Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling Wheelchair Using EEG Signal and Machine Learning

With technology evolving these days, paralyzed patients have found solutions that eased their live a lot. Electrical wheelchairs are commercialized which help patients to move around effortlessly without any assistance. Robotic arms are being commercialized as well and are being connected to the human brain directly for commands. However, extremely paralyzed patients that cannot move any part of their bodies need to feel self-dependent and find something that can ease their lives. This work aims to deliver a wheelchair that can move based on the brain signals of the patient. The brain signals are recorded by an EEG sensor from EMOTIV. There will be a Brain-Computer interface to analyze the brain signals and translate them to commands between the patient’s brain and the computer. The goal of this study is to move the wheelchair freely based on the patient’s intention (i.e. forward, backward, right or left) by building an algorithm that compares a trained set of data from the patient’s brain with the live data that is recorded by the EEG sensor. The outcomes indicated the possibility of BCI-controlled systems being used in complex daily tasks and showed that the suggested BCI could offer satisfactory control accuracy for the wheelchair.
Remaining Useful Life Prediction in Turbofan Engines: PCA and Machine Learing Approach

In this paper, a study of prediction of the Remaining Useful Life (RUL) of an aircraft’s turbofan engine is explored by analyzing a data set of a real-life turbofan engine from the Prognostics Data Repository of NASA and using the Principal Component Analysis (PCA) for data reduction and preprocessing, then selecting machine learning algorithms, mainly the Random Forest (RF) and Gradient Boosted Machine (GBM) so that a model can be trained to predict the possible failures through developing a model to estimate the RUL of the functionality of the turbofan engine.
COVID-19 Misinformation Detection on Weibo by Support Vector Classifier

The ongoing COVID-19 pandemic has led to a simultaneous infodemic, marked by a surge of accurate and inaccurate information circulating online and across social media platforms. This study is centered on creating a Support Vector Classifier model designed to categorize misinformation about COVID-19 shared on Weibo, a prominent Chinese social media platform. The dataset utilized, known as the CHECKED dataset, is a crucial tool for those engaged in misinformation detection, especially within the Chinese linguistic landscape and in the context of the current global situation. The findings demonstrate that the Support vector machine model crafted for this study demonstrates robust performance across several pivotal metrics, including accuracy, F1-score, recall, and precision, with an accuracy of 89.07%, an F1-score of 87.33%, a recall of 89.07%, and a precision of 90.38%.
A Multi-constraint 3D Mesh Models Deformation Method

Aiming at the problems of too many control vertices and difficult operation of the traditional free deformation technique, a multi-constraint 3D mesh models deformation method is proposed. Firstly, the input model is embedded into a sparse tetrahedral control mesh, and the positional relationship between the model and the control mesh is established using tetrahedral coordinates. Secondly, skeleton nodes are set on the model to generate skeleton segments, control mesh vertices are bound to the skeleton segments, the control mesh is deformed by manipulating the rotation and translation of the skeleton nodes, and finally the new positions of the model vertices are computed based on the control mesh. Since the skeleton drives a sparse tetrahedral control mesh, the accuracy of model deformation will be reduced, resulting in a series of problems in the deformed model. In order to optimize the model deformation, this paper sets the influence range of the control mesh on the model deformation by the weight of the constraint region, adds point constraints to the control mesh vertices to set the change range of this vertex, and finally calculates the correction factor according to the stretch constraints and volume constraints, and optimally adjusts the position of the control meshes vertices. The experimental results show that this method can make up for the problems of the traditional free deformation technique, which is complicated and unintuitive to operate, and improve the accuracy of deforming the model.
Experimental Test-Bed for Computation Offloading for Cooperative Inference on Edge Devices

In this paper, we describe the experimental test-bed we are de-veloping to evaluate the efficacy of computation offloading for cooperative inference without in-depth architectural changes to the models under consideration. We describe our test-bed design, functionalities, and work in progress features. We demonstrate a simple use case of the test-bed, consisting of the identification of a split layer in an AlexNet for a particular hardware scenario and the validation of those results.
An Integral-event-triggered Scheme for AVR-LFC Optimization Strategy with Guaranteed L∞-Gain

This paper addresses the challenges related to automatic voltage regulation and load frequency control in a power system. To mitigate the burden of network communication, an improved Integral-event-triggered scheme based on $\mathcal{L}_{\infty}$ control co-design is studied for network control systems. This method incorporates two time delays and external disturbances, and leverages the historical output of the system to determine which data should be transmitted to the controller. Finally, the computer-based experiments provide concrete evidence and quantitative results that demonstrate the efficacy.
Research on Quantitative Remote Sensing Monitoring Method of Ecological Environment Based on Machine Vision

