bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of Machine Learning Approaches in Health Care Sector to The Diagnosis of Breast Cancer
Authors: Wassim Ahrou , Elarbi Elalaouy , Khadija Rhoulami 
Published: April 2022

Abstract
Breast cancer (BC) is a kind of malignant disease that represents the primary reason of women’s death around the world, cancer cells form tumors which lead to weakening the functioning of the immune system. If the main risk factors are known and detected correctly, the cure rate becomes higher, and the inappropriate treatments which are the main cause of death will be avoided. Today, several avenues for advancing breast cancer classification research are being studied, in particular to strengthen screening and develop an early diagnosis plan. The purpose of this paper is to approach the unfolding of machine learning techniques in the clinical field to categorize and discriminate patients between malignant and benign groups. Modeling of cytological characteristics based on machine learning is proposed to improve predictive performance. In this work, three proposed algorithms of machine learning techniques have been used for the analyze and classification of Wisconsin breast cancer database, k Nearest Neighbors (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM). We will compare learning metrics of both, using train/test split and cross validation. The obtained results shows that KNN offers the best accuracy (97.07%), NB classifier (94.15 %) and SVM classifier (94.73%).
Penetration Multilayer Overload Signal Generation Based on TransGAN
Authors: Fang Anqi , Li Rong 
Published: April 2022

Abstract
With the booming development of modern electronic technology, artificial intelligence in the military field is showing a fast-rising trend, but at the same time, it is also facing the dilemma of a serious shortage of data. To address the current problems of low number and unbalanced categories of penetration multi-layer overload signals, a TransGAN-based penetration multi-layer overload signal generation method is proposed in combination with the fruitful neural network model of deep learning. Firstly, the dataset is built using overload signals that have been tested in practice at the range. Secondly, generator and discriminator are constructed. Generator borrowing the model structure of TransGAN, which consists of Transformer Encoder for learning the feature mapping of the overload dataset, and discriminator using a simple attention mechanism to reduce the complexity of the model. Finally, the generator and discriminator are trained and optimized using a generative adversarial network to achieve the penetration multilayer overload signal. Finally, the generative adversarial network is utilized to train and optimize the generator and discriminator to achieve penetration multilayer overload data generation. Experimental results show that the method can generate effective overload data with a different number of layers, which can address the issue of the lack of penetration multilayer overload signals to a certain extent.
Lightweight and Effective Human Pose Estimation Model Based on Multi-Angle Knowledge Distillation
Authors: Li Hua 
Published: April 2022

Abstract
In the field of human pose estimation, most of the existing methods focus on improving the generalization performance of the model, while ignoring the significant efficiency issues. This leads to an increasing amount of model parameters and needs to take up more and more computing resources, which greatly reduces the practical value of the model. In order to solve this problem, we propose a novel lightweight network structure called Effective and Lightweight Pose Network (ELPN). At the same time, for the sake of alleviating the difficulty of lightweight model training, we propose a Multi-Angle Pose Distillation (MAPD) model training method that can more effectively train particularly small pose network models. In quantitative experiments, our models have excellent performance on two mainstream benchmark datasets: the MPII and the COCO. In qualitative testing, our models can accurately locate the keypoints of complex human movements. These fully demonstrates the efficiency and effectiveness of our methods. Our models have the characteristics of high precision, small size and fast inference speed. It is a cost-effective model with greater practical value.
Holographic Study of Non-Stationary Surface Deformations by the Method of Combined Exposure of Interferograms
Authors: Selivanov Yu M , Dzyuba A P , Klymenko S V , Shevchenko V V 
Published: April 2022

Abstract
The article presents a method of holographic research of non-stationary deformations of a surface by method of two exposures, one of which is carried out in its static state, the other in motion. The derivation of formulas for quantitative analysis of the obtained interferograms for a number of typical cases of surface motion is given, as well as a general algorithm for determining the displacements from the interferogram. The features and results of approbation of the method are given. It is shown that the method is very broad in the application of holographic interferometry. Its application makes it possible to obtain highly informative qualitative and quantitative data on the field of unstable displacements of the test element at any moment of its deformation, using for this purpose low-power continuous-wave lasers (instead of expensive high-power sources) and, accordingly, relatively inexpensive and compact holographic measuring devices.
Electromagnetic Waves’ Dynamic Simulation in a Confined Space Under Limiting Conditions
Authors: Shovhelia D , Sokolova N 
Published: April 2022

