Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of road network extraction from High Resolution Images

Study related to high resolution remote sensing (RS) images has been applicable to many areas that are beneficial to us for information related to territory, public, weather and agriculture, the main aim of RS applications is extract appropriate information that will help us to draw meaningful conclusions. Road network extraction is an important and challenging research field, road networks are essential for humans as they provide transportation and other support systems. Distinct factors like sensor, weather, resolution and light etc., can affect the road features from a RS image which imposes the problems in road network extraction. This paper presents a comprehensive analysis of various aspects of road network extraction from RS images like road features, problems in road network extraction and finally different road network methods are classified on the basis of local and global features, automation and algorithm used. Different road extraction techniques are compared on the basis of features used in extraction, number and type of data (aerial, hyper-spectral, remote sensing, urban and semi-urban) used, performance on the basis of different qualitative parameters and the advantage and disadvantages of different methods are discussed. The comparative analysis of road extraction methods is presented emphatically and it is observable that in order to obtain precise road network from RS images only one type of feature is not sufficient. Hence, multiple road features should be combined together but it depends on type of application and data. Road network extraction from RS image is still remains a challenging and important research field.
A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning

The rapid adoption of mobile devices has dramatically changed the access to various networking services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted features, and frequent features update. With the excellent ability of automatic feature learning, Deep Learning (DL) undoubtedly becomes a highly desirable approach for mobile services traffic classification, especially encrypted traffic. This survey paper looks at emerging research into the application of DL methods to encrypted traffic classification of mobile services and presents a general framework of DL-based mobile encrypted traffic classification. Moreover, we review most of the recent existing work according to dataset selection, model input design, and model architecture. Furthermore, we propose some noteworthy issues and challenges about DL-based mobile services traffic classification.
Baseline Estimation Using DEM Prior Knowledge and Capability Analysis for Gaofen-3 Repeat-Pass SAR Interferometry

In this paper, the study on the digital elevation model (DEM) reconstruction using Gaofen-3 satellite repeat-pass synthetic aperture radar (SAR) interferometry is presented. The coherence of the single-look complex (SLC) images and residuals of interferogram provide a powerful guarantee for interferometric processing for Gaofen-3. Baseline estimation is the key factor to generate accurate DEM with SAR interferometry. This paper uses the coarse orbit data and the precise orbit data of Gaofen-3 to carry on the baseline estimation and uses the prior coarse DEM information to refine the baseline estimation. Then, the topographic height is reconstructed with more precise spatial baseline. An experiment is conducted to verify the conclusion, and some mountain and flatten region are chosen as the study area. The results show that the DEM errors based on the prior DEM method are rapidly converged compared with the ones based on the orbit data method. Therefore, benefiting from the precise orbit data of the Gaofen-3 satellite, the accuracy of the baseline estimation can be improved further by using the method based on prior DEM information; thus, higher accuracy of DEM can be achieved so that Gaofen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, and high correlation, which has a good repeat-pass SAR interferometry application prospect.
A Novel Neutrosophic Data Analytic Hierarchy Process for Multi-Criteria Decision Making Method: A Case Study in Kuala Lumpur Stock Exchange

This paper proposes a multi-criteria decision making method called the neutrosophic data analytic hierarchy process (NDAHP) for the single-valued neutrosophic set (SVNS). This method is an extension of the neutrosophic analytic hierarchy process (NAHP) designed to handle actual datasets which consist of crisp values. The proposed NDAHP method uses an objective weighting mechanism whereas all other existing versions of the AHP, fuzzy AHP, and other fuzzy based AHP method in literature such as the NAHP and picture fuzzy AHP uses a subjective weighting mechanism to arrive at the decision. This makes the proposed NDAHP method effective as the weightage of the criteria which forms the input of the evaluation matrix are determined in an objective manner using actual data collected for the problem, and hence will not change according to the opinions of different decision makers which are subjective. The proposed NDAHP method is applied to a multi-criteria decision making problem related to the ranking of the financial performance of five public listed petrochemical companies trading in the main board of the Kuala Lumpur Stock Exchange (KLSE). Actual dataset of 15 financial indices for the five petrochemical companies for 2017 obtained from Yahoo! Finance was used in this paper. Following this, a brief comparative study is conducted to evaluate the performance of our NDAHP algorithm against the results of other existing SVNS-based decision making methods in the literature. The results are compared against actual results obtained from KLSE. To further verify the rankings obtained through each method, the Spearman and Pearson ranking tests are carried out on each of the decision making methods that are studied. It is proved that NDAHP produces the most accurate results, and this was further verified from the results of the Spearman and Pearson ranking tests.
Design and Analysis of an Interleaved Flyback–Forward Boost Converter With the Current Autobalance Characteristic

