Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiclass MRI Brain Tumour Classification with Deep Transfer Learning

A brain tumour is a dangerous form of cancer that happens when cells divide in an abnormal way. Recent advances in deep learning have helped the medical imaging sector in the diagnosis of numerous diseases. This paper presents Multiclass MRI Brain Tumour Classification with Deep Transfer Learning. In the proposed model, VGG-16 is employed as a deep transfer learning model. The dataset is collected from the Kaggle brain tumour MRI dataset, which is a combination of three popular brain tumour datasets such as figshare, SARTAJ, and Br35H datasets. The data are prepossessed by rescaling and random brightness and/or contrast by ±20% before applying to the modified VGG-16 model. The proposed model employs minimum computational resources and achieves better results in accuracy, precision, recall, and F1 score.
Radiative signature of increasing atmospheric carbon dioxide in HIRS satellite observations

We examine the cause and implications of decadal trends observed in the High‐Resolution Infrared Radiation Sounder (HIRS) CO2 channels over the period 1980–1999 in conjunction with Microwave Sounding Unit (MSU) O2 channel measurements and climate model simulations. Simulated HIRS brightness temperatures (TBs) from the climate model with constant carbon dioxide indicate a global warming trend (∼0.2 K/decade) consistent with the corresponding MSU observations. By contrast, simulations with observed time‐varying CO2 concentration result in a global decreasing TB trend (∼−0.3 K/decade), indicating a greater influence of increased CO2 on the HIRS radiances compared to the tropospheric warming. Moreover, comparison between observation and simulations suggests that there may be important regional variations in the CO2 trends, particularly over the northern hemisphere midlatitudes downstream from major CO2 sources (U.S. and China). Thus the HIRS CO2 radiances may provide one opportunity to supplement existing surface networks by constraining regional patterns of decadal CO2 variability.
Research on Image Recognition of Coal and Gangue Based on Improved ResNet

To improve the recognition accuracy of coal and gangue images while avoiding complex model structures, this paper builds an improved convolutional neural network based on residual networks and applies it to the study of coal and gangue image recognition. Firstly, we perform various image enhancement processes on the acquired image data to enrich the experimental samples. Secondly, we analyze the features of the Ghost module and coordinate attention mechanism (CA), combining their advantages to propose coordinate attention augmenting the Ghost module. Then, the proposed module is used instead of convolution operation to build our recognition model according to the underlying network structure. Through comparative analysis, the number of parameters and floating point of operations (FLOPs) of the model are decreased by 46.9% and 46.29%, respectively, in comparison to the basic model, and the prediction accuracy can reach 97.97%. Finally, the model is contrasted with the commonly used convolutional neural networks further to confirm its efficacy. The results demonstrate that the accuracy of the proposed model is increased by up to 2.19% compared to the mainstream networks and that the model's parameters and FLOPs count have been decreased by 89.82% and 93.6%, respectively. The average predicted time for a single picture of coal and gangue is 9.76ms. Therefore, this study has high recognition accuracy and fast inference efficiency for coal and gangue, which can provide effective help for the separation of coal and gangue and has certain practical application value.
Fast Prototyping of Distributed Stream Processing Applications with stream2gym

Stream processing applications have been widely adopted due to real-time data analytics demands, e.g., fraud detection, video analytics, IoT applications. Unfortunately, prototyping and testing these applications is still a cumbersome process for developers that usually requires an expensive testbed and deep multi-disciplinary expertise, including in areas such as networking, distributed systems, and data engineering. As a result, it takes a long time to deploy stream processing applications into production and yet users face several correctness and performance issues. In this paper, we present stream2gym, a tool for the fast prototyping of large-scale distributed stream processing applications. stream2gym builds on Mininet, a widely adopted network emulation platform, and provides a high-level interface to enable developers to easily test their applications under various operating conditions. We demonstrate the benefits of stream2gym by prototyping and testing several applications as well as reproducing key findings from prior research work in video analytics and network traffic monitoring. Moreover, we show stream2gym presents accurate results compared to a hardware testbed while consuming a small amount of resources (enough to be supported in a single commodity laptop even when emulating a dozen of processing nodes).
A High-Order Heuristic Fuzzy Time Series Forecasting Model Based on Hedge Algebras Approach

Nowadays, forecasting models are increasingly being developed and studied a lot to serve various areas of science and public life. One of all is fuzzy time series forecasting models have been developed and are booming because they have been proven to give very good forecasting results. This paper presents a high-order fuzzy time series forecasting model based on a hedge algebras approach. The results of the model have been evaluated for performance through comparison with other models and the effectiveness of the model is proven. The proposed model will serve as reference material, as well as the basis for other research in this area.
Evaluation of the Variability of the Maximum Expected Field Strengths in an MRI Room