Abstract
This study aims at finding the most advantageous algorithm for dynamic simulation of the propagation of electromagnetic waves inside buildings. The method is based on fast estimation of Wi-Fi signal strength when passing obstacle within the building and the modified Smart3D algorithm. A software application for dynamic simulation of the best wireless network coverage under restrictive conditions was developed in Autodesk® Revit® environment. The application calculates Wi-Fi signal attenuation of buildings, estimates position of signal sources to get the best network coverage.
PharmaLab: A Tool to Study the Drug Action on the Mouse Ventricular Myocyte
Authors: Camargo M L A , Goroso D G , Bissaco M A S , Silva R R 
Published: April 2022

Abstract
Mathematical and computational modeling has been a great ally in the development of new drugs. It helps by providing preliminary results that ultimately guide the path that the tests should take. Thus, the objective of this work was to develop the PharmaLab computational tool, which simulates the pharmacological action in the mouse ventricular myocyte using the mathematical model of Mullins & Bondarenko (2013). Having as one of the main differentials the integration with a model for the force of contraction, at PharmaLab it is possible to perform pharmacological tests by changing parameters of the mathematical model and registering drugs to be used in the simulations. The computational tool has its own interface for plotting results and support material for educational use. As a demonstration of the tool, a validation was performed, comparing simulations with experimental data for the drug Niferidil and two “in silico” tests: SS-68 correcting the “Catecholaminergic Polymorphic Ventricular Tachycardia” (CPVT) and Niferidil correcting the Short QT Syndrome (SQTS). The drugs showed good results in the correction of arrhythmias. Finally, it can be concluded that the PharmaLab computational tool has resources for use in both research and teaching.
Physics Concept Space: An Application of Fischer’s Thought Space
Authors: Chekuri N R 
Published: April 2022

Abstract
A Physics Concept Space is constructed using Fischer’s Thought Space, a general thought space that explains how individuals continually construct thoughts and modify them through conscious actions on events (Aes) in Physical Sociocultural Environments (PSCEs) from birth throughout the life span. Thought Developmental Equations (TDE) that operate on the physics concept space take two physics concepts and construct new concepts using the operations augmented-intercoordination (⊗), compounding (⊕), and shift of focus (<>). Diffisimliar metric dp
, a measure for the number of similar, different, and non-diffesimilable properties and an individual’s comprehension of thoughts at a given time, reveals the development and connectedness of the concept space through the life span. At any given time, only the part of the space associated with actions (Aes) is active while they are being performed. No two concept spaces activated simultaneously or at two different times are the same, but the construction mechanism is the same. Granott’s transition process and the dp
explain the underlying cognitive process. Furthermore, several properties of physics concept space such as near and far connectedness, continuum space, bases, subspaces, multiplicity, product spaces, and dynamic nature, along with the skills required to construct physics space, are discussed. The properties of the space show that it is a highly connected space, supporting a tenet of the epistemology of physics.
Motion of Mass Source in Stratified Fluid
Authors: Baydulov Vasiliy G , Knyazkov Dmitri , Shamaev Alexey S 
Published: April 2022

Abstract
The paper considers the problem of determining properties of an underwater moving source by analyzing the perturbation that it creates in electromagnetic or hydrodynamic fields. The computer program has been developed to simulate the spatial propagation of gravitational waves from the mass source moving along an arbitrary trajectory in a stratified fluid. The calculation results are in good agreement with analytical results obtained in the far-field approximation and with the results of experiments on the flow around underwater obstacles, moreover, the proposed technique allows to simulate any arbitrary motion of the source. Two new approaches to solving the inverse problem of determining the characteristics of the source of disturbances are proposed. The first one is based on the analysis of the signal received by radio-sensors, that scan the surface of the ocean. The second one uses data obtained from sensors installed directly in the water column.
Organizing Large Data Sets for Efficient Analyses on HPC Systems
Authors: Gu Junmin , Davis Philip , Eisenhauer Greg , Godoy William , Huebl Axel , Klasky Scott , Parashar Manish , Podhorszki Norbert , Poeschel Franz , Vay JeanLuc , Wan Lipeng , Wang Ruonan , Wu Kesheng 
Published: April 2022

