Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Regulated Evolutionary Multitask Optimization

Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of evolutionary computation. It investigates how to solve multiple optimization problems (tasks) at the same time via evolutionary algorithms (EAs) to improve on the performance of solving each task independently, assuming if some component tasks are related then the useful knowledge (e.g., promising candidate solutions) acquired during the process of solving one task may assist in (and also benefit from) solving the other tasks. In EMTO, task relatedness is typically unknown in advance and needs to be captured via EA’s population. Since the population of an EA can only cover a subregion of the solution space and keeps evolving during the search, thus captured task relatedness is local and dynamic. The multifactorial EA (MFEA) is one of the most representative EMTO techniques, inspired by the bio-cultural model of multifactorial inheritance, which transmits both biological and cultural traits from the parents to the offspring. MFEA has succeeded in solving various multitask optimization (MTO) problems. However, the intensity of knowledge transfer in MFEA is determined via its algorithmic configuration without considering the degree of task relatedness, which may prevent the effective sharing and utilization of the useful knowledge acquired in related tasks. To address this issue, we propose a self-regulated EMTO (SREMTO) algorithm to automatically adapt the intensity of cross-task knowledge transfer to different and varying degrees of relatedness between different tasks as the search proceeds so that the useful knowledge in common for solving related tasks can be captured, shared, and utilized to a great extent. We compare SREMTO with MFEA and its variants as well as the single-task optimization counterpart of SREMTO on two MTO test suites, which demonstrates the superiority of SREMTO.
Single Image based Infant Body Height and Weight Estimation

The collection of infant body data such as height and weight is a useful mean of tracking its growth and wellness. The contact-based measurements using height and weight scales are manual and cumbersome, camera-based methods were proposed to obtain features from face or body for height and weight estimation. In this paper, we created a clinical dataset including 200 newborns collected at obstetrics, and benchmarked four convolutions neural networks for infant height estimation, where MobileNet, VGG16, GoogleNet, and AlexNet were chosen. Moreover, we investigated different MobileNet-based variants for infant weight estimation, including linear regression model, one-task model, and multi-task model. Several sets of experiments were carried out on the newborn dataset to validate the effectiveness of the proposed methods. The results show that the Mean Absolute Error (MAE) of different models are quite similar, with an average MAE < 1.1 cm and < 0.28 kg for height and weight estimation, respectively. Among them, the multi-task MobileNet has better temporal stability given its lower variance of measurement in a video.
Exploring Auditory Attention Decoding using Speaker Features

The auditory attention decoding (AAD) approach aims to determine the identity of the attended talker in a multi-talker scenario using neuro recordings. In the past few years, various AAD methods have been proposed, and most of them rely on speech envelope reconstruction, which unfortunately face challenges with shortened decoding windows. Inspired by the findings that voices with different acoustic features arouse diverse brain activities in a very short period, this paper proposes to use speaker voice features instead of speech envelope as a speaker indicator for conducting AAD in short-time situations. To achieve this, a novel dual-branch convolutional network (DBCNet) is proposed to estimate speaker features from EEG. Results show that the proposed method achieves higher decoding accuracy than existing methods for short decoding windows (approximately 75% for 0.3-s window and 82% for 1.0-s window).
The KP limit of a reduced quantum Euler–Poisson equation

In this paper, we consider the derivation of the Kadomtsev–Petviashvili (KP) equation for cold ion‐acoustic wave in the long wavelength limit of a two‐dimensional reduced quantum Euler–Poisson system under different scalings for varying directions in the Gardner–Morikawa transform. It is shown that the types of the KP equation depend on the scaled quantum parameter H>0$H>0$ . The KP‐I is derived for H>2$H>2$ , KP‐II for 0
Identifying Stable Sections for Formant Frequency Extraction of French Nasal Vowels Based on Difference Thresholds

Formant frequencies of a vowel are generally extracted from midpoints or central sections on the time axis. Nasal vowels present a challenge for obtaining stable formant frequencies, as the midpoint often falls in an anti-formant section where vocal energy is lost through the nasal cavity. This study proposes a stable section for extracting nasal vowel formant frequencies using difference thresholds, which identify a vowel as being distinct when F1 is above 60 Hz and/or F2 is above 200 Hz. For the experiment, 481 disyllabic words (232 nasal vowels and 294 oral counterparts) are selected from an online French-Korean Dictionary. Each vowel is divided into 10 intervals, and the stable section is identified as one or more continuous intervals with lower frequencies than the difference thresholds. The results show that the stable section for nasal vowels is identified in 20%~50% of the vowels, while the stable section for oral vowels is identified in 20%~80% of the vowels.
Generative AI, and Scholarly Manuscripts: WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications

This statement revises our earlier “WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications” (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop.
Phase Behavior and Role of Organic Additives for Self-Doped CsPbI3 Perovskite Semiconductor Thin Films

The phase change of all-inorganic cesium lead halide (CsPbI3) thin film from yellow δ-phase to black γ-/α-phase has been a topic of interest in the perovskite optoelectronics field. Here, the main focus is how to secure a black perovskite phase by avoiding a yellow one. In this work, we fabricated a self-doped CsPbI3 thin film by incorporating an excess cesium iodide (CsI) into the perovskite precursor solution. Then, we studied the effect of organic additive such as 1,8-diiodooctane (DIO), 1-chloronaphthalene (CN), and 1,8-octanedithiol (ODT) on the optical, structural, and morphological properties. Specifically, for elucidating the binary additive–solvent solution thermodynamics, we employed the Flory–Huggins theory based on the oligomer level of additives’ molar mass. Resultantly, we found that the miscibility of additive–solvent displaying an upper critical solution temperature (UCST) behavior is in the sequence CN:DMF > ODT:DMF > DIO:DMF, the trends of which could be similarly applied to DMSO. Finally, the self-doping strategy with additive engineering should help fabricate a black γ-phase perovskite although the mixed phases of δ-CsPbI3, γ-CsPbI3, and Cs4PbI6 were observed under ambient conditions. However, the results may provide insight for the stability of metastable γ-phase CsPbI3 at room temperature.
Purine Metabolism and Pyrimidine Metabolism Alteration Is a Potential Mechanism of BDE-47-Induced Apoptosis in Marine Rotifer Brachionus plicatilis

This present study was conducted to provide evidence and an explanation for the apoptosis that occurs in the marine rotifer Brachionus plicatilis when facing 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) stress. Metabolomics analysis showed that aminoacyl-tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, and arginine biosynthesis were the top three sensitive pathways to BDE-47 exposure, which resulted in the reduction in the amino acid pool level. Pyrimidine metabolism and purine metabolism pathways were also significantly influenced, and the purine and pyrimidine content were obviously reduced in the low (0.02 mg/L) and middle (0.1 mg/L) concentration groups while increased in the high (0.5 mg/L) concentration group, evidencing the disorder of nucleotide synthesis and decomposition in B. plicatilis. The biochemical detection of the key enzymes in purine metabolism and pyrimidine metabolism showed the downregulation of Glutamine Synthetase (GS) protein expression and the elevation of Xanthine Oxidase (XOD) activity, which suggested the impaired DNA repair and ROS overproduction. The content of DNA damage biomarker (8-OHdG) increased in treatment groups, and the p53 signaling pathway was found to be activated, as indicated by the elevation of the p53 protein expression and Bax/Bcl-2 ratio. The ROS scavenger (N-acetyl-L-cysteine, NAC) addition effectively alleviated not only ROS overproduction but also DNA damage as well as the activation of apoptosis. The combined results backed up the speculation that purine metabolism and pyrimidine metabolism alteration play a pivotal role in BDE-47-induced ROS overproduction and DNA damage, and the consequent activation of the p53 signaling pathway led to the observed apoptosis in B. plicatilis.
Use of lightweight FFT-based algorithms for remote measurements using DIC

Abrupt collapse events that result in casualties due to failure of load-bearing structures have been reported in recent years. Continuous remote monitoring of these structures, assessing the behavior under operational load, could provide a reliable prevention, as the developing damage manifests itself through a loss of stiffness, and therefore excessive deflections. Digital image correlation (DIC) algorithms based on fast Fourier transform (FFT) were implemented in a small computer unit Raspberry Pi, capable of remote operation in a low-energy demand mode. The unit is fully autonomous, capable of localizing QR code targets and extracting encoded information. This strategy allows us to instantly evaluate displacements of QR codes attached to any surface and send the data to a server. The software was developed in Python, allowing for two modes: the online mode for current data evaluation with information transfer via the MQTT protocol and the offline mode used for retrospective evaluation.
Contextual Multi-Armed Bandit based Beam Allocation in mmWave V2X Communication under Blockage

Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.
The variance spectrum of phytoplankton in a turbulent ocean

Spatial heterogeneity of ecological variables is recognized as an important stabilizing factor for ecosystem function, but has proved to be a difficult concept to characterize in quantitative, operational terms. In the sea, however, chlorophyll concentration can be estimated by a continuous in vivo measurement, and used to describe the spatial structure of phytoplankton populations in terms of a variance spectrum in either wavenumber or frequency space. In this paper a theoretical represent ation is proposed, from dimensional considerations, of the variance spectrum of phytoplankton abundance in which the critical parameters are the net rate of reproduction of the phytoplankton r and rate of dissipation of turbulent kinetic energy e. The resulting spectrum is shown to have two distinct regions separated by a third boundary region centered about a wavenumber k, = (r"IE)~. (For typical upper ocean values of r and E, k,-, e,; 1 km). For wavenumbers k >> k, , the chlorophyll variance spectrum E~(k) is proportional to k----,,13, not unlike that for a passive scalar like temperature , but for wavenumbers k << k,, it is predicted that E~(k) o: k-• Finally several spectra derived from observations in the upper ocean are shown to be consistent with the proposed kspectrum.
Index Coded PSK Modulation for Prioritized Receivers over Rayleigh Fading Channels

Noisy index coding problem over single-input single-output (SISO) additive white Gaussian noise (AWGN) broadcast channel has been studied in [6]. Further the performance of high priority receivers has been improved by using rotated PSK constellation in [7] for AWGN broadcast channel. In this work, we consider noisy index coding problem in which the source transmits symbols over a SISO Rayleigh fading broadcast channel. We show that even for transmission over Rayleigh fading channel minimum inter-set distance, as seen by the receiver plays a crucial role in its ML decoding performance unlike classical fading cases. For improving the performance of high priority receivers for a chosen index code of length N we are transmitting N bits over Rayleigh fading channel using a rotated M-PSK (M = 2N) constellation where half of the signal points have been rotated in such a way to maximize the minimum inter-set distance. Additionally, we account for the multiplicity of signal pairs along with inter-set distance as a factor to achieve better ML decoding performance for some index coding problems with rotated constellation for prioritized receivers over fading channel.
Model-based and Model-free Prescriptive Maintenance on Edge Computing Nodes

The Industrial IoT era has benefitted immensely from increased data collection and processing, which enables Industry 4.0 and predictive maintenance. Predictive maintenance tries to predict when different types of failures will occur. Prescriptive maintenance goes further by optimizing the maintenance decisions, i.e. what to do and when to do it. This paper considers a distributed factory environment, and firstly, proposes a model-based approach to prescriptive maintenance using the Partially Observable Markov Decision Process (POMDP) framework. A particle filter algorithm performs online estimation of a POMDP model to enable it to adapt to each machine over time. The POMDP is then solved using the point based value iteration (PBVI) method to obtain maintenance decisions. Next, we present a model-free approach to prescriptive maintenance using the Deep Q-Network (DQN) reinforcement learning method to obtain maintenance decisions. The POMDP and DQN methods are implemented on GPU-accelerated edge computing nodes and their performance in terms of reward and downtime are compared.
Energy-Efficient Power Allocation for Full-Duplex Device-to-Device Underlaying Cellular Networks with NOMA

Full-duplex (FD), Device-to-Device (D2D) and non-orthogonal multiple access (NOMA) are promising wireless communication techniques to improve the utilization of spectrum resources. Meanwhile, introducing FD, D2D and NOMA in cellular networks is very challenging due to the complex interference problem. To deal with the complex interference of FD D2D underlaying NOMA cellular networks, power allocation (PA) is extensively studied as an efficient interference management technique. However, most of the previous research works on PA to optimize energy efficiency only consider the system framework of partially joint combining techniques of FD, D2D and NOMA, and the constraints of optimization problem are very different. In this paper, in order to further improve the energy efficiency of a system, a dual-layer iteration power allocation algorithm is proposed to eliminate the complex interference. The outer-layer iteration is to solve the non-linear fractional objective function based on Dinkelbach, and the inner-layer iteration is to solve the non-convex optimization problem based on D.C. programming. Then, the non-convex and non-linear fractional objective function is transformed into a convex function to solve the optimal power allocation. In this approach, FD D2D users reuse the spectrum with downlink NOMA cellular users. Imperfect self-interference (SI) cancellation at the FD D2D users and the successive interference cancellation (SIC) at the strong NOMA user are considered in the system framework. The optimization problem is constructed to maximize the system’s energy efficiency with the constraints of successful SIC, QoS requirements, the maximum transmit power of BS and FD D2D users. Numerical results demonstrate that the proposed algorithm outperforms the traditional orthogonal multiple access (OMA) in terms of energy efficiency with a higher system sum rate.
Improved Channel Estimation and Equalization for Single-Carrier IEEE 802.11ad Receivers