An interleaved flyback-forward boost converter is proposed in this paper. The switched capacitors are used to realize the controllable voltage source function and balance the currents of the interleaved two phases without an additional current-sharing module. Voltage lift function of the switched capacitor also reduces the turns ratio of the transformers and alleviates the voltage stresses of the output diodes. As the transformers operate in flyback and forward modes alternately, part of the output energy is stored in the switched capacitors; therefore, the size of magnetic components can be reduced. Since the current falling rates of the clamping and output diodes, when they turn off, are controlled by the leakage inductance, the diode reverse-recovery problem is alleviated. Active clamp circuits are applied for the interleaved two phases to recycle the leakage energy and absorb the voltage spikes caused by the leakage inductance. The experimental results based on a 1 kW 48 to 380 V dc/dc prototype verify the effectiveness of the theoretical analysis.
Numerical Solution to Nonlinear Biochemical Reaction Model Using Hybrid Polynomial Basis Differential Evolution Technique

In this paper, we present an approximate numerical solution to the well known Michaelis-Menten nonlinear biochemical reaction system using a stochastic technique based on hybrid polynomial basis evolutionary computing. The approximate solution is expanded as a linear combination of polynomial basis with unknown parameters. The system of nonlinear differential equation is transformed into an equivalent global error minimization problem. A trial solution is formulated using a fitness function with unknown parameters. Two popular evolutionary algorithms such as Genetic algorithm (GA) and Differential evolution (DE) are used to solve the minimization problem and to obtain the unknown parameters. The effectiveness of the proposed technique is demonstrated in contrast with fourth-order Runge Kutta method (RK-4) and some well known standard methods including homotopy perturbation method (HPM), variational iteration method (VIM), differential transform method (DTM), and modified Picard iteration method (Picard-Pade). The comparisons of numerical results validate the efficacy and viability of the suggested technique. The results are found to be in sharp agreement with RK-4 compared to some popular standard methods.
Association between Deposition of Beta-Amyloid and Pathological Prion Protein in Sporadic Creutzfeldt-Jakob Disease

Background: Alzheimer’s disease (AD) and prion diseases such as sporadic Creutzfeldt-Jakob disease (sCJD) share common features concerning their molecular pathogenesis and neuropathological presentation and the coexistence of AD and CJD in patients suggest an association between the deposition of the proteolytically processed form of the amyloid precursor protein, β-amyloid (Aβ), which deposits in AD, and the abnormal form of the prion protein, PrPSc, which deposits in sCJD. Methods: We have characterized sCJD patients (n = 14), AD patients (n = 5) and nondemented controls (n = 5) with respect to the deposition of PrPSc and Aβ morphologically, biochemically and genetically and correlated these findings to clinical data. Results: sCJD-diseased individuals with abundant deposits of Aβ present with a specific clinicopathological profile, defined by higher age at disease onset, long disease duration, a genetic profile and only minimal amounts of PrPSc in the cerebellum. Conclusion: The co-occurrence of pathological changes typical for sCJD and AD in combination with the inverse association between accumulation of Aβ and PrPSc in a subgroup of sCJD patients is indicative of common pathways involved in the generation or clearance of Aβ and PrPSc in a subgroup of sCJD patients.
Between Commodification and Engagement: On the Double-Edged Impact of User-Generated Metadata within the Cultural Heritage Sector

This article challenges common assumptions and opinions regarding the use of the social web by cultural heritage institutions by framing the phenomenon of user-generated metadata within the larger context of the commodification and the engagement process of our cultural heritage. Theoretical reflections on both the negative and positive long-term outcomes of the social web for libraries, archives, and museums are presented and confronted with empirical observations regarding the use of social tagging and user comments. This combination of a theoretical and an empirical approach will provide original insights into the long-term implications of user-generated metadata for cultural heritage institutions.
3D Hyper-Dense Connected Convolutional Neural Network for Brain Tumor Segmentation

