Miracle-dz/UniFAR-FTUs huggingface dataset:
Free Market of Multi-Leader Multi-Follower Mobile Crowdsensing: An Incentive Mechanism Design by Deep Reinforcement Learning

The explosive increase of mobile devices with built-in sensors such as GPS, accelerometer, gyroscope and camera has made the design of mobile crowdsensing (MCS) applications possible, which create a new interface between humans and their surroundings. Until now, various MCS applications have been designed, where the task initiators (TIs) recruit mobile users (MUs) to complete the required sensing tasks. In this paper, deep reinforcement learning (DRL) based techniques are investigated to address the problem of assigning satisfactory but profitable amount of incentives to multiple TIs and MUs as a MCS game. Specifically, we first formulate the problem as a multi-leader and multi-follower Stackelberg game, where TIs are the leaders and MUs are the followers. Then, the existence of the Stackelberg Equilibrium (SE) is proved. Considering the challenge to compute the SE, a DRL based Dynamic Incentive Mechanism (DDIM) is proposed. It enables the TIs to learn the optimal pricing strategies directly from game experiences without knowing the private information of MUs. Finally, numerical experiments are provided to illustrate the effectiveness of the proposed incentive mechanism compared with both state-of-the-art and baseline approaches.
Change detection across domains using GAN-based image translation

The problem of visual change detection becomes a challenging one when query and reference images involve different domains (e.g., time of the day, weather, and season) due to variations in object appearance and a limited amount of training examples. In this study, we address the above issue by training a GAN-based image translator that maps a reference image to a virtual image that cannot be discriminated from query domain images, and experimentally verify efficacy of the approach.
The relationship between intraseasonal tropical variability andENSO simulated by the CMIP5

This study evaluates the simulation of relationship between intraseasonal tropical variability (ITV) and El Nino Southern Oscillation (ENSO) in 23 models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). As a first step, the models' skill in simulating ENSO diversity is assessed, which indicates that 16 models among 23 are able to simulate realistically the statistics of the relative size of two types of El Nino. The characteristics of the ITV are then documented revealing that only five models (CMCC-CM, CCSM4, BNU-ESM, INMCM4 and MIROC5) simulate realistically the parameters crucial for proper reproducing of ITV contribution to the El Nino, in particular the total variability, seasonal cycle and propagation along the equator of Madden-Julian oscillation (MJO) and convectively coupled equatorial Rossby waves (ER). At last step the ITV/ENSO relationship in the models are analyzed and compared to observation. It is shown that the key aspects of this interaction such as phase lag between ITV peak activity and El Nino peak and longitude localization of maximum correlation between ITV and ENSO is realistically simulated by CMCC-CM and MIROC5 for MJO and CMCC-CM and INMCM4 for equatorial Rossby waves. These models are capable to reproduce the distinct MJO and ER behavior associated to the two El Nino flavors. Aforementioned models may be used for the investigation of the sensitivity of the ITV/ENSO seasonal dependence to global warming.
Application of Image-based Acquisition Techniques for Additive Manufacturing using Canny Edge Detection

Edge is an indispensable characteristic of an image, defined as the contour between two regions with significant variance in terms of surface reflectance, illumination, intensity, color and texture. Detection of edges is a basic requirement for diverse contexts for design automation. This study presents a guideline to assign appropriate threshold and sigma values for Canny edge detector to increase the efficiency of additive manufacturing. The algorithm uses different combinations of threshold and sigma on a color palette and the results are statistically formulated using multiple regression analysis with an accuracy of 95.93%. An image-based acquisition technique system is designed and developed for test applications to create three-dimensional objects. In addition, graphical user interface is developed to convert a selected design of a complex image to a three-dimensional object with generation of Cartesian coordinates of the detected edges and extrusion. The developed system reduces the cost and time of developing an existing design of an object for additive manufacturing by 20% and 70% respectively.
Sedimentation and dispersion of Brownian particles in spatially periodic potential fields

A generalized Taylor dispersion analysis is presented of the combined diffusion and sedimentation of a Brownian particle subjected to a spatially periodic potential plus a uniform force. At long times this transport process is macroscopically characterized by a uniform mean drift velocity vector of the Brownian particle, and by a dispersion dyadic that quantifies stochastic ‘‘spreading’’ about the (traveling) mean position. A general explicit integral formula is derived for the dispersion coefficient in the one‐dimensional case, which supplements known results for the mean velocity. Illustrative calculations show that, for sufficiently large values of the drift force, the dispersivity exceeds the molecular diffusivity, which characterizes dispersion in the absence of any potential barriers. These calculations also lead to the surprising conclusion that an increase in the height of the potential barriers can sometimes lead to an increase in the dispersivity. A general asymptotic analysis is developed for t.
An Efficient Design for NOMA-Assisted MISO-SWIPT Systems with AC Computing

