Miracle-dz/UniFAR-FTUs huggingface dataset:
PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment

Given the proliferation of sensors and actuators, evaluating the network performance of current and forthcoming city-wide Internet of Things (IoT) applications is a challenging task. To overcome this challenge, we created a large-scale simulator called PIoT over the years that can assess the performance of multiple millions of mobile/static IoT devices using a 4G/5G cellular infrastructure. In PIoT, different Key Performance Indicators (KPIs) are defined and collected to produce data to evaluate applications and network performance. PIoT is an on-going academic simulator project, and its most recent version is accessible to the public through the user interface found on https://www.piotsimulation. com without any installation requirements. It uses a realistic database that contains the real locations and features of Base Stations (BSs) and the real locations of IoT user equipment devices. The interface can be used by operators and researchers to understand network behavior when deploying new applications or to gather data to feed artificial intelligence and machine learning algorithms. The objective of this paper is to present a detailed description of the PIoT modeling architecture, as well as some use cases to help potential users understand the type of capabilities that are available when using the simulator. Limitations and comparisons with other popular engines are also included in the paper.
Accuracy of Human Limb Moment of Inertia Estimations and Their influence on Resultant Joint Moments

Segmental moment of inertia values, which are often required to perform mechanical analyses of human movement, are commonly computed using statistical models based on cadaver data. Two sets of equations for estimating human limb moments of inertia were evaluated: linear multivariable equations and nonlinear equations. Equation coefficients for both sets of equations were determined using the cadaver data of Chandler et al. (1975). A cross-validation procedure was used to circumvent the problem of model evaluation when there is limited data with which to develop and evaluate the model. Moment of inertia values for the longitudinal axes were predicted with similar degrees of accuracy with either set of equations, while for the transverse axes the nonlinear equations were superior. An evaluation of the influence of the accuracy of moment of inertia estimates on resultant joint moments for three activities showed that the influence of these errors was generally small.
Multicast Service Chaining Model Guaranteeing Reliability with Multiple Sources

Service chaining provides network services to users by processing packets with a series of virtualized network functions (VNFs). This paper proposes a multi-source multicast service chaining model that guarantees the reliability of services with flexible routing and VNF placement. In order to obtain cost-efficient feasible solutions, we introduce an algorithm that iteratively solves an integer linear programming problem and an algorithm that conducts the VNF placement with a concept of the betweenness centrality. Numerical results show that the proposed model allocates the network and computation resources with the reduction in the total cost compared to the existing model.
The Analysis of Student Experimental Behavior Based on Multi-source Information Fusion

At present, with the rapid development of Internet technology and the arrival of the big data, more and more smart labs are put into use. The analysis of multi-source data in the labs has a wider application scenario. Experimental teaching based on big data platform has become more and more popular. Learning and using the multi-source massive data generated by the experimental teaching platform has become a hot issue in the field of educational research and application. Based on the surveillance video of the circuit laboratory of the smart labs, this paper uses the open-source posture recognition framework OpenPose to process the human posture in the video, and then obtain the human bone point data. It trains the appropriate classifier to classify and recognize the behavior based on machine learning, and analyzes the correlation between the behavior data and the performance data to establish a learning prediction model, which on the one hand provides scientific guidance for student management, On the other hand, it provides valuable decision-making information for experimental teaching reform.
Make Digital Twins of MCU Systems based on Proteus Software with IoT Platform

More and more monitor and management systems are established based on IoT platform. That provides good environment to make digital twins. The suitable concept of the digital twin that includes the physical object, the virtual object and communication system between tow objects is proposed according to the original digital twin concept. The MCU system is very useful and important for the physical object. And the architecture of the digital twins of MCU systems are innovated using Proteus software and IoT platform. The practical demonstration of the digital twin of one MCU system is established to prove that the architecture is correct and can be realized in the practical projects. The concept of the digital twin and the architecture are useful to improve the performance of the monitor and management systems based on IoT platform.
High-dimensional Data from Financial Statements for a Bankruptcy Prediction Model

A bankruptcy prediction model is essential for stakeholders to avoid losses and market imbalances arising from the misallocation of resources. In the literature, several methods have been proposed for bankruptcy prediction. However, conventional statistical techniques and financial ratios have limitations in correctly identifying financially distressed firms in a reasonable time frame. In this study, we compare several machine learning approaches to define a bankruptcy prediction model using raw accounting data from financial statements. In particular, we used a dataset of 1’826’157 financial statements from 532’255 active and 76’464 bankrupt Italian firms (i.e., the last three financial statements for each firm), considering both financial ratios and the raw accounting data as input variables. To overcome the constraints of imbalance between the two classes of bankrupt and active firms, we used rebalancing techniques. We found that Random Forest is the best-performing model, exhibiting an accuracy of 98% which is an increase of 13.95% compared to the use of the financial ratios. Moreover, relative variable importance analysis shows that equity/total debts ratio and short-term debts are the most essential bankrupt predictors.
Young Adults’ Lived Experiences with Cancer-Related Cognitive Impairment: An Exploratory Qualitative Study