Glioma is one of the most widespread and intense forms of primary brain tumors. Accurate subcortical brain segmentation is essential in the evaluation of gliomas which helps to monitor the growth of gliomas and assists in the assessment of medication effects. Manual segmentation is needed a lot of human resources on Magnetic Resonance Imaging (MRI) data. Deep learning methods have become a powerful tool to learn features automatically in medical imaging applications including brain tissue segmentation, liver segmentation, and brain tumor segmentation. The shape of gliomas, structure, and location are different among individual patients, and it is a challenge to developing a model. In this paper, 3D hyper-dense Convolutional Neural Network(Cnn)is developed to segment tumors, in which it captures the global and local contextual information from two scales of global and local patches along with the two scales of receptive field. Densely connected blocks are used to exploit the benefit of a CNN to boost the model segmentation performance in Enhancing Tumor (ET), Non-Enhancing Tumor (NET), and Peritumoral Edema (PE). This dense architecture adopts 3D Fully Convolutional Network (FCN) architecture that is used for end-to-end volumetric prediction. The dense connectivity can offer a chance of deep supervision and improve gradient flow information in the learning process. The network is trained hierarchically based on global and local patches. In this scenario, the both patches are processed in their separate path, and dense connections happen not only between same path layers but also between different path layers. Our approach is validated on the BraTS 2018 dataset with the dice-score of 0.87, 0.81 and 0.84 for the complete tumor, enhancing tumor, and tumor core respectively. These outcomes are very close to the reported state-of-the-art results, and our approach is preferable to present 3D-based approaches when it comes to compactness, time and parameter efficiency on MRI brain tumor segmentation.
Children's Recollections of a Historic House Visit: Recall of Experiences and Use of Cognitive Tools

ABSTRACT This descriptive study explored the historic site visitor experience through children's recollections of their visits. Following a class field trip to a historic house, 22 third-grade students wrote reflective journal entries that detailed their experiences during the visit, a data collection method designed to focus on the authentic voices of children. After coding the journals using systematic content analysis, the data showed that participants recalled a variety of experiences, including narratives, objects and rooms/areas, on-site activities, affective responses, and physical sensory information. Participants used cognitive tools, such as description, analysis, and different types of responses to narratives, to create individual interpretations from their experiences. The consistency of the data suggests that educators and those conducting visitor studies take advantage of children's abilities to recall experiences and use cognitive tools to encourage further engagement with content and deeper discussion or response.
A Mobile Application for Early Detection of Melanoma by Image Processing Algorithms

Melanoma is the most dangerous skin cancer which causes many deaths annually. However, early detection can help treat it. For accurate detection of melanoma, dermatologists use biopsy which is usually associated with pain, time and cost. With the advancement of technology and the development of smartphones, many mobile applications have been designed for early detection of melanoma. Although they are fast in the detection of melanoma and save time and money, they are not as accurate as the biopsy. In this paper, the authors proposed an application for early detection of melanoma using image processing methods and pattern recognition algorithms by Android Studio software, Java programming language, and the OpenCV library. All detection steps were carried out using the Android smartphone. For better performance in the classification step, in addition to the smartphone, a computer was also used. This application is user-friendly and the calculated Accuracy, Sensitivity, and Specificity are 95%, 98%, and 92.19% on average, respectively. It should be noted that these results are more reliable when the lesions are geometrically distinct.
Testing the Implementation of Concurrent AUTOSAR Drivers Against Architecture Decisions

Concurrent software based on a shared-memory model is predominant in industrial applications that cannot afford to execute complex message-passing libraries. However, direct access to shared memory creates implicit dependencies between concurrently executing components. Therefore, the development and maintenance of such software is hard. In this paper, we argue the need to manage, at the architectural level, the implicitly high coupling between concurrent components that share memory. We suggest an approach that verifies architectural specifications against the implementation and finds potential mismatches. While static analysis approaches can be complete and verify all possible mismatches, they are often imprecise, leading to a large number of false warnings, especially in concurrent software. Instead, we built our approach, using dynamic analysis, on top of one of the most well-known algorithms for detecting data races, Eraser Lockset, and extended its model to support features required for the verification process. Since Lockset operates on the execution traces, test cases that produce these traces must ensure proper coverage. Therefore, we argue the need to use test cases conforming to the strict modified condi-tion/decision coverage criteria (MC/DC). Our version of Lockset takes advantage of the fact that possible shared memory locations are known in advance. We further improved its precision by considering atomic operations as a synchronization mechanism. The approach was evaluated on industrial AUTOSAR drivers that execute concurrently.
ROI and Non-ROI based Medical Image Compression Techniques: A Survey and Comparative Review

