Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning-Based Progressive Image Transmission in MIMO Channels with Inter-cell Interference

This paper studies the transmission of progressive images over multiple-input multiple-output (MIMO) channels with inter-cell interference. For the transmission of progressive images, the optimization of jointly allocating various spectral efficiencies and spatial multiplexing rates to a series of numerous packets has been a challenging problem. In addition, if transmit power control is also involved with the optimization for interference channels, the problem becomes more complicated. To address such issues, in this paper, a neural network based optimization method is proposed, where the input layer of the network takes the distortion-rate characteristics of progressive images and the estimates of channel propagation gains between transmitters and receivers. Unsupervised learning is adopted to train the network, where the softmax activation functions of the output layer are exploited to average the expected distortions of the images over the predicted probability distributions of spectral efficiencies and spatial multiplexing rates. The proposed scheme offers significantly improved performance, compared to the baseline method that separately optimizes transmit power and a set of spectral efficiencies and spatial multiplexing rates. It is demonstrated that a neural network is able to analyze and learn the highly nonlinear distortion-rate characteristics of images that are associated with MIMO channel fading effects in the presence of inter-cell interference.
Large Language Models (LLMs) Inference Offloading and Resource Allocation in Cloud-Edge Networks: An Active Inference Approach

As the research and applications of large language model (LLM) become increasingly sophisticated, it is difficult for resource-limited mobile terminals to run large-model inference tasks efficiently. Traditional deep reinforcement learning (DRL) based approaches have been used to offload LLM inference tasks to servers. However, existing solutions suffer from data inefficiency, insensitivity to latency requirements, and non-adaptability to task load variations. In this paper, we propose an active inference with rewardless guidance algorithm using expected future free energy for offloading decisions and allocating resources for the LLM inference task offloading and resource allocation problem of cloud-edge networks systems. Experimental results show that our proposed method has superior performance over mainstream DRLs, improves in data utilization efficiency, and is more adaptable to changing task load scenarios.
POXIEM: An ELK Integrated SDN Controller Proposal for Improved Control Plane Forensic Visibility and Incident Response

Software-Defined Networking (SDN) has revolutionized network management by decoupling the control plane from the data plane, enabling dynamic and flexible network configurations. This architectural shift introduces new challenges in maintaining control plane visibility and effectively detecting and mitigating potential threats. However, the security of the control plane and the server hosting the SDN controller has received limited attention compared to data plane security. In this study, we address this gap by presenting a comprehensive approach that combines custom log parsers, additional granular logging functions, and the integration of the POX SDN controller with the Elastic Stack (ELK) to monitor both the security of the server hosting controller and control plane operations against potential threats and provide forensically sound evidence for incident response. Our results confirm the efficacy of our proposed solution, with easy-to-parse structured logs and strong filtering and visualization capabilities providing comprehensive analytics. These capabilities contribute to improved control plane visibility and enhanced security for both the control plane and the hosting system in SDN environments.
Any-Radix Efficient Fully-Parallel Implementation of the Fast Fourier Transform on FPGAs

This paper presents the results of a thorough investigation of arbitrary and mixed-radix architectures of the Fast Fourier Transform (FFT) and their efficient implementation on high performance FPGA devices. We have developed a novel methodology that recursively generates the hardware description of the logic architecture of the FFT using parameterized complex matrix multipliers. Our approach allows to easily and efficiently generate fully-parallel implementations of an arbitrary number of points. This is used for example in state-of-the-art systems with non-power-of-two number of points, such as some 6G mobile communications systems. In addition, the proposed architectures accept large amounts of pipeline to meet rigorous timing requirements. The largest implementation provided surpasses the 100 GS/s throughput threshold.
Age-related adaptation of the body's kinematic responses to unpredictable trip perturbations induced by a split-belt treadmill *

