Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Segmentation and Shape, Texture-based Analysis of Glioma Using Fully Convolutional Network

Lower-grade glioma is a type of brain tumor that is usually found in the human brain and spinal cord. Early detection and accurate diagnosis of lower-grade glioma can reduce the fatal risk of the affected patients. An essential step for lower-grade glioma analysis is MRI Image Segmentation. Manual segmentation processes are time-consuming and depend on the expertise of the pathologist. In this study, three different deep-learning-based automatic segmentation models were used to segment the tumor-affected region from the MRI slice. The segmentation accuracy of the three models-U-Net, FCN, and U-Net with ResNeXt50 backbone were respectively 80%, 84%, and 91%. Two shape-based features- (angular standard deviation, marginal fluctuation) and six texture-based features (entropy, local binary pattern, homogeneity, contrast, correlation, energy) were extracted from the segmented images to find the association with seven existing genomic data types. It was found out that there was a significant association between the genomic data type-microRNA cluster and texture-based feature-entropy case and genomic data type-RNA sequence cluster with shape-based feature-angular standard deviation case. In both of these cases, the p values were observed less than 0.05 for the Fisher exact test.
Ion-Selective Ligands: How Colloidal Nano- and Micro-Particles Can Introduce New Functionalities

Abstract Colloidal nano- and micro-particles can introduce new properties and functionalities to existing materials and thus are a valuable building block for the construction of novel materials. This is discussed for the case of ion-selective ligands, hence molecules that can bind specifically ions of one type. First, in case ion-selective fluorescent ligands are attached to the surface of particles, these fluorophores sense the local ion concentration at the particle surface and not the bulk ion concentration. Thus, the ion-response of the ligands can be tuned by attaching them to the surface of particles. Second, in case ligands specific for particular ions are bound to the surface of particles, these ions can provide contrast and thus the particles can be imaged. This involves for example Gd-ions, which provide contrast for magnetic resonance imaging (MRI), and 111In-ions, which provide contrast for imaging of radioactivity. By attaching the ligands to the surface of particles, their physicochemical properties (as for example size and solubility) are changed, which affects their interaction with cells and, consequently, biodistribution. Attachment of ion-chelators for imaging to particles thus allows for tuning their biodistribution. Third, ion-specific ligands can be also attached to the surface of magnetic particles. In this case ions bound to the ligands can be extracted with magnetic field gradients and magnetic separation becomes possible. Therefore, magnetic particles provide a handle to the ligands, which enables the extraction of ions from solution. These examples demonstrate how the attachment of different types of colloidal particles to one existing class of molecules, ion-selective ligands, can open new fields of applications of these molecules.
System Throughput Performances Analyses with Stochastic Geometry in Unlicensed Spectrum Systems

With the increasing shortage of spectrum resources, the use of unlicensed spectrum is garnering more attention. This paper establishes a theoretical framework based on stochastic geometry to analyze the throughput performances of a cellular network with unlicensed spectrum. Considering that both cellular and WiFi users adopt a listen-before-talk (LBT) spectrum sharing scheme, we model it as a hard core point process (HCPP). Our analysis focuses on the combined effect of cellular user and WiFi user density on the throughput performances, and obtains their integral solution. The results show that the use of unlicensed spectrum can increase the throughput of cellular users when the densities of the two types of users are considered, which are of practical value to holistic designs and deployments of License Assisted Access (LAA) systems.
LiDAR and Camera Calibration Using Near-Far Dual Targets

Due to different modality and sampling density between LiDAR and camera, the correspondences between these two sensors are difficult to find. In this paper, we propose a novel method for Camera-LiDAR extrinsic parameters calibration based on the near-far dual targets. In our approach, a chessboard target is placed closer to the combined Camera-LiDAR device and a trihedron object of the structured scene is placed farther. Using the chessboard, camera motion can be estimated through the RGB images captured by the camera. The point cloud captured by LiDAR from the trihedron is then used for LiDAR motion estimation. By coordinating their motion constraints, our approach can reliably estimate the extrinsic parameters between two sensors by a classic hand-eye calibration process without facing the challenges of finding feature correspondences. Finally, the proposed method is evaluated on both simulated data and real data.
On the partitioning of urban networks for MFD-based applications using Gaussian Mixture Models

