Miracle-dz/UniFAR-FTUs huggingface dataset:
A Biased Random-key Genetic Algorithm with a Local Search Component for the Optimal Bucket Order Problem

Aggregating ranks into a consensus is an important task applied in different fields of science. This paper deals with a specific variation that aggregate ranks into a consensus considering ties between its elements. This approach is more flexible and meaningful for modeling some circumstances where a strict order is considered too restrictive. A ranking considering ties is also known as a bucket order in literature, and the problem that considers the rank aggregation of bucket orders is defined as the Optimal Bucket Order Problem (OBOP). It is an NP-hard problem, hence several heuristics have been proposed in the literature. The current state-of-the-art results for this problem were achieved through an Evolution Strategy (ES) metaheuristic. This paper proposes the application of the adaptive Biased Random-key Genetic Algorithm (A-BRKGA) with Variable Neighborhood Descent (VND) as a local search to solve it. The A-BRKGA is a metaheuristic with on-line parameter control, in which the strategy for parameter tuning is based on deterministic rules and self-adaptive schemes. The proposed approach was compared with ES in 152 instances, improving the fitness of the best solutions in 35.52% of the instances, providing better average solutions for 70.39%, and equal results for the remaining instances.
An Automatic Multimedia Likability Prediction System Based on Facial Expression of Observer

Every individual’s perception of multimedia content varies based on their interpretation. Therefore, it is quite challenging to predict likability of any multimedia just based on its content. This paper presents a novel system for analysis of facial expressions of subject against the multimedia content to be evaluated. First, we developed a dataset by recording facial expressions of subjects under uncontrolled environment. These subjects are volunteers recruited to watch the videos of different genre, and provide their feedback in terms of likability. Subject responses are divided into three categories: Like, Neutral and Dislike. A novel multimodal system is developed using the developed dataset. The model learns feature representation from data based on the three provided categories. The proposed system contains ensemble of time distributed convolutional neural network, 3D convolutional neural network, and long short term memory networks. All the modalities in proposed architecture are evaluated independently as well as in distinct combinations. The paper also provides detailed insight into learning behavior of the proposed system.
A kind of node number selection method for electrical machine lumped-parameter thermal model based on the systematic bias

Lumped parameter thermal network (LPTN) is widely used for modelling electrical machine thermal behaviour. However, there are systematic inaccuracies in the LPTN model predicted results. Conventionally, to alleviate this problem, a highly discretized model is favoured. In practice, the discretization level highly depends on experience, or trial and error, which needs to be analysed case by case and has a limited scope of application. In this paper, a method to directly compute the proper LPTN node number is proposed. By analysing the initial values, steady-state results, and governing equations, the biases and approximations in the LPTN model are explicitly derived, where the philosophies behind using more nodes to improve the model accuracy are also revealed. After that, based on the node number related initial-boundary and steady-state temperature biases, a straightforward model discretization level determination method is proposed. To verify the derived results and assess the proposed method in detail, a comparison between the temperature obtained through LPTN and finite element method is presented. The results show that by limiting the biases in the initial and steady-state values, the transient error throughout the whole process can be successfully decreased to a desirable level.
Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification

The automatic classification of electrocardiogram (ECG) signals has played an important role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs), particularly Convolutional Neural Networks (CNNs), have excelled in a variety of intelligent tasks including biomedical and health informatics. Most the existing approaches either partition the ECG time series into a set of segments and apply 1D-CNNs or divide the ECG signal into a set of spectrogram images and apply 2D-CNNs. These studies, however, suffer from the limitation that temporal dependencies between 1D segments or 2D spectrograms are not considered during network construction. Furthermore, meta-data including gender and age has not been well studied in these researches. To address those limitations, we propose a multi-module Recurrent Convolutional Neural Networks (RC-NNs) consisting of both CNNs to learn spatial representation and Recurrent Neural Networks (RNNs) to model the temporal relationship. Our multi-module RCNNs architecture is designed as an end-to-end deep framework with four modules: (i) time-series module by 1D RCNNs which extracts spatio-temporal information of ECG time series; (ii) spectrogram module by 2D RCNNs which learns visual-temporal representation of ECG spectrogram ; (iii) metadata module which vectorizes age and gender information; (iv) fusion module which semantically fuses the information from three above modules by a transformer encoder. Ten-fold cross validation was used to evaluate the approach on the MIT-BIH arrhythmia database (MIT-BIH) under different network configurations. The experimental results have proved that our proposed multi-module RCNNs with transformer encoder achieves the state-of-the-art with 99.14% F1 score and 98.29% accuracy.
Naturalistic paradigm reveals multi-component emotion dynamics in theta and beta bands using DENS dataset

