Miracle-dz/UniFAR-FTUs huggingface dataset:
Highway Condition Analysis and Traffic Safety Monitoring System Through Analysis of Time-Series Data from LiDAR-based Probe Vehicle

Recently, various approaches have been developed for traffic analysis systems, ranging from Vehicle Detection Systems (VDS) to Mobile Detection Systems (MDS). However, VDS has limitations. For example, in actual driving situations such as intersections or exits, each lane may have different traffic conditions, revealing the limitations of traffic information provided by conventional VDS. In this paper, we propose a methodology to efficiently monitor the traffic condition and safety on the road by utilizing a LiDAR sensor installed on a vehicle to collect continuous traffic information about surrounding vehicles. To implement the Mobile Detection System (MDS), we collect point cloud data from LiDAR and detect the position and size of vehicles using a deep learning-based voxel-RCNN. We then convert the data into traffic information for analysis. Furthermore, we propose an efficient method for analyzing road hazards by introducing the MTTC method based on the TTC for hazard assessment. To evaluate the performance of the proposed method, we compare its reliability with that of conventional VDS and perform road hazards analysis using LiDAR-based probe vehicles with data collected directly from highways in Korea.
A Research Study of Heart Health Monitoring Using Deep Learning and IoT

Cardiovascular diseases (CVDs) pose a substantial worldwide health burden, hence requiring sophisticated strategies for their prevention and treatment. This research study presents a novel framework that combines deep learning algorithms and IoT devices to enable ongoing monitoring of heart health by means of heartbeat categorization. The objective of this project is to create a highly accurate and effective system that can classify different cardiac diseases based on real-time electrocardiogram (ECG) signals. This study makes a scholarly contribution by utilizing the capabilities of deep learning and the IoT to improve the timely identification and proactive treatment of cardiovascular conditions. As a result, patient care and overall health outcomes are enhanced. The paper presents a thorough technique that includes many stages such as data collecting, preprocessing, building of a deep learning model, integration of IoT, and evaluation of performance. The expected results include the development of a comprehensive categorization system, the integration of Internet of Things (IoT) technology for continuous monitoring, and a comparative analysis demonstrating the advantages of the system in comparison to current methodologies. In this undertaking, our aim is to achieve significant advancements in the field of cardiac health monitoring and disease identification, hence promoting enhanced healthcare methodologies and patient results.
Ranking Center-based NSGA-II

Multi-objective optimization is a branch of computation to solve mathematical optimization problems when conflicting multiple objective functions must be simultaneously optimized. Many population-based algorithms, such as NSGA-II are used to find an optimal Pareto set of solutions in a short time. However, previous research works showed the performance of classic NSGA-II is degraded when solving problems with many objectives (M ≥ 5). In this work, we aim to take advantage of center-based sampling scheme to increase the exploration and exploitation capability of NSGA-II algorithm. This sampling strategy has demonstrated promising results on several single-objective evolutionary algorithms such as GA, DE, and PSO. Recently, a novel clustering center-based strategy has been proposed, which motivated us to utilize center-based sampling scheme in NSGA-II to solve multi-objective optimization problems. The outcomes confirm that the proposed clustering center-based NSGA-II is able to effectively solve CEC-2017 multi-objective benchmark problems with 2, 3, 5, 10, and 15 objectives.
Design Space Exploration Tool for Mixed-Signal Spiking Neural Network

Spiking Neural Networks (SNNs) are biologically inspired neural network algorithms that process data based on events. Neuromorphic hardware is used to compute SNNs which enables significant energy efficiency. Specifically, mixed-signal circuits such as analog synapses and mixed-signal neurons are used to closely emulate biological neurons and synapses and also to compute the SNNs at low power. However, the mixed-signal implementation of spiking neural networks (SNNs) involves non-linear computation and process variation. This study presents a tool to model these nonidealities while learning the parameters of the SNNs. Currently, the tool allows exploring different network architectures (Fully-Connected and Recurrent) with two hardware-aware neuron models and Floating-Gate (FG) transistor-based synapses. Further, it enables users to train the SNNs using biologically inspired local learning rules while using spiking datasets. The tool estimates the network's area, power consumption, and accuracy based on the measurement and simulations performed in the 65nm CMOS process.
A 0.4-11-GHz Active-Feedforward-Noise-Canceling LNA in Stacked n/pMOS Configurations