This paper defines confidence intervals for the variability of the maximum expected field strengths, at the susceptible frequencies of 1.5T, 3T, and 7T MRI systems. Measurements of the maximum electric field were performed in a 30 m3 MRI room. Based on the absolute values of the measurements, an analysis of the undermoded and overmoded properties of the room is presented. Then, the relation between the expected electric field, created by equipment characterized by its TRP, is empirically described. This relation is mainly characterized by the confidence interval of the maximum expected field strength. This confidence interval is a powerful tool to assess the severity of EMC risks.
Mutual Control of Interleaved and Single Phase DCDC Converters for Improved Light Load Efficiency and Peak Power Increase

The paper describes design and control of bi- directional interleaved GaN transistors-based DC-DC converter combined with a lower power single phase DCDC converter for supercapacitor energy storage integration into DC microgrid. The implementation of GaN transistors allows to increase switching frequency and reduce size of magnetic components thus decreasing size of converter, but at the same time the loss in discontinuous mode is high due to higher voltage drop into reverse conduction mode. In the paper it is proposed mutual control strategy of both converters to extend boundary operation region of interleaved converter thus avoiding current control loop in each phase. The paper describes proposed control method. Proposed algorithm has been implemented into Matlab Simulink simulation model. Simulation results shows that the discontinuous operation of the control method simulation, and conduction mode of the interleaved converter is avoided. The experimental results show measured efficiency of the converter working into boundary conduction mode in a range from 96 to 98.5 percent.
Parameter Selection Method for Accuracy Optimization of Time-Grating Displacement Sensor Based on Orthogonal Experimental Design

Time-grating displacement sensors have been widely used in various application fields such as machineries. , automobiles. , aerospace. , and defense with their merits of high accuracy. , reliability. , and low cost. However. , the problem of over-reliance on experience during the product parameter design process without scientific advisory was exposed during the time-grating sensor manufacturing process. In addition. , it brought about the problems of production efficiency decline due to delayed design and experimental processes. This paper discusses the influence of different stages on the optimum parameters of a Time-grating displacement sensor. By using the orthogonal experiments and the intuitive analysis of the experiment findings. , this paper aims to provide reliable and effective strategies for the component design of a Time-grating displacement sensor. The influence of different parameters on the sensor's accuracy was studied by establishing a sensor model and using finite element simulation software experiments. The experimental results show that other parameters correlate with accuracy. , among which pole number and excitation signal significantly impact sensor accuracy. The research work carried out in this paper can provide a basis for various parameter designs in the production process and has a certain reference value for the optimal design of time-grating sensors.
Differentially Private Two-Party Top-$k$ Frequent Item Mining

Various data analysis tasks take frequent item mining as an essential part of them. Thus, it is crucial to design practical privacy-preserving frequent item mining methods such that private data analysis can be enabled. For two-party data analysis tasks, each party possesses its portion of the data and is reluctant to share the data with the other. Although secure computation can enable two-party frequent item mining, the output of exact top-$k$ items may still leave the adversary a chance to infer the sensitive information. Differential privacy has been utilized in various data analysis tasks to protect participating individuals, but how to provide differential privacy for two-party frequent item mining has not been addressed. To prevent each party's data from being leaked to the other while achieving differential privacy for releasing the output, we study the problem of differentially private two-party frequent item mining in this paper. We have proposed a practical protocol that can efficiently select the frequent items of the union of two confidential databases in a differentially private way but does not need to combine all the elements. We have conducted extensive experiments to evaluate the proposed method on real-world datasets, and the results show that it can achieve satisfactory utility with affordable overheads.
Exploring Deep Learning-Based MRI Radiomics for Brain Tumor Prognosis and Diagnosis

Advancements in deep learning (DL) techniques have brought a ray of hope in the battle against brain tumors by significantly improving their detection and segmentation in MRI scans. This research paper delves into recent studies that have introduced innovative DL models for brain tumor diagnosis and segmentation, achieving exceptional levels of accuracy, precision, recall, and AUC values. The VGG16, VGG19 and Hybrid-DANet architectures, among others, have emerged as beacons of promise, enabling automated tumor delineation and MR image segmentation. The integration of DL algorithms in clinical practice not only enhances surgical and radiotherapy planning but also plays a vital role in survival prediction and tumor subtyping. The discoveries of this study reveals the extraordinary capabilities of DL techniques in elevating brain tumor detection and segmentation accuracy, sensitivity, specificity, and F1-score. It highlights the power of embracing hyperparameter tuning to unlock optimal performance and redefine the landscape of brain tumor diagnosis and treatment. This profound progress instills a sense of warmth and optimism, as DL paves the way for more personalized and effective care for individuals battling brain tumors. With every step forward, DL empowers healthcare professionals and instills hope in patients and their loved ones, offering a brighter future where early detection and precise tumor segmentation become cornerstones of compassionate care.
Adaptively Secure Random Beacons for Ungrindable Blockchains