Cancer-related cognitive impairment (CRCI; e.g., disrupted memory, executive functioning, and information processing) affects many young adults, causing significant distress, reducing quality of life (QoL), and thwarting their ability to engage in professional, recreational, and social experiences. The purpose of this exploratory qualitative study was to investigate young adults’ lived experiences with CRCI, and any strategies (including physical activity) they use to self-manage this burdensome side effect. Sixteen young adults (Mage = 30.8 ± 6.0 years; 87.5% female; Myears since diagnosis = 3.2 ± 3) who reported clinically meaningful CRCI whilst completing an online survey were interviewed virtually. Four themes comprising 13 sub-themes were identified through an inductive thematic analysis: (1) descriptions and interpretations of the CRCI phenomenon, (2) effects of CRCI on day-to-day and QoL, (3) cognitive–behavioural self-management strategies, and (4) recommendations for improving care. Findings suggest CRCI is detrimental to young adults’ QoL and must be addressed more systematically in practice. Results also illuminate the promise of PA in coping with CRCI, but research is needed to confirm this association, test how and why this may occur, and determine optimal PA prescriptions for young adults to self-manage their CRCI.
Detection and Classification of Diabetic Retinopathy using Deep Learning

The most common reason for blindness in adults in developed nations is Diabetic Retinopathy (DR). Currently, diagnosing DR involves an in-depth arduous examination of digital colour fundus pictures of the retina by a qualified practitioner. By looking for lesions connected to the vascular anomalies brought on by the illness, ophthalmologist can recognise diabetic retinopathy. Although this strategy works, it has substantial resource requirements. It has long been understood that a thorough and automated approach of detecting diabetic retinopathy is necessary, and prior initiatives have achieved excellent strides utilising image classification, pattern recognition, and machine learning. This project seeks for automated detection, grading, and segmentation of Diabetic Retinopathy. In our project we aim to improve image segmentation using UNet and to automise the project using Convolutional Neural Networks and VGG16.
Research on application of object detection based on yolov5 in construction site

In the context of the ongoing COVID-19 outbreak, the regulation of the use of personal protective items, specifically helmets and masks, on construction sites is of critical importance. Due to the complex nature of construction environments and the high number of workers, relying on worker inspection and surveillance cameras to detect the wearing of helmets and masks has problems such as poor timeliness, low accuracy, and low efficiency. This paper provides a deep learning approach to address the above issues. We employed YOLOv5s to train separate models for helmet and mask detection, achieving accuracy and mAP@0.5 of greater than 90% for both models. Tested on the divided test set, the mAP@0.5 for both helmet and mask detection were over 96%, demonstrating the effectiveness of our models. These models can be deployed in real environments, effectively solving the problem of monitoring the compliance of construction workers wearing personal protective equipment.
Acceleration of hybrid MPI parallel NBODY6++ for large N-body globular cluster simulations

Abstract Previous research on globular clusters (GCs) dynamics is mostly based on semi-analytic, Fokker-Planck, Monte-Carlo methods and on direct N-body (NB) simulations. These works have great advantages but also limits since GCs are massive and compact and close encounters and binaries play very important roles in their dynamics. The former three methods make approximations and assumptions, while expensive computing time and number of stars limit the latter method. The current largest direct NB simulation has ~ 500k stars (Heggie 2014). Here, we accelerate the direct NB code NBODY6++ (which extends NBODY6 to supercomputers by using MPI) with new parallel computing technologies (GPU, OpenMP + SSE/AVX). Our aim is to handle large N (up to 106) direct NB simulations to obtain better understanding of the dynamical evolution of GCs.
Elliptical cladding elements in negative curvature hollow-core fiber for low-loss terahertz guidance

Optical losses of ellipse-nested elliptical negative curvature hollow-core fibers are numerically presented for the terahertz region. Primary design parameters of all fibers were iteratively optimized and confinement and total losses as low as $2.79 \times 10^{-5} \mathrm{~dB} / \mathrm{m}$ and $0.015 \mathrm{~dB} / \mathrm{m}$ were achieved at 0.55 THz, respectively. Single-mode light guidance of the proposed design was investigated and a higher-order mode extinction ratio of around 3000 was calculated.
Manhattan Nonnegative matrix factorization using the alternating direction method of multipliers

