Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Cultural Gaps in Facial Expression Recognition Systems by Visual Features

This study investigates the cultural dependence of a facial expression recognition (FER) system in an interactive agent by analyzing the performance of several recognition models in different cultural domains. A comprehensive cross-domain classification performance assessment reveals disparities in model performance across different cultural contexts, indicating challenges in cross-cultural FER. To further investigate these characteristics, several public datasets across regions and our cross-cultural dataset of facial expressions derived from Thai and Japanese TV shows are analyzed. By evaluating the capacity of existing FER models to interpret our newly collected data, we found significant variations in emotion interpretation across these cultural contexts, highlighting the necessity for culturally inclusive algorithms. These findings underscore the critical need for more consideration of cultural diversity in FER research, marking a crucial step toward more inclusive and culturally sensitive artificial intelligence technologies.
A Method for Detecting Red Tide Information Using EOS/MODIS Data

In recent years,red tides appear to have increased in frequency,intensity and geographic distribution.It has been one of familiar ocean disasters in near shore area of China.It had serious impacts on ecosystem,economic and life of inhabitants of the coast.In this paper,based on comparing the spectrum of red tides and its around water,and analyzing the changing characters of chlorophyll-a concentration in the period of red tide occurring,we developed a method of extracting red tide information with EOS/MODIS data.The method considers the ratio of channel 4 and channel 3 reflectance and the ratio of channel 11 and channel 9 water-leaving radiance,combined with suspended sediments.In 15th June 2002 and 31st May 2004,red tides occurred in Bohai Sea of China.We analyzed the EOS/MODIS data of the Bohai Sea of China using the method during the red tides.Results indicate that the method is useful to retrieval red tides information from the surrounding waters.
Full Compensation of Doppler Effect in Satellite Geolocation

The paper presents the problem of the influence of the Doppler effect on the accuracy of determining the location of a radio source signals of unknown form in a satellite geolocation system (SGS). In contrast to the traditionally used Doppler frequency shift-only compensation method, a direct Doppler scaling factor compensation method is considered here. By modeling a real satellite geolocation system using the SGP model and orbital parameters in the TLE data format, the possibility of a significant increase in the accuracy of the satellite geolocation system using the considered method is shown.
Spectral and Luminescent Properties of Borosilicate Glass with CsPbBr3 Nanocrystals

Experimental results of the study of spectral and luminescent properties of borosilicate glass with CsPbBr3 nanocrystals were demonstrated. Changes in the absorption spectra with temperature and time had a non-monotonic increasing character. The maxima of the luminescence spectra also had a long-wavelength shift from 519 to 538 nm, which opens up prospects for precise local adjustment of the emission wavelength.
Fire Detection Using Level Set Segmentation Based Fractional Order Optical Flow and 4D Fire Features with Mixed Data CNN-LSTM Model

As we are aware that the world witnesses a huge number of fire breakouts everyday, which results in high numbers of hazardous events and severe losses to property and forest vegetation. Therefore, early stage fire detection is of vital importance, for once it spreads it becomes unmanageable and disastrous. The early detection of fire can be performed with the help of vision based deep learning techniques. The novelty of the work lies in performing the fire detection using the static and dynamic features of fire. The static fire features are taken as shape, texture, and color, while the dynamic feature accounts for its flickering motion. For this purpose, the fire motion is estimated in terms of optical flow from videos (image sequences) by using a motion edge preserving level set segmentation based fractional order variational model. Level sets provide nicely segmented flow fields, while fractional order derivatives are capable to deal with discontinuities in the motion field. The estimated optical flow field is used to derive four fire features, which are constituted as 4D vectors. These 4D vectors reduce the data dimensionality and mitigates over-fitting problem. Finally, the fire detection is carried out by implementing a mixed data CNN-LSTM model. The mixed data presented in the work is composed of a reference image frame and the corresponding 4D vector sequence. Also, the significance of the model is manifested through an ablation study. The model performance validation is performed thorough a comparison study conducted with several existing models.
Reducing the Carbon Footprint of Ensemble Weather Forecasting with GPUs

Climate and Weather Modelling is a highly complex and computationally intensive task which consumes substantial amounts of energy. A desire to improve forecast skill demands further advances in these forecasts, such as increased model fidelity and more comprehensive physical representations of the underlying processes. Another driver towards better forecasts is the goal of uncertainty quantification, with ensembles of forecasts a popular technique. But ensembles place much higher demands on the computational workload as N ensemble members require N times the compute cycles. This leads to an even higher energy demand. This study examines one approach to reducing the energy demands of ensembles by taking advantage of a hardware feature in modern NVIDIA GPUs known as Multi Instance GPU (MIG). This feature allows us to run multiple ensemble members on hardware-isolated GPU slices to maximize efficient use of the GPU resources and subsequently reduce the Carbon Footprint for an Ensemble forecast. We examine both small and large test cases across a range of setups to determine the optimal runtime configuration. Our study shows a 2.S-2.8x reduction in CO2 emissions across all cases which translates into a savings of between 141–171 tonnes of carbon emissions annually per GPU.
Numerical Simulation on Anisotropic Squeezing Phenomenon of New Guanyin Tunnel