IEEE 802.11ad uses mmWave technology for multi-gigabit wireless access networks. Multipath with large delay spread severely reduces performance due to insufficient guard interval. In this paper, we improve single-carrier IEEE 802.11ad receivers by proposing channel estimation and equalization methods for a frequency domain equalizer. Channel estimation is improved by leveraging on sparsity of the channel impulse response, while equalization is combined with an interference cancellation algorithm. The log-likelihood ratio demapper is also improved by correct power estimation of signal, interference, and noise. Simulation re-sults show that the proposed methods are effective on channels whose length exceeds the guard interval.
CIPF: Crossing Intention Prediction Network based on Feature Fusion Modules for Improving Pedestrian Safety

As the development of autonomous driving technology continues, pedestrian safety is becoming an increasingly important issue. The ability of an autonomous car to accurately predict whether a pedestrian will cross the road is essential for ensuring their safety, as the vehicle can slow down in time or stop to avoid any potential accidents. However, predicting pedestrian behavior is a complex task influenced by various environmental and contextual factors. To deal with this issue, we propose a novel method, Crossing Intention Prediction based on feature Fusion modules (CIPF) that combines eight different input features extracted from both pedestrians and vehicles through three fusion modules using RNN layers and attention mechanisms. We demonstrated state-of-the-art performance of prediction accuracy in the PIE dataset, which is the most widely used for pedestrian crossing intention prediction. We also demonstrated the superiority of the performance of our CIPF network through qualitative and quantitative analysis. In particular, we also performed ablation studies on the verification of the effectiveness of the eight input features, the validity of VGG encoders, and performance comparison of our CIPF over time by adjusting the prediction time.
Ensemble Spatial and Temporal Vision Transformer for Action Units Detection

Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient Vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video Vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the Vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge [8], with a notable 14% difference in result. Our team has achieved a position within the top 5 teams in the ABAW 2023 competition, scoring slightly below the top three and four teams by a narrow margin of 0.27% and 0.43%, respectively.
Effects of smartphone addiction on cognitive function and physical activity in middle-school children: a cross-sectional study

Introduction This study aimed to investigate the effects of smartphone addiction on cognitive function and physical activity in middle-school children. Methods A population of 196 children (boys and girls) from middle schools were recruited for this study with an average age of 12.99 ± 0.81 years, a height of 153.86 ± 6.50 meters, a weight of 48.07 ± 7.31 kilograms, and a body mass index of 20.22 ± 2.08 kg/m2. Smartphone addiction was determined using Arabic versions of the Smartphone Addiction Scale-Short Version, and physical activity levels were assessed by a physical activity questionnaire for older children. The working memory and selective attention domains of cognitive function were evaluated using a laptop screen's digital version of the memory automaticity and Flanker tasks, respectively. A one-way MANOVA was conducted to determine the differences in working memory between the smartphone-addicted and non-addicted groups. The relationship between smartphone addiction and physical activity was analyzed using Pearson's chi-squared test. Results The cognitive function-attention domain accuracy component showed a statistically significant difference between the groups, with a p-value of 0.05). The reaction time between smartphone-addicted and non-addicted children showed no statistically significant difference (p = 0.817). The relationship between smartphone addiction and physical activity was statistically significant (p < 0.001). Discussion The interaction effects between physical activity and smartphone addiction on reaction times showed statistically insignificant (p = 0.25) differences, showing that physical activity's effect on reaction times did not depend on smartphone addiction levels. The non-addicted children had significantly higher physical activity levels than the addicted children, indicating that smartphone addiction reduced physical activity.