Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and classification of Green Leafy Vegetables using CNN models

Identifying and classifying vegetables in big farms is a challenge, especially when the vegetables are similar in colour and shape. Manual identification of vegetables takes time and is prone to errors. Therefore, the automatic classification process of the precision farming, increasingly using image processing and pattern recognition to identify fruits and vegetable, is becoming essential to identify and classify vegetables in big farms. In this paper, an automatic system for the identification and classification of green leafy vegetables, similar in colour and shape was evaluataed using five different deep learning models such as CNN, MobileNet, VGG-16, Inception V3 and ResNet 50. The accuracies of these models achieved in this paper vary from 67% to 99%. The model with the highest accuracy is the MobileNet.
Fine-Grained Alignment for Boundary Samples under Open Set Domain Adaptation

Open set domain adaptation aims to transfer knowledge in the presence of unknown samples in the target domain. Previous approaches use additional classifiers or threshold-based methods to identify unknown samples and try to investigate the information of class diversity within the unknown samples. Despite achieving excellent adaptation results, these methods ignore those samples that lie on the cluster boundaries, especially the clustering-based methods. In this paper, we propose a novel Neighbor Prototype Contrastive Clustering (NPC2) method, which uses the Local Semantic Structure (LSS) to help these low-confidence samples located on the boundary of clusters to return to their own clusters. Further, we propose Local Semantic Consistency (LSC) to evaluate the clustering result and apply it to the domain adaptation process as a metric to assess the reliability of the samples. Results on four benchmarks show that our NPC2 significantly outperforms most state-of-the-art methods with higher LSC.
Automatic Arrhythmia Detection Using Deep Learning

Arrhythmia is a common cardiac disease that can be a potential life threat. Arrhythmias are detected using an Electrocardiogram (ECG) signal record. ECGs are usually performed with 12 lead channels. However, this arrangement requires extensive equipment and expertise to handle. The doctors also need much time to find problematic instances when there are many records. A single-channel system is necessary to make a portable, wearable embedded system to detect arrhythmias. Such a system could assist the patients for frequent monitoring and the caregivers to follow up treatment plans and/or immediate actions. Therefore, an automated arrhythmia-detecting mechanism using single-channel information is required to improve cardiac treatment. In so doing, a Convolutional Neural Network (CNN) based lightweight deep learning model is developed to detect arrhythmia. The dataset is prepared from the MIT-BIH arrhythmia database of ECG records to validate the model. 2-D images are taken from the snapshots of R-R peaks, including regular, ventricular premature beats, and atrial premature beats of the MLII lead. The proposed classifier's overall performance on test data to detect arrhythmia and the normal rhythm is 0.9167, with 8,658 parameters.
Impact of Extreme Class-Imbalance on Landslide-Risk Prediction and Mitigation Using Two-Stage Deep Neural Network

The classification of landslides is the one of the most challenging topics because of the complexity of the relationships of various dynamic and uncertain factors and the physical gaining data processes. Landslide incidents frequently occur in the upper northern region of Thailand due to its topography. The landslide classification method is proposed to capture the significant features from an extreme case of class-imbalance dataset. The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. A study area of this research covers an area of 25 square kilometers at Chiang Rai, in Thailand which contains 30,408 non-landslide data and 1,077 landslide data. The percentage of landslide data is 3.54% of the total data. This paper proposed the solution of landslide classification given by extreme class-imbalance dataset. The proposed method has two main steps i.e., firstly, mitigating class-imbalance dataset. and secondly two-stage learning. Performance of the proposed method benchmarks the baseline, the logistic regression (LR), the random forest classifier (RFC) methods given by enhanced dataset. In the case of imbalance dataset, the one-class method is assessed against the proposed method along with the LR and the RFC methods. Experimental results demonstrate that the proposed method has improved the landslide-risk prediction performance over the baseline, the LR, the RFC and the one-class SVM methods in terms of an average area under the curve scores by 0.48, 0.48, 0.03, and 0.06, respectively, in both enhanced dataset and imbalance dataset.
Improving Automatic Singing Skill Evaluation with Timbral Features, Attention, and Singing Voice Separation

Most automatic singing skill evaluation (ASSE) models focus only on solo singing, resulting in a limited application scope since singing is usually mixed with instrumental accompaniment in music. In this paper, we propose a more general ASSE model which applies to both solo singing and singing with accompaniment. For this purpose, we employ an existing singing voice separation tool for accompaniment removal and compare ASSE models trained with and without accompaniment. Results show that accompaniment removal achieves better performances. Furthermore, we explore different features and model architectures, concluding that the additions of timbral features, attention mechanism, and dense layer further improve the performance. Finally, we show that our proposed model achieves a Pearson correlation coefficient of 0.562, a 62.4% relative improvement compared to 0.346 for the baseline model.
Lipid–Polymer Hybrid Nanosystems: A Rational Fusion for Advanced Therapeutic Delivery

