Miracle-dz/UniFAR-FTUs huggingface dataset:
A Portable and Less Time Consuming Wireless Biometric Attendance System for Academic Purpose Using NodeMCU Microcontroller

In this paper, a portable wireless biometric attendance system for academic purpose has been designed and developed using fingerprint sensor and NodeMCU microcontroller. One of the cardinal features of this system is the portability of this system and also the reduction of time consumption compared to the conventional stationary wired/wireless systems where students have to stand in serials and wait for his/her turn which costs good amount of time during a class period. The smaller size of our system has enabled the students to pass the device from one to another without interrupting the class lecture and saves valuable time of the class lecturer which is generally a fixed period of time. All the data of a particular student are stored in a database when a student's finger is registered for a particular institute and after that whenever the student enrolls, his/her attendance is counted automatically and stored in the destined database wirelessly. The attendance marks based on attendance percentages of the students are automatically calculated and uploaded on the webpage. So whenever a semester finishes the course teacher can get percentage information as well as the corresponding attendance marks of all the students without having a headache of it in the entire semester. Our system also notifies the user if any student is absent for 10 successive classes (as per Govt. rule in Bangladesh, course teacher has to inform authority if any student is absent for 10 days successively).
Design of a Novel Parallel-Hybrid-Excited Dual-PM Machine Based on Armature Harmonics Diversity for Electric Vehicle Propulsion

A novel parallel-hybrid dual permanent magnet (PM) machine topology with both enhanced torque density and improved flux weakening capability is proposed for electric vehicle propulsion in this paper. The key is to artificially construct the harmonics diversity with a single-layer concentrated armature winding in the stator and thus simultaneously couple the armature field with the excitation field produced by slot PM, rotor PM, and stator dc source, respectively. Enhanced torque density is obtained in this new topology due to the symmetrical flux-modulation effect between dual PM sources. Moreover, benefiting from a parallel excitation characteristic of hybrid magnetic circuit, the demagnetization risk is significantly mitigated for slot PM source and thus an extended flux weakening range is obtained. In this paper, the proposed new topology and its design mechanism are investigated, along with its electromagnetic performance evaluated based on finite-element analysis. Further, some leading design parameters are analyzed and determined to provide a design guideline for the proposed new topology. Finally, a prototype is built and fully tested. Relevant experimental results agree well with the finite-element predication.
Revenue Maximization in Cloud Federation Based on Multi-Choice Multidimensional Knapsack Problem

Cloud federation is introduced to eliminate the resource limitation problem of individual Cloud Service Provider (CSP). In a federation, one CSP can outsource their overhead requests by hiring unused resources from other CSPs in the federation and CSPs cash greater revenue. In this paper, we propose a system that maximizes this revenue while putting some factor constraints in satisfactory standard. Factors include response time of instances that is analogous to Quality of Service (QoS), and profit that maintains a certain reputation. Response time is reduced by considering geographical location of the user and the data center of desired resources. In this system, CSPs choose suitable resources from resource pool of cloud federation that maximizes the profit while preserving Quality of Service (QoS) high and minimizing the rejection rate of user requests. We map factor constraints to MMKP (Multi-choice Multidimensional Knapsack Problem) algorithm and define objective function for revenue maximization. We calculate response time by Cloud Analyst to measure the QoS. Experimental results show that the proposed system can effectively increases QoS level by reducing response time and rejection requests in a cloud federation that eventually increases the revenue of the CSP.
DeepClean: Data Cleaning via Question Asking

As one critical task in the data analysis pipeline, data cleaning is notoriously human labor-intensive and error-prone. Knowledge base-assisted data cleaning has proved a powerful tool for finding and fixing data defects; however, its applicability is inevitably bounded by the natural limitations of knowledge bases. Meanwhile, although a vast number of knowledge sources exist in the form of free-text corpora (e.g., Wikipedia), transforming them into formats usable by existing data cleaning tools can be prohibitively costly and error-prone, if not at all impossible. Here, we present DeepClean, the first end-to-end data cleaning framework powered by free-text knowledge sources. At a high level, DeepClean leverages a knowledge source through its question-answering (QA) interface and achieves high-quality cleaning via iterative question asking. Specifically, DeepClean detects and repairs data defects in three stages: (i) Pattern extraction - it automatically discovers the semantic types of the data attributes as well as their correlations; (ii) Question generation - it translates each data tuple into a minimal set of validation questions; (iii) Completion and repair - by checking the answers returned by the knowledge source against the data values, it identifies erroneous cases and suggests possible fixes. Through extensive empirical studies, we demonstrate that DeepClean is applicable to a range of domains, and can effectively repair a variety of data defects, highlighting data cleaning powered by free-text knowledge sources as a promising direction for future research.
MixDir: Scalable Bayesian Clustering for High-Dimensional Categorical Data