We consider a multiple-input single-output simultaneous wireless information and power transfer (MISO-SWIPT) system, where a power-splitting protocol is employed at users near the base station (BS) to provide both energy harvesting (EH) and information decoding. For the considered system, it is of practical interest to adopt non-orthogonal multiple access (NOMA) to improve the network spectral efficiency, while still meeting the EH requirements. In addition, an alternating current computing (ACC) logic is incorporated into EH receivers to directly use the wirelessly harvested AC power, which in turn achieves higher energy efficiency than traditional direct current computing (DCC). We formulate a problem of maximizing the spectral efficiency subject to the constraints of quality-of-service for the individual user, EH requirements, and BS’s maximum transmit power, where the beamformers and PS ratios are jointly optimized. To achieve an efficient solution to this nonconvex problem, we propose an iterative algorithm based on the inner approximation (IA) framework, where the approximate convex problem solved in each iteration can be cast as a second-order-cone program with convergence guaranteed. To further simplify the problem design, we propose a zero-forcing beamforming-based NOMA approach to partially eliminate interference, which has the potential to significantly reduce the number of variables. The extensive numerical results are presented to demonstrate the effectiveness of the proposed algorithms, compared with the baseline schemes.
Robust Performance Analysis for a Cascade Nonlinear H∞ Control Algorithm In Quadrotor Position Tracking

This paper presents a new hierarchical robust algorithm to solve the position tracking problem, in presence of exogenous disturbances and modeling uncertainties, of a quadrotor helicopter. The suggested controller includes a nonlinear H∞ algorithm to track the reference trajectory in the outer loop and a nonlinear H∞ controller to stabilize the rotational movements in the inner loop. The resultant controller consists of three important parts to regulate tracking errors for translational and rotational motions, maintain robust performance confronting random disturbances and modeling uncertainties and reject the sustained disturbances from the system to vanish the steady-state errors. Analytical study on the stability of the cascade system is mentioned to verify the compatibility of two controllers considering coupling terms. Numerical performance analysis is accomplished using Monte-Carlo simulation. Statistical results obtained from 1000 simulations considering environmental disturbances and modeling uncertainties depict less than 5 cm for position tracking error and less than 2 degrees for attitude tracking error in steady state performance. The closed-loop performance of the controller is also compared with two previous algorithms by determining two numerical indexes for state tracking performance and control efforts, respectively. Simulation results of the suggested control algorithm depict a significant reduction in both indexes for a similar mission. Review History: Received: 31 Dec. 2018 Revised: 7َ Apr. 2019 Accepted: 5 May. 2019 Available Online: 11 May. 2019
A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.
A Comprehensive Experimental and Reproducible Study on Selfie Biometrics in Multistream and Heterogeneous Settings

This contribution presents a new database to address current challenges in face recognition. It contains face video sequences of 75 individuals acquired either through a laptop webcam or when mimicking the front-facing camera of a smartphone. Sequences have been acquired with a device allowing to record visual, near-infrared, and depth data at the same time. Recordings have been made across three sessions with different, challenging illumination conditions and variations in pose. Together with the database, several experimental protocols are provided and correspond to real world scenarios, when a mismatch in conditions between enrollment and probe images occurs. A comprehensive set of baseline experiments using publicly available baseline algorithms show that extreme illumination conditions and pose variations are remaining issues. However, the usage of different data domains—and their fusion—allows to mitigate such variation. Finally, experiments on heterogeneous face recognition are also presented using a state-of-the-art model based on deep neural networks, and showed better performance. When applied to other tasks, this model proved to surpass all existing baselines as well. The data, as well as the code to reproduce all experiments are made publicly available to help foster research in selfie biometrics using latest imaging devices.
On the Application of Situational Language Teaching Method to Mongolian English Majors