As remote sensing technology is widely used in quantitative monitoring of ecological environment, it is very important to segment and extract the transformation of ecological environment accurately and efficiently from remote sensing images. some image semantic segmentation models based on machine vision have been proposed and have shown good performance in ecological environment monitoring. Based on machine vision, this paper proposes a quantitative remote sensing monitoring algorithm for ecological environment with improved DeepLab V3+semantic segmentation model. In this paper, lightweight network MobileNetV2 is used as the feature extraction network of semantic segmentation model to effectively reduce the number of parameters and floating point calculation amount of the model and improve the segmentation efficiency of the model. Meanwhile, mixed depth convolution is introduced in MobileNetV2 to allow the model to extract more abundant feature information through convolution kernel of different sizes. It is beneficial to improve the segmentation accuracy of the model. The R-Drop regularization method is proposed to further constrain the model. The difference between the training model and the prediction model is reduced by constrains the output results of the same data sample, which is conducive to improving the segmentation accuracy and generalization performance of the model. Finally, relevant experiments were carried out using the collected data set to prove its effectiveness. The optimized model has better segmentation accuracy, which fully proves that the optimization method proposed in this paper has good generalization performance, and is suitable for quantitative remote sensing monitoring of ecological environment.
Research on Chemical Accident News Classification Based on CNN and BiLSTM-Self-Attention

Chemical accident news data encompasses essential information such as news headlines, news content, and news sources, with the context of news content playing a crucial role. To enhance the accuracy of text feature extraction and improve the efficiency of chemical accident news classification, this paper introduces a feature fusion-based classification approach. The proposed model employs a multi-layer convolutional neural network (CNN) to extract local features from the text of chemical accident news. Additionally, a Bidirectional Long Short-Term Memory (BiLSTM) network is utilized to capture global features, supplemented by the integration of a Self-Attention mechanism behind the BiLSTM network to assign weights to the features and reduce noise. The local and global features are then fused to enrich the semantic information. Furthermore, the feature fusion information undergoes maximum pooling and average pooling to reduce dimensionality and enhance the training speed. Finally, the information is fed into a Softmax layer for classification. Experimental results demonstrate that the proposed neural network model, namely ABLSACNN (Add-CNN-BiLSTM-Self-Attention), outperforms the CNN-Self-Attention model. The ABLSACNN model exhibits an improvement of1.59% in accuracy, 2.46% in recall rate, and 1.93% in F1 score on the chemical accident news dataset, thereby showcasing its superiority.
Hate Speech Detection using CNN and BiGRU with Attention Mechanism on Twitter

Indonesia is the country that leads the most use of social media in Asia. Twitter is one of the most popular social media platforms in Indonesia. Twitter is a social media that is typically used for opinion exchange, criticism, and storytelling. This is frequently abused by Internet users, including the dissemination of hate speech directed at an individual or groups. Therefore, the aim of this research is to address these issues by building a hate speech detection system for Indonesian Twitter. This research uses 69.484 tweet data and implements hybrid deep learning models with Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU). Other methods applied are Bidirectional Encoder Representation from Transformer (BERT) used as word embedding to help the system better understand the context and meaning of tweets and attention mechanism to help the system to find the essential word from tweets. This research examines eight hybrid approaches in classification process, either CNN-BiGRU or BiGRU-CNN and adds attention mechanisms for those models. The result shows BIGRU-CNN hybrid model with attention mechanism for both layers achieving the highest accuracy of 88.12%.
Fast Computation Flow Restoration with Path-Based Two-Stage Traffic Engineering

The emerging edge networks are cloud-native. Flows with computation needs are processed in-flight by compute nodes inside the network. Routing with In-Network Processing (RINP) not only has to maintain network-wide load balance on communication and computation elements, but also has to quickly restore flows upon various types of failures. In this paper, we propose a novel path-based two-stage traffic engineering scheme to trade-off between routing model complexity, network performance in the normal stage, and restoration efficiency upon failures. For the normal stage, our model jointly optimizes computation demand allocation and traffic flow routing. We further speed-up RINP calculation by controlling the path budget and decoupling computation allocation and traffic routing. For the restoration stage, we develop a fast restoration scheme that only re-routes the flows traversing the failed elements to achieve close-to-optimal network delay performance while minimizing the fraction of unrestored flows. Evaluation results on real network instances demonstrate that in the normal stage, our scheme achieves near-optimal performance with up to 50-100x speedup compared to link-based routing models. In the restoration stage, our scheme can restore most of the affected traffic with up to 10x speedup compared to globally rerouting all the flows.
Neural Network-based Synthesis for Digital Sound Processing

