Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Binary Particle Swarm Optimization With Support Vector Machine for Enhancing Rice Varieties Classification Accuracy

The objective of this study is to classify the rice grains of seventeen different varieties popularly planted in Vietnam. Image processing is used to extract color, morphological, and texture features of the rice grains. Five feature subsets are formed, namely, morphological, basic color, clustering color, statistical, and gray level co-occurrence matrix (GLCM). These subsets and combined sets are evaluated for classification ability with a support vector machine (SVM). A dataset of 248 features, including a total of color, morphological, and texture features classified with the SVM gives an overall accuracy of 88.29%. To decrease the number of used features and to improve the classification accuracy, the proposed method combining binary particle swarm optimization (BPSO) and the SVM, called BPSO+SVM, is applied to the dataset. In the results, classification accuracy from BPSO+SVM reaches 93.94% using only 96 selected features. The obtained result shows the proposed method achieves higher classification accuracy than the SVM alone, and the required number of features is only 39% of the total dataset. This result can be applied for developing an automatic classification and identification system of rice varieties.
Transitional Asymmetric Non-local Neural Networks for Real-World Dirt Road Segmentation

Understanding images by predicting pixel-level semantic classes is a fundamental task in computer vision and is one of the most important techniques for autonomous driving. Recent approaches based on deep convolutional neural networks have dramatically improved the speed and accuracy of semantic segmentation on paved road datasets, however, dirt roads have yet to be systematically studied. Dirt roads do not contain clear boundaries between drivable and non-drivable regions; and thus, this difficulty must be overcome for the realization of fully autonomous vehicles. The key idea of our approach is to apply lightweight non-local blocks to reinforce stage-wise long-range dependencies in encoder-decoder style backbone networks. Experiments on 4,687 images of a dirt road dataset show that our transitional asymmetric non-local neural networks present a higher accuracy with lower computational costs compared to state-of-the-art models.
Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring

We design a video analytics system to measure gait over time and detect trend and outliers in the data. The purpose is for health monitoring, the thesis being that trend especially can lead to early detection of declining health and be used to prevent accidents such as falls in the elderly. We use the OpenPose deep learning tool for recognizing the back and neck angle features of walking people, and measure speed as well. Trend and outlier statistics are calculated upon time series of these features. A challenge in this work is lack of testing data of decaying gait. We first designed experiments to measure consistency of the system on a healthy population, then analytically altered this real data to simulate gait decay. Results on about 4000 gait samples of 50 people over 3 months showed good separation of healthy gait subjects from those with trend or outliers, and furthermore the trend measurement was able to detect subtle decay in gait not easily discerned by the human eye.
LightChain: Scalable DHT-Based Blockchain

As an append-only distributed database, blockchain is utilized in a vast variety of applications including the cryptocurrency and Internet-of-Things (IoT). The existing blockchain solutions show downsides in communication and storage scalability, as well as decentralization. In this article, we propose LightChain, which is the first blockchain architecture that operates over a Distributed Hash Table (DHT) of participating peers. LightChain is a permissionless blockchain that provides addressable blocks and transactions within the network, which makes them efficiently accessible by all peers. Each block and transaction is replicated within the DHT of peers and is retrieved in an on-demand manner. Hence, peers in LightChain are not required to retrieve or keep the entire ledger. LightChain is fair as all of the participating peers have a uniform chance of being involved in the consensus regardless of their influence such as hashing power or stake. We provide formal mathematical analysis and experimental results (simulations and cloud deployment) to demonstrate the security, efficiency, and fairness of LightChain, and show that LightChain is the only existing blockchain that can provide integrity under the corrupted majority power of peers. As we experimentally demonstrate, compared to the mainstream blockchains such as Bitcoin and Ethereum, LightChain requires around 66 times smaller per node storage, and is around 380 times faster on bootstrapping a new node to the system, and each LightChain node is rewarded equally likely for participating in the protocol.
A Hierarchical Framework for Leaf Instance Segmentation: Application to Plant Phenotyping

Image based analysis of plants is a high-throughput and non-invasive approach to study plant traits. The quantitative estimation of many plant traits (leaf area index, biomass etc.) from plant images is primarily based on accurate segmentation of individual leaves. This is a challenging task due to the presence of overlapped leaves and lack of discernible boundaries between them. To overcome these limitations, state-of-the-art supervised deep learning algorithms have been recently employed. However, the annotations of individual leaf instances is time consuming, in addition the variability in leaf shapes and its arrangement among different plant species limits the broad utilisation of these algorithms. To relieve this bottleneck, we propose a novel framework that relies on a graph based formulation to extract leaf shape knowledge for the task of leaf instance segmentation. These shape priors are generated based on leaf shape characteristics independent of plant species. Evaluation of the proposed framework on multiple plant datasets i.e. Arabidopsis, Komatsuna and salad demonstrates its broad utility.
Exploring the Use of Artificial Intelligence Techniques to Detect the Presence of Coronavirus Covid-19 Through Speech and Voice Analysis

