Miracle-dz/UniFAR-FTUs huggingface dataset:
Caching-aided coded multicasting in dynamic scenarios

New technologies that incorporate affordable storage at the edge, such as caching, are anticipated to facilitate the growth of wireless communication and accommodate increased traffic. In this paper, the focus is on designing an adaptive coded caching schema in dynamic scenarios with respect to the users mobility and their continuous requests. The proposed technique is based on the original coded caching schema that can be seen as index coding problem and modeled as a conflict graph. The approach is to adapt the conflict graph to any request and apply the necessary coloring in a adaptive way. A greedy approach is used, comparing the performance to the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm proposed by Vettigli et al. The aim of the adaptive greedy based technique is improving the performance while significantly reducing the complexity of the algorithm. Simulation results highlight the performance of the proposed technique.
Automatic Sleep Staging for EEG Signals Using GoogLeNet Deep Architecture and Continuous Wavelet Transform

Majority of automatic sleep stage scoring studies rely on the use of feature engineering and classical machine learning algorithms. Recently, several automatic sleep stage classification methods have been proposed based on convolutional neural networks (CNN). However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set-up and sufficiently train a deep CNN from scratch. More importantly, many of the previous deep learning studies were trained in limited scenarios which might limit their ability to generalize in different settings. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. The proposed approach uses a GoogLeNet deep architecture to set-up and train an end-to-end CNN model for automatic feature extraction and sleep scoring using data from a single EEG source. In order to obtain imaging data inputs needed for the CNN, the continuous wavelet transform (CWT) is used to obtain time-frequency images from EEG sleep epochs. The proposed model was evaluated over the three primary EEG signals recommended by the American Academy of Sleep Medicine (AASM) from data sets that were collected by different devices that are used by two major sleep centers in USA and Germany, respectively. Evaluation results show that this method can achieve a promising performance and can be further deployed in real-time for online sleep monitoring and wearable systems.
Temperature Dependence of the Anisotropic Dielectric Properties of Semiinsulating β-Ga2O3 in the Terahertz Region

We conducted measurements using terahertz timedomain spectroscopy on a semi-insulating bulk sample of (001)oriented beta gallium oxide $\left(\beta-\mathrm{Ga}_{2} \mathrm{O}_{3}\right)$ in the temperature range of $90 \mathrm{~K}$ to $400 \mathrm{~K}$. We analyzed the complex dielectric function parallel to the a-axis $[100]$ and b-axis $[010]$ in the frequency range from 0.2 to 3THz using the Lorentz model. Our analysis enabled us to investigate the temperature dependence of the static dielectric constants and phonon absorption, as well as the anisotropy of $\beta-\mathrm{Ga}_{2} \mathrm{O}_{3}$.
Improving Credibility Detection with Combined Transformers-Based with Machine Learning and Deep Learning Techniques

This research article covers the crucial challenge of determining whether or not tweets are rumors. To reach this goal, we suggest and contrast the use of machine learning and deep learning models. Our experiments demonstrate that KNN, which achieves an accuracy of 87.68%, is the best machine-learning model for this task. Additionally, our deep learning model surpasses all other models and achieves a state-of-the-art accuracy of 91.89% on this dataset. This model embeds tweets with the T5 xlarge embedding pre-trained model and preprocesses them using lemmatization. Our findings demonstrate the potential of both machine learning and deep learning methods for rumor identification in social media platforms and highlight the significance of pre-processing methods for raising the models’ level of accuracy. Overall, our research helps to build automatic rumor detection systems on social media, which can help reduce the spread of false information in the digital age.
Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system’s active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals.
Surrogate-Assisted Hybrid Metaheuristic for Mixed-Variable 3-D Deployment Optimization of Directional Sensor Networks