In this paper, we present a broadband low-noise amplifier (LNA), using active-feedforward noise-cancellation and complementary n/pMOS configurations. Based on traditional common-gate(CG) and common-source(CS) noise canceling topology, the applied active feedforward stage reduces the CG's dc current and alleviates its resistive load tradeoff between noise and headroom. Meanwhile, the active feedforward stage follows the same noise cancellation (NC) principle as the CG transistors. A low noise figure is thus obtained. In addition, the complementary n/pMOS configuration is used to cancel second-order distortions and double current efficiency. Two n-type matching networks are utilized to broaden the bandwidth of S11 and gain. After applying these techniques, an LNA prototype was designed in standard 65nm CMOS technology. Simulation results display a gain of 15–18 dB and a noise figure (NF) of 1.8-3.5 dB within a bandwidth of 0.4-11 GHz. The linearity of IIP3 and IIP2 are -2.4 and 23 dBm at 5 GHz. The presented circuit consumes only 7 mW of dc power while occupying an area of 0.21 mm2.
Synergy or Redeployment? Examining Environmental Spillovers in Multi-Unit Firms

We study how environmental regulation affecting one unit impacts sibling units belonging to the same firm. A resource reallocation perspective suggests shifting pollution activities from regulatory-constrained units to unconstrained sibling units. In contrast, the forced adoption of pro-environmental practices in the regulated units may have positive spillovers on sibling units if cleaner operating practices are adopted all across the firm. Examining this theoretical tension, we find evidence of both logics. More importantly, we demonstrate that multi-unit firms face a trade-off between pollution reallocation and knowledge sharing about pro-environmental practices. Consistent with this trade-off, we report that firms that redeploy pollution are less likely to share best environmental practices. Overall, our findings indicate that on average multi-unit firms have weaker environmental performance having 44% higher excess emissions than single-unit firms, especially those that prioritize redeploying polluting activities within the firm over sharing pro-environmental practices.
A Review on Code Generation with LLMs: Application and Evaluation

Code generation is a longstanding subject in the field of computer science and software engineering, which aims at realizing an agent capable of writing code automatically aligning with human desire. With the booming development of large language models (LLMs) in recent years, code generation techniques powered by LLMs with strong coding ability have caught many researchers' interest. In this study, we conduct a review of recent studies about code generation with LLMs, from the application of LLM-based code generation to the evaluation of LLM-generated code. We find, with the powerful code understanding and writing ability LLMs provide, these novel techniques can be applied to manage various software engineering tasks, and indeed boost the productivity of developers to a great extent. But we also find, as an equally important subject, the evaluation receives less attention from researchers than the application. We conclude some limitations in existing studies about the evaluation of code generated by LLMs, like inadequate quality characteristics considered. And we think more effort is needed to narrow the gap between research on the evaluation and the application.
Investigation on Network Survey Questionnaire Evaluation Based on Neural Network Algorithm

With the development of internet big data analysis technology and the current surge in the number of various types of questionnaire surveys, deep learning based online questionnaire survey analysis is becoming increasingly important. In the construction of the entire online questionnaire survey system, how to improve the accuracy of feedback submitted by respondents and the efficiency of system processing is currently a key issue that needs to be urgently addressed. This article conducts research on online questionnaire surveys and related traditional technologies, constructs an evaluation index system for questionnaire surveys, and combines the application of BP (Back Propagation) neural network algorithm and sample size calculation formula to draw the following conclusion: the questionnaire survey system using BP neural network algorithm on five types of questionnaire survey samples has an average improvement of 6.9% in survey accuracy, and an average improvement of 9.74% in system processing efficiency. This study can achieve personalized analysis and prediction of questioning data from different individuals through personalized model training and prediction, providing more accurate and targeted advice and guidance for individuals.
Comparing the Effectiveness of Static, Dynamic and Hybrid Malware Detection on a Common Dataset

The detection of malicious Android applications is a major security challenge. A number of machine learning-based techniques have been put forth, and some of them have attained great accuracy. However, the diversity of apps and frequency at which new malware families are found means that the issue remains unresolved. In this paper, we use both static, dynamic and hybrid analysis to automatically classify Android apps as benign or infected. We compare all three approaches on a common dataset — the TwinDroid dataset which contains over 15,000 system call traces from over 9,000 benign and infected app. This method allows comparison on equal footing. We make further contributions on the topic of feature selection and trace abstraction.
A Pedestrian Re-Identification Network Based on Multi-Granularity Feature Extraction

Pedestrian re-identification aims to distinguish and recognize a particular pedestrian within a set of images taken by various cameras. Previous research has demonstrated that simple, global and local features may not yield optimal results for the model. In this study, an end-to-end pedestrian recognition network is enhanced by incorporating multi-granularity pedestrian features. This network comprises multiple deep network modules, specifically, two global feature branch modules and two local feature branch modules. To augment the model's feature extraction capability, we have integrated an improved parameterless attention module into the baseline model, resulting in significant performance improvements. We have conducted comprehensive experiments on well-established evaluation datasets, including Market-1501, DukeMTMC ReID, and MSMT17, and the results have empirically demonstrated advanced performance that surpasses most current methods. Taking the Market-1501 dataset as an illustration, our approach demonstrated a mean Average Precision (mAP) of 94.0% and a Rank-1 accuracy of 95.8% in single query mode, with the application of reordering techniques.
CNN-based Boreal Peatland Fertility Classification from Sentinel-1 and Sentinel-2 Imagery

