Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspect Dependence of Parametric Instability Excitation by X-mode at EISCAT

Recent studies of X-mode wave heating experiments observed by EISCAT (European Incoherent Scatter Scientific Association) UHF radar indicate that parametric instability (PI) can be excited unexpectedly. In this paper, we re-investigate the ionospheric heating experiment on October 21, 2012 in details. The observed HF-enhanced ion lines (HFILs) and HF-enhanced plasma lines (HFPLs) observed during the experiment presents dependence on the heating incidence angles. The observational evidence and numerical calculation that the parallel electric field of X-mode heating wave can exceed the PI excitation threshold at certain angles indicate the decisive role of parallel electric field for the PI excitation. The excited HFPLs and HFILs during X-mode heating cycles also present the evidence for the non-Maxwellian distribution of the electrons in the heated region.
Open and Short Circuit Fault detection in Alternators using the rectified DC output voltage

The alternator in automotive systems with built-in rectifier can be subjected to various open circuit and short circuit faults. These faults result in harmonics in the rectified output voltage from the alternator. Most of the methods for fault diagnosis in AC machines and its power electronics require measurements using expensive sensors for current, temperature, torque etc., other methods have high computational requirements and therefore are not suitable for real time applications. The paper discusses an approach to detect the electrical faults by identifying the fault harmonics in the rectified output voltage of the alternator. In the paper, the effectiveness of this technique is demonstrated in fault detection and its severity in both simulation and practical measurements.
Modality Classification and Concept Detection in Medical Images Using Deep Transfer Learning

Medical image classification and concept detection are two important tasks for efficient and robust medical retrieval systems and also help with downstream tasks such as knowledge discovery, medical report generation, medical question answering, and clinical decision making. We investigate the effectiveness of transfer learning on the modality classification task using state-of-the-art deep convolutional neural networks pretrained on generic images. We also compare the performance of the traditional pipeline of handcrafted features with multi-label learning algorithms with end-to-end deep learning features for the concept detection task. Experimental results on the modality classification task show that transfer learning can leverage the patterns learned from large training data to the medical domain where little labeled data is available. Moreover, results on the concept detection task show that the deep learning approach provides better and more powerful feature representations compared to handcrafted feature extraction methods. The results on both tasks suggest that deep transfer learning methods are effective in the medical domain where data is scarce.
Maximum Correntropy Criterion-Based Sparse Subspace Learning for Unsupervised Feature Selection

High-dimensional data contain not only redundancy but also noises produced by the sensors. These noises are usually non-Gaussian distributed. The metrics based on Euclidean distance are not suitable for these situations in general. In order to select the useful features and combat the adverse effects of the noises simultaneously, a robust sparse subspace learning method in unsupervised scenario is proposed in this paper based on the maximum correntropy criterion that shows strong robustness against outliers. Furthermore, an iterative strategy based on half quadratic and an accelerated block coordinate update is proposed. The convergence analysis of the proposed method is also carried out to ensure the convergence to a reliable solution. Extensive experiments are conducted on real-world data sets to show that the new method can filter out the outliers and outperform several state-of-the-art unsupervised feature selection methods.
Adaptive Weighted Deformable Part Model for Object Detection

We describe an adaptive weighted deformable part model for object detection based on traditional deformable part model(DPM). In the original DPM model, we find that the high response score region calculated by the template filter as high-energy regions, which indicates that the influence on the detection results is greater. The parts can affect the results of object detection, some important parts may directly determine the accuracy of the results, and some unimportant parts even produce bad impacts. To reduce the adverse effects caused by unimportant part filter, we add an adaptive coefficient strategy to the traditional method, which could improve the accuracy of object detection without efficiency loss. The proposed algorithm is better in accuracy compared with the traditional deformable part model, especially in the case of occlusion, with the same efficiency.
Viral Cascade Probability Estimation and Maximization in Diffusion Networks

People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a <italic>viral cascade</italic>? If so, can we find a set of users that are more likely to trigger viral cascades? These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient <bold>vi</bold>ral <bold>c</bold>ascade probability <bold>e</bold>stimation method, <sc>ViCE</sc>, that leverages an special <italic>importance sampling</italic> approximation to achieve high accuracy, even in the cases of very <italic>small probability</italic> of influence. We then show that the most influential nodes in this model is NP-hard, and develop an efficient <bold>vi</bold>ral <bold>c</bold>ascade probability <bold>m</bold>aximization method, <sc>ViCEM</sc>, that maximizes a surrogate submodular function using a greedy algorithm. Experiments on both synthetic and real-world data show that <sc>ViCE</sc> can accurately estimate viral cascade probabilities using fewer samples than naive sampling by at least two orders of magnitude, and also <sc>ViCEM</sc> finds a set of users with higher viral cascade probability than alternatives. Additionally, experiments show that these algorithms are robust across different network topologies.
Control of DC Motor Using Genetic Algorithm Based PID Controller