The traditional English teaching method has gradually been replaced by new teaching methods in English class. Situational Language Teaching Method is a way to activate the teaching of language knowledge through the design of authentic and concrete situation. A survey is carried out in Inner Mongolia University for Nationalites. It investigates the practical application of Situational Language Teaching Method among Mongolian English majors in this university. Through the analysis of the questionnaire results, the paper reveals the obstacles existing in the application of the method, namely: passive participation due to improper text parsing; weakening role of teachers; and ineffective transfer among language, image and meaning. At the same time, it puts forward appropriate strategies to solve the problems during the application of SLT to Mongolian English majors.
FORMULATIONS ABOUT ELASTODYNAMICS IN ROBOTICS

The main objective of this paper consists in the establishment of the generalized elastokinematics and elastodynamics equations for robot structures with flexible links. For kinematics and differential matrices in the case of the robot structures with rigid and elastic links will be applied, among of these, the matrix exponentials, in accordance with the algorithms developed by the author. For dynamical study of robot structures with rigid links, the author of paper will develop kinetic energy with important formulations. According to the author researches, in this paper will be also presented new expressions for acceleration energies of higher order, corresponding to suddenly movements. For the study of elastodynamics, author will establish with new formulations the kinetic energy, acceleration energy of first and second order, corresponding in exclusivity to elastic structure. So, the expressions for generalized inertia forces typical to suddenly motions will be determined, when the robot structures are dominated of elastic links. Key words: elasto-kinematics, elasto-dynamics, advanced mechanics, robotics.
Handbook of fish age estimation protocols and validation methods

The Planning Group on Commercial Catch, Discards and Biological Sampling (PGCCDBS) 2012 was approached by the ICES Publications and Communications Group (PUBCOM) with the suggestion to combine the existing protocols on the age estimation of fish species within the ICES Area and publish them as an ICES Cooperative Research Report (CRR). This idea was received favourably by PGCCDBS. It was deemed important to (i) summarize the state of knowledge for key species, (ii) scrutinize, by peer review, the work done during the many calibration exercises, and, by doing so, (iii) promote an increase in quality. The aim of the present publication is to provide a comprehensive manual on the methodology of age estimation and validation and represents a collation of the state-of-the-art scientific work on the methods and validated age estimation of commercially exploited fish species across Europe. Having a collation of the latest methodologies by species grouping will also facilitate rapid and quality-assured development of methods suitable for new species.
Global Solar Radiation Prediction for Makurdi, Nigeria Using Feed Forward Backward Propagation Neural Network

The optimum design of solar energy systems strongly depends on the accuracy of solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial neural network model for prediction of global solar radiation in Makurdi, Nigeria (7.7322 N long. 8.5391 E) using MATLAB 2010a Neural Network toolbox. The training and testing data were obtained from the Nigeria metrological station (NIMET), Makurdi. Five meteorological input parameters including maximum and temperature, mean relative humidity, wind speed, and sunshine hour were used, while global solar radiation was used as the output of the network. During training, the root mean square error, correlation coefficient and mean absolute percentage error (%) were 0.80442, 0.9797, and 3.9588, respectively; for testing, a root mean square value, correlation coefficient, and mean absolute percentage error (%) were 0.98831, 0.9784, and 5.561, respectively. These parameters suggest high reliability of the model for the prediction of solar radiation in locations where solar radiation data are not available.
A Computational Model to Investigate the Influence of V1 Cell Properties and Topographic Organization on V2 Response to Illusory Contours, with Applications in the Study of Cortical Injuries in the Primary Visual Cortex

ii LIST OF TABLES . x LIST OF FIGURES . xi 1 Introduction . 1 2 Neurobiological Background . 4 2.1 The Brain: an Overview . 4 2.2 The Visual System . 7 2.2.1 The Eye . 8 2.2.2 The Lateral Geniculate Nucleus . 9 2.2.3 The Primary Visual Cortex . 10 2.2.4 The Extra-Striate Visual Cortex: V2, V4, IT . 14 2.3 Experimental Background for Illusory Contour Detection . 15 2.3. 1 Single Cell Recording . 17 2.3. 2 Optical Imaging . 22 2.4 Computational Background . 31 2.5 Modeling Topographic Organization of Neurons: Self-Organizing Maps . 33 2.6 Simulating Supervised Learning: Support Vector Machines . 34 2.6. 1 The Linear, Separable Case . 35
Novel Adaptive Background Segmentation Algorithm for Multiple Object Tracking