The allure of synthesizers built upon analog circuits lies in their individuality and idiosyncratic traits, which have captivated enthusiasts since the 1960s. In contrast, the digital age ushered in sound modules that generate a clean and almost predictable sound, setting them apart from their analog counterparts. Venturing into the expansive realm of neural networks, this research delves into the field of machine learning and the significant attention it has garnered in recent years. The widespread application of these technologies now permeates our daily lives. Exploring the intersection of creativity and machine learning, researchers have investigated how these same techniques can be harnessed for expressive purposes. In the domain of digital sound processing, this paper serves as a proof of concept, demonstrating how machine learning methods can synthesize sound and infuse it with an analog essence.
Classification of Tree Species by Federated Learning

This article focuses on applying federated learning methods to the use case of tree species classification on the basis of hyperspectral imaging data. A pixel-based approach is applied, using multiple inputs representing various spectral frequency bands. A deep neural network with a relatively simple structure was used for the classification of 14 different types of trees. The article includes an investigation of how the number of federated learning sites (worker nodes) and the communication frequency with the central aggregation server affect the overall accuracy of the classification model and the convergence speed. The presented work also introduces and explains the most commonly used Federated Learning techniques.
Feature Expansion with GloVe for Hate Speech Detection Using Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) Methods on Twitter

Hate speech is unwanted behavior that aims to attack individuals or groups. The spread of hate speech can be found on social media, one of which is on the Twitter platform. This problem can be solved by classifying hate speech. This research proposes to build a hybrid model of hate speech detection using three deep learning models CNN (Convolutional Neural Network), LSTM (Long-Short Term Memory), and a combination of both models. The dataset used comes from Twitter in the form of Indonesian tweets. TF-IDF (Term Frequency - Inverse Document Frequency) and GloVe (Global Vector) are used as feature extraction and feature expansion to improve the accuracy of each model. Several scenarios were tested to find the best features. The best results in this study used a dataset with a ratio of 90% train and 10% test after preprocessing, TF-IDF with Unigram + Bigram + Trigram weighting, and 10,000 feature vectors. The method with Top 10 feature expansion in the tweet corpus achieved the highest accuracy of 90.83% for the CNN-LSTM hybrid model and improved by 0.79% against the previously determined baseline followed by the LSTM-CNN model with a result of 91.72% which improved by 1.88% against the baseline.
Exploiting Doppler Diversity for Inter-satellite Multiple Access in Massive LEO Constellations

We consider a communication framework between a swarm of satellites, belonging to a massive low-Earth orbit (LEO) constellation, and a receiving satellite (i.e., the sink). The employed multiple access scheme combines non-orthogonal multiple access (NOMA), where intersatellite links (ISLs) share the same time-frequency resource blocks, and orthogonal multiple access (OMA), where ISLs make use of orthogonal resource blocks. We develop (and numerically test) design strategies of the proposed hybrid NOMA-OMA scheme, which exploit differences in Doppler frequencies (called Doppler frequency diversity) to enhance fairness among the satellites, in comparison to a pure-NOMA scheme, while ensuring a remarkably higher sum-rate capacity compared to a pure-OMA technique.
Simultaneous and diverse dual-band microwave signals generator based on RF domain dual-loop Fourier domain mode locking optoelectronic oscillator

We propose and demonstrate experimentally a simultaneous and diverse dual-band microwave signals generation technique based on RF domain dual-loop Fourier domain mode locking optoelectronic oscillator (FDML-OEO). Two rapidly-tunable bandpass microwave filters (TBF) are parallel in the RF feedback link, and used for mode selection. Adjusting the drive signal of two TBFs, the TBFs are reconfigured and dual-band and dual-format linearly chirped microwave waveforms (LCMWs) can be generated based on the FDML OEO. In addition, the central frequencies of two frequency bands are independently tunable, and the modulation formats of waveforms at two frequency bands can also be diverse, this approach has potential in generating agile broadband multi-format radar waveforms with low phase noise and excellent pulse-to-pulse coherence directly from an OEO. This RF domain dual-loop diode-tuned FDML-OEO can be applied to advanced multi-band radar and wireless communication systems.
Research On Grid-Connected Performance Analysis Algorithm of Battery Energy Storage System

As new energy generation and microgrid applications become more and more widespread, their instability and intermittency have a great impact on the stable and reliable operation of power grids, and energy storage technology has been rapidly developed as an effective method to solve this problem. The existing energy storage systems are not highly reliable and safe, and there is a lack of methods and cases to comprehensively evaluate the grid-connected performance of battery energy storage systems (BESS), which makes current practical application effect of BESS not satisfactory, so there is an urgent need to conduct research on the evaluation of this area. This paper analyses several analysis algorithms of energy storage systems commonly used for BESS grid-connected performance test, and introduces in detail the analysis algorithms for active power, reactive power and harmonics of the system. At the same time, a corresponding test platform is built, and various operating states of the grid are simulated by the grid simulation device, and finally a complete communication architecture is established and actual tests are carried out. After testing, it was found that the proposed battery grid-connected performance testing system architecture and analysis algorithms are highly feasible and accurate.
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust

Machine learning models play a vital role in time series forecasting. These models, however, often overlook an important element: point uncertainty estimates. Incorporating these estimates is crucial for effective risk management, informed model selection, and decision-making.To address this issue, our research introduces a method for uncertainty estimation. We employ a surrogate Gaussian process regression model. It enhances any base regression model with reasonable uncertainty estimates. This approach stands out for its computational efficiency. It only necessitates training one supplementary surrogate and avoids any data-specific assumptions. Furthermore, this method for work requires only the presence of the base model as a black box and its respective training data.The effectiveness of our approach is supported by experimental results. Using various time-series forecasting data, we found that our surrogate model-based technique delivers significantly more accurate confidence intervals. These techniques outperform both bootstrap-based and built-in methods in a medium-data regime. This superiority holds across a range of base model types, including a linear regression, ARIMA, gradient boosting and a neural network.
WOOD COLORIMETRY OF NATIVE SPECIES OF MYRTACEAE FROM A ARAUCARIA FOREST

Colorimetric parameters have potential to differentiate tree species through their wood type. Therefore, this can be used to aid the inspection of illegal trade. Thus, considering the need to identify species with precision and speed, this study aimed to explore the potential of these parameters in order to characterize wood of some species of native Myrtaceae from the Araucaria Forest: Campomanesia xanthocarpa Berg. , Eugenia pyriformis Cambess. , Myrcia retorta Cambess. , and Plinia Peruviana (Poir.) Govaert. Twelve trees were collected randomly – three per species – for the removal of a disc at diameter at breast height (DBH). We took samples from three regions of the disc (next to the bark, intermediate and next to the pith) and analyzed them in three anatomical sections (transversal, radial and tangential) by the system CIE L*a*b* using a bench colorimeter. We analyzed the data based on the following criteria: test of means in order to differentiate species, quantitative factor analysis (considering the region of the disc and the anatomical section as factors), and principal components analysis (PCA). Then, we used the original spectrum and the values of parameters L*, a* and b* in order to verify the distribution of the species. The analysis determined that the species present different values for the colorimetric parameters. In addition, the factors region of the disc and anatomical section have specific influence for each species. Finally, the PCA indicated that only E. pyriformis differs from the others.
An Exploration of Large Language Models for Verification of News Headlines

This study explores the capabilities of ChatGPT in news headline verification across different prompts and languages. We introduce an optimal prompt design and a novel difficulty ratio metric to analyze ChatGPT’s performance across various statement resources. Our findings highlight ChatGPT’s promising accuracy and cross-linguistic adaptability in fact-checking, while also identifying areas for further investigation, especially in expanding the analysis beyond headlines and exploring other Large Language Models (LLMs). Our findings suggest it is highly promising to leverage an LLM such as ChatGPT as a general tool in combating misinformation, enhancing the trustworthiness of digital information, and increasing trust-worithiness of text data mining algorithms by providing more reliable data sources for the algorithms.
Protocol and Logical Models for Robustness and Survivability in Autonomous UAV Systems

Unmanned aerial autonomy is a widely discussed topic with the potential of applications in many areas of modern society. In this work, the concept of robustness in the operation of autonomous groups of Unmanned Aerial Vehicles was examined, understood as the capacity to maintain the target mission despite individual and multiple faults. The formal definition of robustness is given, connected to established characteristics of reliability in complex systems such as fault rate and fault-free operation. A matrix model of operational states is proposed for autonomous UAV groups, with applications in the development of protocols and policies to assure safe and reliable operation of autonomous aerial systems.
Distributed PV Cluster Partition Based on KNN-DPC Clustering Algorithm

This paper addresses the issue of voltage violation caused by numerous distributed PV systems connected to distribution networks. Considering that the DPC algorithm is greatly influenced by subjective definition of the cutoff distance during its application, a K-nearest neighbor optimization DPC algorithm (KNN-DPC) is proposed. By utilizing the comprehensive sensitivity matrix to characterize electrical distances as inputs and accelerating the search for density peak points, the impact of cutoff distance can be avoided, and the precision of the cluster division can be efficiently enhanced. The IEEE33 node and IEEE69 node distribution system with distributed PV integration is used to verify the cluster division, and the modularity function is adopted as an evaluation index for the division results. The evaluation index for different K values is calculated, and the implementation effects of different algorithms are compared correspondingly, demonstrating the efficacy and superiority of the proposed approach.