The Covid-19 pandemic represents one of the greatest global health emergencies of the last few decades with indelible consequences for all societies throughout the world. The cost in terms of human lives lost is devastating on account of the high contagiousness and mortality rate of the virus. Millions of people have been infected, frequently requiring continuous assistance and monitoring. Smart healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of patient care but also in order to support the early diagnosis, prevention and evaluation of Covid-19 in a faster and more accurate way. On the other hand, the necessity to realise reliable and precise smart healthcare solutions, able to acquire and process voice signals by means of appropriate Internet of Things devices in real-time, requires the identification of algorithms able to discriminate accurately between pathological and healthy subjects. In this paper, we explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis. Several studies report, in fact, significant effects of this virus on voice production due to the considerable impairment of the respiratory apparatus. Vocal folds oscillations that are more asynchronous, asymmetrical and restricted are observed during phonation in Covid-19 patients. Voice sounds selected by the Coswara database, an available crowd-sourced database, have been e analysed and processed to evaluate the capacity of the main ML techniques to distinguish between healthy and pathological voices. All the analyses have been evaluated in terms of accuracy, sensitivity, specificity, F1-score and Receiver Operating Characteristic area. These show the reliability of the Support Vector Machine algorithm to detect the Covid-19 infections, achieving an accuracy equal to about 97%.
Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F1-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.
A Novel Fire Identification Algorithm Based on Improved Color Segmentation and Enhanced Feature Data

In order to improve the accuracy of fire identification based on video in the Internet-of-Things environment, this article proposes a new fire identification algorithm by merging fire segmentation and multifeature fusion of fire. First, according to the relationship between R and Y channels, the improved YCbCr models are established for initial fire segmentation under reflection and nonreflection conditions, respectively. Simultaneously, the reflection and nonreflection conditions are judged by comparing the areas obtained by the two improved YCbCr models. Second, an improved region growing algorithm is proposed for fine fire segmentation by making use of the relationship between the seed point and its adjacent points. The seed points are determined using the weighted average of centroid coordinates of each segmented image. Finally, the quantitative indicators of fire identification are given according to the variation coefficient of fire area, the dispersion of centroid, and the circularity. Extensive experiments were conducted, and the experimental results demonstrate that the proposed fire detection method considerably outperforms the traditional methods on average in terms of three performance indexes: precision, recall, and $F1$ -score. Specifically, compared with the deep learning method, the precision of the proposed method is slightly higher. Although the recall of the proposed method is slightly lower than the deep learning method, its computation complexity is low.
Video Object Detection Using Object's Motion Context and Spatio-Temporal Feature Aggregation

The deep learning technique has recently led to significant improvement in object detection accuracy. In many applications, object detection is performed on video data consisting of a sequence of two-dimensional (2D) image frames. Numerous object detection schemes have been designed to detect objects independently in each video frame. Though temporal information within adjacent image frames can be exploited in subsequent object tracking stage, it has been shown that the object detection accuracy can be significantly improved by exploiting the temporal structure in the image sequence in the object detection stage. In this paper, we propose a novel video object detection method that exploits both the motion context inferred from the adjacent frames and the spatio-temporal features aggregated over the image sequence. First, correlation between the spatial feature maps over two adjacent frames are computed and the embedding vector, representing the motion context, is obtained by encoding the $N$ correlation maps using long short term memory (LSTM). In addition to utilizing the motion context, the spatial feature maps for ($N+1$) consecutive frames are aggregated to boost the quality of the feature map. The gated attention network is employed to selectively combine the temporal feature maps based on their relevance to the feature map in the present image frame. While most video object detectors have been developed for two-stage object detectors, our proposed idea applies to one-stage detectors with the advantage of low computational complexity in practical real-time applications. Our numerical evaluation conducted on the ImageNet object detection from video (VID) dataset demonstrates that our proposed network achieves significant performance gain over the baseline algorithms and outperforms the existing one-stage video object detectors.
Fine-Resolution Ranging Scheme Based on Signal Strength in Indoor Hallway With Rough-Surface Slab Waveguide