Multiple object tracking plays a vital role in many applications. The objective of this paper is to track multiple objects in all the scenes of the video sequence. In this paper, an algorithm is proposed to identify objects between scenes by dividing the scenes in the video sequence. Within each scene, objects are identified and tracked between scenes by segmenting the background adaptively. The proposed method is tested on four publicly available datasets. The experimental results substantially proved that the proposed method achieves better performance than other recent methods.
Understanding the Sense of Community and Continuance Intention in Virtual Communities: The Role of Commitment and Type of Community

Virtual communities (VCs) have become essential in current organizations and society, and so their sustainability is a topic of interest for researchers and practitioners. We focus on the sense of virtual community (SoVC) and commitment as relevant antecedents in achieving the success and maintenance of different types of VCs (communities of interest, virtual learning communities, and VCs of practice). Specifically, this study examines a moderated mediation model in which the type of VC moderates the indirect effect of a SoVC on the intention to continue through the perceived commitment of the users of the VC. The sample consists of 299 members of VCs. The results showed that SoVC influenced the intention to continue via commitment to VCs. Additionally, the relationship between SoVC and commitment was higher for communities of interest and virtual learning communities than for VCs of practice. This article contributes to previous literature by identifying the importance of participants’ engagement and the contingent effect of the type of community. Implications of the study and directions for future research are discussed.
Family diversity and the division of domestic labor: How much have things really changed?

Few studies have examined the distribution of housework across family types. Using a nationally representative sample, this paper examines the division of household labor in first-marriage families, stepfamilies, families headed by divorced mothers, and families headed by never-married mothers. Findings indicate that, across family types and regardless of women's employment status, women perform two to three times more housework than their husbands or cohabiting partners. Implications for family life education are discussed.
Optimization for Image Segmentation

Image segmentation, i.e., assigning each pixel a discrete label, is an essential task in computer vision with lots of applications. Major techniques for segmentation include for example Markov Random Field (MRF), Kernel Clustering (KC), and nowadays popular Convolutional Neural Networks (CNN). In this work, we focus on optimization for image segmentation. Techniques like MRF, KC, and CNN optimize MRF energies, KC criteria, or CNN losses respectively, and their corresponding optimization is very different. We are interested in the synergy and the complementary benefits of MRF, KC, and CNN for interactive segmentation and semantic segmentation. Our first contribution is pseudo-bound optimization for binary MRF energies that are high-order or non-submodular. Secondly, we propose Kernel Cut, a novel formulation for segmentation, which combines MRF regularization with Kernel Clustering. We show why to combine KC with MRF and how to optimize the joint objective. In the third part, we discuss how deep CNN segmentation can benefit from non-deep (i.e., shallow) methods like MRF and KC. In particular, we propose regularized losses for weakly-supervised CNN segmentation, in which we can integrate MRF energy or KC criteria as part of the losses. Minimization of regularized losses is a principled approach to semi-supervised learning, in general. Our regularized loss method is very simple and allows different kinds of regularization losses for CNN segmentation. We also study the optimization of regularized losses beyond gradient descent. Our regularized losses approach achieves state-of-the-art accuracy in semantic segmentation with near full-supervision quality.
Combined Transmission-Distribution System (CoTDS) co-simulation modeling and its applications for dynamic studies

Combined Transmission and Distribution Systems (CoTDS) simulation requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in practical systems. The dynamic behavior of transmission and distribution systems are vastly different. With the increased deployment of distribution generation, especially power electronic inverters, the complexity is further increased. The time scales of simulation can be orders of magnitude apart making the combined simulation extremely challenging. This has led to increased research in applying coupled simulation (also referred to as co-simulation) techniques for integrated simulation of the two systems. In this thesis, two methods for co-simulation of CoTDS are proposed using parallel and series computation with integration impact on numerical convergence. The proposed cosimulation methodology is validated against commercial EMTP software. The results show the limits and benefits of applying co-simulation by using test transmission and distribution systems. A detailed phasor domain Distribution Generation (DG) inverter model is developed for power system dynamic simulation using which the effectiveness of the proposed co-simulation methodology is demonstrated in dynamic studies. The co-simulation method is then applied to model reduction where the CoTDS based dynamic load modeling with distributed load serves as a guiding tool to calculate some of the key aggregated WECC Composite Load Model (CLM) parameters. As a further addition, a Reduced Distribution System Model (RDSM) is proposed with a new single-phase A/C motor model for the WECC CLM with fractional stalling and recovery. Such a model can be used for developing measurement based control schemes that can mitigate events such as fault-induced delayed voltage recovery in distribution systems.