Nonnegative matrix factorization (NMF) was a classic model for dimensional reduction. Manhattan NMF is a variant version of NMF that uses a L1 -norm cost function as the objective function instead of the L2 -norm cost function. Manhattan NMF can be formulated as a nonconvex nonsmooth optimization problem. An algorithm framework for solving the Manhattan NMF problem based on the alternating direction method of multiplication is presented to us. Compared with the existed algorithm, our proposed algorithm is more effective by experiments on synthetic and real data sets.
GraphDescriptor: Augmenting Node-Link Diagrams With Textual Descriptions

Node-link diagrams are the most popular form for graph visualization. Yet, salient information of a node-link diagram cannot be fully depicted by solely presenting the visualization. We propose to augment node-link diagrams by creating textual descriptions for interested information. We conduct an expert review and a user interview to identify six requirements of generated interpretations, including three requirements for connection extraction and three requirements for visual expression. Our solution, GraphDescriptor, generates textual descriptions with two stages: feature extraction and description generation. The first one identifies and extracts features of node-link diagrams, like node connections, visual designs, and types of graph layouts. The second stage creates a group of hierarchical sentences based on a pre-defined schema. To the best of our knowledge, our approach is the first attempt to generate textual descriptions automatically. Three use cases and the in-lab user study confirm the superiority of our approach.
Aux-ViT : Classification of Alzheimer's Disease from MRI based on Vision Transformer with Auxiliary Branch

Alzheimer's disease (AD) has become a major public health concern, and reliable screening and diagnosis remain difficult. Magnetic resonance image (MRI) can be helpful in distinguishing AD patients from individuals with normal cognition (NC). Deep neural networks have demonstrated strong capacities for extracting intricate nonlinear correlations from brain imaging data recently. However, this requires large amounts of data for training to avoid overfitting problems, but data is scarce and precious in medical field. In this work, we propose a Vision Transformer network architecture called Aux-ViT, which solves the problem of losing shallow features by adding a class auxiliary branch. Specifically, we choose ViT as the backbone network and add an Auxiliary Multi-layer Perceptron Head to output auxiliary prediction results for calculating prediction errors. Based on the characteristics of MRI, we also developed a brain MRI data preprocessing method called Multi-Information Fusion Improvement, while further achieving data enhancement using a random synthetic mask based on pixel weighting fusion. We conducted extensive experiments using the ADNI-3 dataset to validate our algorithm. When compared to the baseline ViT model, the Aux-ViT model obtains an accuracy of 89.58%, which is an increase in accuracy of 3.93% and a decrease in training time of 47.7%. Our study provides a practical approach for early Alzheimer's diagnostics utilizing MRI data.
Graph Construction Based on Nonnegative Sparse Representation for Semi-supervised Learning

It is well known that the graph construction is the key part of graph-based semi-supervised learning algorithms, and the performance of algorithms relies heavily on the graph weight matrix given by graph construction process. In this paper, we propose two graph construction models based on nonnegative sparse representation. These two models accommodate small possible noise, and moreover, their solutions are sparse and nonnegative which can be used as the graph weights directly. Weights generated in such a way can reflect the point neighborhood structure well, thereby providing favorable similarity measures for the sample pairs. Numerical experiments on several UCI and face datasets indicate that in most cases the results yielded by the proposed algorithms are comparable even superior to the best ones yielded by the algorithms based on traditional graph construction methods and L1 graph.
A Mobile-Transformer Algorithm for Car Paint Defect Detection

To improve the accuracy of car paint defect detection, this paper used a deep learning method to realize car paint defect detection based on small dataset. Aiming at detecting car paint defects and improving the accuracy of the detection algorithm, this paper proposed a Mobile-Transformer algorithm for automatic detection of car paint defects by replacing partial Convolutional Neural Networks (CNN) with the Transformer architecture (Transformer), improving the feature layer of the network, and optimizing the parameters. The Mobile Transformer algorithm combined the benefits of CNN and the Transformer architecture, learned global features of car paint defect images, and built a lightweight, low-latency mobile vision task network algorithm. In the training process, since the car paint defects dataset was a small dataset, it was necessary to introduce the transfer learning method to improve accuracy. The Mobile Transformer algorithm was used to extract the image features to complete the automatic detection of car paint defects more efficiently. Experiments showed that the Mobile-Transformer algorithm realizes the lightweight of the model, and the accuracy is as high as 99.5%, which is 9% more accurate than the Vision Transformer algorithm, and 5% more accurate than the MobileNet V2 algorithm.
HookNet: Efficient Multiscale Context Aggregation for High-Accuracy Detection of Fabric Defects