Peatland semantic segmentation is a sub-problem of land cover classification conducted to provide valuable information for environmental monitoring and land management. In this paper, we propose a Convolutional Neural Network (CNN) to fuse two open-source satellite input image sources, Sentinel-1 SAR and optical Sentinel-2, for pixel-level classification of peatland fertility classes. To overcome the overfitting problem and reduce the time and computation complexity, we proposed a window-based approach which consider the defined spatial resolutions around each pixel to perform classification. Two early and late fusion architectures combining inputs at different stages in the network are evaluated. The multi-modal architectures are compared with uni-modal architecture which only uses one input data for training the CNN. Moreover, we investigate how the performance of CNN changes when it is trained with a high number of bands or only the best bands of each data source. The experimental results are acquired on a study area in Finland which cover a variety minerotrophic aapa mire peatlands. They show that our late fusion architecture able to classify peatland fertility levels around 56.73% total accuracy in the study area when the CNN is trained based on the best bands of both optical data and radar signals.
Remote Sensing Image Classification Fusion Cross Convolution and Attention Mechanism

With the continuous development of remote sensing technologies has led to the generation of high resolution remote sensing imagery data, which has created new opportunities and challenges for the classification of remote sensing images. To further enhance the distinguishability and local semantic capability of remote sensing images, this study proposes a cross-conference attention module that combines the cross-conference and attention mechanism, thereby improving the distinguishability of the images.In this method, 3D cross-convolution is used to find the potential location of key objects in remote sensing images to improve the global semantic ability, and then attention mechanism is used to enhance the key information of key objects to improve the discriminant feature ability. Secondly, the instance level loss is introduced to strengthen the local semantic description and assist the global loss function classification. The experimental results demonstrate that the method proposed in this study exhibits competitive performance in the task of remote sensing image classification.
Irradiation-and Bias-Stress-Induced Charge Trapping and Gate Leakage in AlGaN/GaN HEMTs

Effects of proton irradiation and bias-stress are evaluated for the degradation and gate leakage of commercial AlGaN/GaN HEMTs. These devices are sensitive to bias stress and robust to proton irradiation. Gate leakage is shown to increase significantly with short-term high-current stress and/or high-fluence proton irradiation.
Medical Record Management System Using Blockchain Technology

The EHR is a patient's medical history in digital form. Electronic health records allow for record collection, creation, and storage. Electronic health records are sluggish to implement by healthcare practitioners. Our national government recently passed legislation requiring the use of an Electronic Record System. The electronic health record (EHR) will improve clinical recording, healthcare utilisation tracking, quality control, and invoicing. It will also simplify healthcare record management and mobility. Administrative activities, lab systems, computerised physician order entry, radiography systems, clinical documentation, and pharmacy systems are basic parts of electronic health records. Customers save money, increase quality and safety, and improve healthcare efficiency using electronic health records. EHR challenges include pricey software, system security, patient confidentiality, and unknown government regulations. EHR management will benefit from bar coding, speech recognition, and RFID.
Impact of Substituting Hydrogen for Natural Gas on Compressor Station Operation in Gas Networks

Hydrogen is a chemical energy carrier and plays an important role as a storage for renewable energies and the decar-bonization of many energy sectors. The re-utilization of existing gas network infrastructure including its compressor stations is a prerequisite for the development of a hydrogen market. The extent to which compressor stations can be repurposed depends on how the substitution of hydrogen for natural gas changes the operating parameters. To investigate this question, we have developed a model, which we present in this paper. As a case study, we take a compressor station with centrifugal compressor and investigate how the compression power and the power of the cooler at the station outlet change as a function of hydrogen admixture up to the point where hydrogen completely replaces natural gas. The thermal power of the transported gas mixture remains constant. Our results show that the required compressor power is almost quadrupled and the cooling power increases by a factor up to 4.7. Consequently, extensive measures are required to upgrade compressor stations for the compression of hydrogen if a similar thermal power is to be transported in the gas network.
Variability of upper-ocean characteristics and tropical cyclones in the South West Indian Ocean