The strength and deformability anisotropy of rock masses caused by discontinuities is highly complicated in analysis and design of rock engineering. As the excavation sequence and support systems during tunneling cannot control deformation of surrounding rock in time, anisotropic squeezing behavior may occur and make tunneling very difficult. This study uses a numerical model that considers both elasto-plastic behavior and time-dependent deformation of rock masses to investigate the squeezing phenomenon in the New Guanyin Tunnel, Taiwan. The effects of discontinuities on surrounding rock deformation of a tunnel are discussed, and the mechanism and associated mobilization of squeezing phenomenon are elucidated.
Formation Control of Multiple Unmanned Aerial Vehicles Based on Two-Layer Controller of Model Predictive Control Algorithm

This paper addresses the multiple unmanned aerial vehicle (UAV) formation motion control problem and presents a two-layer model predictive control (MPC) control algorithm. The global control framework consists of an outer distributed formation controller and an inner tube-based MPC optimization controller. The outer controller employs distributed model pre-dictive control (DMPC) to obtain the formation control vector. In the inner tube-based MPC controller, the state output of the upper controller is received as a control input and used as a reference state to solve for the optimal control vector. This optimal control vector is then combined with the control vector from the external DMPC controller to generate the final output. Compared to traditional MPC controllers, this two-layer MPC controller reduces computational complexity and effectively compensates for errors caused by external disturbances and model inaccuracies, improving convergence speed and reducing control errors in multi-UAV formation control. Simulation results demonstrate the effectiveness of the proposed algorithm in achieving formation control in the presence of disturbances.
A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, like edge AI systems, requires a) careful optimization of computing patterns, leveraging sparsity as an opportunity to lower computational requirements, and b) using dedicated hardware. In this paper, we introduce a novel near-memory floating-point computing architecture dedicated to the parallel processing of sparse matrix-vector multiplication (SpMV). This architecture can be integrated at the periphery of memory arrays to exploit the inherent parallelism of memory structures to speed up computation. In addition, it uses its proximity to memory to achieve high computational capability and very low latency. The illustrated implementation, operating at 1GHz, can compute up to 370 MFLOPS (millions of floating-point operations per second) while computing SpMV multiplications, while incurring a modest 17% area overhead when interfaced with a 4KB SRAM array.
High Gain Dual-band Compact Antenna Array for Millimeter Wave Applications

To meet the solicitations of high gain requirements, we proposed a single port, dual-band, and compact antenna array operating at the ranges of 28 and 38 GHz of millimetre - wave frequencies. The single array segment is comprised of a rhombus-shaped radiator with inset feeding and a quarter wave transformer for improved impedance matching. On the radiating patch, the pair of horizontal rectangular slots near to upper and lower edges, as well as three circular shaped slots at the three corners, are etched out. This distinct geometric configuration of the slots provides uniqueness and enhancement in the performance of the proposed design. This design is printed on a Rogers 5880 substrate that is 0.508 mm in thickness. The proposed optimised antenna is employed to construct a corporatefed $1\times 2$ element array. The suggested antenna array has a total volume of $12\times 13\times 0.508 \ mm^{3}$. The design equations for the entire structure are presented, along with various elements like return loss, gain, and radiation characteristics. The proposed antenna array produced by the computer aided design process covers the 28 GHz band from 27.95-29.68 GHz and the 38 GHz band from 36.82-38.27GHz. While peak gain values of 10.3 dBi at 28 GHz and 10.1 dBi at 38 GHz are achieved. From the obtained results, it is appropriate to suggest the proposed design as a viable candidate for the 5G and beyond 5G communication systems.
Review of Free Space Optical Communication: Advantages and Disadvantages

Free-space optical communication (FSO) is a revolutionary technology that utilizes optical signals to transmit data wirelessly, providing a viable alternative to conventional wired systems. This paper aims to explore the FSO system, analyze previous research, and discuss the challenges associated with information transmission in FSO networks, particularly under adverse weather conditions that can lead to weakened signals. FSO technology has garnered significant attention due to its numerous advantages, such as high data rates, quick installation, cost-effectiveness, and enhanced security. Many researchers have delved into this field, contributing valuable insights and advancements in the FSO domain. However, one of the major challenges faced by FSO systems is their sensitivity to weather conditions in the vicinity of the transmitter site. Adverse weather factors, such as rain, fog, snow, and dust, can attenuate the optical signals, leading to a reduction in signal strength and potentially affecting data transmission reliability.
Personalized Chemotherapy Selection for Lung Cancer Patients Using Machine Learning and Computed Tomography

