Miracle-dz/UniFAR-FTUs huggingface dataset:
Decomposition-based Multi-Objective Evolutionary Optimization for Cluster-Head Selection in WSNs

A Wireless Sensor Network (WSN) is composed of a set of energy- and processing-constrained devices that gather data about a set of phenomena (e.g., temperature, humidity, pressure, etc.). An efficient way to extend the lifetime of a WSN is using a clustering organization, which hierarchically structures the sensors in groups and one of them is chosen as a cluster head (leader). Such cluster head performs specific tasks such as gathering data from other cluster sensors (cluster members) and resending this data through the network to the base station. By using a cluster-head organization, data gathering process is improved and, by extension, the network lifetime is extended. In this paper, we formulate a multi-objective approach for the optimal cluster-head selection in a WSN aiming to minimize: i) the distance from the cluster head to the base station, ii) the distance from cluster members to their leader, and iii) the residual energy of the leaders. The goal of our study is two-fold. First, as currently it is not known the conflict relation among those objectives, we carry out an analysis to discover which objectives are essential for solving the cluster-head selection problem. Second, we investigate the performance of multi-objective evolutionary approaches based on decomposition and the impact of their main parameters when solving the multi-objective cluster-head selection problem.
Design of Reinforced Interval Type-2 Fuzzy C-Means-Based Fuzzy Classifier

This paper is concerned with a new design methodology of a reinforced interval type-2 fuzzy c-means (FCM) based fuzzy classifier (FC). The key point of this study is to reduce the computational complexity of type-2 fuzzy set-based models and to alleviate the deterioration of its generalization abilities through the synergistic effect of two algorithms: First, interval type-2 FCM (IT2FCM) is used in the hidden layer of the network and connections (weights) are adjusted by invoking the least squares error estimation method. Second, an L2-norm regularization is considered in the cost function to avoid the construction of the network suffering from overfitting. In more detail, the hidden layer of the proposed FC is realized by interval type-2 FCM clustering to deal with the factor of uncertainty involved in the problem. This type of clustering is realized by using two values of the fuzzification coefficient resulting in the interval type-2 membership functions. Once completing type reduction, the membership grades of IT2FCM are used as the outputs of the hidden layer. Instead of the backpropagation training, least squares estimator based learning is applied to adjust the functional connection being regarded as linear functions mapping the hidden layer to the output layer. In order to reduce potential overfitting, L2-norm regularization is taken into account. The effectiveness of the proposed classifier is analyzed with the aid of a number of machine learning datasets as well as face image datasets. Thorough comparative studies are also included.
Plane Wave Spectrum Method Applied on Radiated Magnetic Field from RFID Reader Antenna

Near electromagnetic field characterization for radiating devices requires the 3D field mapping in the space near this device, especially for dosimetric applications. In this work we present the plane wave spectrum (PWS) method to extract the 3D magnetic field components from the transverse ones, in addition to the extraction of the filed at different planes above the device under test (DUT). This operation was performed on an RFID reader antenna operating at 13.56 MHz that was designed and simulated on HFSS (high frequency structural simulator). Good agreements were found by comparing the theoretical, simulated, and extracted magnetic field quantities at different heights.
Design of seamless handover between APs based on traffic load in SDWN

Software Defined Network(SDN) decouples control plane and data plane as a new type of network architecture. Centralized control is beneficial to obtain global network topology quickly and provides greater convenience for seamless handover between AP. In view of the handover between AP in Software Defined Wireless Network (SDWN, this article mainly researched in SDWN client’s seamless handover between AP. Considering the influence of traffic load for the AP performance, this paper proposed a design of Inter-AP handover scheme based on traffic load perception and a seamless handover algorithm between AP based on traffic load perception in SDWN. The design by considering the wireless client received signal strength (RSSI) and traffic load on the AP can effectively solve the problem of load imbalance between AP. Finally, applying the scheme on a small test platform, and shows that the scheme is feasible. By compared with the traditional handover algorithm based on RSSI, the handover algorithm is proved that it can effectively enhance the network throughput.
A Nature-inspired System for Mental State Recognition

In this article, we apply metaheuristics and Neural Networks for classifying human mental activities using EEG signals. We developed a Brain-Computer Interface system that is able to classify mental concentration versus relaxation. We collect the brain information during specific activities of the subject. Besides, we selected the best combination of the input features using the following two metaheuristic techniques: Simulated Annealing and Geometric Particle Swarm Optimization. The classification problem is solved using Neural Networks. We show that is possible to identify the human concentration using few EEG signals. In addition, the proposed system is developed with a fast and robust learning technique that can be easily adapted according to each subject.
Three Categories of Set-Valued Generalizations From Fuzzy Sets to Interval-Valued and Atanassov Intuitionistic Fuzzy Sets

