Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT-based PID Controller for Light Control System

This research developed a low cost smart control system that enables virtual monitoring and control of a closed-loop system using a NodeMCU microcontroller linked to the cloud or internet network. The designed system allows adjustments for Proportional-Integral (PI) controller and setpoint settings of the real-time operating system to be performed anywhere and anytime via Blynk IoT platform. The analysis is demonstrated using experimental test to a light control system, where the PI controller controls light luminosity emitted by Light Emitting Diode (LED). The designed Graphical User Interface (GUI) is capable of notifying system failure by sending an alarm notification message to the electronic device connected to the physical system virtually, so the biased or unstable process parameters could be immediately mitigated when it occurs. Empirical model identification is conducted via MATLAB simulation, followed by the calculation of controller tunings. MATLAB analysis further analyses gain values, that can affect the adjustment of proportional and integral gains for the stability and good control. Data downloading can be performed either via the Integrated Development Environment (IDE) software or the Superchart in the Blynk. The experimental analysis showed that the light control system has perform well while the Blynk is connected virtually to the light control system.
Derivative-Free Observability Analysis of a Stochastic Dynamical System

This paper proposes a novel approach to analyze the observability of a stochastic linear or non-linear dynamical system. Unlike the traditional observability analysis approach, our approach is fully derivative-free, resulting in a low complexity and an easy implementation. Furthermore, it can not only analyze the observability of each system state, but also it can assess the effect of the observation and the system noise on the system observability, which is not considered by the traditional approach. Specifically, using the generalized polynomial chaos expansion, an observability-coefficient matrix is first calculated, which enables us to judge whether the system is observable or not. Then, the puny and brawny observability indexes are assessed to quantify the degree of observability. The equivalence between the conventional method and the proposed method for a continuous-time linear time-invariant system is proved in the appendix. The effectiveness of the proposed approach is mathematically proved and its good performance is demonstrated in several test cases.
Multi-Modal Emotion Recognition Based On deep Learning Of EEG And Audio Signals

Automatic recognition of human emotional states has attracted many researchers' attention in Human-Computer Interactions and emotional brain-computer interface recently. However, the accuracy of emotion recognition is not satisfying. Considering the advantage of information supplement based on deep learning of multi-modal signals related to emotion, this study proposed a novel emotion recognition architecture to fuse emotional features from brain electroencephalography (EEG) signal and the corresponding audio signal in emotion recognition on DEAP dataset. We used convolutional neural network (CNN) to extract EEG features and bidirectional long short term memory (BiLSTM) neural networks to extract audio features. After that, we combine the multi-modal features into a deep learning architecture to recognize arousal and valence levels. Results showed an improved accuracy compared with previous studies that merely used the EEG signals in both arousal level and valence level, which suggests the effectiveness of our proposed multi-modal fused emotion recognition model. In future work, multi-modal data from nature interaction scenes will be collected and inputted into this architecture to further validate the effectiveness of the method.
Determination of Trace Elements in Dendrobium Officinale Cultivated in Various Conditions

ABSTRACT Dendrobium officinale is an important medicinal plant in traditional Chinese medicine. The consumption of D. officinale has increased rapidly in recent years due to the health awareness among Chinese people. The present study aimed to determine trace elements levels in 42 D. officinale samples and to utilize the elemental data for identifying the cultivation technique. Eighteen trace elements (B, V, Cr, As, Mn, Fe, Mo, Cu, Zn, Se, Sr, Sn, Ti, Al, Co, As Pb, Cd, and Bi) were determined by inductively coupled plasma–mass spectrometry. Of the 11 essential elements, the mean concentrations ranged from 30.6 µg kg−1 (Mo) in D. officinale with greenhouse cultivation to 161 mg kg−1 (Fe) in D. officinale with rock cultivation, and the contributions of Mn may exceed the dietary reference intake value by the uptake of 20 g of D. officinale. Of the seven toxic elements, the mean concentrations ranged from 3.29 µg kg−1 (Bi) in the D. officinale with greenhouse cultivation to 342 mg kg−1 (Al) in the D. officinale with rock cultivation, and the contributions of Al may exceed the provisional tolerable daily intake value by the uptake of 20 g of D. officinale. The concentrations of seven metals (Bi, Pb, Fe, Al, Cr, As, and Mo) were utilized to identify the cultivation technique of the analyzed D. officinale samples through linear discriminant analysis.
Road Garbage Segmentation and Cleanliness Assessment Based on Semantic Segmentation Network for Cleaning Vehicles

