Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Stitching Algorithm for Fake News Detection Problem

Nowadays, we can see how social media networks are developing. We must accept the fact that the opinion of an expert is frequently just as valuable and crucial as that of a non-expert. It is feasible to see how traditional media is undergoing changes and processes that diminish the importance of the traditional ”editing office” and place a growing focus on journalists’ remote labour.As a result, social media has evolved into a component of national security since fake news and disinformation spread by nefarious individuals can influence readers and spark pointless debates on social issues that are inherently unimportant. This has a domino effect, instils dread in the populace, and eventually puts the security of the state in jeopardy.Recently, deep machine learning techniques have proven to be one of the technologies thought to be an effective way to combat the false news problem. However, due to shortages of labelled data, these methods often have poor model generalization capabilities when applied in real-world cases.In this paper, we address this problem by utilizing lightweight model stitching, which serves as a foundation for a hybrid method for fake news detection. Six distinct benchmark datasets have been used in our varied experiments. The outcomes are promising and pave the way for additional studies.
SP-VSLAM:Monocular Visual-SLAM Algorithm Based on SuperPoint Network

For scenes with significant changes in light intensity or perspective, the traditional ORB feature extraction method is prone to the problems of local homogenization of feature points and errors arising from the accumulated drift. A monocular visual Simultaneous Localization and Mapping algorithm (SLAM) based on SuperPoint network (SP-VSLAM) is proposed. The SuperPoint network is used to extract feature points and calculate binary descriptors simultaneously, combined with Non-Maximal Suppression methods to achieve uniform detection and description of image feature points. Then construct robust and accurate feature association information. It is fused with the back-end local mapping, loop & map merging modules of the ORB-SLAM3 system to construct a complete monocular vision SLAM system. Using the public measurement datasets TUM, the localization accuracy is calculated for five sequences in monocular mode, and the experimental results show that the proposed method has smaller trajectory error and higher pose estimation accuracy in static, partial low dynamic and high dynamic environments, which can effectively enhance the robustness of tracking.
Exploring Transverse Mode Suppression with Tilted IDTs in TF-SAW Resonators

The TF-SAW (thin film-surface acoustic wave) resonator with POI (piezoelectric on insulator) substrate exhibits remarkable Q factor and low TCF (temperature coefficient of frequency), making it a subject of extensive research and widespread application. In TF-SAW devices, tilted IDTs (interdigitated transducers) are commonly employed to suppress the unwanted transverse modes. This study investigates the correlation between the tilted angle of IDTs and the number of electrode fingers. A general formula for determining the optimal tilted angle of IDTs at approximately 800 MHz is derived. The results indicate that the IDTs at the optimal tilted angle exhibit the highest Q factor and superior suppression capability of transverse modes.
Deep Learning and Ensemble Machine Learning for Bengali Vowel Recognition from Consonant Pronunciation using Face and Lip Images

Phonetic feature identification, particularly vowels, is crucial for speech recognition systems. Visual speech recognition in Bengali is a relatively unexplored area. This work focuses on Bengali vowel identification using consonant pronunciation from images. For this, we created a dataset consisting of six Bengali vowel pronunciations, each from four speakers; and 27 Bengali consonants, each with six corresponding categories from one speaker. After manual processing, the dataset contained a total of 2030 consonant-pronouncing images. We considered face and lip images in different steps. To establish a benchmark we applied four popular Machine Learning algorithms, namely Support Vector Machine, K-nearest Neighbors, Decision Tree, and Naive Bayes. Then, we proposed an ensemble ML model, which outperformed all the benchmark performances. We identified the challenging vowels through performance analysis, revealing two pairs of homonym-like vowels: “i” (in) and “e” (everything), as well as “u” (push) and “o” (bold). Next, we created two subsets of the dataset to address these challenges. The first subset contained data for four vowel pronunciations, including “i” and “u”, while the second one contained data for four vowel pronunciations, including “e” and “o”. Both the benchmark and proposed ML models were applied to these subsets, consistently demonstrating superior performance by the proposed ML approach. To further enhance classification efficiency, a Deep Learning architecture was proposed specifically for classifying the two subsets. The proposed DL model surpassed ML algorithms, including the proposed ensemble ML approach. This work will greatly benefit researchers and practitioners working on visual speech recognition in Bengali.
Production-Ready Deep Learning Model Optimization for Anomaly Detection in Photovoltaic Systems