The paper deals with the optimized tuning of P.I. D controller which is used to control the D C Motor. In this research work different PID tunings techniques are discussed.D.C Motor due to its very simple regulated characteristics is used in this paper. Due to this fact D.C. Motors are widely used in many industrial applications like steel mills, electric trains and many more. Here G.A-P.I. D (Genetic Algorithm – P.I.D) with different performance indices namely Mean Square Error (M.S.E) and Integral of Time multiplied by Absolute Error (I.TA.E) optimization technique is compared with standard P.I. D parameter adjustment technique i.e. Ziegler & Nichols method. Comparison of results are based on standard step responses parameters i.e. maximum overshoot, steady state and rise time.
Programming Situational Mobile Web Applications with Cloud-Mobile Convergence: An Internetware-Oriented Approach

Mobile Web applications (a.k.a., Web apps) stand for an important trend for next-generation Internet-based software. Currently popular mobile Web apps need to be adapted to various and ever-changing contexts and personalized user requirements. Based on our over-decade research experiences and practice on the Internetware paradigm, this position article describes an Internetware-oriented approach to designing, developing, and deploying situational mobile Web apps, by synthesizing the resources and services of mobile and cloud. Guided by a novel Service-Model-View-Controller (SMVC) software model, a mobile Web app is organized into a well-defined structure that facilitates adaptation including online/offline data access, computation offloading, user interface optimization, hybrid composition, etc. We provide efficient runtime support spanning mobile and cloud to make mobile Web apps more flexibly adaptive. The proof-of-concept evaluation demonstrates that our approach can benefit end-users with optimized user experience of mobile Web apps.
Game State Evaluation Heuristics in General Video Game Playing

In General Game Playing (GGP), artificial intelligence methods play a diverse set of games. The General Video Game AI Competition (GVGAI) is one of the most famous GGP competitions, where controllers measure their performance in games inspired by the Atari 2600 console. Here, the GVGAI framework is used. In games where the controller can perform simulations to develop its game plan, recognizing the chance of victory/defeat of the possible resulting states is an essential feature for decision making. In GVGAI, the creation of appropriate evaluation criteria is a challenge as the algorithm has no previous information regarding the game, such as win conditions and score rewards. We propose here the use of (i) avatar-related information provided by the game, (ii) spacial exploration encouraging and (iii) knowledge obtained during gameplay in order to enhance the evaluation of game states. Also, a penalization approach is adopted. A study is presented where these techniques are combined with two GVGAI algorithms, namely, Rolling Horizon Evolutionary Algorithm (RHEA) and Monte Carlo Tree Search (MCTS). Computational experiments are performed using 20 deterministic and stochastic games, and the results obtained by the proposed methods are compared to those found by their baseline techniques and other methods from the literature. We observed that the proposed techniques (i) presented a larger number of wins and F1-Scores than those found by their original versions and (ii) obtained competitive solutions when compared to those found by methods from the literature.
Optimal Design of Slotless PM Halbach Array Linear Generator for Wave Energy Converters at Maximum Power Transfer Condition

Linear generators have been proposed as a proper power takeoff system for direct drive wave energy conversion. Coupled directly to a reciprocating wave energy device, it was declared that linear generators could be a respected replacement for hydraulic and pneumatic systems. In this paper, slotless permanent magnet Halbach array linear generators is designed to extract the wave energy. The design of these generators is suitable for direct energy extraction from sea waves at small wave amplitude. The electromagnetic design is performed using finite element method FEM and the genetic algorithm GA as an optimization technique. In this paper, the parameters affect the maximum power extraction from wave is studied according to the hydrodynamics modeling of direct wave energy converters. Equivalent circuit based model is utilized to investigate the dynamic performance of the Halbach array linear generator according to maximum power transfer condition. The control strategy of the converter is explained to justify the maximum power transfer conditions.
SCIQ-CD: A Secure Scheme to Provide Confidentiality and Integrity of Query results for Cloud Databases

Database outsourcing introduces a new paradigm, called Database as a Service (DBaaS). Database Service Providers (DSPs) have the ability to host outsourced databases and provide efficient facilities for their users. However, the data and the execution of database queries are under the control of the DSP, which is not always a trusted authority. Therefore, our problem is to ensure the outsourced database security. To address this problem, we propose a Secure scheme to provide Confidentiality and Integrity of Query results for Cloud Databases (SCIQ-CD). The performance analysis shows that our proposed scheme is secure and efficient for practical deployment.
Analyzing Attitude of Second Screen Social Media Messages