The emotion research with artificial stimuli does not represent the dynamic processing of emotions in real-life situations. The lack of data on emotion with the ecologically valid naturalistic paradigm hinders the knowledge of emotion mechanism in a real-world interaction. To this aim, we collected the emotional multimedia clips, validated them with the university students, recorded the neuro-physiological activities and self-assessment ratings for these stimuli. Participants localized their emotional feelings (in time) and were free to choose the best emotion for describing their feelings with minimum distractions and cognitive load. The obtained electrophysiological and self-assessment responses were analyzed with functional connectivity, machine learning and source localization techniques. We observed that the connectivity patterns in the theta and beta band could differentiate emotions better. Using machine learning, we observed that the classification of affective self-assessment features, namely dominance, familiarity, and self-relevance, involves midline brain regions responsible for mentalization and event construction activity compared to valence and arousal, which were mainly associated with lateral brain regions. This finding advocates the need for more than two dimensions for emotion representation. In addition, the channels with high predictability were source localized to the brain regions in default-mode, sensorimotor and salience networks. Hence, in this naturalistic study, we find that the domain-general systems contribute to emotion construction.
An AI-guided signature reveals the nature of the shared proximal pathways of host immune response in MIS-C and Kawasaki disease

A significant surge in cases of multisystem inflammatory syndrome in children (MIS-C, also called Pediatric Inflammatory Multisystem Syndrome - PIMS) has been observed amidst the COVID-19 pandemic. MIS-C shares many clinical features with Kawasaki disease (KD), although clinical course and outcomes are divergent. We analyzed whole blood RNA sequences, serum cytokines, and formalin fixed heart tissues from these patients using a computational toolbox of two gene signatures, i.e., the 166-gene viral pandemic (ViP) signature, and its 20-gene severe (s)ViP subset that were developed in the context of SARS-CoV-2 infection and a 13-transcript signature previously demonstrated to be diagnostic for KD. Our analyses revealed that KD and MIS-C are on the same continuum of the host immune response as COVID-19. While both the pediatric syndromes converge upon an IL15/IL15RA-centric cytokine storm, suggestive of shared proximal pathways of immunopathogenesis, they diverge in other laboratory parameters and cardiac phenotypes. The ViP signatures also revealed unique targetable cytokine pathways in MIS-C, place MIS-C farther along in the spectrum in severity compared to KD and pinpoint key clinical (reduced cardiac function) and laboratory (thrombocytopenia and eosinopenia) parameters that can be useful to monitor severity.
Collision avoidance decision-making by altering course based on Pareto cuckoo algorithm in multi-ship encounter

Aiming at collision avoidance decision-making problem in multi-ship encounter situation, the Pareto optimal theory is applied to the course altering decision making, a multi-ship encounter course alternation decision making model considering ship maneuverability is established, with collision avoidance effect, range loss, and rudder effect as three objective functions. A multi-objective cuckoo algorithm based on optimal solution set is designed to solve the optimal value of course angle alternation. Consider the visibility clause and define the feasible area according to the navigation environment. Compared with the traditional weight-based multi-objective function solution, an example was verified in a certain area to verify the effectiveness of the model and algorithm. The results show that the Pareto cuckoo algorithm has a good effect in multi-ship encounter situations, and can provide the driver with various optimal collision avoidance plan.
Context-Aware Fog Computing Implementation for Industrial Internet of Things

The connectivity of devices has increased in the last decade enabling multiple innovative applications and solutions to serve industries and societies. This has solved multiple challenges and facilitated the improvement of methodologies and techniques adapted by humanity. One of the newly created paradigms that changed industries and technology is the Industrial Internet of Things (IIoT). IIoT is currently being adapted by various industries creating interactive supply chain ecosystems through the use of cloud computing. The size and distributions of these ecosystems introduced latency and Quality of Service (QoS) issues for edge devices sending data to the cloud. This research paper explores a paradigm called “Fog Computing” which aims to reduce the latency between IIoT devices and the cloud by deploying a “cloud-like” computing layer closer to the IIoT devices. In addition, a Context-Aware implementation of fog computing is proposed in this paper to provide the most optimised service to edge devices. Furthermore, this paper includes various experiments that examine the different context-awareness perspectives this paper proposes for fog computing. The results and outcomes of these experiments show reduction in latency and automated resource scaling from the use of context-awareness with fog computing over cloud computing for IIoT.
Low-voltage (~1.3V), Arsenic Free Threshold Type Selector with Ultra High Endurance (> 1011) for High Density 1S1R Memory Array