Many different notions included in the fuzzy set literature can be expressed in terms of functionals defined over collections of tuples of fuzzy sets. During the past decades, different authors have independently generalized those definitions to more general contexts, like interval-valued fuzzy sets and Atanassov intuitionistic fuzzy sets. These generalized versions can be introduced either through a list of axioms or in a constructive manner. We can divide them into two further categories: set-valued and point-valued generalized functions. Here, we deal with constructive set-valued generalizations. We review a long list of functions, sometimes defined in quite different contexts, and we show that we can group all of them into three main different categories, each of them satisfying a specific formulation. We respectively call them the set-valued extension, the max–min extension, and the max–min varied extension. We conclude that the set-valued extension admits a disjunctive interpretation, whereas the max–min extension can be interpreted under an ontic perspective. Finally, the max–min varied extension provides a kind of compromise between both approaches.
Component Failure Analysis (CFA): A New Method for Implemented FPGA Design Failure Analysis

FPGAs are reconfigurable hardware architectures that have been used in implementing safety-critical embedded systems where failure analysis is required. Single Event Upset (SEU) in digital electronics is one of the critical safety hazards in digital electronics. The effect of SEUs must be analyzed according to the system functional safety. The SEU failure analysis is one of the well-established research topics due to the continuous shrinking size of new IC technology and the growing risk of SEU failure. This paper introduces a new probabilistic method, the Component Failure Analysis (CFA), that uses FPGA specific techniques and algorithms for analyzing SEUs in implemented FPGA designs. The CFA focuses on providing an early insight to the designer about the implemented design during the product development phase. This paper provides step by step illustrative examples and several tests to verify and validate the performance of CFA on FPGA implemented benchmark circuits. From these tests, CFA is observed to have a faster computation time as well as high accuracy. In this work, Xilinx FPGAs have been investigated, but the method can be easily extended to cover other reconfigurable digital systems.
Stereoscopic Image Quality Assessment by Considering Binocular Visual Mechanisms

With the booming of 3-D image processing in the entertainment industry and 3-D multimedia applications todays, the technology for assessing the quality of stereoscopic image faces more challenging tasks than its 2-D counterparts, such as binocular combination, stereo matching, and binocular rivalry. In this paper, a novel stereoscopic image quality assessment method is proposed by jointly exploring binocular fusion and rivalry models. In the quality-aware feature extraction stage, multi-scale binocular combination and binocular rivalry energy responses are first generated from reference and distorted stereopairs. In addition, considering that the changes of luminance affect the quality of stereoscopic image greatly, multi-scale visual features relating to image quality are obtained from its luminance maps as another binocular combination and rivalry features. Then, the dissimilarity of quality-aware features between the reference stereopair and its distorted version is quantified. Finally, such dissimilarities are mapped into an objective score to represent the perceptual quality of stereoscopic image through the pooling strategy of support vector regression. Experiments conducted on LIVE 3-D databases demonstrate that the proposed method achieves 96.61% and 96.03% in terms of Pearson’s linear correlation coefficient on Database Phase I and Phase II, respectively, outperforming most of the state-of-the-art methods.
Serverless is More: From PaaS to Present Cloud Computing

In the late-1950s, leasing time on an IBM 704 cost hundreds of dollars per minute. Today, cloud computing, that is, using IT as a service, on-demand and pay-per-use, is a widely used computing paradigm that offers large economies of scale. Born from a need to make platform as a service (PaaS) more accessible, fine-grained, and affordable, serverless computing has garnered interest from both industry and academia. This article aims to give an understanding of these early days of serverless computing: what it is, where it comes from, what is the current status of serverless technology, and what are its main obstacles and opportunities.
Relative Saliency and Ranking: Models, Metrics, Data and Benchmarks

Salient object detection is a problem that has been considered in detail and many solutions have been proposed. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. Initially, we present a novel deep learning solution based on a hierarchical representation of relative saliency and stage-wise refinement. Further to this, we present data, analysis and baseline benchmark results towards addressing the problem of salient object ranking. Methods for deriving suitable ranked salient object instances are presented, along with metrics suitable to measuring algorithm performance. In addition, we show how a derived dataset can be successively refined to provide cleaned results that correlate well with pristine ground truth in its characteristics and value for training and testing models. Finally, we provide a comparison among prevailing algorithms that address salient object ranking or detection to establish initial baselines providing a basis for comparison with future efforts addressing this problem. The source code and data are publicly available via our project page: ryersonvisionlab.github. io/cocosalrank.
Understanding surface degradation on polymeric insulators using rotating wheel and dip test under DC stress