The treatment of lung cancer remains a significant challenge in modern medicine, with no universally optimal treatment regimen established to date. In this study, we focused on investigating the efficacy and suitability of Carboplatin as a chemotherapy drug for lung cancer treatment. Utilizing machine learning algorithms and analyzing computed tomography (CT) data, we aimed to determine if Carboplatin or Cisplatin emerges as the best therapeutic option. Our methodology involved collecting and preprocessing clinical data, extracting deep features from CT images, developing a machine learning algorithm for image classification, and evaluating the performance of our approach. The results of our study provide valuable insights into the effectiveness of Carboplatin as a chemotherapy drug for lung cancer, aiding physicians in making informed treatment decisions based on individual patient characteristics. This research contributes to the advancement of personalized medicine and highlights the potential of machine learning and CT imaging in optimizing lung cancer treatment outcomes.
Tackling Renewable Energy Intermittency in Hydrogen-Based Solar Energy System Control Using DRL

Despite the potential significant demand for hydrogen produced from renewable energy, few studies have explored strategies for addressing intermittency challenges commonly experienced with renewable energy sources such as wind or solar generation. To address this gap, our study proposes a hybrid solar-battery-hydrogen system optimised to manage uncertainty using a combination of a machine learning technique, specifically deep reinforcement learning (RL), and linear programming. By leveraging this approach, we optimised hydrogen production using solar PV power while meeting hourly demand and adapting to disruptions in solar power generation. Our RL model trained with uncertainty in mind is designed to operate in various geographic locations and seasonal conditions, showcasing its versatility and potential for real-world implementation.
An Envelope Model for Efficient Prediction of the Impact of Wideband Signals on Power Amplifier Performance

This paper presents a simplified simulation method for estimating the impact of baseband terminations in advanced power amplifier architectures when used with wideband signals. Previous work has introduced the idea of using the baseband signal as a separate domain from the RF signal in order to estimate continuous wideband power amplifier performance with different baseband terminations. In this paper, this idea is expanded, the underpinning equations are generalized, and a new circuit simulator is designed utilizing the idea of baseband, or envelope, domain. The results are compared to 2-tone simulations using a commercial harmonic balance solver. The new envelope model appears to be able to predict issues with baseband impedance matching for wideband signals. It allows for efficient simulations of complex wideband signals without the escalation of harmonics as required for N-tone harmonic balance solvers, and allows for more complex amplifier architectures, such a Doherty power amplifier, to be studied.
CNN-based Community Detection: A Comparison Study

Community detection is indispensable for comprehending the structural organization and functional modules of complex networks. Convolutional neural networks (CNNs) have emerged as powerful instruments for graph data analysis in recent years. In this paper we start by providing a classification of CNN community detection approaches in networks based on node and edge transformations. Then we conduct a comparative analysis of these two categories. We assess the performance of the two CNN models on diverse network datasets using normalized mutual information a widely accepted evaluation metric. According to the findings of the study, CNN models can identify cohesive communities within networks. Moreover, they demonstrate that the node-based transformation method is more straightforward than the edge-based transformation method, while still producing satisfactory community detection results. This indicates that the node-based transformation method simplifies community detection and requires fewer computational resources.
Unleashing the Power of EfficientNet-ConvNeXt Concatenation for Brain Tumor Classification

Brain tumors, impacting a substantial global population annually, necessitate precise detection and classification for timely intervention and effective therapy. Though deep learning models have exhibited potential in medical image interpretation, a demand persists for enhanced accuracy and efficiency. This study introduces an optimized solution for brain tumor detection and classification via a concatenated EfficientNet-ConvNeXt model. This novel approach merges the power of EfficientNet and ConvNeXt—two formidable neural networks—to attain extraordinary precision in categorizing various brain tumor types, namely glioma, meningioma, pituitary tumor, and non-tumor. Experimental evaluations validate the model’s superiority over standalone architectures and existing deep learning techniques in terms of accuracy, sensitivity, and specificity. Demonstrating robustness against image quality fluctuations and variability in tumor types, the model exhibits strong potential for real-world clinical usage. Implementation of our proposed concatenated EfficientNet-ConvNeXt model resulted in substantial performance elevation, achieving an exceptional 99% predictive accuracy. These findings underscore our approach’s accuracy and efficiency, offering substantial aid to radiologists and clinicians in early-stage brain tumor detection and classification. The model’s predictive capabilities can considerably influence patient prognosis and therapy planning through enabling early intervention.
VINARCH: A Visual Analytics Interactive Tool for Neural Network Archaeology