Road garbage recognition and cleanliness assessment are very important for intelligent cleaning of vehicles. However, widely used garbage detection methods cannot provide accurate information to assess cleanliness. Also, only a few contributions are available in the road cleanliness assessment, and they too mainly depend on the category and quantity of garbage. In this paper, deep supervision UNet++ (DUNet++) is proposed to solve the problem of road garbage classification and segmentation, which can directly impact the output of the category of garbage and the occupied ground area. A new, simple and accurate method is established to assess road cleanliness by combining the results of garbage segmentation. Compared with the cleanliness index (CI), the road cleanliness index based on semantic segmentation (CISS) not only considers the road area occupied by different types or categories of the garbage but also considers the difficulty of cleaning them. A road garbage segmentation dataset, which consists of four categories (stones, leaves, sand, and bottles), is collected to train the designed garbage segmentation model, and it has achieved considerable garbage segmentation improvement with an MIoU (Mean Intersection over Union) of $76.73\pm 0.11$. Especially when compared with the state-of-the-art methods, the model we designed has greatly improved the accuracy of garbage segmentation. Experimental results have shown that our road garbage segmentation and cleanliness assessment methods are relatively simple, and can provide accurate road cleanliness information for cleaning vehicles.
Graph Synthesis for Pig Breed Classification From Muzzle Images

Non-intrusive and automated detection of pig breeds, particularly from visual standpoint, is important from a food quality tracking perspective. In this work, colour as well as texture based visual descriptors from muzzle images have been identified, which, serve as breed-identifiers to separate four common pig-breeds: Duroc, Ghungroo, Hampshire and Yorkshire. While these handcrafted visual descriptors by themselves are fairly robust and discriminative, it is recognized that by controlling the decision space by choosing the feature-type based on colour or texture or both and the order in which particular breeds are siphoned, classification accuracy can be improved considerably. In that light, a stable, relatively data-independent, breed-specific, hierarchical tree synthesis and feature selection procedure is proposed based on a breed-pair cluster separation table. The proposed approach has been compared with the state of the art Phylogenetic distance based Hierarchical Agglomerative Clustering algorithm (AGNES) and also with the standard decision tree classification algorithm. On cross-validation, When completely different sets of pigs were used for training and testing (50-50 split), the proposed algorithm reported relatively high mean classification accuracies of 86.45% for Duroc, 93.02% for Ghungroo, 86.91% for Hampshire and 98.54% for Yorkshire, respectively.
Identification of Adversarial Android Intents using Reinforcement Learning

Android malware has grown manifold in the last decade and has become a threat to the ecosystem. Recently published literature suggests that the data mining based models have shown promising results in android malware detection. However, adversaries can identify vulnerabilities in these detection models using adversarial learning. Adversaries can further exploit these vulnerabilities by performing adversarial attacks on these detection models and reduce their performance. Therefore in this work, we first put ourselves in the adversary's shoes and propose an evasion attack to find vulnerabilities in various malware detection models using reinforcement learning. The attack aims to add perturbation(s) in malware samples to generate adversarial samples such that they are forcefully misclassified as benign samples by the malware detection models. The attack is designed for a limited knowledge scenario where an adversary does not know the learning algorithm used to build detection models, which is similar to a real-world scenario. We validated the proposed adversarial attack against ten malware detection models constructed using different learning algorithms. Our proposed attack with limited knowledge and capabilities accomplish more than 58% forced misclassification rate against all ten detection models. We also identified ten vulnerable intents that an adversary could exploit to decrease the performance of malware detection models. Lastly, we propose a defense against evasion attacks to increase the adversarial robustness of all malware detection models.
Efficient Probabilistic K-NN Computation in Uncertain Sensor Networks