Anomaly detection in photovoltaic systems using deep learning models can help further automatize classification tasks and support the operator in decision making. However, the performance of pretrained deep learning models decreases in production owing to the difference between training and production data (domain shift). This performance gap can lead to serious problems when monitoring photovoltaic (PV) farms. To this end, this study proposes an approach to adapt new instances to fit the constraints of the input layer of the pretrained model by image segmentation using Niblack thresholding, padding, and resizing. In addition, the localization of single panels was calculated using metadata. Moreover, a retraining cycle was proposed to include new instances in the training process to fine-tune the models and adapt the weights to the images taken during production. The proposed algorithm extracted 81% of the panels from the images fed into it. However, the pretrained model, which was used to classify the segmented images to test its performance, did not perform well. This indicates the importance of the retraining process for optimizing the model for production.
Clustering and Association Rules Based Missing Data Filling Algorithm

Filling missing data is crucial to preprocessing data. Missing data reduces the accuracy of statistics, causes statistical errors. Traditional methods for dealing with missing data include simple deletion, mean filling, or simply ignoring missing values. However, these methods have limitations in accuracy and computational feasibility. Therefore, it is essential to select a effective method to fill missing data. In this paper, we propose a method based on Fuzzy c-means clustering algorithm, Dynamic division of connected graph algorithm and Association Rules named FDAR to fill missing data. First, we use Fuzzy c-means clustering (FCM) and Dynamic division of connected graph (CABDDCG) algorithm to cluster, and then mine association rules (AR) in each part of cluster. Next, if there are association rules, the association rules are filled; Otherwise, the nearest first $k$ values in the same cluster are used to fill. Experiments show FDAR algorithm in filling missing data has high accuracy on six UCI datasets.
Momentum-Based Balance Control of a Lower-Limb Exoskeleton During Stance

In this work, we present the implementation of a momentum-based balance controller in a lower-limb exoskeleton that can successfully reject perturbations and self-balance without any external aid. This controller is able to withstand pushes in the order of 30 N in forward and sideways directions with little sway. Additionally, with this controller, the system can perform balanced weight-shifting motions without the need for an explicit joint reference trajectory. There is potential, with fine parameter tuning, for a more robust balance performance that can reject stronger pushes during the presented tasks. Backward pushes were not rejected due to practical limitations (the mass of the device is concentrated in the back) rather than due to the control method itself. This controller is a preliminary result that brings paraplegic patients closer to crutch-free balance in a lower-limb exoskeleton.
Benefits of Optimistic Parallel Discrete Event Simulation for Network-on-Chip Simulation

The end of Moore's law has placed a two-fold demand on hardware simulation. Firstly, efficient co-design requires fast simulation of hardware systems in order to vet proposed designs. Secondly, modern simulator platforms need to become increasingly concurrent as well. To address these challenges, we develop an optimistic time warp-based parallelization for the Structural Simulation Toolkit (SST). Our optimistic PDES engine hides synchronization costs by speculatively executing tasks, leading to better compute resource utilization on modern multicore architectures. Given the significant engineering effort to make custom existing hardware models reversible, we also develop a new SST component, called escher. The escher workflow instruments arbitrary SST applications and replays event traces through the different SST parallelizations. This is a useful tool for hardware engineers who want to understand the benefits of optimistic parallelization before undertaking the significant software engineering effort to make their hardware models reversible. We demonstrate this workflow by generating traces for a tiled mesh-NOC architecture and show a 2.1x to 3.7x speed-up using our optimistic SST parallelization versus the current conservative SST parallelization.
Signature topographique et gravimétrique des panaches du manteau dans le Pacifique

Grâce a une nouvelle methode de filtrage que nous avons mise au point, nous montrons que l'anomalie de profondeur liee au Superbombement du Pacifique sud est composee de deux branches : la branche sud requiert une composante dynamique alors que la branche nord peut etre expliquee par une compensation isostatique. Nous caracterisons egalement les bombements relatifs aux alignements de type point-chaud de Polynesie francaise et montrons ainsi que l'origine de la Societe est due a l'action d'un panache profond, alors que pour les autres archipels, l'emplacement du volcanisme est essentiellement controle par la structure de la lithosphere. Nous effectuons egalement une cartographie de l'epaisseur elastique sur l'alignement volcanique des Cook-Australes qui permet de reconstituer l'histoire du chargement de la plaque lithospherique par plusieurs phases distinctes de volcanisme.
Implementation of an accelerated electrical ageing protocol for Capacitive Micromachined Ultrasonic Transducers (CMUT)

In this paper, an accelerated electrical ageing protocol for CMUT devices is presented. The ageing protocol was applied on bare CMUT chip and then on fully packaged CMUT devices. The protocol is based on the use of a biasing voltage higher than the collapse voltage value, with an application time which varies up to a few thousand seconds. In parallel, the CMUT characteristics are monitored at regularly spaced times. Experimental characteristics are the capacitance-voltage cycle for the bare chip and sensitivity for the encapsulated device.
Fast marching Phase-aberration Correction in Plane-Wave Transcranial Imaging