The partition of urban networks for the application of aggregated traffic models based on the Macroscopic Fundamental Diagram (MFD) is a challenging task and an active research question in the literature. The partitioning of urban networks should yield fully connected and compact regions, ensuring the homogeneity of traffic conditions within each of them. This requires having rich datasets of traffic data available, which can be difficult to gather. Moreover, one should also decide the optimal number of regions to partition urban networks. Several studies have addressed some of these research questions; but, the models in the literature fail to ensure all the required properties of a good partitioning. In this paper, we propose to use Gaussian Mixture Models to partition urban networks. The covariance matrix allows the model to learn the topological features and dependencies of the urban network. We also discuss proxies that can be utilized to ensure the homogeneity of traffic conditions in the regions, when traffic data is not available.
Trust and Quality Computation for Cyber Threat Intelligence Sharing Platforms

Information sharing has been considered a critical solution against the ever-increasing complexity of cyber-attacks. In this effort Cyber Threat Intelligence is undergoing a process of increasing its maturity levels. The quantification of the quality of shared information and the assessment of trust amongst information sharing entities is an important part of the process. The Trust and Quality Tool has been designed as a tool with the aim of improving the trust in the relevancy of shared information by enabling an option to assess its trustworthiness and defining a set of metrics for trust and quality.
Driving Behavior Safety Levels: Classification and Evaluation

Driving simulators and naturalistic driving studies are often used to understand driving behavior characteristics. It is essential to evaluate the traffic safety of driving behavior in real time, which is helpful to trigger interventions of Advanced Driver Assistance Systems (ADAS) to ensure the driving safety. Therefore, this paper aims to propose a framework of driving behavior safety level classification and evaluation in real time, which was validated by a case study based on a driving simulation experiment. The proposed methodology focuses on finding the optimal number of safety "levels" or "zones" for driving behavior, classifying the safety levels with the help of different clustering techniques, and evaluating the driving safety levels based on developed classification models in real-time. Three clustering techniques were applied, including k-means clustering, hierarchical clustering and model-based clustering. The optimal number of clusters was found to be four using k-means clustering, and the clusters of safety levels will be labelled as "normal" driving, "low risk" driving, "middle risk" driving and "high risk" driving. A Support Vector Machine (SVM) and a decision tree were thereafter developed as the classification model. The accuracy of the combination of model-based clusters and SVM models proved to be the best with four clusters, yet no significant difference to other models was found.
Construction of knowledge graph of drug-action mechanism

Background: Medical texts contain a large amount of information on the mechanism of action of drug ingredients, and it is impossible to manually collate the massive information in dozens or millions of papers. The method of relationship abstraction in data mining can be used to extract the mechanism of drug action. Methods: First, 770000 papers’ abstracts on the mechanism of the drug in Chinese journals were collected; an algorithm to identify the sentence patterns and semantic elements of the mechanism of drug action were developed; the corresponding semantic elements were extracted by this algorithm. Finally, a knowledge graph was established by the Neo4j tool. Results: A total of 30710 mechanisms of drug action were found, and 57865 drug-action mechanism relations were established. The recall rate of the algorithm to extract semantic elements and their relations was 0.59, the accuracy is 0.86. Conclusion: The algorithm can accurately and comprehensively extract semantic elements and their relationships. Comparison with drug knowledge graphs such as DRKG, this knowledge graph of drug-action mechanism covers a large number of drugs and mechanism of action more comprehensively, which can facilitate new drug development and query of drugs-action mechanism.
A Smart Sensor-Based cEMD Technique for Rotor Bar Fault Detection in Induction Motors

