transfer the feature extraction ability of the source domain intelligent diagnostic agent to the target intelligent diagnostic agent. Finally, the obtained target intelligent diagnostic agent is evaluated on fault data in the target domain and compared with other methods. The results indicate that the proposed method exhibits remarkable advantages and has great potential for practical application in fault diagnosis.
CHERI-TrEE: Flexible enclaves on capability machines

This paper studies the integration of two successful hardware-supported security mechanisms: capabilities and enclaved execution. Capabilities are a powerful and flexible security mechanism for implementing fine-grained memory access control and compartmentalizing untrusted or buggy software components. Capabilities have a long history but have gained significant momentum recently, as evidenced by ARM’s experimental Morello processor that supports the Capability Hardware Enhanced RISC Instructions (CHERI). Enclaved execution is a popular mechanism for dynamically creating Trusted Execution Environments (TEEs), called enclaves. Enclaves are isolated execution contexts that protect the integrity and confidentiality of software in the enclave (even against compromised system software) and that support attestation.Integrating capabilities and enclaved execution in a single processor is challenging because they overlap partially in their security objectives, and a clean integration should unify the way in which these overlapping objectives are achieved. In addition, it is not obvious how attestation should interact with capabilities. In this paper, we propose CHERI-TrEE: a novel design for a processor that cleanly integrates support for both capabilities and enclaved execution. CHERI-TrEE targets low-end embedded systems without virtual memory. We show that CHERI-TrEE is greater than the sum of its parts by showing how it naturally supports useful features that have traditionally been hard to support in enclaved execution, like dynamically growing and shrinking enclaves, non-contiguous and nested enclaves, sharing of memory between enclaves etc. We implement our proposal both in hardware on a RISC-V processor, as well as in a small software hypervisor on top of ARM Morello, and evaluate impact on performance and hardware resources.
Stochastic Volatility Effects on Correlated Log-Normal Random Variables

The transition density function plays an important role in understanding and explaining the dynamics of the stochastic process. In this paper, we incorporate an ergodic process displaying fast moving fluctuation into constant volatility models to express volatility clustering over time. We obtain an analytic approximation of the transition density function under our stochastic process model. Using perturbation theory based on Lie–Trotter operator splitting method, we compute the leading-order term and the first-order correction term and then present the left and right skew scenarios through numerical study.
Video-based detection of hemiparetic weakness side in post-stroke patient

One of the key symptoms of stroke is hemiparesis, a weakness with loss of movement in one or more sides of the body. Early detection of stroke-related weakness can improve patient outcomes and minimize the risk of long-term disability. This study presents a novel deep learning approach for efficiently and objectively detecting and quantifying stroke-related weakness. This approach addresses the limitations of current technologies, which can be invasive and time-consuming. The study describes a model that processes video data to classify left or right-sided stroke-induced motor weakness. The model extracts the movement of key human body joints and applies time series analysis to classify the stroke-affected side of the body. The model was developed using data collected from 23 post-stroke patients in a clinical setting over 48 hours.
Identification of Predictive Subnetwork for Brain Network-Based Psychiatric Diagnosis: An Information-Theoretic Perspective

Graph neural networks (GNNs) have recently been applied to develop useful diagnostic tools for psychiatric disorders. However, due to the lack of interpretability, clinicians are hard to identify quantifiable and personalizable biomarkers which provide biologically and clinically relevance. We introduce three recently proposed GNN-based psychiatric disorders diagnostic models, namely BrainIB, Graph-PRI and CI-GNN, from an information-theoretic perspective. These models are able to discriminate psychiatric patients from healthy controls and identify predictive subgraph, a.k. a. biomarkers, solely from observations. We demonstrate their improved classification accuracy and interpretability on ABIDE database. We also put forward three proposals for future research.
Fast Quality-Aware AMDF Based Pulse Rate Estimation from Compressed PPG Measurements for Wearable Vital Signs Monitor