Uncertain data management has recently attracted much research interest in the networking community and database community, as in many emerging applications, e.g., sensor data monitoring, location-based services and vehicle tracking, where data are inherently uncertain due to measurement errors, update delays etc. The probabilistic k nearest neighbor (k-PNN) query returns k objects with the highest probability of being the k-th nearest neighbor from a given query point Q. However, compared to the traditional k-NN over precise data, the computation cost of k-PNN is rather expensive due to the costly numerical integration or Monte-Carlo approach it adopts, which raises a challenge in answering k-PNN in very large uncertain sensor networks. To address this challenge, we propose an efficient strategy for processing k-PNN queries. Specifically, we introduce two effective pruning methods, spatial pruning and probabilistic pruning, to speed up the query procedure by reducing the search space. The spatial pruning is based on the bounding region of the k-th nearest neighbor, while the probabilistic pruning is based on the lower and upper probability bounds of each k-NN candidate object after spatial pruning. Extensive experiments have been implemented to demonstrate the efficiency and effectiveness of our proposed method under various settings, in terms of both wall clock time and the number of candidate objects to be evaluated.
Decomposing Non-Stationary Signals With Time-Varying Wave-Shape Functions

Modern time series are usually composed of multiple oscillatory components, with time-varying frequency and amplitude contaminated by noise. The signal processing mission is further challenged if each component has an oscillatory pattern, or the wave-shape function, far from a sinusoidal function, and the oscillatory pattern is even changing from time to time. In practice, if multiple components exist, it is desirable to robustly decompose the signal into each component for various purposes, and extract desired dynamics information. Such challenges have raised a significant amount of interest in the past decade, but a satisfactory solution is still lacking. We propose a novel nonlinear regression scheme to robustly decompose a signal into its constituting multiple oscillatory components with time-varying frequency, amplitude and wave-shape function. We coined the algorithm shape-adaptive mode decomposition (SAMD). In addition to simulated signals, we apply SAMD to two physiological signals, impedance pneumography and electroencephalography. Comparison with existing solutions, including linear regression, recursive diffeomorphism-based regression and multiresolution mode decomposition, shows that our proposal can provide an accurate and meaningful decomposition with computational efficiency.
An Answer Driven Model For Paragraph-level Question Generation

Answer-aware question generation aims to generate answerable questions from a given context and answers. Most of the current models are based on the attention-based sequence-to-sequence ($s$eq2seq) structure. However, these models do not make full use of the answer information, resulting in generating questions unrelated to the answer. We propose an answer driven model, which dynamically incorporates the interactive information between answer and previously generated words in the decoder to help the model decide which aspect of the question to focus on. Further, we use the answer distribution difference as a reward and use reinforcement learning to fine-tune the model. Experimental results show that our model performs better than the baseline models.
Learning Knowledge Graph Embedding in Semantic Space: A Novel Bi-linear Semantic Matching Method

Knowledge Graphs represent facts with triples containing head entity $h$, tail entity $t$ and relation $r$ which facilitate applications of many scenarios, for example intelligent web search, community detection and question answering. Knowledge Graph Embedding (KGE) represents elements of triples in a low-dimensional continuous vector space. Though it has been widely studied by both academic and industry communities, most researches focus on learning embeddings of entities and relations separately rather than considering the interactive semantic information between them. However, neither relations nor entities exist lonely out of context. In this paper, we define an interactive semantic space to model the context of triples and propose a novel Bi-linear Semantic Matching Method using Convolutional networks (BiSC). Specifically, we use 1D convolutional neural networks to extract features of the interactive semantics and then compute the similarity scores in the bi-linear space. Compared to existing complex graph network methods, BiSC needs lower computational cost to reach competitive results on link prediction task. The consistent state-of-the-art performance through extensive experiments over two benchmarks demonstrates the advantages of the proposed BiSC model. Further analysis on convergence study and case study of interactive semantic space show the efficiency of our model.
Deep Convolutional Neural Networks based on Manifold for Smoke Recognition

Smoke recognition has been actively studied in the computer vision domain. Due to large variance of smoke color, texture and shapes, smoke recognition is a challenging task. Traditional smoke recognition methods are based on handcrafted features. In the past few years, some methods which are based on convolutional neural networks have been proposed that achieved great improvement of smoke recognition. However, previous methods cannot capture the internal structure of smoke well. Manifolds can represent the internal characteristics of smoke data in lower dimensions, reduce the redundancy of data representation, and obtain more discriminative capabilities. In this paper, an end-to-end deep convolutional neural network which based on manifold structure was proposed to capture more discriminative features for smoke recognition. Experimental results show that the proposed method achieves promising results on the public smoke recognition dataset. The proposed method can obtain high detection rate, high accuracy rate and low false alarm rate in the same time.
Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