Abstract
Upcoming exascale applications could introduce significant data management challenges due to their large sizes, dynamic work distribution, and involvement of accelerators such as graphical processing units, GPUs. In this work, we explore the performance of reading and writing operations involving one such scientific application on two different supercomputers. Our tests showed that the Adaptable Input and Output System, ADIOS, was able to achieve speeds over 1TB/s, a significant fraction of the peak I/O performance on Summit. We also demonstrated the querying functionality in ADIOS could effectively support common selective data analysis operations, such as conditional histograms. In tests, this query mechanism was able to reduce the execution time by a factor of five. More importantly, ADIOS data management framework allows us to achieve these performance improvements with only a minimal amount of coding effort.
Research on Electromagnetic Interference of Lightning Current on Secondary Cable in Substation
Authors: Liu Shengjun , Yin Yanzeng , Qi Hongbin , Wang Xuejia 
Published: April 2022

Abstract
Secondary cable is an important equipment for ubiquitous power Internet of things to transmit information. When the substation grounding grid is struck by lightning, part of the lightning current flows through the shielding layer of secondary cable and produces core-to-sheath potential difference, which seriously threatens the safety of the secondary system. Fourier transform was used to decompose the lightning current in frequency domain, and the core-to-sheath potential difference at the end of the secondary cable was obtained by calculating the shielding layer current and transfer impedance at each frequency component. The effects of soil resistivity, lightning current waveform, distance between lightning stroke point and cable and grounding grid on the core-to-skin potential difference of secondary cable were studied. It is shown that the core-to-sheath potential difference of secondary cable increases with the increase of soil resistivity, the peak value, and the tail time of lightning current. The core-to-sheath potential difference decreases with the increase of the distance, while the core-to-sheath potential difference increases with the increase of conductor spacing of the grounding grid. The core-to-sheath potential difference with copper grounding grid is smaller than that with steel grounding grid.
Influence of Normalization Techniques on Multi-criteria Decision-making Methods
Authors: Mhlanga Sandile Thamie , Lall Manoj 
Published: April 2022

Abstract
One of the most significant steps in solving multi criteria decision-making (MCDM) problems is the normalization of the decision matrix. The consideration for the normalization of the data in a judgment matrix is an essential step as it can influence the ranking list. This study investigates the effects of normalization on an AHP-VIKOR hybrid method in the selection of Web services. The Web services considered in this research offer similar functionalities but with different Quality of Services (QoS). For the purpose of this study, ten web services were selected. Each of these services were evaluated against five difference QoS requirements namely, response time, throughput rate, latency, availability, and reliability to determine the most appropriate Web services. The five normalization techniques employed are linear sum, linear max, linear max-min, enhanced accuracy and vector normalization. It was observed that different ranking lists are produced when applying different normalization techniques to the VIKOR method and normalization has an influence on the final ranking list.
Forensic Analysis of the IoT Operating System Ubuntu Core
Authors: Castelo Gómez Juan Manuel , Roldán Gómez José , Martínez José Luis Martínez , Amo Mínguez Álvaro del 
Published: April 2022

Abstract
The number of cyber incidents in which an Internet of Things (IoT) device or system is present is increasing every day, requiring the opening of forensic investigations that can shed light on what has occurred. In order to be able to provide investigators with proper solutions for performing complete and efficient examinations in this new environment, IoT systems and devices are being studied from a forensic perspective so that tools and procedures can be designed accordingly. In this article, the IoT version of one of the most used Linux distributions, namely Ubuntu, is studied to determine in what way a forensic investigation of this system should be performed, detailing how to approach the acquisition and analysis phases. In addition, both the volatile and non-volatile artifacts that might held useful information are listed and described.
Scale-Adaptive Selection Network with Dynamic Focal IoU Loss
Authors: Xu Wenxiong , Yin Jun , Sun Zepei , Wang Keyang , Shao Ming 
Published: April 2022

Abstract
Feature pyramid network is wildly applied to predict the targets at different scales in many one-stage detectors. However, most of them treat all feature levels without level-wise attention. As a result, different levels may capture the same object and predict totally different labels, which lead to lower AP on evaluation. Besides, many classic detectors aim at relieving the imbalance between hard and easy samples on classification task, while we find the fact that hard-easy imbalance also exists in localization. To general detection task, bboxes regressed at high IoU are much less than those at low IoU so that detector can easily be biased towards suboptimal bboxes and localization accuracy will be harmed. Based on these two points, in this paper, we propose a Scale-Adaptive Selection Network (SASNet) with a novel Dynamic Focal IoU (DF-IOU) loss. The Scale-Adaptive Selection Network introduces multi-scale attention mechanism into feature pyramid so as to assign attention weight for feature maps on each level, which enables the network to select the dominant levels for prediction and alleviate the prediction conflicts between different levels. Furthermore, we design Dynamic Focal IoU loss to increase loss contribution of easy targets so that the coordinates of these easy targets can regress better and the bounding box will fit tighter. Our experimental results show that our SASNet with DF-IoU loss can increase average precision of objects at small, medium and large scale on the MS COCO and CCPD dataset.
A Trusted IoT Communication Architecture Based on Blockchain and Named Data Network
Authors: Sun Yizhe , Chen Shiyou , Fang Yadong , Xu Wei , Luo Qun , Rui Lanlan 
Published: April 2022