A major concern in designing sensor networks is the deployment problem. However, establishing an efficient algorithm for the real-world deployment problem is challenging due to three issues, which are 1) the realistic mixed-integer nonlinear programming problem (MINLP) with mixed-variable; 2) the combinatorial subset selection problem; and 3) the expensive computational cost for fitness evaluation in the 3-D coverage problem. Therefore, this paper addresses these challenges and proposes a surrogate-assisted hybrid metaheuristic for mixed-variable 3-D deployment optimization of directional sensor networks (DSNs). First, an MINLP with flexible coordinate transformation technique and an efficient mixed-variable encoding scheme are introduced to model and represent the problem. We propose hybrid metaheuristic which applies two reproduction methods respectively for discrete and continuous variables. Second, we design sparse population-based incremental learning (s-PBIL) to handle inherent subset selection problem. s-PBIL could accurately learn the required information, and automatically learn a sparse distribution. Third, a mixed-variable surrogate with unifying space under Bayesian model management is incorporated to reduce the expensive computational cost. Experiment results on real-world deployment scenarios scaling from small-size to large-size show the effectiveness of the proposed algorithm.
Assessing the Vulnerabilities of the Open-Source Artificial Intelligence (AI) Landscape: A Large-Scale Analysis of the Hugging Face Platform

Artificial Intelligence (AI) has rapidly proliferated as a critical disruptive technology in the 21st century. Hugging Face hosts pre-trained models, facilitating the sharing and use of open-source code. Hugging Face has been used by 22,000+ organizations, including Intel and Microsoft, with 2.6+ billion model downloads. While Hugging Face democratizes access to AI models, these models may contain unknown security vulnerabilities. In this research, we automatically collect models from Hugging Face, link them to their underlying code bases on GitHub, and perform a large-scale vulnerability assessment of these repositories. Through our approaches, we collected about 110,000 models from Hugging Face and over 29,000 GitHub repositories. Our vulnerability assessment revealed a larger percentage (35.98%) of high-severity vulnerabilities compared to low-severity vulnerabilities (6.79%). This trend in severity levels contradicts the results of severities detected in repositories forked from root repositories and searched repositories. Given that many of the vulnerabilities reside in fundamental AI repositories such as Transformers, the results of this vulnerability assessment have significant implications for supply chain software security and AI risk management more broadly.
Investigation of High Voltage Electrical Cable Loading in Substation Basement Spaces

Underground transmission and distribution cables have seen increased adoption in recent times and are increasingly an essential part of the electrical grid for power transmission in challenging topographies. This work examines the thermal effects of continuous and emergency loading on a 110 kV transmission cable in air within a high-voltage transformer substation to ascertain whether there is a risk of overheating in such an environment. A validated multi-physics thermal-CFD model was used for this purpose, simulating continuous and emergency operation. The overall current carrying capability of the cable in unrestricted air convection settings was confirmed to be greater than the close trefoil ducted underground cable section based on existing continuous current rating specifications. Thus, the potential for cable overheating at transformer substation basements does not pose a risk to electrical grid integrity for the close trefoil ducted situation examined in this study. However, for other higher ampacity cable burial arrangements, cable overheating during short term overloads in basements may be a limiting factor for overall circuit rating. This would need to be evaluated on a case by case basis.
A Deep Learning Framework for Noise Elimination of Partial Discharge Signals

Noises can contaminate acoustic partial discharge (PD) signals, resulting in inaccurate detection and interpretation. As a result, denoising acoustic PD signals has achieved significant attention to enable precise diagnosis and analysis. This manuscript presents a deep learning (DL) method for denoising acoustic PD signals obtained from a high-voltage power apparatus. The obtained signals are contaminated with various levels of white Gaussian noises followed by two-dimensional time-frequency transformation via continuous wavelet transform (CWT). The CWT images are then fed to a fully convolutional network (FCN) based autoencoder (AE), that supports skip connections, to perform denoising on the acoustic PD signals. The proposed FCN based AE is validated upon four mother wavelets, and it has been observed that the best performance is achieved for the ‘Daubechies’ wavelet. Moreover, the denoising performance has been assessed using various evaluation metrics, where different levels of white Gaussian noises are added. Experimental results reveal that the proposed denoising method outperforms the existing DL quantitatively. The proposed scheme can also be used to monitor and diagnose other signal modalities in real time.
Deep Learning-Based Flood Detection Using Multi-Temporal Sentinel-1 SAR Data: A Case Study in Beijing Area, 2023