Lipid nanoparticles (LNPs) are spherical vesicles composed of ionizable lipids that are neutral at physiological pH. Despite their benefits, unmodified LNP drug delivery systems have substantial drawbacks, including a lack of targeted selectivity, a short blood circulation period, and in vivo instability. lipid–polymer hybrid nanoparticles (LPHNPs) are the next generation of nanoparticles, having the combined benefits of polymeric nanoparticles and liposomes. LPHNPs are being prepared from both natural and synthetic polymers with various techniques, including one- or two-step methods, emulsification solvent evaporation (ESE) method, and the nanoprecipitation method. Varieties of LPHNPs, including monolithic hybrid nanoparticles, core–shell nanoparticles, hollow core–shell nanoparticles, biomimetic lipid–polymer hybrid nanoparticles, and polymer-caged liposomes, have been investigated for various drug delivery applications. However, core–shell nanoparticles having a polymeric core surrounded by a highly biocompatible lipid shell are the most commonly explored LPHNPs for the treatment of various diseases. In this review, we will shed light on the composition, methods of preparation, classification, surface functionalization, release mechanism, advantages and disadvantages, patents, and clinical trials of LPHNPs, with an emphasis on core–shell-structured LPHNPs.
A Sparse Array DOA Estimation Approach Based on Matrix Completion

In order to improve the performance of direction of arrival (DOA) estimation for sparse array, this paper applies low-rank matrix completion theory to DOA estimation and proposes an improved matrix completion model and optimization solution. The proposed method utilizes the Hermitian and Toeplitz structural information of the observation matrix as a priori information. We split the augmented Lagrangian function and minimize one of the subproblems by Dykstra alternating projection which makes the completion matrices keep the Toeplitz structure and accomplishes the denoising of the known data. The simulation results show that the proposed method can effectively realize the reconstruction of sparse array, the performance of DOA estimation is excellent, and it can be applied to coherent sources.
Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation

Although self-supervised learning methods based on masked image modeling have achieved some success in improving the performance of deep learning models, these methods have difficulty in ensuring that the masked region is the most appropriate for each image, resulting in segmentation networks that do not get the best weights in pre-training. Therefore, we propose a new adaptive-masking policy self-supervised learning method. Specifically, we model the process of masking images as a reinforcement learning problem and use the results of the reconstruction model as a feedback signal to guide the agent to learn the masking policy to select a more appropriate mask position and size for each image, helping the reconstruction network to learn more fine-grained image representation information and thus improve the downstream segmentation model performance. We conduct extensive experiments on two datasets, Cardiac and TCIA, and the results show that our approach outperforms current state-of-the-art self-supervised learning methods.
Transmit Beamforming for Dual-Function Radar- Communication System Based on FDA

Dual-function radar-communication (DFRC) methods employed phased array and multiple-input multiple- output (MIMO) systems are “point-to-line”. The enemy hidden in coverage can intercept the transmit signals leading to poor confidentiality. Frequency diversity array (FDA) can provide an angle-time-dependent transmit steering vector by introducing a stepping frequency increment among each array element. This paper proposes an anti-interception DFRC system based on FDA transmit beamforming. The proposed system adopts a sub-pulse transmit mode to realize a “point-to-point” detection and communication. The information symbols are embedded into the transmit beampattern by beam level control and orthogonal waveform transmission. The receiver can obtain the original information by detecting the received signal amplitude in multiple channels. The simulation results demonstrate that the proposed system can perform “point-to-point” detection and communication capability with a low bit error rate and high confidentiality.
TED: A Python-Based Analog Design Environment for Agile Circuit Development

The paper introduces TED, a Python-based analog hardware design environment that simplifies the design process for both netlist and layout. With its domain-specific syntax and built-in engines for automated placement and routing, TED enables designers to describe analog circuits using fewer lines of code compared to other solutions. Results from experiments show that a SAR-ADC design can be created with just 800 lines of code in TED, while a similar design would require 40,000 lines of code in BAG. Additionally, TED supports technology-generic design, allowing for highly reusable designs across different process technologies and enabling a no-human-in-the-loop design process when transitioning from one technology to another, saving significant time and effort in repeated design work.
Test Vector Generation for Array-form Multipliers Using a Genetic Algorithm

Digital multipliers are often designed in the array form. Traditional timing closure of multipliers is issued by static timing analysis (STA) which only gives a conservative timing report. Sometimes we would like to find the critical paths of a multiplier array and the corresponding input words (test vectors) that activate such critical paths. This is a very time-consuming task if using an exhaustive test vector enumeration method. There exists only one heuristic method in the open literature that can generate suboptimal test patterns corresponding to relatively long critical paths for multipliers with quite long word lengths. However, this method does not guarantee that the generated test vectors would activate the longest critical paths. In this paper we propose a genetic algorithm (GA) to conduct the search of the longest critical path test patterns for the array multipliers using carry-save adders (CSA). Experiments show that the GA method can find the longest critical paths for a set of CSA array multipliers up to the word length of 21 bits. Improvements over the previous method have been observed.
DBIA: Data-Free Backdoor Attack Against Transformer Networks