The received signal strength (RSS) is widely used in range-based localization because of its practical advantages, such as low cost and low complexity. For accurate ranging based on the RSS in indoor environments, well-defined propagation models (PMs) that consider the distortion of the RSS due to abrupt obstructions are required. This article proposes an exponential PM for indoor hallways whose structures can be modeled as a slab waveguide. This waveguide-shaped structure has two main power loss mechanisms: wave penetration and scattering. These loss mechanisms are quantified as exponential coefficients according to the law of conservation of energy. The surface-scattering theory and the multimode waveguide concept are utilized for the quantification of scattering and penetration, respectively. The proposed model is verified through actual site measurements using the vector network analyzer (VNA)-based channel-sounding system at IEEE 802.11ax frequency bands. The estimated range obtained from the measured data indicates that the proposed model leads to the ranging estimation performance that could not be achieved with existing sounding-based indoor channel models.
Design, development, EUVL applications and nano mechanical properties of a new HfO2 based hybrid non-chemically amplified resist

A new HfO2 based hybrid polymer HfO2–methacrylate–MAPDST (HMM, Mw ∼27885 g mol−1) was synthesized by reacting the hybrid monomer HfO2–methacrylate (HM) with (methacryloyloxy)phenyldimethylsulfoniumtriflate (MAPDST) in the presence of azobisisobutyronitrile (AIBN) as a radical initiator in tetrahydrofuran and acetonitrile (2:1; v/v) at 60 °C for Extreme Ultraviolet Lithography (EUVL) applications as a non-chemically amplified resist (n-CAR) material. Transmission Electron Microscope (TEM) and Dynamic Light Scattering (DLS) analyses revealed the particle sizes of HM and HMM hybrids as ∼3–5 nm and ∼12 nm respectively. Thin films of thickness ∼53 nm were prepared by spin coating 3% (w/v) solutions of HMM in methanol onto 4′′ p-type Si wafers consisting of a 40 nm hexamethyldisilazane (HMDS) under layer. The films were then subjected to EUV exposure at a dose of 51.7127 mJ cm−2 on a micro exposure tool (MET) with an Advanced Light Source (ALS) at SEMATECH Berkeley using standard mask IMO228775 with field R4C3 (LBNL low flare bright-field). The EUV E0 dose used for the exposure was 22 mJ cm−2. Field Emission Scanning Electron Microscope (FE-SEM) micrographs of the EUV exposed hybrid resist films showed isolated line patterns of 80, 70, 60, 50, 45, 40, 35 and 30 nm for 5:1 duty cycle, of which 80 to 50 nm line patterns were well resolved, whereas patterns of 45 to 30 nm or higher resolutions exhibited undesirable traits like bridging, fracturing, de-adhesion, peel-off and pattern collapse. To comprehend the reasons behind these undesirable traits, nano-mechanical properties (modulus and adhesion) of the EUV exposed and developed resist patterns were investigated by using Peak Force-Quantitative Nano-Mechanical (PF-QNM) tapping mode Atomic Force Microscope (AFM). These studies revealed ∼18% and ∼19% reductions in Derjaguin–Muller–Toporov (DMT) modulus and adhesion magnitudes respectively with an increase in isolated line pattern resolution from 80 to 30 nm for 5:1 duty cycles. The reduction in modulus and adhesion magnitudes might have caused the observed fracturing, peel-off and collapse of the high resolution line patterns during the pattern development processes.
A Novel Feature Points Tracking Algorithm in Terms of IMU-Aided Information Fusion

Feature tracking plays a vital role in a monocular visual–inertial system (VINS) or a visual task based on feature points. However, in terms of feature points tracking, most of the existing VINS solutions adopt the classical method where the feature extraction and matching are carried out independently. Due to such nonintegrated working manner, the matching performs traversal operation globally rather than in a reasonable search space, which increases the probability of false matches, and reduces the accuracy. In this article, a novel feature points tracking algorithm in terms of inertial measurement unit (IMU)-aided information fusion is presented, which can reduce the search space to improve accuracy, and boost efficiency. This method starts with a preintegration-based predictor which can predict the position of the feature points in the current frame according to the feature points that need to be matched in the previous frame, and the measurements of IMU between two frames. Then, a variable-sized search window, in which the feature extraction and matching are locally carried out, is built at the predicted location. Furthermore, to solve the convergence and overlap problem of feature points in the tracking process, a feature update module of the bionic population is attached to the local matcher. Finally, the comparison experiments are performed on the public datasets to show the effectiveness and superiority of our method. It should be emphasized that the proposed algorithm is a universal framework of solution, which can meet various task requirements by choosing different feature points extraction algorithms, and improve the efficiency of matching in an integrated way.
Revisiting the Training of Very Deep Neural Networks without Skip Connections