This study quantitatively investigated motor adaptations to unpredictable trip perturbations repeatedly induced by a commercially available split-belt treadmill. Using a motion capture system, three outcome measures (i.e., maximum trunk flexion angle, maximum right hip flexion angle, and minimum whole-body center of mass (COM) position) quantified the kinematics of 10 healthy young (YG) and 10 healthy older adult (OG) groups. In each of the five trials, random trip perturbations were induced between the 31st and 40th steps. The three outcome measures were computed for the pre-trip period (from the baseline gait to the five steps before the trip perturbation) and the recovery period (after the trip perturbation to the baseline gait). The results showed that both groups progressively adapted the body's kinematic responses to the repetitive trip perturbations. The findings suggest that our trip-inducing technology may train young and older adults to improve the body's kinematic responses and reduce the risk of falling.
Transmit Power Minimization for STAR-RIS aided Bistatic Backscatter Networks

Bistatic backscattering communication (BackCom) allows passive tags to send signals over long distances, but requires proximity to the carrier emitter (CE). Otherwise, the system would suffer from severe path loss, which need to be compensated by a high transmitting power of the CE. This paper presents an inventive BackCom system that makes use of the Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS), which could improve the deployment flexibility of BackCom devices at both sides of STAR-RIS simultaneously. With the ensurance of the BackCom performance, we focus on minimizing the CE transmitting power through optimizing STAR-RIS phase shift and power splitting coefficient of tags. Numerical results reveal that the CE transmitting power in bistatic BackCom systems can be greatly saved with the assistance of STAR-RIS.
Assessing GBM-Model Characteristics for Improving GNSS Standard Positioning

Since the official launch of BeiDou and Galileo, there are four operational satellite navigation systems with global coverage, and signals from more than 100 satellites are available to Global Navigation Satellite Systems (GNSS) users. Improving key GNSS characteristics is the main goal of every modernization stage, and one of the most important is positioning accuracy. To improve GNSS positioning accuracy a machine-learning model was trained that can predict the residual pseudorange error and consider it at the stage of navigation equations system solution. Although the model showed strong performance, it is also vital to understand how the model works. This work assesses the model parameters in terms of learning curves and the number of iterations of the gradient boosting algorithm. The study showed that by increasing the complexity of the model, its accuracy could be improved. However, it is possible to reduce the sample size with a slight decrease in the accuracy of the model as well as the model training time. Two different methods (Mean Decrease in Impurity and Mean Decreased Accuracy) were used to identify the most important elements of the feature vector. Data consistency and adequacy of the model were also confirmed.
Exploring the Impact of Absorber Material on the Performance of a Terahertz Microbolometer by Finite Element Analysis

Microbolometers, functioning as sensitive detectors, play a crucial role in terahertz imaging systems by converting terahertz radiation into measurable electrical signals. The performance of microbolometers heavily relies on the properties of the absorber material employed. In this study, we numerically investigate the impact of two distinct absorber materials, namely gold and pyrolytic carbon (PyC), on the performance of terahertz microbolometers. Finite element simulations are conducted to analyze the absorber material effect on the thermal transient time constant, that is the limiting factor for the bolometer response time, and the mechanical resonance frequency shift, that is related to the sensitivity of microbolometers. The results will help improving detectors for terahertz imaging systems, which represent powerful tools for non-invasive inspection and characterization in various fields.
An Ensemble-based Dates Classification Systems

Recently, there has been an exponential growth of applications that adopt artificial intelligence (AI) techniques in a variety of domains. This trend is supported by three main factors namely, recent advances in AI techniques, in particular machine learning, the growing size of digital data resources and the increasing power of computation. The agriculture domain no exception; there is a growing number of publications that aim to utilise these three main factors to introduce AI-based applications that tackle challenges in the agriculture field. The palm tree, of which there are more than 200 varieties, is one of the main plant species in the Middle East and North Africa region that has more than 200 varieties. Classifying different varieties is currently conducted manually, which is costly and prone to error. This work aims to develop an ensemble-based model that provides a more effective approach. The proposed approach adopts the bootstrap aggregating paradigm and nonlinear radial basis function support vector machines classifier and gives a superior performance compared with other state-of-the-art models.
Generalized Robot Dynamics Learning and Gen2Real Transfer