Recently, transformer architecture has demonstrated its significance in both Natural Language Processing (NLP) and Computer Vision (CV) tasks. Although other network models are known to be vulnerable to the backdoor attack, which embeds triggers in the models and controls the models’ behavior when the triggers are presented, little is known about how such an attack performs on the transformer models. In this paper, we propose DBIA, a novel Data-free1 Backdoor Attack against the CV-oriented transformer networks, leveraging the inherent attention mechanism of transformers to generate triggers and injecting the backdoor using a poisoned substitute dataset. We conducted extensive experiments using three benchmark transformers, i.e., ViT, DeiT, and Swin Transformer, on four mainstream image classification tasks, i.e., ImageNet, CIFAR-10, GTSRB, and Youtube Face. The evaluation results demonstrate that, with fewer resources, our approach can embed backdoors with a high success rate and a low impact on the performance of the victim transformers.
Motion Analysis of Passive-Dynamic Walker with Flexible Structure: Exploration for Novel Support Phase

In the robotics, various walking models have been proposed to study the mechanisms of walking, and for better investigations, the walking model is excepted that can capture the most essence of walking problem with minimal complexity. According, propose a model that describe and explain the walking mechanism of creatures is our research target. In this study, we proposed a passive-dynamic walker which has flexible structure, and analyzed the walking performance by numerical simulation. The simulation result shows the influence of vibration on walking performance, and the flexible rimless wheel (FRW) can generate not only double-limb support phase, but also triple-limb support phase. Our work not only identified new gait patterns, but has also provided a theoretical basis for the mechanical design of locomotion.
Multiphase Multilevel NPC Converter for MVDC Electric Ship Applications

Electric propulsion has been used in marine systems for a while, but it was the availability of power electronic converters that allowed for the electrification of large ships. MVDC power systems have become of interest for future low emission, fuel-efficient vessels, but they require effective analysis tools due to their complexity. Modeling and simulation are essential to design, analyze, and optimize the system. This paper proposes a multi-phase multi-level power electronics-based converter for medium voltage DC electric ship applications, along with a generalized framework for multiphase multilevel space vector modulation (MPML-SVM) technique to control the converter effectively. The proposed approach is based on a hierarchical method that involves SVM basic calculations, SVD for MPML-SVM, and integration of parametrized calculations and SVD to solve dwell time equations and extract switching signals. Simulation studies are performed and compared for MPML converters in a medium-voltage electric ship system (MVESS) to validate the analytical models under different conditions.
Smart Prediction System for Classifying Mirai and Gafgyt Attacks on IoT Devices

The proliferation of Botnet attacks on IoT devices indicates that IoT network traffics is more vulnerable than other IT-based device network traffic. Mitigating this threat has led to new techniques for identifying attacks initiated by infected IoT-based devices. The study proposed an intelligent system (Maltect) that could predict botnet attacks on IoT-based devices by utilizing the machine learning model as the prediction engine. During the implementation process, there are two main parts; firstly, to perform the model classification using machine learning (ML) algorithms to determine the best-fitted model, and second, to develop the web-based system. For model classification, the study deployed the N-BaIoT dataset to train two renowned superior performance classifiers based on previous studies. Support-Vector Machine (supervised learning algorithm) and Extreme Gradient Boosting (ensemble learning algorithm) were the deployed classifiers. The study evaluated the models based on the Accuracy, Precision, Recall, and F-measure performance metrics for each attack type according to three types of IoT devices: the doorbell, security camera, and thermostat. The results denote that Extreme Gradient Boosting was the most performing model, achieving 99.9% accuracy in predicting attacks on all the IoT devices.
Knowledge-Infused Dynamic Embedding for Predicting the Severity of Suicidal Ideation in Social Media

Depression accounts for an increased number of suicides in the United States. To identify those at risk and notify the authorities, social media can play an invaluable role. Posts made on social media can be collected to train predictive models capable of identifying individuals at risk. However, studies with an unsupervised method of data labeling are susceptible to misidentify individuals presenting signs of depression from those who provide support. Moreover, these studies tend to classify depression as a binary outcome, not considering its severity levels. This necessitates the use of accurate and efficient modeling techniques for the existing scarce but professionally annotated datasets. While pre-trained embeddings proved to be an efficient method of text representation, not all may fully encode emotions and sentiment polarities present in mental health related posts. In this work, we propose the use of a dynamic embedding infused with emotion and polarity knowledge for a more accurate representation of depression severity and suicidal ideation. The emotion, polarity, and context-aware generated embeddings are then utilized by bidirectional RNN (GRU and LSTM) model to provide the most accurate predictions. The results show that knowledge-infused dynamic embeddings with bidirectional LSTMs leads to an 8% increase in the overall AUC with improved prediction accuracy of suicidal ideations at its highest level.
A Review on the Breast Histopathological Images, Processing and Classification