We describe and analyze a simple protocol for $n$ parties that implements a randomness beacon: a sequence of high entropy values, continuously emitted at regular intervals, with sub-linear communication per value. The algorithm can tolerate a $(1-\epsilon)/2$ fraction of the $n$ players to be controlled by an adaptive adversary that may deviate arbitrarily from the protocol. The randomness mechanism relies on verifiable random functions (VRF), modeled as random functions, and effectively stretches an initial $\lambda$-bit seed to an arbitrarily long public sequence so that (i) with overwhelming probability in k-the security parameter-each beacon value has high min-entropy conditioned on the full history of the algorithm, and (ii) the total work and communication required per value is $O(k)$ cryptographic operations. The protocol can be directly applied to provide a qualitative improvement in the security of several proof-of-stake blockchain algorithms, rendering them safe from “grinding” attacks.
Sparse Linear Arrays of Vector Antennas for Fourth-Order Direction Finding

Sparse linear arrays such as nested arrays, coprime arrays, and minimum redundancy arrays (of omnidirectional or vector antennas) have reduced inter-antenna mutual coupling as compared to the uniform linear arrays. However, there still exist at least one antenna pair in these arrays whose spacing is less than or equal to the half of signal wavelength. To reduce further the inter-antenna mutual coupling, a new sparse linear array of vector antennas (VAs) has been designed in the context of fourth-order (FO) cumulant based direction finding of statistically independent narrowband non-Gaussian signals. The minimum inter-VA spacing of the array is constrained to be no less than one signal wavelength while synthesizing a virtual linear VA array with maximum contiguous aperture. The FO direction finding scheme for the designed array has also been presented. The effectiveness of the designed array in reducing inter-VA mutual coupling and achieving unambiguous FO direction finding is validated by using both simulated and real-world data.
Systematic analysis of occurrence of equatorial noise emissions using 10 years of data from the Cluster mission

We report results of a systematic analysis of equatorial noise (EN) emissions which are also known as fast magnetosonic waves. EN occurs in the vicinity of the geomagnetic equator at frequencies between the local proton cyclotron frequency and the lower hybrid frequency. Our analysis is based on the data collected by the Spatio‐Temporal Analysis of Field Fluctuations–Spectrum Analyzer instruments on board the four Cluster spacecraft. The data set covers the period from January 2001 to December 2010. We have developed selection criteria for the visual identification of these emissions, and we have compiled a list of more than 2000 events identified during the analyzed time period. The evolution of the Cluster orbit enables us to investigate a large range of McIlwain's parameter from about L∼1.1 to L∼10. We demonstrate that EN can occur at almost all analyzed L shells. However, the occurrence rate is very low (<6%) at L shells below L=2.5 and above L=8.5. EN mostly occurs between L=3 and L=5.5, and within 7° of the geomagnetic equator, reaching 40% occurrence rate. This rate further increases to more than 60% under geomagnetically disturbed conditions. Analysis of occurrence rates as a function of magnetic local time (MLT) shows strong variations outside of the plasmasphere (with a peak around 15 MLT), while the occurrence rate inside the plasmasphere is almost independent on MLT. This is consistent with the hypothesis that EN is generated in the afternoon sector of the plasmapause region and propagates both inward and outward.
Runtime Adaptation Framework for Fulfilling Availability and Continuity Requirements of Network Services

The Network Function Virtualization (NFV) framework is an enabler for the automation of Network Service (NS) management. In the context of NFV, an NS is realized by interconnecting Virtual Network Functions (VNF) using Virtual Links (VL). Availability and continuity are among the important characteristics of an NS. These characteristics depend on the availability of the VNFs and VLs composing the NS, which are usually selected at NS design time. VNFs and VLs utilize the resources of the underlying infrastructure, and their availability (partially) depends on the availability of these resources. To design an NS to fulfill availability and continuity requirements, the availability required from the resources is constrained at design time. However, the characteristics of these resources may change at runtime due to the dynamicity of NFV infrastructure. Thus, impacting the availability of the VNFs and the VLs, which in turn may impact the availability and continuity of the NS. To fulfill these requirements at runtime despite the changes in the infrastructure, the NS should be adapted. In this paper, we propose a framework for the runtime adaptation of NSs that reacts to changes and adapts the NS configuration so that it can fulfill its availability and continuity requirements during the NS lifetime. We also propose a method to develop machine learning models that are used within the framework to determine the required adjustments at runtime. We implemented the proposed framework, the method for developing the machine learning models, a testbed, and NSs to assess the feasibility and validity of our approach through experiments.
SDN-based Port Hopping Technique for Mitigating Network Attacks