Low voltage selectors are critical for low power operation of high density non-volatile memories. In this work, selectors based on arsenic free chalcogenide materials are demonstrated with record high endurance over 1011 cycles together with threshold voltage ~1.3V and leakage current ~5nA. The enhanced endurance is attributed to suppression of phase separation with more stable amorphous network by proper dopants.
Supervised Learning Classifier Based Transmit Antenna Selection for SM-MIMO System

In this paper, we propose the supervised learning classifier (SLC) based transmit antenna selection (TAS) for spatial modulation (SM) multiple-input multiple-output (MIMO) system. Firstly, the classical Euclidean distance based antenna selection (EDAS) method is investigated. Secondly, we convert the typical TAS problem into a classification problem and propose a low complexity multi-output classifier design. Then, two SLC schemes, random forest decision (RFD) and deep neural network (DNN) with adaptive momentum (ADAM) optimization, are employed to solve the TAS problem. Numerical results illustrate that the proposed DNN based TAS (DNN-TAS) scheme obtains better average bit error rate (ABER) performance than the RFD based TAS (RFD-TAS) scheme and outperforms the typical TAS methods with much lower complexity. Moreover, the proposed DNN-TAS scheme achieves the suboptimal ABER performance at high signal-to-noise ratio (SNR) region compared with the optimal high complexity EDAS algorithm.
First Demonstration of Multi-VT Stacked Ge0.87Sn0.13 Nanosheets by Dipole-Controlled ALD WNxCy Work Function Metal with Low Resistivity and Thermal Budget ≤ 400 °C

Dipole-controlled WN<inf>x</inf>C<inf>y</inf> work function metal (WFM) by plasma-enhanced atomic layer deposition (PEALD) is integrated with stacked GeSn nanosheet transistors to enable multi-V<inf>T</inf> with a wide tunability of 510 mV at the low process temperature ≤ 400 °C. The effective work function (EWF) of WN<inf>x</inf>C<inf>y</inf> is modulated by the relative thicknesses of pWFM and nWFM while maintaining the constant total thickness. EWF tuning of 500 meV around Si midgap is obtained, and the best achievable resistivity of ALD WN<inf>x</inf>C<inf>y</inf> is 990 μΩ-cm, much lower than industrial ALD TiAl value (2000 μΩ-cm [1]). Multi-V<inf>T</inf> stacked Ge<inf>0.87</inf>Sn<inf>0.13</inf> nanosheets are demonstrated by controlling the different group electronegativity and dipole strength at nWFM/WN<inf>x</inf>C<inf>y</inf>:O interface.
An Entropy-Based Approach: Compressing Names for NDN Lookup

NDN (Named Data Networking) is one of the most popular future network architecture, a “clean slate” design for replacing the traditional TCP/IP network. However, the lookup algorithm of FIB entry in NDN is the bottleneck of the current NDN. Owing to the unique identifier of content name, whose length is variable, the size of FIB entries is proliferating, and the effectiveness of lookup algorithms is low. This paper proposed an entropy-oriented name processing mechanism, compressing the content names effectively by bringing in an encoding scheme. This mechanism can be split into two parts: name compression and lookup. The first part compressed the content names and converted them into a kind of code with a smaller size by considering the information redundancies of content names; the second part built a compact structure to minimize the memory footprint of FIB entries with keeping the high lookup performance. This mechanism outperformed many traditional name lookup algorithms, had better flexibility and cost less memory footprint.
Towards a Realistic Maximum Flow Model in Hybrid Multi-Channel Wireless Mesh Networks

There is a continuous interest in multi-hop multichannel Wireless Mesh Networks (WMNs) based on IEEE 802.11 for years now. Many design problems have been proposed to calculate performance metrics like the maximum flow to improve network capacity. However, these usually presume global coordination of transmissions in a slotted time model, a highly questionable presumption for IEEE 802.11. Additionally to this shortcoming, existing data rate models and link correlations are not realistic, rendering results imprecise. Furthermore, they are not incorporating other means of communication, e.g., wired connections, which could exist in real-world scenarios due to geographical proximity of selected nodes or availability of access to a wired LAN or WAN infrastructure – leading to the use case of hybrid WMNs. This paper proposes a model for maximum flow in hybrid multi-channel WMNs based on shared airtime. It is tailored to incorporate an exact representation of data rates and link correlations in IEEE 802.11 networks to produce dependable results and it furthermore supports wired connections. Packet-level simulation results verify key aspects of our model, supporting its validity with only a small relative error of less than 5% on average and no severe outliers.
The Challenge for the Nature-Inspired Global Optimization Algorithms: Non-Symmetric Benchmark Functions

Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capability of algorithms in optimization, but not all of them could be optimized, because they were different from each other in dimensionality, separability, scalability, and modality et.al. In this paper, we introduced another property called symmetry or non-symmetry, which should be another embedded characteristic of functions affecting the capability of algorithms in optimization. 67 non-symmetric benchmark functions were collected and 9 popular capability-verified algorithms were introduced in four types of simulation experiments. Experimental results show that most of the non-symmetric algorithms could not be optimized. And none of the algorithms involved could optimize them all. Efforts remain in need of new methods and improvements of nature-inspired algorithms.
The Advancement of Radio Frequency Energy Harvesters (RFEHs) as a Revolutionary Approach for Solving Energy Crisis in Wireless Communication Devices: A Review

In this paper, the evolution and milestones of RFEH and related breakthroughs over the years are reviewed and presented. The review begins with an overview of the surging energy crisis due to the increasing demand for wireless communication devices. Furthermore, the RFEH mechanism that discusses the frequency bands utilized, the near field, and the far-field propagation are also presented. Various rectification techniques in CMOS RFEH, from the fundamental rectification method to cross-coupled-differential-drive (CCDD), are also examined and discussed. The performance is gauged through harvested output DC voltage and the power conversion efficiency (PCE) as well as the RF input power sensitivity. An assessment on the non-CMOS RFEH that includes the Schottky diode and Rectenna is also concisely described to analyze their contribution to RFEH’s performances enhancement. Finally, a particular highlight of the 5G RFEH in mmWave is provided in presenting this work as an ongoing future solution to reduce the electronic circuit’s battery dependency. The pros and cons of all the presented RFEHs are summarized and discussed as well. Continuous research and development activities are extensively being executed in developing a system that could ultimately solve the world’s energy crisis, and RFEH seems to be a revolutionary approach.
Big Data and AI Revolution in Precision Agriculture: Survey and Challenges

Sustainable agricultural development is a significant solution with fast population development through the use of information and communication (ICT) in precision agriculture, which produced new methods for making cultivation further productive, proficient, well-regulated while preserving the climate. Big data (machine learning, deep learning, etc.) is amongst the vital technologies of ICT employed in precision agriculture for their huge data analytical capabilities to abstract significant information and to assist agricultural practitioners to comprehend well farming practices and take precise decisions. The main goal of this article is to acquire an awareness of the Big Data latest applications in smart agriculture and be acquainted with related social and financial challenges to be concentrated on. This article features data creation methods, accessibility of technology, accessibility of devices, software tools, and data analytic methods, and appropriate applications of big data in precision agriculture. Besides, there are still a few challenges that come across the widespread implementation of big data technology in agriculture.
Cooperative Chaotic Evolution

We introduce two new strategies into the conventional chaotic evolution (CE) to facilitate the exchange of information between individuals instead of searching independently, and propose a cooperative CE (CoCE) with stronger performance. The first strategy designs a new crossover operation to ensure that mutant individual’s exchange genes with other better individuals. Specifically, a mutant individual does not cross with its parent individual, but with a randomly selected individual whose fitness is not worse than that of the parent individual. The second strategy introduces an additional attraction generated by the randomly selected individual acting on the mutant individual, which can allow the offspring individual to prefer potential areas and speed up the search process. To evaluate the performance of our proposed strategies, we configure CoCE and CE with the exact same parameter settings and run them on 28 benchmark functions from CEC 2013 test suites. Each benchmark function is run 30 times independently on three different dimensions (i.e., 2-D, 10-D, and 30-D), and we also apply the Friedman test and Holm’s multiple comparison test to check significant differences between CE and its three variants at the termination condition. The experimental results confirmed that our proposed CoCE has a faster convergence speed and higher convergence accuracy, and the acceleration effect is more significant for high dimensional optimization problems.
Evaluation of Fuzzy Membership Function Effects for Maximum Power Point Tracking Technique of Photovoltaic System

The photovoltaic generation system (PGS) is considered a potential renewable energy harvesting system. However, the low conversion efficiency of PGS and maximum power point tracking (MPPT) technique are the main challenges that must be solved. In addition, the switching frequency of the converters employed also affects the MPPT system performance. A high gain voltage DC-DC converter is proposed to replace conventional power converter and fuzzy logic controller (FLC) is applied in the MPPT for optimizing solar energy harvesting system. Nevertheless, evaluation of suitable fuzzy membership function is needed for optimal MPPT technique of photovoltaic system. In this paper, FLC of MPPT for photovoltaic application system was built using various membership functions in MATLAB/Simulink environment. The switching frequency of the high gain voltage DC-DC converter is varied to test the robustness of the performance of each FLC membership function. The results showed that the FLC-based MPPT technique for high gain voltage DC-DC converter with GBell membership function type has the capability to track the maximum power point (MPP) accurately and to achieve optimum power conversion. Furthermore, GBell membership showed having stable and consistent performance at various switching frequencies.