Induction motors (IMs) are commonly used in industrial and domestic applications. Reliable IM fault detection can improve machinery production quality and operation safety and prevent unexpected failures. However, reliable motor fault diagnosis is still a challenging research and development task, especially in real machinery monitoring applications, due to the limitations in data acquisition (DAQ) systems and fault detection techniques. The first objective of this work is to develop a wireless smart sensor DAQ system for the current signal measurement. The second objective is to propose a new correlation empirical mode decomposition (cEMD) technique to detect broken rotor bar faults. The proposed cEMD technique can differentiate the line current and its sideband components from other frequencies and accentuate fault features over intrinsic mode function sidebands so as to improve fault detection accuracy. The effectiveness of the developed smart sensor DAQ system and the cEMD technique is verified experimentally under different motor conditions.
Intelligent Spectrum Allocation for mmWave Integrated Backhaul and Access Network

Prediction of user behavior can be used to preallocate resources of the wireless network, the performance of the mmWave Integrated Backhaul and Access Network (IBAN)can be improved. In this paper, we jointly optimize the multi-base station and multi-user spectrum resource allocation in mmWave IBAN based on prediction with aim to improve spectrum efficiency and network energy efficiency, subject to user’s (Quality of Service) QoS and network throughput requirements. We model the spectrum resource allocation problem of mmWave IBAN as a model-free Markov Decision Process (MDP). Then We propose to employ deep reinforcement learning algorithm incorporated with recurrent neural network LSTM, called DRQN, to solve the problem. Numerical results demonstrate the effectiveness of the proposed algorithm.
Application of Machine Learning and Statistics in Banking Customer Churn Prediction

Application of the core concepts of Machine Learning and Statistics for predicting whether the customer would leave the services of the bank in future or not. Machine learning model is trained by considering the data of 10,000 customers of the bank. Statistical Techniques are applied so as to investigate the data in depth and infer the relationships between different features or variables of data. The web application uses the trained model in the backend to predict the probability of the customer leaving the bank. Hence, the website can prove to be extremely useful for the bank managers and decision makers of the bank to get an idea of those customers who are likely to leave the services of the bank in future and can retain them by formulating some new policies.
Re-Ranking System with BERT for Biomedical Concept Normalization

In recent years, various neural network architectures have been successfully applied to natural language processing (NLP) tasks such as named entity normalization. Named entity normalization is a fundamental task for extracting information in free text, which aims to map entity mentions in a text to gold standard entities in a given domain-specific ontology; however, the normalization task in the biomedical domain is still challenging because of multiple synonyms, various acronyms, and numerous lexical variations. In this study, we regard the task of biomedical entity normalization as a ranking problem and propose an approach to rank normalized concepts. We additionally employ two factors that can notably affect the performance of normalization, such as task-specific pre-training (Task-PT) and calibration approach. Among five different biomedical benchmark corpora, our experimental results show that our proposed model achieved significant improvements over the previous methods and advanced the state-of-the-art performance for biomedical entity normalization, with up to 0.5% increase in accuracy and 1.2% increase in F-score.
Comparative Study of Pretrained Network Feature Extraction and Classifiers for COVID-19 Detection

Severe Acute Respiratory Syndrome (SARS-CoV-2) causes COVID-19, an infectious disease. It has since spread worldwide, leading to an ongoing pandemic. A coronavirus is a virus that infects the nose, sinuses, and upper throat. Fever, cough, trouble in breathing, loss of smell and taste are some of the symptoms. COVID-19 causes mild to moderate infection in most infected patients, who recover without the need for additional treatment. However, it is critical in the lives of older persons and persons with different diseases like diabetes, cancer, cardiovascular disease, and so on. In this study, we propose a method for detecting COVID-19 from CT images. Here the features are extracted using the pretrained network, ResNet-50, and categorized as COVID-19 infected or not using the KNN classifier. This study also focuses on the efficiency of pre-trained networks and other classification approaches for the automatic detection of COVID-19. The AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101, and DenseNet-201 pre-trained networks are used to extract features for analysis. We explored the Support Vector Machine(SVM), Ensemble based method, K Nearest Neighbour(KNN), Discriminant approach, Tree-based, and Naive Bayes classifiers to get the best classifier. The method was tested on the SARS-CoV-2 CT data set. The ResNet-50 with KNN classifier has a sensitivity, specificity accuracy, and F1-score of 95.99 %, 99.16%, 97.52%, and 97.56%, respectively, which is superior to the work reported.
A New Cross-Six Inverter for Transformerless Grid-Connected Photovoltaic Systems