Acquiring dynamics is critical for robot learning and is fundamental to planning and control. This paper concerns two fundamental questions: How can we learn a model that covers massive, diverse robot dynamics? Can we construct a model that lifts the data-collection pain and domain expertise required for building specific robot models? We learn the dynamics involved in a dataset containing a large number of serial articulated robots and propose a new concept, “Gen2Real”, to transfer simulated, generalized models to physical, specific robots. We generate a large-scale dataset by randomizing dynamics parameters, topology configurations, and model dimensions, which, in sequence, correspond to different properties, connections, and numbers of robot links. A structure modified from the generative pre-trained transformer is applied to approximate the dynamics of massive heterogeneous robots. In Gen2Real, we transfer the pre-trained model to a target robot using distillation, for the sake of real-time computation. The results demonstrate the superiority of the proposed method in terms of its accuracy in learning a tremendous amount of robot dynamics and its generality to transfer to different robots.
Seminal fluid mediates ejaculate competition in social insects

Queens of ants and bees normally obtain a lifetime supply of sperm on a single day of sexual activity, and sperm competition is expected to occur in lineages where queens receive sperm from multiple males. We compared singly mated (monandrous) and multiply mated (polyandrous) sister groups of ants and bees and show that seminal fluid of polyandrous species has a more positive effect on the survival of a male ’ s own sperm than on other males ’ sperm. This difference was not observed in the monandrous species, suggesting that incapacitation of competing sperm may have independently evolved in both bees and ants. In Atta leafcutter ants, the negative effect of the seminal fluid of other males was negated by secretion from the queen sperm-storage organ, suggesting that queens may control ejaculate competition after sperm storage.
SleepSIM: Conditional GAN-based non-REM sleep EEG Signal Generator

Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time series generation still needs to be studied. This paper proposes a Conditional GAN-based system to generate unique samples of non-REM sleep electroencephalographic (EEG) signals. The CGAN model had a 1-D Convolution Neural Network based architecture. The model was trained using real EEG from healthy controls. The trained model can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.Clinical relevance— Sleep EEG simulation can be used to train and enhance the skillset of fellows and technicians in the sleep medicine field. Variations in EEG signals can be highly complex to model mathematically; however, here, we harness the power of deep learning, using generative models such as CGANs to train, model complex data distributions, and generate diverse and artificial but realistic EEG signals during non-REM sleep.
A Systematic Literature Review of Machine Learning Approaches for In-Browser Cryptojacking Detection

This paper analyzes the state-of-the-art machine-learning techniques for detecting in-browser cryptojacking. Our study follows a systematic literature review encompassing three phases: planning, conduction, and synthesis. We provide a comprehensive overview of the approaches, highlighting gaps, areas for improvement, and unique features while categorizing each technique. We identified 14 studies that propose novel methodologies. Most of these studies employed hardware metrics analysis, such as CPU instructions or usage, followed by network traffic analysis, hybrid models, and static feature analysis. The most commonly used algorithms were neural networks, convolutional and recurrent neural networks being the most prevalent, followed by support vector machines. These studies showed improvements in detection compared to traditional blocklist approaches, although they still faced issues with not being updated often enough. We found that the availability of a public and scalable dataset would benefit all research in this area. Additionally, few studies in network traffic analysis address privacy concerns.
Lean Six Sigma: Integrating Knowledge, Data, and Innovation for Organizational Excellence

Lean Six Sigma presents a prominent methodology for organizations seeking to improve efficiency, eliminate waste, and improve overall performance. The integration of knowledge, data, and innovation within Lean Six Sigma plays a crucial role in driving organizational excellence. Lean Six Sigma represents an important business improvement method which merges the basics principles of Lean manufacturing and Six Sigma. It aims to streamline processes, reduce variation, and maximize value for customers. In the pursuit of excellence, organizations must integrate knowledge, data, and innovation to facilitate informed decision-making, promote learning, and drive transformative change. Knowledge management involves capturing, sharing, and utilizing organizational insights, best practices, and lessons learned. In Lean Six Sigma, effective knowledge management ensures that valuable information generated during improvement projects is documented and disseminated. This knowledge repository serves as a foundation for future projects, enabling teams to build upon past successes and avoid repeating mistakes.
A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data

Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings are bounded by the limitation of each modality. In addition, the lack of validity of current psychosis nosology created more complications in understanding biomarkers. In this study, we introduced a deep convolutional framework to classify and identify label noises using structural and functional magnetic resonance imaging data. We applied our method to functional and structural MRI data from a schizophrenia dataset and evaluated the model’s performance in a cross-validated form. In addition, we introduced a noise criterion to distinguish a potentially noisy subject for each modality. Our results show the learned model using resting-state functional MRI data is more informative and has higher performance in comparison with structural MRI data. Lastly, based on the noise level, we investigated potential borderline subjects as possible subtypes and made a statistical analysis to distinguish differences between resting-state static functional connectivity features.Clinical Relevance— Results show schizophrenia patients are separable from the healthy control group based on their neuroimaging data and resting-state functional MRI data is more informative than structural MRI data and hence contains less label noise.
Distillation-based Chinese Food Ingredient Recognition and Nutrition Estimation System

The foods’ ingredients and nutrition are of great significance for human health so that people can meet their fitness needs or avoid consuming allergenic and post-operative contraindicated foods. However, the diversity of recipes and the randomness of combinations in Chinese cuisine make great challenges for Chinese food identification. To address the above issues, we built a new lightweight end-to-end food query and nutrition recognition system, which is based on knowledge distillation and deep learning methods. Firstly, well-performed DenseNet-121 is used to recognize the categories of food. At the same time, ResNet-50 is used as the Net-T, and pre-trained VGG-16 is used as the Net-S in the knowledge distillation framework, which is used to recognize the ingredients of the food. Finally, ingredient nutrition is obtained by querying the ingredient table. Experiments illustrate the good performance of the proposed method, with 91.65% Accuracy of food classification and 92.01% Accuracy of ingredients recognition.
MA-TDMPC: Multi-Agent Temporal Difference for Model Predictive Control

Model-based reinforcement learning has achieved substantial progress in recent years. However, it still faces challenges when applied in multi-agent systems. These challenges in multi-agent environments include the curse of dimensionality, partial observability, which pose difficulties in environment modeling. To address these problems, a novel model-based multi-agent reinforcement learning algorithm, named multi-agent temporal difference for model predictive control (MA-TDMPC), is proposed in this paper. In MA-TDMPC, each agent maintains its own multi-agent environment model: multi-agent communication-based local environment model (MACLM). MACLM is built on the local state-action space of each agent, thus reducing modeling complexity. To overcome the limitation of partial observability in multi-agent environment, MACLM makes prediction based on multi-agent communication. In terms of model utilization, MA-TDMPC uses the MACLM for action trajectory optimization within a finite horizon. Terminal value functions learned by temporal difference are used to estimate long term returns of trajectories, which is combined with the environment model for trajectory value estimation. Our method, MA-TDMPC, outperforms model-free multi-agent reinforcement learning and prior TDMPC in terms of superior sample efficiency and asymptotic performance on MPE tasks.
Overcoming the Impedance Range Limitations of Portable Bioelectrical Impedance Spectroscopy Clinical Devices

Several portable commercial bioimpedance spectroscopy (BIS) devices are used in clinical research, but are limited by their reduced impedance measuring range to less demanding four-electrode configuration measurements. Some of these devices provide raw bioimpedance data for research purposes. The SFB7 device from ImpediMed® is a typical portable clinical device which perform 256 measurement points from 3 kHz to 1 MHz, providing for each point the resistance R and the reactance X. Its main drawback is its impedance range, limited to only 1100 Ω, making it less suitable for more demanding, higher impedance, two-electrode measurements which are potentially of great clinical interest. Some benchtop reference devices have larger frequency and impedance ranges than the SFB7 but are not portable and are not designed specifically for BIS measurement on humans.This article proposes a method to overcome the impedance range limitation of the SFB7 by connecting in parallel to the investigated bioimpedance a fixed and known impedance, whose value (and hence that of the total measured impedance) is within the device’s range. The investigated impedance is then calculated by eliminating the contribution of the known impedance via a post processing of the data.This concept has been successfully tested on a physical model, and an in vivo example has validated the practicality of performing demanding two-electrode measurements on individuals using a SFB7, with relative errors of R and X inferior to 2.5% and 7% respectively.Clinical Relevance— Using the proposed setup and after post processing of the data, the SFB7 BIS device can now be used to perform demanding two-electrode measurements, hitherto impossible, enabling a range of novel clinical, high-impedance studies, for example for localized skin characterization.
USBDAN: Unsupervised Scale-aware and Boundary-aware Domain Adaptive Network for Gastric Tumor Segmentation

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.