Floods inflict widespread and severe damage on human lives, property, ecosystems, and economies. Remote sensing technology is crucial in monitoring flood disasters, offering critical information and data to support decision-making, disaster response planning, and resource allocation. Leveraging deep learning techniques for the timely detection and targeted response to disasters in affected areas represents a cost-effective approach to reducing economic losses resulting from flood disasters. This study utilized four Sentinel-1 Synthetic Aperture Radar (SAR) images from July to August 2023 to analyze flood disasters in Beijing and Hebei province. The research introduced a framework for flood detection. It analyzed the disaster situation in urban zones, agricultural land, and reservoir affected by the extraordinary rainfall on July 23rd in Beijing. This study offers valuable insights for effectively reducing disaster-related losses. It is a reference point for future disaster management and mitigation efforts.
Measurement Reporting Enhancement for 5G Cellular-Connected Aerial Vehicles

Measurement reporting is essential in every cellular network to guarantee the best radio resource management and performance to its users. The information in reports is mainly used to perform handovers and interference management between cells. In the case of cellular-connected UAVs, where the UAV impacts a high number of cells due to its flight height, measurement reporting becomes essential. However, applying the same reporting schemes used for terrestrial users, the UAV appears to generate a large number of reports, creating a substantial signalling overhead. To overcome this problem, we introduce in this paper a new measurement reporting scheme adapted for UAVs, which reduces the number of reports compared to LTE-supported aerial vehicles. Our scheme shows a 30% reduction in number of reports compared to LTE, without having a major impact on the network’s knowledge about the most relevant cells.
Comparative Analysis on Bidirectional DC-DC Converter Topologies

Recent advances in energy storage electric vehicles, renewable energy systems, etc. require a variety of features from a highly functional DC-DC converter. Among different topology configurations, bidirectional ones are used because of their reduced weight, size, and cost-effectiveness. A review of each bidirectional group converter has been addressed here based on advantages, disadvantages, voltage gain, characteristics, efficiency, and power density along with their typical application. The decision will make a ground of comparison for producing new topology and finding the appropriate converter for the specific application for the researchers.
Prototype filter design for weighted overlap-add filter bank based sub-band adaptive filtering applications

Weighted overlap-add (WOLA) filter bank based sub-band adaptive filtering has been widely used in various speech and audio signal processing applications. The WOLA filter bank uses a prototype analysis filter and prototype synthesis filter. The design of these prototype filters significantly impacts the performance of the adaptive filtering. In this paper we propose an optimization criterion for the prototype synthesis filter design, derived from the analysis of the oversampled uniform discrete Fourier transform (DFT) modulated filter bank for WOLA based sub-band adaptive filtering. The proposed prototype synthesis filter design involves a constrained convex optimization problem with quadratic cost function and linear equality constraints. The proposed prototype synthesis filter design method shows performance improvement for WOLA based acoustic echo cancellation (AEC) compared to the conventional prototype synthesis filters.
Stress memory of crystalline rocks in acoustic emission

In acoustic emission studies on granite under uniaxial compression, clear hysteresis of emission numbers related to the stress history (Kaiser effect) was observed below dilatancy onset stress. The maximum stress loaded in the past is memorized in rocks. Above dilatancy, we could not observe such a hysteresis. These features are explained by the nature of irreversible sudden advances of microcrack tips. Recovery process time of this hysteresis is over one month under room humidity and temperature conditions. The results in this paper cast foundation for the applicability of geostress measurements by the acoustic emission method.
Parameter Estimation of Magnetic Growing Rod with Output Error Method

This study focuses on a magnetic controlled growing rods (MCGR) used in the treatment of early-onset scoliosis. In this study, the Lyapunov direct method-based output error method, which is iterative adaptive by online, is used as a parameter estimation method to predict the velocity of the telescopic bar during the MCGR distraction process. The system parameters are estimated. by the proposed model. The main purpose is to minimize the error between the actual and desired states by the online configuration of the controller’s parameters. By using online models, we can continuously update and refine system parameters to improve prediction accuracy.
Spotting Parodies: Detecting Alignment Collapse Between Lyrics and Singing Voice