Multivariate analysis of high-dimensional datasets with multiple categorical variables (e.g. surveys, questionnaires) is a challenging task but can reveal patterns of responses that are masked from univariate analyses. In this paper we propose a novel variational inference algorithm to cluster high-dimensional categorical observations into latent classes. Variational inference is an approximate Bayesian inference algorithm, which combines fast optimization methods with the ability to propagate the uncertainty to the clustering (soft clustering). The model is robust to misspecification of the number of latent classes and can infer a reasonable number from the data. We assess the performance on synthetic and real world data and show that our algorithm has similar performance to the best other tested method if the correct number of classes is known and outperforms the other methods if it the number of classes needs to be inferred. An R-package implementing our algorithm is available at the Comprehensive R Archive Network
Enhanced CLWPR Protocol with Social Properties Exploiting Physical Proximity in VANETs

Opportunistic mobile networks have attracted a lot of attention and several routing protocols have been proposed. However, most algorithms do not consider the importance of users' properties of human mobility in physical world. Given this fact, in this work we enhance CLWPR, a cross-layer optimized position-based routing protocol for urban VANET environments, combining social properties and users' individual mobility traces in order to design an efficient content dissemination scheme. We introduce an offline social metric (OSM), which is based on physical proximity and opportunistic contacts of nodes. Our proposed protocol favors nodes with close offline social relationship as next forwarder nodes. We present a comprehensive study of the performance of our enhanced CLWPR protocol routing against other routing protocols in distributed vehicular networks using realistic representative urban scenarios with synthetic and real traffic.
HYBRID SIMULATION CHALLENGES AND OPPORTUNITIES: A LIFE-CYCLE APPROACH

The last 10 years have witnessed a marked upsurge of attention on Hybrid Simulation (HS). The majority of authors define HS as a joint modelling approach which includes two or more simulation approaches (mainly Discrete Event Simulation, System Dynamics and Agent Based Simulation). Whilst some may argue that HS has been in existence for more than 5 decades, the recent rise tended to be more problem driven rather than technical experimentation. Winter Simulation Conference (WSC) 2015, 2016, 2017 have witnessed 3 panels on the purpose, history and definition of HS, respectively. This paper reports on a comprehensive review conducted by the panelists on HS and its applications. The aim of the paper is to move the debate forward by exploring potential platforms for developing concrete avenues for research on HS in conjunction with the modelling life cycle.
Adaptive Power Transformer Lifetime Predictions Through Machine Learning and Uncertainty Modeling in Nuclear Power Plants

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the prediction accuracy among a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a particle filtering framework to improve thermal modeling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant.
Modeling Cost Versus Flexibility in Optical Transport Networks

Optical transport networks are progressively being designed around reconfigurability. Operators require an infrastructure capable of carrying large amounts of data, but also able to deliver that data when and where needed. While centralized control plane architectures based on software-defined networking are pushing flexibility in automation and interoperability, many of these objectives are reliant on a hardware-enabled flexible data plane. At the same time, reduced operating margins imply that capacity planning through resource overprovisioning is increasingly unsustainable. Therefore, a natural tradeoff between cost and flexibility emerges in many aspects of optical transport network architectures. More complex and reconfigurable hardware, such as flexible-rate transponders or switching fabrics, can be pitted against cheaper purpose-built modules as contending alternatives for incrementally deploying networks. The added value of adaptability to changing conditions must be evaluated against its potential upfront cost and capacity overprovisioning risk. In this context, technoeconomic analysis based on multiperiod capacity optimization plays a pivotal role in identifying the target network scenarios for fixed and flexible hardware. In this paper, use cases where the cost/flexibility tradeoff emerges in optical transport scenarios are identified, such as in the design of line cards and multilayer grooming architectures, and multiperiod optimization frameworks based on integer linear programming (ILP) models are presented. More generally, this paper also discusses scalability issues affecting the use of ILPs for multiperiod capacity optimization, and proposes some simple design and modeling guidelines to help overcome them. These include either reformulating the models themselves, or identifying specific subproblems within the global framework that can be offloaded without undermining the validity of the results.
Alternative Approach to Analysis and Design of Series Resonant Converter at Steady State