Due to the advancement in the field of information technology, medical image processing has become the most eminent image processing field in this era. The medical image needs to be stored and transmitted for telemedicine purpose. Due to the huge size of medical images, it becomes very difficult to store and transmit it over the network. Image compression is a significant toolto reduce the redundancies and irrelevant informationof the image for efficient archival and fast transmission. The paper provides the state-of-the-art survey fornon-ROI (i.e. lossless) and ROI based (i.e. hybrid region of interest)techniques available in the area of medical image compression. The paper will provide help for researchers to understand the different techniques of medical image compression with theircomparative analysis for further research in this area.
Study on the Efficiency Evaluation System of Distribution Communication Collaboration Network

Distribution network itself has a different from other network characteristics, the geographical distribution of the vast number of equipment is extremely large, the type of equipment varies, incomplete data and the actual change is not synchronized and other issues makes the distribution network maintenance, management, troubleshooting difficulty straight up, but with the rapid growth of distribution network users in various regions. The current manual maintenance work even more stretched. Under such circumstances, the construction of distribution network automation system becomes an effective way to improve the reliability of distribution network , the maintenance and management efficiency. Based on the analysis of the typical service data model of smart distribution network and its demand for network communication capacity to establish a data model. A network performance evaluation model system is designed to evaluate the effectiveness of the model.
Information Technology and Boundary of the Firm: Evidence from Plant-Level Data *

We study the relationship between different margins of information technology (IT) use and vertical integration using plant-level data from the U.S. Census of Manufactures. Focusing on the short-run decision of whether to allocate production output to downstream plants within the same firm or to external customers, we find that customer-focused IT, by itself, has surprisingly little impact. In contrast, adoption of upstream supplier-focused IT at a plant is associated with a significant decline in downstream vertical integration. However, the greatest decline in within-firm transfers occurs when supplier- and customer-facing IT are adopted together, suggesting the presence of complementarities in supply chain technology adoption. These results are consistent with the view that, by reducing external coordination costs, IT investments promote a decline in plant-level vertical integration, but only when those investments are made jointly with both suppliers and customers. Our results provide less support for the view that IT investments led to a decline in vertical integration by lowering transactions risks.
Spatially Adaptive Retina-Like Sampling Method for Imaging LiDAR

To mitigate the conflict between imaging quality and speed, a spatially adaptive retina-like sampling method for 3-D imaging Lidar based on time-of-flight method is proposed. The differences between previous retina-like sampling method and the proposed method are described. Sampling points with dense distribution is for the area of interest while sparse distribution is for the area of uninterest, which obtains high imaging quality while consuming much less data acquisition time. Mathematical models of the spatially adaptive retina-like method are developed, and the key parameters are analyzed. To validate the spatially adaptive retina-like sampling method, we perform situational simulations to compare the proposed method with the previous one. Results demonstrate that the proposed method is capable of decreasing data acquisition time without considerable distortion of the interested target. Furthermore, the proposed method is analyzed under different scenes for single and multiple targets. Results illustrate that the proposed method performs better than the previous method.
A comparative study of classifiers used in facial embedding classification

Face recognition, recently, has been a fast and effective method of authentication with the advent of deep learning and powerful hardware. This paper investigates different classifiers used in classifying facial embeddings and evaluates their performance. The paper also focuses on an easily deployable pipeline for face recognition using Python which can be used to develop a face recognition system on portable low-power hardware devices. The methodology discussed uses pretrained models and frameworks which results in state-of-the-art performance without the need of any powerful hardware. The proposed methodology achieves an F1 score of 0. 9947with an AUC score of 0.9997 on LFW dataset.
Flyback Converter Controlled by Model-Based Current MPPT for a Photovoltaic Power System

A model is derived to estimate the primary-side inductor current of a discontinuous current mode Flyback converter. The model is validated by comparing the current estimated by the model to the current sensed using a sense resistor. The model was applied in a photovoltaic (PV) system which interfaced a PV module to a battery via a Flyback converter in order to charge a 12V lead acid battery. The system successfully controlled power flow from the PV module according to a model-based current perturb and observe (P&O) maximum power point tracking (MPPT) algorithm.