Modern wearable or portable health monitoring devices are capable of photoplethysmogram (PPG) sensing, processing, analyzing, storing and transferring signal and parameters wirelessly but are generally energy constrained and have more false alarms under noisy PPG recordings. In this paper, we present computationally-efficient reliable pulse rate (PR) estimation in compressed sensing (CS) domain. The proposed CS-PPG based PR estimation method consists of measurement generation, high-pass filtering, average magnitude difference function (AMDF) features based signal quality assessment (SQA), and AMDF based quality-aware PR estimation. The proposed unified framework is evaluated using a wide variety of normal and pathological PPG signals taken from five standard databases. The proposed framework had an average sensitivity (SE) of 98.75% and specificity (SP) of 63.35%. Results show that the CS-PPG based quality-aware PR estimation method had a mean absolute error (MAE) of 3.1 ± 4.6%, Bland-Altman ratio (BAR) of 9.2% and root-mean-square error (RMSE) of 5.6 which are not only comparable with the results of the PR estimation method with the original PPG signals but also the proposed framework reduces 80% of the overall computational load. The proposed unified framework has great benefits in reducing processing time and energy consumption and thus can maximize battery lifetime of battery-operated health monitors.
Confused vs Non-Confused Electroencephalography Signal Classification Using Deep Learning Algorithm

When students are doubtful or uncertain about a certain subject, they frequently face confusion. If students did not pose questions, it would be difficult for the teacher to see this circumstance. Eventually, this will slow down the pupils’ learning development and impact their academic performance. While confusion is a mental state, it is proposed to utilise the electroencephalogram (EEG) signal to evaluate if a pupil is confused or not. Unfortunately, it is challenging to distinguish between a confused and non-confused EEG signal based on basic characteristics like as frequency domain and power spectral density. As technology progresses, deep learning in artificial intelligence is currently a prevalent technique. Thus, this research aimed to run a series of experiments to determine which deep learning model is the best at classifying EEG signals as confused or not confused. The results indicate that the hybrid CNN-biLSTM deep learning model is superior to the six other deep learning models included in this study. In identifying the EEG signals of confused and non-confused pupils, it obtains an AUC of 82%, 76.7% accuracy, 76.9% recall rate (76.9%), 71.4% precision, and 76.5% specificity. The dependability of the hybrid CNN-biLSTM deep neural network indicates that it has the potential to be utilised in the classroom in the future to identify whether a student is confused or has fully grasped the curriculum that the teacher has taught. This can guarantee that the teacher efficiently transferred knowledge to the pupil.
Powering IoT Systems with 5G Wireless Communication: A Comprehensive Review

In today's modern world, IoT plays an important and multifaceted function in all industries. 5th Generation Wireless Systems - 5G is the primary technical platform on which the Internet of Things plays a major role in smart technologies. Incorporating 5G technology into the architecture of an IoT system is now simple. Using plug-and-play technology, remote access to configuration and control is possible. Smart technology always results in faster data transfer rates, more bandwidth, greater capacity, lower latency, and a quicker output response. Based on this notion, IoT brings about a dramatic shift in heterogeneous networks. So, there is a need to implement 5G-IoT technologies. In the age of the Internet of Things, the fifth-generation (5G) cellular networks give the smart and complex technical environment. 5G Cellular Networks offer great growth potential for IoT technologies. This article gives a thorough study of the role of 5G Cellular Networks in the expansion of IoT technologies across all industries. Since the inclusion of renewable energy emerges as a new trend in the digital world, 5G is very beneficial in the age of the Internet of Things. To achieve the link between 'Things and People,' 'Things and Things,' and 'People and People' in power Systems, it is necessary to combine IoT with 5G in power systems to a high degree of success in various power-based industry-based systems. This article demonstrates a smart scenario and provides a thorough analysis of "how 5G drives the IoT System intelligently in every way and how IoT affects as the "most promising technology" with this smart 5G Wireless Communication System. " In addition, the IoT era 5G application possibilities were demonstrated.
A biologically-inspired locally-connected spiking network for efficient and robust ground reaction force estimation in a legged robot

The paper introduces a new structure of Liquid State Machine (LSM) characterised by local connectivity within the excitatory neurons of the reservoir layer. The architecture learning and testing capabilities are compared with the classical LSM network on an important robotic task of estimating exteroceptive information from proprioceptive signals coming from a simulated quadruped robot. The main advantages of the proposed architecture are discussed. The actual arrangement of the network resembles specific learning structures within the insect brain, endowed with interesting reaction-diffusion dynamics and classification capabilities. Simulation results are reported and compared with those ones obtained using a classic LSM. The proposed architecture, resembling typical biological solutions present in simple brains, through reaction-diffusion local mechanisms, significantly reduces the computational requirements needed by standard massively-connected neural network solutions. Moreover, the robustness of the introduced network, against faults in the sensory system, is demonstrated. In these conditions, the network is able to partially reconstruct the lacking sensory input signal from the learned relation with the other sensory input streams.
Revisiting Quantum Optics with Surface Plasmons and Plasmonic Resonators