Automatic defect detection is a critical stage of quality control in the textile industry. Because defects vary in texture structure, size, and spatial distribution, existing defect detection methods have difficulty in achieving a perfect tradeoff between detection efficiency, accuracy, and generalizability. This article presents an advanced fabric defect detection model based on a deep learning algorithm. A novel parallel dilated attention module (PDAM) was designed: this is nested in the deep layers of the neural network backbone to establish global channel dependencies and capture multiscale contextual information. Furthermore, a hook-shaped feature pyramid network (HookFPN) was developed for multiscale context aggregation: this directs the network to focus on lower level features and is helpful for further improving detection efficiency without sacrificing detection accuracy. In addition, the Alpha-GIoU loss is used to improve the accuracy of bounding box regression because of its modulated power parameter $\alpha $ . The advantages and effectiveness of the proposed method were statistically analyzed using data from several public datasets. The code is available at https://github.com/aabb605/HookNet.
Approaches to dynamic provisioning in multiband elastic optical networks

Adopting multiband transmission in optical networks can cost-effectively increase network capacity without deploying new fibre. In this paper, we focus on the solutions explored by the research community to address the problem of resource allocation in dynamic multiband elastic optical networks. We start by summarising the main challenges and contributions of the design of ad-hoc heuristics. Next, we review the few recent approaches based on deep reinforcement learning and evaluate the efficacy of different techniques to improve their performance. We also discuss possible future directions for research in the area.
Evaluation of Tourism Sustainable Development Ability Based on Neural Network Model

In order to provide scientific basis for the improvement of tourism economic quality and green environmental protection, an evaluation method of tourism sustainable development ability based on neural network model is proposed. Firstly, by collecting and managing tourism resource information through investigation and analysis, an evaluation index system based on the main influencing factors of the sustainable development ability of tourism economy and tourism is constructed. Then the data standardization, training and output are performed based on BP neural network, and the comprehensive ranking of various indicators of sustainable development is acquired. Finally, according to the actual demand, MATLAB is adopted to verify the effectiveness of the evaluation scheme based on BP neural network. The experimental results show that it improves the sensitivity and reliability of the evaluation and early warning system, which provides new measures and ideas for the sustainable development of tourism economy.
The method of WDM network reserving based on the p–cycle

There are several schemes to ensure the reliability of WDM networks, such as hardware redundancy and network redundancy. The problem of choosing a rational option for network redundancy for WDM networks is no less relevant than for SDH networks. The WDM network, like the SDH network, can be represented as a set of typical structures: linear (single-connected structure), ring (two-connected) and cellular (multi-connected). For redundancy of optical networks based on cellular topology, a redundancy mechanism based on p-cycles is proposed. Currently, the concept of p-cycles is increasingly being used on European transport communication networks.
Support Vector Machine, Naïve Baye’s, and Recurrent Neural Network to Detect Data Poisoning Attacks on Dataset

Machine learning is a subset of artificial intelligence, that has revolutionalized the world in recent days. The machine learning model will be trained and tested on the collection of huge data, a large portion is for training and others for testing. data has been collected from trusted or untrusted sources, and results will be predicted using different algorithms. People are getting used to these predictions, are these predictions secure, trustful, or guaranteed? Well, that will be decided by the quality of the datasets. Although the datasets can be poisoned easily. Here the proposed work is providing a solution to check the quality of the dataset used for machine learning algorithms. The basis of the research is to detect poisoning attacks in the data set and to determine the most accurate estimate of the detection of poisoning attacks. comparing accuracies of the supervised machine learning algorithms Support Vector Machine(SVM), Naive Baye’s Classifier (NBC), and deep learning algorithms Recurrent Neural Network(RNN). The evaluation report was submitted for the results obtained using the rating report. The classification report considers performance measures the Precision, Accuracy, and F1 score of each algorithm.
CNN-based Articulatory Feature Recognition for Kunqu-Singing Pronunciation Evaluation

In order to achieve ‘Accurate pronunciation and proper melody’ in Kunqu-singing, the automatic evaluation of pronunciation based on ASR (Automatic Speech Recognition) can be greatly beneficial for learners. In modern phonetics, the articulatory feature describes the articulatory movement during speech production, which directly impact the quality and characteristics of pronunciation. To provide effective feedback for mispronunciation detection in Kunqu-singing, we propose a CNN-based articulatory feature recognition model. To tackle the issue of limited training corpus, we incorporate transfer learning into the model training by utilizing both Jingju and Kunqu corpus. The experimental results from our self-built Kunqu corpus show that the incorporation of transfer learning led to a 6% improvement in the recognition rate of articulatory feature, and the average recognition rate of various articulatory features reached 83.7%, which is 24.4% better than phoneme recognition.