In this paper, new steady-state analysis is proposed for a series resonant converter (SRC) that provides closed-form expressions for converter waveforms. Using the proposed analysis, explicit equations are obtained to design the converter components. It is shown that an SRC can be modeled through a nonlinear differential equation with discontinuous inputs. A Laplace-based theorem (LBT) is provided to obtain the steady-state analytic solution of the resonant converter differential equations. Using the LBT, a flowchart is proposed to analyze the converter where the nonlinearity of differential equation is removed by defining intermediate variables. The variable duty ratio SRC is analyzed using the proposed flowchart. Accurate, closed-form, and explicit equations for converter waveforms, voltage gain, current phase lag, zero voltage switching, and discontinuous conduction mode boundaries are derived. The proposed analysis is compared with conventional methods and its accuracy is validated through simulations and experimental results. Moreover, using the proposed method, a novel procedure is provided for the optimal design of the converter and is compared with conventional design approaches.
A Low-Profile and High-Gain CP Patch Antenna With Improved AR Bandwidth Via Perturbed Ring Resonator

A low-profile circularly polarized (CP) patch antenna with improved axial-ratio (AR) bandwidth and high gain is proposed in this letter. The antenna consists of a pin-loaded patch and a probe-fed ring resonator beneath the patch radiator. On the one hand, the radiative patch dominates the radiation, and the loading of shorting pins facilitates its high gain due to enlarged radiating area at resonance. On the other hand, the ring resonator with perturbation produces two additional split degenerate modes, which not only widen the bandwidth of magnitude, but also generate the required quadrature phase shift for CP radiation. The working mechanism is presented with an equivalent circuit model, allowing us to easily demonstrate how the ring resonator and patch radiator work together to effectively widen AR bandwidth. With the proposed method, stable high gain of about 11 dBic has been realized over an AR bandwidth of 6% for a single-fed CP patch antenna with profile of 0. 05λ0 at 2.33 GHz.
Denial of Service Attack Generator in Apache JMeter

Cyber attacks are currently a major threat to individuals as well as businesses or institutions. Denial of service (DoS) attacks are very common cyber attacks that focuses on availability of network services or network devices. This paper deals with developing of new modules of DoS attacks and network traffic generator into Apache JMeter that is designed to load test functional behavior and measure performance. Modules developed together with Appache JMeter tool can test a network infrastructure and find weak localities prone to DoS cyber attacks. One goal of this paper is to compare tools for stress testing of web servers and services. The network generator module of DoS attacks allows stress testing of networks based on the Transmission Control Protocol/Internet Protocol (TCP/.IP). With using the generator developed it is possible to test the availability of network devices when exposure to DoS attacks. Selected cyber attacks targeting on the availability of network resources (DoS attacks) that can test a resistance of a network infrastructure are described in the article. DoS attacks described have been implemented in the Apache JMeter tool together with the design and the development of the network generator of DoS attacks. New modules can be used to test a resistance of network infrastructure. Finally, the measured values using the generator developed are presented and discussed.
A New Urban Functional Regions Minig Method with MPETM

Urban functional regions mining plays an important role in Smart Cities. At present, the main approaches to deal with this problem are probabilistic topic models. Some researchers also use deep learning methods to solve this problem. In this paper, we present a new method for urban functional regions mining called MPETM which combines deep learning and probabilistic topic models. Experiment results show that the MPETM framework has superior accuracy than other current popular methods.
Soil Classification Using Machine Learning Methods and Crop Suggestion Based on Soil Series

Soil is an important ingredient of agriculture. There are several kinds of soil. Each type of soil can have different kinds of features and different kinds of crops grow on different types of soils. We need to know the features and characteristics of various soil types to understand which crops grow better in certain soil types. Machine learning techniques can be helpful in this case. In recent years, it is progressed a lot. Machine learning is still an emerging and challenging research field in agricultural data analysis. In this paper, we have proposed a model that can predict soil series with land type and according to prediction it can suggest suitable crops. Several machine learning algorithms such as weighted k-Nearest Neighbor (k-NN), Bagged Trees, and Gaussian kernel based Support Vector Machines (SVM) are used for soil classification. Experimental results show that the proposed SVM based method performs better than many existing methods.
A New Approach for Selecting Aggregated Multicast Trees to Reduce Forwarding States