Most neural networks (NNs) generated by neural architecture search (NAS) are discarded except for the final output to limit the memory usage on high performance computing (HPC) systems on which the search is performed. However, discarded NNs are vital for understanding the NAS structure’s evolution and reproducibility. We design a visual interactive tool for NN archaeology that explores the evolution of NAS structures, finds matching subsequences in the structures, and visualizes NN similarities across NAS outputs, including discarded NNs. We demonstrate the capabilities of our tool to discover and visualize matching subsequences on a dataset of NNs generated by NSGA-Net, a genetic NAS.
Analyzing the Impact of Different Real Number Formats on the Structural Reliability of TCUs in GPUs

1 Modern Graphics Processing Units (GPUs) boost the execution of tiled matrix multiplications by extensively using in-chip accelerators (Tensor Core Units or TCUs). Unfortunately, cutting-edge semiconductor technologies are increasingly prone to fault defects. Indeed, faults may affect TCUs when processing massive amounts of data under classical floating-point formats, raising reliability concerns when used in the safety-critical and High-Performance Computing (HPC) domains. In this scenario, the characterization of faulty TCUs supporting different arithmetic formats is still missed. This work for the first time quantitatively evaluates the effects of hardware faults arising in TCU structures when using two different formats for real number representation (i.e., Floating-Point and Posit). For the experimental evaluation, we resort to an architectural description of a TCU core (PyOpenTCU) and perform 60 fault simulation campaigns, injecting 57,344 faults per campaign and requiring around 24 days of computation. The experimental results indicate a relation between the corrupted spatial areas in the output matrices and the TCU’s scheduling policies. Moreover, the numeric analysis shows that hardware faults in TCUs in most cases affect up to 2 bits in the output results for both considered formats. The results also demonstrate that the Posit formats are less affected by faults than Floating-Point formats by up to one order of magnitude.
Automatic lung nodule classification in CT images using Two-stage CNNs and Soft-voting of Multi-scale Classifiers

Lung nodule classification in Computed Tomography is an essential procedure for the diagnosis of lung cancer. Though some automatic methods have been proposed with high accuracy, the performance of these methods was heavily dependent on the amount and quality of data annotation and sensitive to the distribution of tissue density in CT. In this study, we propose a novel lung nodule classification system based on convolutional neural networks, achieving nodule detection and classification with good accuracy even using coarse annotated and low-quality data. Firstly, we constructed a nodule center-point detection method to predict the coarse coordinate of the nodule and the distance offset between the corner-points and the center-point of the nodule. Then, we extracted the multi-scale region of interest s and shape features of the nodule within an region of interest and then input them into the nodule classification network for predicting the nodule grade and type label. The proposed method was tested on 822 cases yielding a precision of 0.962 and a recall of 0.934 for nodule detection and an accuracy of 0.759 for nodule grade classification.
Making Ping Feint To Avoid Service State Desynchronization

This article presents a novel approach aimed at minimizing state desynchronization faults between two networked applications. The approach addresses the challenge of working with a legacy application that solely supports a synchronous HTTP API and cannot undergo modifications. To tackle this limitation, an algorithm leveraging statistical data on service response time and a Markov transition matrix has been developed. The algorithm strategically sends commit messages during optimal network conditions, while ping messages provide valuable insights into the network state.Laboratory experiments were conducted to evaluate the effectiveness of the proposed approach. The results showcased a significant 72% reduction in desynchronization errors, with an average ping/commit message ratio of 2.2.
Enhanced Deep Learning Explainability for COVID-19 Diagnosis from Chest X-ray Images by Fusing Texture and Shape Features

In this paper, we propose a novel method of explainable deep learning for COVID-19 detection in Chest X-ray (CXR) images, employing a Texture-Shape approach. The method extracts texture information from the first convolution layer and shape features from the last convolution layer of Deep Neural Networks, namely CNN, VGG16, and ResNet50. To generate the final explainable map, the extracted texture and shape heatmaps are fused using a guided filter-based method. Our approach is versatile and can be adapted to work with classical explainability methods commonly used in the literature. We validate the efficacy of our method on a well-known COVID-19 database comprising CXR images. We conduct extensive experiments to assess its performance against classical explainability methods such as FEM (Feature Extraction Map) and GradCam (Gradientweighted Class Activation Mapping). For evaluation, we calculate metrics such as the average drop and increase in confidence scores. The obtained results demonstrate that fusing texture and shape information leads to significantly improved explainability compared to conventional methods.