Software Defined-Network (SDN) scheme has recently proven to be efficient in resisting different attack vectors which plague the Internet of Things (IoT) and other networks. Logically, the SDN utilizes port hopping as an active defense mechanisms through consolidated regulation and network programmable characteristics for the mitigation of network attacks such as Denial-of-Service (Dos), port scanning attempts, and other kinds of reconnaissance attacks in a network. However, existing port hopping techniques are limited in terms of the inability to completely mask the service port and the use of extra hardware capabilities which apparently results in overhead. To address these issues, this paper proposes a port hopping technique based on masked communication services (PHCSS) which not only have the capacity of detecting and clarifying malevolent data packets more efficiently as compared to existing port hopping techniques but also reduce the cost of port hopping for the protected server, and resist both DoS, internal attacks, and port scanning attempts. Results of our experiments suggest that the approach can successfully secure a network from both port scanning and DoS attacks, while not overwhelming excessive resources on the SDN controller.
Knee Osteoarthritis Detection And Classification Using Transfer Learning

Knee osteoarthritis (OA) is a common disease that causes pain, stiffness, and decreased mobility by wearing down the cartilage between the bones in the knee joint. Early and automatic detection of knee OA is important for ensuring effective treatment for patients. The aim of this article is to develop a deep learning model using ResNet50 architecture for the accurate classification of knee osteoarthritis (OA) from images of X-rays originally labeled using the Kellgren–Lawrence (KL) Scale. The dataset used in the current study involves images taken from 4796 participants of ages between 45 and 79 years, with approximately nine thousand knee X-ray images. The ResNet50 model is trained on this dataset, and various evaluation metrics were used to evaluate its performance. A flask-based web application was also developed for using the trained model. Our model achieved a high degree of accuracy (97.4%) in classifying knee OA, suggesting that it could be a valuable tool for diagnosing this condition quickly and accurately.
Error-Exponent of Distributed Hypothesis Testing for Gilbert-Elliot Source Models

This paper considers Distributed Hypothesis Testing (DHT), in which a source X is encoded given that a side information Y is available only to the decoder. The decoder’s task is to make a decision between two hypothesis $\mathcal{H}_{0}$ and $\mathcal{H}_{1}$ related to the joint probability distribution of X and Y. While most works on DHT have adopted an information-theoretic approach by providing generic error exponents for the decision error, we focus on a specific and practical source model: the Gilbert-Elliot (GE) model. The later is a two-states hidden Markov model which describes time-varying correlation between X and Y. Starting from the generic error exponents, we develop a method for numerically estimating the error exponent for the GE model. We provide numerical results to evaluate the impact of the model parameters on the error exponents and explore the tradeoff between two types of error events, namely the testing error and the binning error.
Numerical Filtering of Electric Field in Human Head Models Exposed to High-Frequency EMF

This paper is on the use of a hybrid boundary element method/finite element method (BEM/FEM) to determine the induced electric field in several human head models exposed to high-frequency electromagnetic field (EMF). The models including the homogeneous and non-homogeneous spherical and anatomically detailed one, respectively, are irradiated by plane EM wave at several frequencies of interest, including 3500 MHz pertaining to 5G communication systems but also 6000 MHz representing the transition frequency in the EMF safety standards. The numerical results for the induced electric field along propagation axis are then post-processed using a numerical filtering technique in order to obtain the signal envelope. The results show that envelope can be subsequently utilized to correlate the exponentially decaying electric field with calculated penetration depth at the high frequency of interest.
Automatic Characterization of a Fully-Digital Doppler Cancellation Technique for Local Ultra-Stable Frequency Dissemination

SummaryWe demonstrate a fiber link with instability in the 10-18 range for optical frequency dissemination. It relies on a fully-digital Doppler cancellation platform that makes a novel use of aliasing to generate signals above Nyquist frequency. Furthermore, we present an automatic method to measure the disturbance rejection of the system without a direct comparison between the input and the output of the fiber link. Finally, we have investigated the low-frequency noise at the Red Pitaya output.