We analyze more than 3,000,000 Twitter, 800,000 Instagram, and 50,000 Tumblr posts concerning a single major in-real-life event, Super Bowl XLIX, to determine attitude. We consider three event phases (<italic>Pre</italic>, <italic>During</italic>, and <italic>Post</italic>). Findings show link-based recommendations and undirected broadcast patterns positively correlate with attitude in the <italic>Pre</italic> and <italic>Post</italic> phases, respectively. The usage of these specific features highlights the differing information needs of viewers during these phases, specifically the sharing of information in the <italic>Pre</italic> phase and the sharing of opinions in the <italic>Post</italic> phase. The volume of postings indicates a negative attitude for all social media platforms, demonstrating that adverse information is more likely to be shared than positive information; this finding contradicts prior findings. This second screens phenomenon research is important in identifying the sharing information on multiple social media platforms during an in real life event.
Quadruple Cross-Coupled Latch-Based 10T and 12T SRAM Bit-Cell Designs for Highly Reliable Terrestrial Applications

In this paper, quadruple cross-coupled storage cells (QUCCE) 10T and 12T are proposed in 130 nm CMOS technology. The QUCCE 10T and 12T are about <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3.4\times $ </tex-math></inline-formula> the minimum critical charge of the conventional 6T, respectively. Compared with most of the considered state-of-the-art SRAM cells, both QUCCE 10T and 12T have comparable or better soft error tolerance, time performance, read static noise margins, and hold static noise margins, and besides, QUCCE 10T also has similar or lower costs in terms of area and leakage power. The QUCCE 10T is designed for high-density SRAMs at the nominal supply voltage. Furthermore, the QUCCE 12T saves more than 50% the read access time compared with most of the referential cells including the 6T, making it suitable for high speed SRAM designs, and it also has the best read margin, except for the traditional 8T, in terms of <inline-formula> <tex-math notation="LaTeX">$\mu /\sigma $ </tex-math></inline-formula> ratio in the near threshold voltage region among all the other considered cells which nearly have no write failure in that region. Hence, the QUCCE 12T is a promising candidate for future highly reliable terrestrial low-voltage applications.
Extended Topology for a Boost DC–DC Converter

In this paper, a new structure for a nonisolated boost dc–dc converter is proposed. The proposed converter generates higher voltage gain than some conventional nonisolated boost dc–dc converters. In this paper, the voltage and current equations of the elements and voltage gain in continuous conduction mode and discontinuous conduction mode are extracted. Then, the critical inductance converter is extracted and the current stresses in the switches are calculated. To achieve high voltage gain, a generalized structure based on the proposed structure generates for dc–dc converters. Meanwhile, the root mean square current relations of devices are obtained for an extended structure. Finally, the results of PSCAD/EMTDC software and laboratory prototype are used to reconfirm theoretical concept.
Latency and Alphabet Size in the Context of Multicast Network Coding

We study the relation between latency and alphabet size in the context of Multicast Network Coding. Given a graph $G =$ (${V}$, E) representing a communication network, a subset $S \subseteq V$ of sources, each of which initially holds a set of information messages, and a set $ T\subseteq V$ of terminals; we consider the problem in which one wishes to design a communication scheme that eventually allows all terminals to obtain all the messages held by the sources. In this study we assume that communication is performed in rounds, where in each round each network node may transmit a single (possibly encoded) information packet on any of its outgoing edges. The objective is to minimize the communication latency, i.e., number of communication rounds needed until all terminals have all the messages of the source nodes.For sufficiently large alphabet sizes (i.e., large block length, packet sizes), it is known that traditional linear multicast network coding techniques (such as random linear network coding)) minimize latency. In this work we seek to study the task of minimizing latency in the setting of limited alphabet sizes $(\mathrm {i}.\mathrm {e}.,$ finite block length), and alternatively, the task of minimizing the alphabet size in the setting of bounded latency. Through reductive arguments, we prove that it is NP-hard to (i) approximate (and in particular to determine) the minimum alphabet size given a latency constraint; (ii) to approximate (and in particular to determine) the minimum latency of communication schemes in the setting of limited alphabet sizes.
Design of Ka-band High Integrated Array Antenna with Wide Beam Width for Security Inspection