Although transcranial ultrasound imaging (TUI) has shown promise for clinical application, it has been shown that variable sound speed would severely affect the quality of TUI, leading to target deterioration and inaccurate depth position in the conventional constant velocity assumed plane-wave imaging (PWI). To overcome these limitations, this work proposed a multi-stencils fast marching (MSFM) phase distortion compensated method with PWI. The MSFM method relies on the approximate Eikonal equation to calculate the time of wavefront propagation through the skull. The proposed phase distortion correction method is more accurate, has a high framerate for transcranial ultrasound path estimation, and offers promising applications in TUI. Simulation experiments demonstrate that for point targets, the mean lateral resolution of MSFM-restored images increased by 55%, and the center position shift decreased by 85%. For the cyst targets, the eccentricity of fitting ellipse decreased by 65%, and the center position shift decreased by 69%.
Highlighting the Names of Geographical Objects when Processing Text Messages

Identification of emergency situations of a local nature and their subsequent geocoding involves the selection of names of geographical objects in text messages. As one of the tools for solving such a problem, the natasha library was proposed, which allows you to select named entities in the text. However, the study showed that such a tool highlights the entity as a proper name, which creates difficulties in determining the exact address where the abnormal situation occurred.
A Lightweight-CNN Model for Efficient Lung Cancer Detection and Grad-CAM Visualization

The lungs' abnormal cell growth leads to the development of lung cancer. Early cancer identification could make treatment easier, potentially saving millions of lives annually. This study's main goal is to more rapidly and effectively classify various types of lung cancer by employing a lightweight, computationally efficient convolutional neural network (CNN) model to categorize three different types of lung cancer. With an outstanding validation accuracy of 99.48%, the suggested model surpasses the achievements of previous works. The 15,000 CT scan images in our dataset include three different forms of lung cancer. The proposed model performs exceptionally well, as evidenced by its astounding precision, recall, and F1-score, all above 99%, and by its flawless Area Under Curve (AUC) score of 100%. The proposed model has fewer parameters than the existing transfer learning models. Gradient Weighted Class Activation Mapping (Grad-CAM) was used to create class activation maps, which were then used to create a heatmap to display the classification zone.
Dynamic Beamforming Strategy for Sidelobe Level Suppression in Piezoelectric Micromachined Ultrasonic Transducer (PMUT) Sparse Arrays

In this paper, we proposed a novel method to suppress the sidelobe level for a presented quasi-periodic sparse 2D array based on piezoelectric micromachined ultrasonic transducer (PMUT) technology. We applied a dynamic apodization based on Dolph-Chebyshev weights to the transducer for both transmitting and receiving for different steering angles. The apodization weights were obtained by iterative calculations in a simulation model built with Field II. Based on the calculated weights, intensity profiles were simulated and compared with that without weighting. The results showed that the side lobe level was reduced from - 14.3 dB to -38 dB when no steering angle, and from -12 dB to - 16 dB when the steering angle was 45°. It is shown that the side lobe level was largely suppressed, with the cost of an acceptable larger main lobe.
Shear wave elastography using diverging wave imaging with sliding-window coherent compounding

Shear wave elastography is a promising tool for non-invasive assessment of myocardial stiffness. As cardiac shear waves (SW) are very short-lived, their detection using ultrasound necessitates high frame rate imaging. More specifically, diverging wave imaging with coherent compounding is typically used, requiring a trade-off between time resolution and image quality. Hence, the aim of this study was to investigate if this trade-off can be circumvented for SW imaging by using a sliding-window approach during coherent compounding. In this approach, DW transmits are re-used to create a compounded frame and the number of re-used DW transmits depends on the selected overlap percentage. The proposed method was tested using phantom data and in vivo recordings. The preliminary phantom results show that frame rate can be increased using a sliding-window coherent compounding approach. However, in case of compounding 24 diverging waves, the shear wave slope in the compounded M-mode panel appeared noisier and the median value of tissue acceleration in the wave front was significantly lower (-94% without and -74% with sliding), indicating that motion information is blurred due to high number of compounded transmits. Moreover, in the in vivo recording it was observed that wave pattern and underlying motion of the heart might further blur the motion information. Hence, further optimization of the number of transmits and overlap percentage, together with advanced motion compensation should be investigated.
Multiuser Communication Aided by Movable Antenna

This paper investigates the multiuser communication aided by movable antenna (MA) via antenna position optimization, where the uplink transmission from multiple user terminals (UTs) each equipped with a single MA to a base station (BS) with a fixed-position antenna (FPA) array is considered. Specifically, we formulate an optimization problem for minimizing the total transmit power of UTs, subject to a minimum-achievable-rate requirement for each UT, where the positions of MAs and the transmit powers of UTs, as well as the receive combining matrix at the BS are jointly optimized. To solve this non-convex optimization problem, we first express the combining matrix of the BS and the total transmit power of UTs as a function of the MAs’ position vectors, which are then optimized by using the gradient descent method in an iterative manner. Simulation results demonstrate that compared to conventional FPA systems, the proposed MA-enhanced multiple access systems can significantly decrease the total transmit power of UTs by moving antennas within small regions of several-wavelength size.
Quality, Quantity and Type detection of Fruits inside Refrigerator through Smart Vision in IoT