Cancer in the breast is a very fatal cancer among the women. A confirmation of its presence can be made mainly by the biopsy. The histopathological image analysis exhibits an indispensable role in the spotting, care and survival among the patients. For the sufficient accuracy and predictability breast histopathological image analysis and artificial intelligence has to work hand by hand. In this retrospection, we have gathered an overview on the breast histopathology images, its processing, analysis and classification based on different algorithms and learning techniques.
Active Haptic Exploration based on Dual-Stage Perception for Object Recognition

Haptic exploration in robotics is prone to sensing ambiguities. Actively selecting actions during exploration can provide crucial information to mitigate these ambiguities and improve object recognition. This study presents a dual-stage active haptic exploration technique that enables a robot to adapt its actions to optimise information acquisition for object recognition. In the initial stage of rough perception, the algorithm employs actions that maximise mutual information to swiftly identify the likely categories of an object. Subsequently, during the fine perception stage, it selects actions that maximise the Kullback-Leibler (KL) divergence between the most likely pair of ambiguous objects, thus facilitating their differentiation. To evaluate the performance of our algorithm, a robot with a sensorised finger collected tactile information from the interaction with ten objects using the primary actions of pressing, sliding, and tapping. In comparison with existing active exploration strategies that optimise a single information metric, our algorithm achieves superior recognition rates while requiring fewer exploration actions. By conducting only necessary comparisons between similar objects, it also reduces the computational cost. These results suggest that the proposed algorithm effectively diminishes ambiguities by adapting actions and enhancing the recognition outcomes in haptic exploration.
On the Design and Development of an Intelligent Safety Warning System for Slow-Moving Vehicles

This paper explored different technologies for designing an intelligent safety warning system for slow-moving vehicles. With the recent advancements in edge computing devices and sensors, it is now possible to develop such a system with a reasonable cost, especially for human-powered small vehicles – such as bicycles. In our design, we utilized the recent advancements in computer vision, AI, machine learning, and Convolutional Neural Networks (CNNs) technologies to design and implement a prototype of such an intelligent safety warning system for slow-moving vehicles. We discussed the pros and cons of different technologies we tested and gave an in deep discussion of the design we chose. We also discussed possible future research directions in this area and the lessons learned from this project.
Supplementary Material for Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

In this supplementary document , we ﬁrst discuss our network architecture, implementation details, the metrics we used and the detailed training protocol in Section 1. More information regarding data pre-and post-processing steps can be found in Section 2. Finally, we provide additional experimental results, both qualitatively and quantitatively in Section 3
A Multi-Dimensional Weight Partition Algorithm for FPGA Prototype Simulation

As the complexity of circuit design increases, single FPGA can no longer meet the requirements of logic simulation and rapid prototyping, so the circuit design needs to be partitioned into multiple parts, and then it can use multiple FPGA systems for simulation. When RTL-level code is modeled as an undirected graph, the weight of vertex is multidimensional and the edge is weighted. In order to meet the demand of multi-FPGA platform, we modeled the RTL-level code into an undirected graph, whose vertex weight is multidimensional, corresponding to different resources of FPGA platform, and the edge is weighted.A new partitioning algorithm should satisfy the following two requirements. Firstly, the multi-dimensional weight of each partition is below the FPGA threshold in each dimension. Secondly, the partition algorithm should minimize max_cut, where max_cut is the largest cut size between all partitions. In this paper, we propose a partition algorithm that meets the above requirements.
Classification and Detection of Cyanosis Images on Lightly and Darkly Pigmented Individual Human Skins using a Fine-Tuned MobileNet Architecture

The classification and detection of cyanosis using in-vivo and in-silico image processing approaches are intriguing and very special. In this study, a peripheral and central cyanosis image classification approach, using lightweight-deep learning Convolutional Neural Networks (CNNs), referred to as pre-trained MobileNet architecture, was introduced. This modified MobileNet model was assessed using the sanctioned dataset of 1300-image collected from multiple cyanosis published datasets. The augmentation technique was applied on the training dataset to enrich the productivity. Emphatic results, validation-accuracy and accuracies on the training and test datasets of 95% and 97%, respectively; were obtained as compared to the validation-accuracy of 79% and 82% of the Simple Convolutional Neural Networks (SCNNs) and Fine-tuned VGG16 models attained from prior stud.