Track and intensity are key aspects of tropical cyclone behavior. Intensity may be impacted by the upper-ocean heat content relevant for TC intensification (known as Tdy) and barrier layer thickness (BLT). Here the variability of Tdy and BLT in the South West Indian Ocean and their relationships with tropical cyclones are investigated. It is shown that rapid cyclone intensification is influenced by large Tdy values, thick barrier layers and the presence of anticyclonic eddies. For TC generation in the South West Indian Ocean, the parameter Tdy was found to be important. Large BLT values overlay with large Tdy values during summer. Both fields are modulated by the westward propagation of Rossby waves, which are often associated with ENSO. For example, the 1997–1998 El Nino shows a strong signal in Tdy, SST, and BLT over the South West Indian Ocean. After this event, an increasing trend in Tdy occurred over most of the basin which may be associated with changes in atmospheric circulation. Increasing SST, Power Dissipation Index and frequency of Category 5 tropical cyclones also occurred from 1980 to 2010. To further examine the links between tropical cyclones, Tdy, and BLT, the ocean response to Category 5 Tropical Cyclone Bansi that developed near Madagascar during January 2015 was analyzed. Its unusual track was found to be linked with the strengthening of the monsoonal north westerlies while its rapid intensification from Category 2 to Category 4 was linked to a high-Tdy region, associated with a warm core eddy and large BLT.
Collision Avoidance Strategy for Autonomous Intersection Management by a Central Optimizer Algorithm

The increasing volume of traffic worldwide enlightens the problem of ensuring driving safety and preventing collisions at unsignalized intersections. In this regard, with the advent of Connected Autonomous Vehicles (CAV), Collision Avoidance (CA) and Autonomous Intersection Management (AIM) problems have been intensively studied and many cooperative and optimization-based approaches have been proposed. In this paper, we introduce a double-level collision-free control system, performed by a Central Optimizer (CO) and local CAV controllers, that allows CAVs to safely cross the intersection at the same time. The CO collects data from CAVs and imposes waiting times at specific waypoints that are then used by the low-level controllers to regulate the vehicle speed. The main advantage of the presented method is that a less complex optimization problem is formulated by imposing waiting times rather than determining the speed profile. A simulation campaign conducted on Matlab shows the performance of the proposed approach.
Self-supervised Defocus Map Estimation and Auxiliary Image Deblurring Given a Single Defocused Image

In this paper, we propose an end-to-end self-supervised Deep Neural Network (DNN) for Defocus Map Estimation (DME). Currently, such defocus maps are estimated by DNNs with fully supervised learning. For training, such networks need large datasets annotated by defocus amount or scene depth, which are challenging to obtain. The networks with self-supervised training arrive to overcome the limitation represented by the Ground Truth (GT) data. In this line, we propose a self-supervised learning neural network for DME from a single defocused image. Our method is based on a recently proposed DNN called 2HDED:NET that we enriched with a defocus simulation module that makes possible the self-supervised training for DME. In addition to the defocus map, our network reconstructs the All-in-Focus (AIF) image through supervised learning. We test the network on synthetic and realistic benchmarks and demonstrate that it is an effective solution for DME and image deblurring when a single defocused image is available.
Computer Aided Disease Prediction Using Machine Learning

In recent years, new technologies have helped avoid numerous diseases when diagnosed early. Health is a popular research topic. Developing a user-friendly platform that can help end-users get nearly accurate predictions so they can take timely actions to prevent or manage medical situations by consulting a clinical expert and living a healthy lifestyle can help prevent disease and death graphs from growing faster. Thus, a user-friendly platform is created using different machine learning methods to make accurate predictions based on specified characteristics and deliver them to the user. For diabetes and cardiovascular disease prediction, five machine learning models-LR, DT, KNN, SVM, and Perceptron-are used.
High-Precision Indoor Fingerprint Localization Based on Graph Neural Network

Thanks to the rapid development of machine learning and deep learning, the fingerprint localization community has achieved high localization performance through advanced models. However, current approaches mainly focus on machine learning or pure convolutions algorithms for example k-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN). Despite their success, their localization ability is mainly derived from the development of model learning ability without in-depth study of the physical meaning of Channel State Information (CSI), such as the varying degrees of correlation and interference between subcarriers of antennas located at different locations in a wireless communication system. In particular, we develop a novel graph neural network (GNN)-based method to embed subcarriers from different antennas at different positions of the CSI as nodes and connect them to K-nearest neighbours (KNNs) to obtain a cohesive graph structure. Furthermore, we introduce a dilated KNN to perform graph-level learning using graph convolution, effectively modeling the correlation and interference between subcarriers. Finally, to prevent the degradation of positioning performance caused by reduced node feature diversity, we introduce a feedforward neural network (FFN) module for node feature transformation. Experimental results on real datasets indicate that our method achieves an average localization error of 0.17m, which is almost 39.3% better than the existing optimal baseline.