A broadband high integrated array antenna with wide beam width is proposed to realize the 3-dimensional imaging in the Ka-band active imaging system for security inspection. On the basis of the antenna element which is distinctively designed as a union of the open-ended waveguide and the microstrip feeding network, excellent radiating performance combined with compact structure would come true in comparison with traditional design scheme of the antenna element for security inspection. When aimed at high resolution and fast scanning speed, the array antenna adopts the high integrated arrangement scheme and takes full advantage of the polarization theory just as presented in this paper. Simulation results demonstrate that the array antenna possesses low VSWR of less than 1.5 and radiating gain of more than 6dB when the whole bandwidth of 26.5∼31.5GHz is covered. And the cross polarization level of the receiving element and the axial ratio (AR) of the transmitting element can achieve as low as −20dB and 3dB respectively in the whole half-power beam width (HPBW) in excess of 60 degrees, which approves its practicality in imaging area for security inspection.
Relationship between Renewable Energy Consumption and Sustainable Economic Growth: The Case of Turkey

Energy is a fundamental factor in increasing individual and social well-being. The sustainability of growth depends on the diversification of energy resources. Dependence on foreign energy, such as Turkey’s national income in countries with high employment, investment opportunities, environmental factors, and the importance of security of energy supply in areas such as renewable energy resource diversification is increasing with each passing day. In this study, by considering Turkey’s renewable energy potential, it aimed to analyze the relationship of economic growth-renewable energy consumption.According to the results of the analysis, all the variables except for GDP-GEO variables have reached to the result that they act together in the long run. In addition, a one-way causality relationship between GDP, WIND, SOLAR and HYDRO was established and it was determined that BIOMASS and HYDRO variables are positive and GEO variable has negative effect on GDP.
Broadside Scanning Asymmetric SIW LWA With Consistent Gain and Reduced Sidelobe

In this paper, a backward-to-forward continuous beam scanning leaky wave antenna is presented in substrate integrated waveguide technology. The antenna radiates from a continuous longitudinal slot etched on its broad wall. The slot is excited by using periodic H-plane steps. An impedance matched unit cell structure is used to suppress the open stopband in the broadside direction. Bloch wave analysis is used to obtain the propagation characteristics of the antenna. However, due to radiation being obtained only from the shunt-type radiating element, namely, the longitudinal slot, a gain dip is observed in the broadside direction. Transversal asymmetry is then introduced in the structure to eliminate the gain dip and obtain the consistent gain. A prototype of the antenna is fabricated and measured. Continuous beam scanning is achieved from −29º to +30º about the broadside direction with a gain variation of less than 2 dB over 8.0–12.4 GHz. A measured peak gain of 16.1 dBi is obtained. Key structural design parameters of the antenna are identified for controlling the leakage rate. Next, Taylor tapered aperture illumination is used to obtain low sidelobe level (SLL). The Taylor tapered antenna is also fabricated with the measured SLL below −21 dB.
Channel Simulation and Validation by QuaDRiGa for Suburban Microcells under 6 GHz

3GPP (3rd Generation Partnership Project) recommends QuaDRiGa (Quasi-Deterministic Radio Channel Generator) as the 5G preferred simulation platform. In this work, based on experimental measurements performed at 2.55 GHz for both LOS and NLOS scenarios, channel parameters like delay spread (DS), angle spread (AS) are analyzed. Furthermore, based on QuaDRiGa simulation platform, the accuracy of channel parameters like azimuth spread of arrival (ASA), elevation spread of arrival (ESA), azimuth spread of departure (ASD) are validated. The 3D features of received powers are illustrated. Results of QuaDRiGa simulation match quite well with experimental measurement data. This indicates that QuaDRiGa platform can be used for design and simulation of 5G wireless communications systems.
Selection of Rich Model Steganalysis Features Based on Decision Rough Set  $\alpha$ -Positive Region Reduction

Steganography detection based on Rich Model features is a hot research direction in steganalysis. However, rich model features usually result a large computation cost. To reduce the dimension of steganalysis features and improve the efficiency of steganalysis algorithm, differing from previous works that normally proposed new feature extraction algorithm, this paper proposes a general steganalysis feature selection method based on decision rough set <inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula>-positive region reduction. First, it is pointed out that decision rough set <inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula>-positive region reduction is suitable for steganalysis feature selection. Second, a quantization method of attribute separability is proposed to measure the separability of steganalysis feature components. Third, steganalysis feature components selection algorithm based on decision rough set <inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula>-positive region reduction is given; thus, stego images can be detected by the selected feature. The proposed method can significantly reduce the feature dimensions and maintain detection accuracy. Based on the BOSSbase-1.01 image database of 10 000 images, a series of feature selection experiments are carried on two kinds of typical rich model features (35263-D J+SRM feature and 17000-D GFR feature). The results show that even though these two kinds of features are reduced to approximately 8000-D, the detection performance of steganalysis algorithms based on the selected features are also maintained with that of original features, which will remarkably improve the efficiency of feature extraction and stego image detection.