This article proposes a novel cross-six (X-6) inverter topology for low voltage distributed grid-connected solar photo-voltaic (PV) systems that operates transformerlessly. The proposed topology’s primary feature is that it significantly reduces the effects of common-mode leakage current in existing inverter topologes used in transformerless operation. Since the leakage current is reduced with the proposed X-6 inverter topology, it improves the power quality and switching losses as these are directly related to the leakage current. The performance of the proposed X-6 inverter employing on a single-phased grid-connected PV system is determined in the MATLAB/SIMULINK platform and compared with different existing transformerless inverter topologies used for similar applications. The comparative simulation results are presented and discussed in terms of total harmonic distortions (THDs), leakage current, and efficiency. Simulation results demonstrate that the proposed X-6 inverter topology has the ability to achieve an efficiency of 98.34% while injecting current into the grid with lower values of THDs.
Quantum chemical insight into molecular structure, density functional theory calculations, vibrational dynamics, natural population analysis, Hirshfeld analysis, and molecular docking approach to chalcone 1-4-bromophenyl-3-(2-methoxyphenyl)prop-2-en-1-one

ABSTRACT The structure of the novel chalcone 1-4-bromophenyl-3-(2-methoxyphenyl)prop-2-en-1-one has been characterized by Fourier transform infrared and Fourier transform Raman. Density functional theory with Becke, 3-parameter, Lee-Yang-Parr functional was used for the optimization of geometry. The comprehensive assignments of the vibrational spectra have been performed with the aid of normal coordinate analysis. Stability of the molecule and intra/intermolecular charge transfer have been analyzed using natural bond orbital analysis. The existence of intermolecular C-H⋯O, blueshifted hydrogen bond was investigated by bond length variation. Hirshfeld and two-dimensional fingerprint plot analyses have been performed to study the nature of interactions present in the molecule. The docked complex gives a constancy of −8.2 kcal/mol toward the androgen receptor.
Improved Transfer Learning Based Deep Learning Model For Breast Cancer Histopathological Image Classification

In recent years, the demand for prompt detection and classification of breast cancer is rising sharply as breast cancer has become leading cancer type among women throughout the world. Convolutional Neural Networks(CNNs) are widely being used for performing mentioned tasks.However, they need a large number of labeled images which may appear to be infeasible for some kinds of medical images data such as mammographic tumor images. To address this difficulty, Transfer Learning becomes convenient. In this paper, we proposed a deep learning model for classifying Benign and Malignant types of breast tumor that trains an InceptionV3 model which pulls out features from the histopathological images of various magnification. These features are then used for classification. Introduced system is validated on BreakHis dataset and gains average validation set accuracy of 99.50%, 98.90%, 98.96% and 98.51% for magnification factor 40X, 100X, 200X and 400X respectively which outperforms all studied baseline models. Different performance metrices such as precision, recall, F1 score, Specificity have additionally been used for performance estimation purposes.
Origin-destination matrix estimation by deep learning using maps with New York case study

Origin-destination (OD) matrix estimation has conventionally required costly surveys or other forms of sensor data. In this paper, we propose that map data can supplement or partially replace such data if integrated in a deep learning method. Road network information and point of interest (POI) data are used for predicting interzonal trips within a city. The proposed learning approach is a hybrid model, combining a graph convolutional approach at the lower level with an upper level multilayer perceptron. We apply our method for estimating taxi trips in Manhattan and Brooklyn, New York, and obtain promising results for expanding an OD matrix with limited observations.
Social Media Monitoring for IoT Cyber-Threats