“One person’s data or experience is another person’s information” this has become the golden rule of the 21st century which has resulted in a massive reservoir of data and immense amounts of information generation. However, there is no control over the source of this information, accessibility of this information, or the quality of it, which has given rise to the presence of “misinformation. ” The research community has reacted by proposing frameworks and difficulties, which are helpful for (different subtasks of) recognizing misinformation. Most of these frameworks, however, fail to consider all the aspects that can contribute to making information “credible”. Furthermore, a valid explanation for each considered feature’s contribution to the model’s decision stands missing in most work. With this in mind, the authors have attempted to produce a system that yields highly accurate decisions, thus effectively separating credible health blogs from their non-credible counterparts while providing valid user-friendly explanations. The study proposes an Explainable AI-assisted Multimodal Credibility Assessment System that examines the credibility of the platform where the blog is hosted, the credibility of the author of the blog and the credibility of the images that contribute to the blog. This novel framework contributes to the existing body of knowledge by assessing the credibility of misleading beauty blogs using multiple crucial modalities which would lead to an insightful information consumption by the users. The proposed pipeline was successfully implemented on multiple carefully curated datasets and correctly identified 274 non credible blogs out of 321 blogs with an accuracy of 97.5%, Precision of 0.973 & F1score of 0.986. Further, the Explainable AI model, with the help of several visualizations displayed the feature contributions for each blog & it’s impact and magnitude in a concise comprehensible format. The framework can be further customized and applied to various domains where presence of misinformation is of high concern such as pharmaceutical drug information, pandemic management, financial advisories, online healthcare services and cyber frauds.
Research on Inversion Algorithm of the Power Transmission Corridor Information Based on Radar Ranging

As for the information theory of Energy Internet, image signal processing and deep learning theory are important theories to support the information communication system of Energy Internet. The application of advanced remote sensing technology, such as radar, makes the theoretical system of Energy Internet improved. Power line safety is an important factor to be considered in the power transmission corridor, and radar technology can be used to effectively monitor it. This study proposes an interferometric inversion method of wide-area vegetation elevation range information, using radar satellite polarization data, by the simulation data of polarization interferometric Synthetic Aperture Radar (PolInSAR), firstly. There are three kinds of algorithms compared and analyzed, that is, Digital Elevation Model (DEM) difference algorithms, coherent amplitude inversion algorithm, and coherent amplitude and phase joint inversion algorithm. The actual ALOS 1-PALSAR telemetry data in Hechi has inverted the height of vegetation with coherent amplitude and phase joint inversion algorithm. The result is 16m of the average vegetation height, which is in line with the height grade of local pines and firs species. Then, the distance of ground object points and power line points range between poles and towers is measured by Light Detection and Ranging (Lidar) technology, to determine the distance between components in the power transmission corridor and external objects. This can identify the vegetation invasion. In conclusion, this study has guiding significance for the threat of vegetation in distant and close range. The methods can be used as an effective means to monitor the safety and stability of power lines. It can provide the basis for the operation optimization strategy of power transmission corridors.
Adaptive Finite-Time Tracking Control for Nonlinear Time-Varying Delay Systems With Full State Constraints and Input Saturation

This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear time-varying delay systems subject to full state constraints and input saturation. The nonlinear state-dependent functions (NSDFs) are introduced to handle the asymmetric time-varying full state constraints without feasibility conditions. To cope with the unknown time-varying delays, the radial basis function neural networks (RBF NNs) and the finite covering lemma are utilized. Avoiding the use of the Lyapunov-Krasovskii functionals (LKFs), no restriction on the derivative of the time-varying delays is needed. Meanwhile, the effect of the input saturation is eliminated by the augmented function with an auxiliary control signal. The adaptive finite-time tracking controller with only one adaptive parameter is constructed by applying the command filter approach and backstepping technique. It is proved that the proposed controller can ensure that all signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed scheme is demonstrated by two simulation examples.
A Survey of Millimeter-Wave Communication: Physical-Layer Technology Specifications and Enabling Transmission Technologies

Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.