Surface plasmon polaritons can be used to confine fields at the nanoscale and are now one of the working horses of nanophotonics. This perspective deals with recent studies aiming at doing quantum optics experiments with surface plasmons. A first class of studies deals with one or two single plasmons and aims at observing wave-particle duality, squeezing, and coalescence of plasmons. A second class of studies deals with cavity quantum electrodynamics with localized plasmons in both the weak coupling regime and the strong coupling regime.
MePof: A Modular and End-to-End Profile-Guided Optimization Framework for Android Kernels

Profile-Guided Optimization (PGO) is a novel compiler optimization leveraging runtime feedback and has been applied successfully to optimize Android kernels gaining significant performance improvements. However, current studies as well as implementations of PGO-based Android kernel optimizations still suffer from three problems: 1) optimization inflexibility due to restricted algorithms for generating profiles and for simulating real-world usage scenarios; 2) considerable optimization efforts due to the extensive manual interventions needed; and 3) optimization failures due to kernel version fragmentation.This paper presents MePof, the first modular and end-to-end PGO framework for Android kernels. The MePof framework consists of three key components: 1) a tool orchestration, that integrates two novel algorithms for generating profiles, and three methods for simulating real-world scenarios that can be flexibly switched according to the usage scenario; 2) a domain-specific language (DSL) that can specify PGO-based optimization strategies and a corresponding compiler translating the DSL programs into configuration files necessary for optimization; and 3) an adapter that automatically triggers and completes the optimization when the Android kernel version changes.We have implemented a prototype for MePof and have conducted extensive experiments to evaluate its effectiveness, performance, and usability. Experimental results demonstrated that: 1) MePof is effective, with performance improvement 9.39% on average; 2) MePof is efficient by saving up to 39.07% of time than manual optimizations; and 3) MePof is highly usable by requiring only one manual intervention instead of more than 30 manual interventions as in the existing optimization framework.
A Unified Semantic Segmentation and Object Detection Framework for Synthetic Aperture Sonar Imagery

Manually identifying objects in synthetic aperture sonar (SAS) imagery is costly and time consuming, making identification through computer vision and deep learning techniques an appealing alternative. Depending on the application, a generalized map (semantic segmentation) and/or a characterization of each individual object (object detection) may be desired. Here, we demonstrate a framework that allows us to simultaneously generate both semantic segmentation maps and object detections with a single deep learning model by chaining together a U-Net model with k-means clustering and connected components. This framework streamlines the model training phase by allowing us to utilize a set of semantically segmented training data to yield both semantic segmentation and bounding box predictions. We demonstrate that the deep learning model can achieve accurate predictions with a small training set through transfer learning from a convolutional neural network pretrained on optical imagery. Results from this unified framework will be presented on images of boulders collected during various surveys using a Kraken Robotics miniature SAS (MINSAS).
Trajectory Planning for Autonomous Mine Inspection Using UAV

In this paper, we propose a trajectory planning algorithm for autonomous mine inspection using unmanned aerial vehicles (UAV). Mine inspection presents a set of serious challenges such as narrow roadways and sharp corners. To overcome the disadvantages, the safe flight corridors are calculated to guarantee the safety of the UAV and a multi-objective function is used for optimization to enable the comprehensive performance of the trajectory. A manual inspection with a laser is taken as the input to generate the initial path and point cloud map of the mine environment using the Simultaneous Localization and Mapping (SLAM) algorithm. The initial path can be transformed into a set of waypoints after b-spline smoothing and discrete sampling. The safe flight corridors composed of convex polyhedrons are built based on the waypoints. A multi-objective optimization strategy considering smoothness and time cost is used to generate the piecewise polynomial inspection trajectory and safety is ensured by limiting the trajectory inside the safe flight corridors. Experimental results demonstrate that the proposed approach can perform well in autonomous mine inspection.
Effective wavefront aberration measurement of spectacle lenses in as-worn status

An effective wavefront aberration analysis method for measuring spectacle lenses in as-worn status was proposed and verified using an experimental apparatus based on an eye rotation model. Two strategies were employed to improve the accuracy of measurement of the effective wavefront aberrations on the corneal sphere. The influences of three as-worn parameters, the vertex distance, pantoscopic angle, and face form angle, together with the eye rotation and corresponding incident beams, were objectively and quantitatively obtained. The experimental measurements of spherical single vision and freeform progressive addition lenses demonstrate the accuracy and validity of the proposed method and experimental apparatus, which provide a potential means of achieving supernormal vision correction with customization and personalization in optimizing the as-worn status-based design of spectacle lenses and evaluating their manufacturing and imaging qualities.
Sentiment Detection through Emotion Classification Using Deep Learning Approach for Chinese Text