The rapid development of IoT applications and their use in various fields of everyday life has resulted in an escalated number of different possible cyber-threats, and has consequently raised the need of securing IoT devices. Collecting Cyber-Threat Intelligence (e.g., zero-day vulnerabilities or trending exploits) from various online sources and utilizing it to proactively secure IoT systems or prepare mitigation scenarios has proven to be a promising direction. In this work, we focus on social media monitoring and investigate real-time Cyber-Threat Intelligence detection from the Twitter stream. Initially, we compare and extensively evaluate six different machine-learning based classification alternatives trained with vulnerability descriptions and tested with real-world data from the Twitter stream to identify the best-fitting solution. Subsequently, based on our findings, we propose a novel social media monitoring system tailored to the IoT domain; the system allows users to identify recent/trending vulnerabilities and exploits on IoT devices. Finally, to aid research on the field and support the reproducibility of our results we publicly release all annotated datasets created during this process.
Methacrylated hyaluronic acid and pectin cell-laden hydrogels for skin emulation

vii Resumo . ix List of figures . xiii List of Tables . xv List of Abbreviations . xvii Chapter 1: Introduction . 1 1.1. In vitro assays for drug delivery studies . 2 1.1.1. In vitro transdermal transport and skin barrier function models . 4 1.1.1.1. Low-throughput models . 5 1.1.1.2. High-throughput models . 6 1.2. In vitro assays for wound healing studies. 7 1.2. 1. In vitro wound healing models . 8 1.2. 1.1 Low-throughput models . 8 1.2. 1.2. High-throughput models . 9 1.3. In vitro assays for skin toxicity studies . 10 1.4. Microfluidic strategies for skin emulation . 13 Chapter 2: Objectives . 19 Chapter 3: Materials and Methods . 21 3.1. Materials . 21 3.2. Pectin Purification. 21 3.3. Synthesis of Methacrylated Hyaluronic Acid . 21 3.4. Synthesis of Methacrylated Pectin . 22 3.5. Preparation of HAMA/PectMA hydrogels . 22 3.5. 1. Preparation of HAMA/PectMA hydrogel precursor (pre-polymer) solutions and functionalization with RGD . 22
BER Performance Comparison of AF and DF Assisted Relay Selection Schemes in Cooperative NOMA Systems

This study investigates the bit error rate (BER) performance analysis for both amplify-and-forward (AF) and decode-and-forward (DF) assisted relay selection algorithms used in cooperative non-orthogonal multiple access (NOMA) systems. For the relay selection schemes, two different approaches are analyzed; Maximum-Minimum (Max-Min) relay selection and Two-Stage relay selection strategies are evaluated. Unlike existing works, the BER performance analysis for the near and far users are investigated with Monte Carlo simulations over the Rayleigh fading channel, considering the number of relays, power allocation coefficient, distance between nodes, and path loss exponent parameters. From the observed results, the AF assisted Max-Min relay selection method performs better for the far user. Moreover, the AF assisted Two-Stage relay selection method performs almost 10 dB gain compared to the DF scenario for the far user. Finally, the BER performance decreases as the far user is apparently moving away from the relay, while AF relaying outperforms compared to DF relaying for both users.
An Efficient Information Sampling Method for Multi-Category RFID Systems

In Radio Frequency Identification (RFID) applications, multiple tags may be deployed on the same object or area for security or precision purposes. These tags carry similar information and so can be grouped into a category. To collect such information, most existing works have to query all tags in each category, which is time-consuming. However, sampling a subset of tags from each category is sufficient. In this letter, we propose a new solution called arithmetic coding based sampling (ACS) protocol. Specifically, we construct a sparse vector to sample only a subset of tags from each category, which can not only avoid repetitive information collection but also reduce interference from unsampled tags. Moreover, we compress the sparse vector through arithmetic coding, which significantly reduces its transmission time. Both theoretical analysis and simulation results demonstrate that ACS outperforms existing solutions in time efficiency.