Deep neural networks (DNNs) with many layers of feature representations yield state-of-the-art results on several difficult learning tasks. However, optimizing very deep DNNs without shortcut connections known as PlainNets, is a notoriously hard problem. Considering the growing interest in this area, this paper investigates holistically two scenarios that plague the training of very deep PlainNets: (1) the relatively popular challenge of ‘vanishing and exploding units' activations', and (2) the less investigated ‘singularity’ problem, which is studied in details in this paper. In contrast to earlier works that study only the saturation and explosion of units' activations in isolation, this paper harmonizes the inconspicuous coexistence of the aforementioned problems for very deep PlainNets. Particularly, we argue that the aforementioned problems would have to be tackled simultaneously for the successful training of very deep PlainNets. Finally, different techniques that can be employed for tackling the optimization problem are discussed, and a specific combination of simple techniques that allows the successful training of PlainNets having up to 100 layers is demonstrated.
Optical Proximity Correction Using Bidirectional Recurrent Neural Network With Attention Mechanism

Recurrent neural network (RNN) is employed as a machine learning model for fast optical proximity correction (OPC). RNN consists of a number of neural network instances which are serially connected, with each instance in charge of one segment. RNN thus allows some localized segments to be corrected together in one execution, which offers higher accuracy. A basic RNN is extended by introducing gated recurrent unit (GRU) cells in recurrent hidden layers, which are then connected in both forward and backward directions; attention layer is adopted to help output layer predict mask bias more accurately. The proposed RNN structure is used for OPC implementation. A choice of input features, sampling of training data, an algorithm of mapping segments to neural network instances, and final mask bias calculation are addressed toward efficient implementation. Experiments demonstrate that the proposed OPC method corrects a mask layout with 36% lower EPE compared to state-of-the-art OPC method using artificial neural network structure.
Uniform and Non-uniform Sampling Methods for Sub-linear Time k-means Clustering

The k-means problem is arguably the most well-known clustering problem in machine learning, and lots of approximation algorithms have been proposed for it. However, many of these algorithms may become infeasible when data is huge. Sub-linear time algorithms with constant approximation ratios are desirable in this scenario. In this paper, we first improve the analysis of the algorithm proposed by [1] by sharpening the approximation ratio from $4(\alpha+\beta)$ to $\alpha+\beta$. Moreover, on mild assumptions of the data, a constant approximation ratio can be achieved in poly-logarithmic time by the algorithm. Furthermore, we propose a novel sub-linear time clustering algorithm called $Double -K-M C^{2}$ sampling as well. Experiments on the data clustering task and the image segmentation task have validated the effectiveness of our algorithms.
Holographic-Based Leaky-Wave Structures: Transformation of Guided Waves to Leaky Waves

The term space wave refers to a wave that is propagating through an open medium with a diverging wavefront as it moves away from the source. The term guided wave denotes a variety of wave configurations that are being carried by a closed/open/partially open structure [1]. A guided wave may also be known as a bounded wave, which is a term inspired by its propagation mechanism, where a traveling wave is bounded by two dissimilar mediums with different electromagnetic (EM) characteristics; the wave propagates along the interface and decays exponentially to the surrounding mediums.
Deep Real-time Hand Detectoin Using CFPN on Embedded Systems

Real-time HI (Human Interface) systems need accurate and efficient hand detection models to meet the limited resources in budget, dimension, memory, computing, and electric power. In recent years, object detection became a less challenging task with the latest deep CNN-based state-of-the-art models, i.e., RCNN, SSD, and YOLO; however, these models cannot provide the desired efficiency and accuracy for HI systems on embedded devices due to their complex time-consuming architecture. In addition, the detection of small hands ($<30\times 30$ pixels) is still a challenging task for all the above existing methods. Thus, we propose a shallow model named Concatenated Feature Pyramid Network (CFPN) to provide above mentioned performance for small hand detection. The superiority of CFPN is confirmed on a HandFlow dataset with mAP:0.5 of 95.6 and FPS of 33 on Nvidia TX2. The COCO dataset is also used to compare with other state-of-the-art method and shows the highest efficiency and accuracy with the proposed CFPN model. Thus we conclude that the proposed model is useful for real-life small hand detection on embedded devices.