In our automated world, time is the most precious of all. Technology is crucial in saving time and protecting it from being wasted. Being a technology student, it's my responsibility to contribute to automation. People nowadays never attain enough duration to visit the marketplace, purchase veggies, and examine the quality and number of vegetables. Automation is handled with Computer vision and Artificial Intelligence as its eyes and brain. Without these two, the system cannot be said a fully autonomous. Data science is chosen for core in implementing an intelligent refrigerator system which is capable of sensing fruits, veggies and other familiar entities in the fridge, such as eggs, also sensing their respective quantities and quality, such as rotten or good in this case, in addition, an auto notifying and purchasing interface is the proposal. A CNN based supervised learning model that checks quality and quantity, as well as auto-shops online, might be an excellent addition to the automation arsenal. An advanced model equipped with data analysis and artificial intelligence could be a good fit. This article discusses the concept, architecture, construction process, and final product. The study describes the concept, architecture, construction process, and functionality of a refrigerator that is exposed as an IoT object and interacts with the items stored inside, gathers information about them, processes that information into relevant data, and then communicates that data to its owners via an IoT platform, in other words, a smart refrigerator.
Security Analysis in Multicast Networks over Generalized Gamma Fading Channels Operating Dual-Branch Diversity Receiver

In this paper, a single input multiple output (SIMO) multicasting network scheme is proposed over generalized gamma fading channels with dual-branch diversity receivers in the presence of multiple eavesdroppers. The main goal of this paper is to analyze the effects of shaping parameters $\omega$ and $\beta$ with a number of $\theta$ receivers and $\phi$ eavesdroppers on the secrecy performance of the proposed scheme. The closed-form analytical expressions of the probability of non-zero secrecy multicast capacity (PNZSMC) and the secure outage probability for multicasting (SOPM) have been derived and verified via the monte carlo simulation. Our results revealed that the secrecy performance degrades with increasing in the shaping parameters related to eavesdropper and upgrades with increasing in the main channel shaping parameter. Secrecy performance is also greatly affected by the number of receivers and eavesdroppers. Increasing in the number of receivers and eavesdroppers, the secrecy performance degrades.
DNA Network Construction by Optically Induced Dielectrophoresis

In this work, an optically induced dielectrophoretic force field was used in manipulating deoxyribonucleic acid (DNA), and a DNA network was formed at the position of virtual electrode which was observed by atomic force microscope (AFM). The amorphous hydrogenated silicon (a-Si:H) film was used to structure the virtual electrodes when a beam of light from a projector was illuminated into it and the illuminated area became conductive. The mica was used as the substrate of DNA and placed between the two ITO electrodes. An AC electric field of 2 V peakto-peak at 50 kHz was used across the electrodes. The mica substrate with DNA solution was naturally air dry after functioned with the optically induced dielectrophoresis (ODEP) force and scanned by AFM. The results show that DNA was attracted by positive dielectrophoretic force and converged at the position of virtual electrode, and the overall shape and the specific position of DNA network on substrate could be controlled by changing the virtual electrode shape and position. The average height of the DNA networks was close to the real height of DNA, which was helpful for analyzing the DNA network electrical conductivity. This work has proven that the ODEP can be used as the tool to manipulate DNA and structure DNA molecular devices.
Rate-Compatible Puncturing and Shortening of Short PAC Codes for 6G URLLC

The future 6G technology is envisioned to support ultra-reliable (BLER < 10−6) and ultra-low latency (< 1 ms) communications (URLLC). Achieving performance close to the maximum likelihood bound is extremely challenging, especially for short blocklengths. The polarization-adjusted convolutional (PAC) codes, devised by Arıkan, are attracting great attention due to their error-correction performance, being close to the finite-length capacity bound. While, Arıkan’s PAC codes, whose blocklengths are limited to 2n, can not meet the rate-compatible requirement for mobile communications. To the best of our knowledge, the puncturing or shortening schemes for PAC codes are rarely studied, though there are many rate-compatible schemes for polar codes. In this paper, we propose some low-complexity schemes, including puncturing and shortening to construct rate-compatible PAC codes. Besides, a joint shortening and puncturing scheme of PAC codes is also explored to modify the blocklength of PAC codes, by combining the shortened and punctured bit sets. Simulation results show that our proposed rate-compatible PAC codes significantly outperform the rate-compatible polar codes in the 5G new radio standard.