In the present work, surface degradation studies are performed on silicone rubber based polymeric insulators using rotating wheel and dip test under DC voltage application. This work gains importance as number of HVDC lines are being constructed for transferring bulk power over long distances particularly in the country. A newly developed rotating wheel and dip test arrangement is used for the study; provision is made in the experimental set up to apply two different contaminants simulating different environmental conditions simultaneously. In one tank standard contaminant NH4Cl as per IEC 62217/IEC TR 62730 is used and in other tank acidic contaminant is used to simulate normal and acidic rain conditions. A comparative study is conducted on the insulator specimen under different environmental stresses to evaluate long term performance. The conductivity of both normal and acidic contaminants is kept 2.5 mS/cm approximately to observe the effect of acidic environment on long term performance. During the experimental duration of 1000 hours leakage current was regularly measured and recorded. Samples were visually investigated and degradation was found to be more under acidic environment and tracking was observed. After the experiment, physico-chemical analysis is conducted on the degraded samples. Fourier transform infrared (FTIR) spectroscopy is utilized to analyze the surface chemical changes on degraded insulators samples. Salt deposition was detected on aged samples in the FTIR spectrum. Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDAX) were conducted to detect elemental presence over the surface of degraded/aged samples. Thermo-Gravimetric Analysis (TGA) is utilized to analyze thermal stability and loss of ATH fillers before and after aging experiment.
Model Predictive Load Frequency Control of Isolated Micro-Grid with Electrical Vehicles

This paper design the load frequency controller based on model predictive control (MPC) for micro-grid with electrical vehicles. The MPC controller is designed by minimizing the objective function. Meanwhile, the limitations of the output power increment and the output power increment rate of each subsystem are also considered in this paper. The simulation results show that when the system is subjected to a load step fluctuation, the proposed MPC can reasonably allocate the output of each subsystem, and quickly eliminate the frequency fluctuation, while respecting the equivalent EVs and diesel power system constraints.
Detecting logical argumentation in text via communicative discourse tree

ABSTRACT We solve the argument mining problem by investigating discourse and communicative text structure. A new formal graph-based structure called communicative discourse tree (CDT) is defined. It consists of a discourse tree with additional labels on edges, which stand for verbs. These verbs represent communicative actions. Discourse trees are based on rhetoric relations, extracted from a text according to Rhetoric Structure Theory. The problem is tackled as a binary classification task, where the positive class corresponds to texts with arguments and the negative class corresponds to texts with no arguments. The feature engineering for the classification task is conducted, deciding on which syntactic and discourse features are associated with logical argumentation. Text classification framework based on syntactic, discourse and communicative discourse text structures with a number of learning approaches is implemented. Evaluation on a combined data-set is provided.
Evolving Controllers for Mario AI Using Grammar-based Genetic Programming

Video games mimic real-world situations and they can be used as a benchmark to evaluate computational methods in solving different types of problems. Also, machine learning methods are used nowadays to improve the quality of non-player characters in order (i) to create human like behaviors, and (ii) to increase the hardness of the games. Genetic Programming (GP) has presented good results when evolving programs in general. One of the main advantage of GP is the availability of the source-code of its solutions, helping researchers to understand the decision-making process. Also, a formal grammar can be used in order to facilitate the generation of programs in more complex languages (such as Java, C, and Python). Here, we propose the use of Grammar-based Genetic Programming (GGP) to evolve controllers for Mario AI, a popular platform to test video game controllers which simulates the Nintendo's Super Mario Bros. Also, as GP provides the source-code of the solutions, we present and analyze the best program obtained. Finally, GGP is compared to other techniques from the literature and the results show that GGP find good controllers, specially with respect to the scores obtained on higher difficulty levels.
A clinically oriented computer model for radiofrequency ablation of hepatic tissue with internally cooled wet electrode

Abstract Purpose: To improve the computer modelling of radiofrequency ablation (RFA) by internally cooled wet (ICW) electrodes with added clinically oriented features. Methods: An improved RFA computer model by ICW electrode included: (1) a realistic spatial distribution of the infused saline, and (2) different domains to distinguish between healthy tissue, saline-infused tumour, and non-infused tumour, under the assumption that infused saline is retained within the tumour boundary. A realistic saline spatial distribution was obtained from an in vivo pig liver study. The computer results were analysed in terms of impedance evolution and coagulation zone (CZ) size, and were compared to the results of clinical trials conducted on 17 patients with the same ICW electrode. Results: The new features added to the model provided computer results that matched well with the clinical results. No roll-offs occurred during the 4-min ablation. CZ transversal diameter (4.10 ± 0.19 cm) was similar to the computed diameter (4.16 cm). Including the tumour and saline infusion in the model involved (1) a reduction of the initial impedance by 10 − 20 Ω, (2) a delay in roll-off of 20 s and 70 − 100 s, respectively, and (3) 18 − 31% and 22 − 36% larger CZ size, respectively. The saline spatial distribution geometry was also seen to affect roll-off delay and CZ size. Conclusions: Using a three-compartment model and a realistic saline spatial distribution notably improves the match with the outcome of the clinical trials.
Fast and Accurate THz Permittivity Measurement Using a Self-Heterodyne Technique and Multitone Signal With Nonuniform Intervals