We present a method for detecting parodies in karaoke singing by evaluating alignment collapse between lyrics and singing voice. Parody detection is a crucial technique for catching copyright infringement in online vocal recordings and enhancing the accuracy of singing scoring algorithms. Assuming ground-truth transcriptions (i.e., lyrics) are fixed for a specific song, we can use an acoustic model to obtain a forced alignment between lyrics and the corresponding singing voice. However, if the lyrics are not sung correctly and contain parodies, the alignment may not be estimated accurately due to mismatches in acoustic features. Our aim is to utilize this alignment collapse to create a robust and effective system for detecting parodies in singing voice. To this end, we explore various metrics to assess the degree of collapse in the estimated alignments. We also design metrics robust against singers' variations, such as differences in rhythm arrangements. In our experiments, we construct a Japanese singing voice dataset for training an acoustic model specific to the singing domain, which is essential for estimating proper alignments. The results demonstrate that the proposed system, adopting an edit-distance-based metric, is highly effective in detecting parodies, achieving the area under the curve (AUC) of 0.963.
Investigating the Response of Ferrofluid Droplets in a Uniform Rotating Magnetic Field: Towards Splitting and Merging

Ferrofluid droplets are of great importance due to their ability to be manipulated by magnetic actuation, enabling progress in various areas, such as nanotechnology, robotics, and optics. Moving, splitting, and merging the ferrofluid droplets need a comprehensive analysis of the behavior of the ferrofluid droplets in a magnetic field. Here, we analyze the response of ferrofluid droplets when they are under the effect of a uniform rotating magnetic field. A water-based ferrofluid droplet is immersed in a high-viscosity fluid, and the frequency of the applied field is changed from 0.01 to 0.30 Hz for performing experiments. Three distinct types of motion are observed during experiments: pure rotation at a low-frequency magnetic field, splitting the droplet at medium frequency field, and merging the small droplets into one droplet at high frequency. The responses of ferrofluid droplets are explained through the interaction of surface tension, drag, and magnetic force.
Multiobjective African Vulture Scheduling Algorithm in Green Mobile Edge Computing

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the mobile edge cloud (MEC) becomes a dramatic technique. With the introduction of energy harvesting (EH) technology, hybrid energy supply has been considered an effective way to save on-grid energy (brown energy). However, most current work on their offloading schemes does not consider both the task dependencies and the hybrid energy supply. In this paper, we proposed a novel model that is able to use EH technique to reduce brown energy while equipping energy storage to maintain stable system performance. We then investigate the dependent task scheduling problem in this model and formulate it as a multiobjective task scheduling problem. Later, an improved multiobjective African vulture optimization algorithm (IMOAVOA) is proposed to solve this problem with the goal of minimizing the brown energy consumption and execution latency of the application. Simulation results demonstrate that IMOAVOA outperforms the other three benchmark algorithms, which can obtain better Pareto fronts and provide optimal task scheduling policy for this system.
Are computer skills the new basic skills? : the returns to computer, writing and math skills in Britain

The large increase in computer use has raised the question whether people have to betaught computer skills before entering the labour market. Using data from the 1997 SkillsSurvey of the Employed British Workforce, we argue that neither the increase in computer use nor the fact that particularly higher skilled workers use a computer provides evidence that computer skills are valuable. We compare computer skills with writing and math skills and test whether wages vary with computer skills, given the specific use that is made ofcomputers. The regression results show that while the ability to write documents and to carry out mathematical analyses yields significant labour-market returns, the ability to effectively use a computer has no substantial impact on wages. These estimates suggest that writing and math can be regarded as basic skills, but that the higher wages of computer users are unrelated to computer skills.
Dual-State-Driven Evolutionary Optimization for Expensive Optimization Problems with Continuous and Categorical Variables

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems.
NeuroPDR: Integrating Neural Networks in the PDR Algorithm for Hardware Model Checking

The property-directed reachability (PDR) algorithm has been one of the most successful hardware model checking algorithms powering modern formal property verification tools. Inductive generalization is the key to the efficiency of the PDR algorithm. In this paper, we present NeuroPDR, a message-passing graph neural network that learns to generalize inductive clauses to accelerate the PDR algorithm. Experiments show that on average, the integration of NeuroPDR reduces around 26.4% of the time of convergence, and the network trained on one set of benchmarks can also benefit solving another. The speed-up and transferability indicate a promising future for hybrid model checkers with machine learning methods integrated.