Abstract
The trusted cross-domain sharing capability of the Internet of Things (IoT) terminals needs to be improved urgently. Aiming at the technical issues of routing overhead and authentication security of data sharing of IoT devices, this paper proposes a trusted transmission architecture for network-chain collaboration. Introduce the mobile edge computing (MEC) platform in named data network (NDN) to deploy blockchain light nodes and improve the PBFT consensus algorithm. Besides, a cross-domain authentication algorithm based on elliptic curve encryption is proposed. Simulation results show that the improved PBFT and cross-domain authentication algorithm can effectively improve IoT cross-domain sharing capabilities.
Control of a Semi-autonomous Powered Wheelchair
Authors: Sanders D , Tewkesbury G , Haddad M , Kyberd P , Zhou S , Langner M 
Published: April 2022

Abstract
A model reference controller for a powered wheelchair is described. The chair is fitted with sensor systems to assist a disabled user with steering their chair. The controller can cope with varying circumstances and situations. Non-linear terms are compensated for using an adaptive and automatic scheme. Consistent and dependable veer-controll is considered and the system was able to deal with uncertainties, for example changing surfaces, different shifting weights of users, hills, bumps, slopes and differences in tires and wheels. The controller has a quasi-linear closed-loop behaviour and that means that extra outer control loops can be appended later, for example path-following algorithms. An assistive agent was also created so that sperate wheelchairs will be able to communicate with each other in the future.
Adaptive Cruise Control of Virtual Coupled Trains Based on Sliding Mode
Authors: Wang Di , Cao Yuan 
Published: April 2022

Abstract
In this paper, the adaptive cruise control method for the virtually coupled train set (VCTS) in rail transit is proposed. First of all, a leader-following VCTS dynamics model is constructed by analyzing the dynamic evolution of VCTS in a real-world environment, taking into account uncertain parameters, unknown disturbances and controller input saturation. Next, to cope with the position and speed constraints, the position artificial potential field function and the speed barrier function are embedded into the designed sliding manifold. And a novel adaptive cruise control protocol is designed, which can simultaneously deal with uncertain resistance parameters and unknown disturbances while tracking the desired cruise speed and maintaining the desired distance. Based on Lyapunov stability theory, the proposed control protocol guarantees the ultimate boundedness of all subsystems. Finally, the simulation results verify the effectiveness of the theoretical analysis.
A Comparative Study: The Benefits of a Novel Illuminance Calculation Method over Luminance Calculation Method for Optimal Roadway Lighting Design Applications
Authors: Thurairajah U 
Published: April 2022

Abstract
This paper discusses the challenges and impacts of the luminance calculation method for roadway lighting applications and recommends a novel illuminance calculation method as the most suitable for Roadway Lighting Applications. This change would reduce the costs of roadway lighting design without increasing risks, which will benefit society and the profession. Luminance calculations are complicated and take approximately five times longer than the new illuminance calculation method, provide no additional insight, and result in the same design outcome. This paper makes a comparative study and analyzes both methods using regular comparative assessment and quantitative and qualitative assessments and provides a solution to the over 50-year-old challenge. The quantitative evaluation uses a sample case study and examines its corresponding benefit-cost ratio. The qualitative approach is to complete a survey among the peers and the lighting designers. This is the first paper to address all these parameters of roadway lighting holistically. This paper will be helpful for academics, researchers, scientists, engineers, consultants, architects, lighting designers, contractors, developers, financial institutions, and government agencies funding outdoor lighting.
Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection
Authors: Faham Ali Zaidi Syed , Xu Longting 
Published: April 2022

Abstract
The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It’s verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there’s a demand for feature selection algorithms that are computationally effective and sensitive to feature interactions so that useful features aren’t falsely excluded during the ranking process. We experiment on the ASVspoof 2019 challenge for the assessment of spoofing countermeasures. After the evaluation of our given algorithms and data gives us an equal error rate (EER) and tandem discovery cost function (t-DCF) values. Experimental results on ASVspoof 2019 physical access referring to multiple feature selection approaches show a breakthrough compared to the baseline.