A permittivity measurement system operating in the terahertz waveband around 300 GHz achieves fast measurement by using a self-heterodyne technique to measure phase differences. Errors are reduced because a multitone technique with nonuniform frequency intervals enables us to apply a phase-unwrapping method to the self-heterodyne system. For multitone signal generation, just single-frequency oscillators and local oscillator leakage from a modulator are used. The system achieves a measurement time of 0.03 ms at one point, which is 1/200 compared to a conventional system using a vector network analyzer. With the same measurement time, the maximum measurement error can be reduced to less than half compared to the conventional two-tone self-heterodyne system.
Inhibition of Autophagy in Microglia Alters Depressive-Like Behavior via BDNF Pathway in Postpartum Depression

Postpartum depression (PPD) is associated with mood disorders and elevated inflammation. Studies have evidenced the activation/inhibition of autophagy and excessive activation of microglia to have a close relationship with depression. C57 and microglia-specific autophagy-deficient mice (Cx3Cr1Cre/+ATG5loxp/loxp) were employed to establish the chronic unpredicted mild stress depression mice model from embryonic day 7 (E7) to embryonic day 16 (E16). Fluoxetine was administered for 3 weeks (commencing from 1 week after birth). Behavioral tests (open field, forced swimming, and sucrose preference tests) were implemented. Western blot and immunofluorescence staining were employed to assess the brain-derived neurotrophic factor (BDNF) expression level, autophagy-associated proteins, and inflammatory factors. Depressive behavior was reversed following fluoxetine treatment; this was evidenced via open field, sucrose preference, and forced swimming tests. Both BDNF and autophagy-associated proteins (ATG5, Beclin-1, and LC3II) were upregulated following fluoxetine treatment. Inflammatory factors including nuclear factor kappa B and inducible nitric oxide synthase were reduced while anti-inflammatory factor interleukin-10 (IL-10) was increased after fluoxetine treatment. Microglia-specific autophagy-deficient mice (Cx3Cr1Cre/+ATG5loxp/loxp) showed a curtailed autophagy level, higher inflammatory level, and reduced BDNF expression when compared with C57 mice. Autophagy inhibition in microglia contributes to inflammation, which further instigates PPD. Fluoxetine might mediate its antidepressant effect in PPD through the autophagic pathway while upregulating BDNF expression. In view of this, regulating BDNF in microglia is a potential novel therapy target for PPD.
Nonlinearity Activated Noise-Tolerant Zeroing Neural Network for Real-Time Varying Matrix Inversion

Real-time varying matrix inversion is widely used in the fields of science and engineering, e.g., image processing, signal processing and robot technology, etc. In this paper, a nonlinearity activated noise-tolerant zeroing neural network (NANTZNN) is constructed and employed to the time-dependent matrix inversion in the noisy environment. Compared with the gradient approach related neural network (GNN) and the existing noise-tolerant zeroing neural network (NTZNN), the proposed NANTZNN model is activated by specially-constructed nonlinear activation functions, and thus possesses the better convergence performance. Additionally, theoretical analyses are provided to guarantee the convergence of the proposed model. Finally, simulations are conducted to demonstrate the efficiency and superiority of the NANTZNN model for time-dependent matrix inversion, as compared with the NTZNN model.
Pose Estimation for Multicopters Based on Monocular Vision and AprilTag

This paper deals with the problem of pose estimation for multicopters by using a downward-looking monocular camera and AprilTag. First, successive AprilTag images are captured by the downward-looking camera and the relative pose between them is estimated using a vision algorithm directly. Then, a Kalman filter combined with a constant-velocity process model is adopted to improve the accuracy and robustness of pose estimation. Experimental results show that the proposed pose estimation method can satisfy closed-loop control requirements of multicopters.
LQI-Key: Symmetric Key Generation Scheme for Internet-of-Things (IoT) Devices Using Wireless Channel Link Quality

One of the important part of IoT ecosystem is wearable device technology, that has many applications in day to day life. Due to resource constraints, complex security protocols are not feasible for tiny wearable devices. Hence, alternative approaches employing wireless channel characteristics have been proposed by many researchers. In this work, we propose a novel symmetric key generation scheme for wearable IoT devices that exploits the unique features of wireless channel link quality indicator (LQI), compared to other parameter e.g., received signal strength (RSS) used in prior work. We select LQI as a parameter of interest since it is the measure of the channel link quality connecting two communicating parties, whereas RSS is the total energy measured by radio including noise. We implement our solution on of f-the-shelf IoT devices and demonstrate experimentally that our scheme is lightweight and suitable for practical applications.