Emotion classification and sentiment analysis represent crucial research areas within the field of Natural Language Processing. Previous studies have primarily focused on conducting sentiment classification and emotion classification as separate tasks. Only a limited number of researchers have delved into exploring the relationship between these two and invested efforts in deriving one from the other. This study aims to determine sentiment by employing emotion classifications. Specifically, we utilise the ERNIE Tiny deep learning model to classify emotions in Chinese texts, and detect sentiments through our devised rules. For instance, if emotions such as ‘happiness' or ‘like’ are present, the sentiment is classified as positive. Conversely, emotions like ‘sadness', ‘disgust’, ‘anger’, or ‘fear’ classify the sentiment as negative. The experimental results demonstrate the F1 score of 93.00% and 90.14% for positive and negative sentiment, respectively, in Chinese song reviews. These findings substantiate the validity and feasibility of utilising emotions to extract sentiment
Revealing Causal Graph Structure in Pigeon Flocks Using Attentions in Temporal ConvNet

The study of collective animal behavior has long been of interest to researchers across various disciplines. In this study, a modified version of the Attention-based Dilated Depthwise Separable Temporal Convolutional Networks (AD-DSTCN) architecture was used to analyze high-resolution spatiotemporal data of moving pigeons with the goal of producing causal graph structures in pigeon flocks. The modification allowed for causality inference on data where individuals have multidimensional feature representations. The study revealed several important findings. Firstly, the results showed that, although weak external influences may still be present, each pigeon primarily relies on its own knowledge and decision-making. Secondly, the results also indicated that edges in the graph correspond to pairs of pigeons that are close to each other, suggesting that pigeons that are physically near each other tend to have stronger social connections. Thirdly, the study suggested that there is no single persistent pigeon that holds the most influence in the flock. Instead, the most influential node appears to shift at various time intervals, a concept known as intermittent switching. These findings offer new insights into the complexity of animal behavior and its underlying mechanisms, as well as a deeper understanding of how pigeons make decisions and interact within a flock. Ultimately, the use of the modified AD-DSTCN architecture offers a promising approach for understanding collective animal behavior.
The Art of Multi-Classification: Detecting Rice Sheath Rot Disease Severity Levels using a Hybrid CNN-SVM Model

Rice sheath rot is a dangerous disease that affects rice crops all over the globe. It is responsible for substantial yield losses and considerable economic damage. To effectively handle an illness, early discovery and accurate classification of the disease’s severity are both necessary. For this research, the authors suggested a method that combines Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) for the diagnosis of the illness known as rice sheath rot. The suggested method consists of two stages: a binary classification step to determine whether or not the illness is present, and a multi-classification stage to categorize the severity to which the disease has manifested itself into five distinct stages. The current study evaluates the performance of the suggested strategy on a dataset consisting of images of rice sheath rot disease and compares it to other approaches that are considered to be state-of-the-art for disease identification in rice crops. When it came to detecting and categorizing the illness in rice crops, the suggested method was able to achieve an accuracy of 95.2% overall. According to the findings of the research, the suggested hybrid strategy consisting of CNN and SVM will be more successful in identifying rice sheath rot disease than other methods that are considered to be state-of-the-art. Further research is needed to validate and extend the proposed approach to other plant diseases and to ensure its generalizability to other variations of rice sheath rot disease.
Demo: Robotics meets Augmented Reality: Real-Time Mapping with Boston Dynamics Spot and Microsoft HoloLens 2

In recent years, Simultaneous Localization and Mapping (SLAM) algorithms have gained popularity in the field of robotics for generating maps of environments. This paper presents a novel approach for generating 3D maps of environments visited by a Boston Dynamics robot dog named Spot. Five stereo cameras mounted on the robot dog capture images that are transmitted to a remote machine that generates 3D maps of the environment in real-time. The human operator wears an Augmented Reality (AR) device that embeds the 3D maps into his field of view. Tasks in human demanding environments, where the operator actively intervenes in the performed processes, can be performed remotely. The intention of this application is to stay in a safe place and at the same time take the robot’s perspective.