Multicast is an efficient method for sending data to multiple destinations in a single transmission. Conventional multicast routing often suffers from scalability issues. Here, the number of forwarding states maintained in the Network layer generally increases with the number of concurrently active multicast groups. Consequently, large-size routing tables get generated, which in turn causes degradation in router performance. There exist only a limited number of studies on aggregated multicast to address this issue. The existing studies such as AM (Aggregated Multicast), STA (Scalable Tree Aggregation for Multicast), and STS (Shared-Tree Selection for Aggregated Multicast) attempt to perform tree aggregation, however, still retain high number of forwarding states in the routers. Besides, they mostly ignore the effect of tree aggregation ratio in terms of network performance metrics (for example delay and throughput). Hence, the research on aggregated multicast is still at an elementary stage. To this extent, in this study, we propose a novel aggregated multicast approach to reduce Network layer forwarding states. Our approach proposes new methods for selecting aggregated multicast trees, refining search range, and replacing stale trees in process of the aggregation. We show the effect of our proposed tree aggregation in terms of network performance metrics (delay and throughput). We also show that performance of our approach is better than previous approaches by comparing different performance metrics such as the number of trees, the number of forwarding states, delay, and throughput. Ns-3 simulation results confirm that our approach can reduce up to 92% forwarding states and 34% delay compared to conventional multicast, and reduce up to 88% forwarding states and 29% delay compared to the STS method.
Light Field Spatial Super-resolution via CNN Guided by A Single High-resolution RGB Image

Light field (LF) has proven to be promising in immersive representation of the real world. However, a major limitation of micro-lens array based LF camera is the low spatial resolution, due to the inherent trade-off between angular and spatial dimensions. In this paper, we propose a framework to show that a single high-resolution (HR) RGB image effectively improves the performance of LF spatial super-resolution. We adopt an end-to-end convolutional neural network, which takes a low-resolution (LR) light field image (LFI) and a single HR center view as inputs. The LFI provides the information about LF structure in angular domain, while the HR center view provides more details in spatial domain. Experimental results on 57 test LFIs with various challenging natural scenes demonstrate that our algorithm outperforms current state-of-the-art methods.
An Ontology-Based Approach to Semi-Automate Systematic Literature Reviews

A Systematic Literature Review (SLR) allows us to combine and analyze data from multiple (published and unpublished) studies. Though it provides a complete and comprehensive empirical evidence of an area of interest, the results we usually get from the data synthesis phase of an SLR include huge tables and graphs and thus, for users, it is a tedious and time-consuming job to get the required results. In this work, we propose to semi-automate some steps which can be used to fetch the information from an SLR, beyond the traditional tables, graphs, and plots. The automation is performed using Semantic Web technologies like ontology, Jena API and SPARQL queries. The Semantic Web, also called Web 3.0, provides a common framework and thus allows us to share and re-use the data across the applications and enterprises. It can be used to integrate, extract, and infer the most relevant data required by the users, which are hidden behind the huge information on the Web. We also provide an easy-to-use user interface in order to allow users to perform different searches and find their required SLR results easily and quickly. Finally, we present the results of a preliminary user study performed to analyze the amount of time users need to extract their required information, both via the SLR tables and our proposal. The results revealed that with our system the users get their required information in less time compared to the manual system.
Prediction of Introduced Distortions Parameters in Lossy Image Compression

The paper addresses a problem of predicting parameters of distortion introduced by lossy image compression. Such a prediction is needed to produce a desired quality of compressed images quickly. It is shown that mean square error and peak signal-to-noise ratio can be predicted with high accuracy if images subject to compression are practically noise-free an with appropriate accuracy if images subject to compression are noisy. Approaches to improve accuracy of parameter prediction in compressing of noisy images are proposed and compared. Practical recommendations are given.
A Novel Design and Control to Improve Positioning Precision and Robustness for a Planar Maglev System

In this paper, we have proposed a novel 6-DOF magnetic levitation (maglev) system to improve the robustness and upgrade positioning precision. The design concept attempts to keep the good performance in the whole journey of moving rather than the point-to-point positioning precision. Furthermore, we endeavor to develop this system with an expectable large moving range. Based on these concepts, we built the force model that considers the variation from the displacement to the magnetic forces first, and avoids the constraint of the attractive levitation in replacing the repulsive levitation. Finally, we adopt the concept of relative place to build the measuring system. All of the performance of the improved framework is demonstrated in the experimental results.