Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), which aims to extract emotions and the corresponding causes in documents, has a wide range of applications in network public opinion analysis. Current two-stage methods first extract emotion and cause clauses, and then pair them. However, there are two problems in these methods: 1) the unidirectional enhancement between emotion and cause extraction fails to make full use of the correlation between them; 2) the errors from the first stage directly degrade the performance of the second stage. To address these problems, we firstly propose a mutually auxiliary multitask model to promote the extraction of emotion and cause clauses by adding two auxiliary tasks which are identical to the original tasks. The proposed model uses the predicted results generated by the two auxiliary tasks as extra features of each other’s main tasks, so as to establish the bidirectional correlation between emotion and cause extraction. Secondly, to reduce the influence of error propagation on the second stage, we design a self-distillation method for pairwise tasks to train the proposed model, which further improve the accuracy of emotion and cause extraction. Experimental results on the ECPE benchmark dataset show that the proposed model has achieved good performance on emotion-cause pair extraction, outperforming the baseline models by 1.92% in F1 score.
StreamDL: Deep Learning Serving Platform for AMI Stream Forecasting

Advanced Metering Infrastructures (AMIs) facilitate individual load forecasting. The individual load forecasting not only improves the accuracy of aggregated load forecasting but is a fundamental component of various power applications. With the highlight of deep learning (DL) in the individual load forecasting, a serving platform specialized in deep learning is required to forecast with AMI stream data. However, the existing serving platforms for DL models do not consider stream data as an input but usually support image or text data through RESTful API. To solve this problem, we propose StreamDL that is a serving framework providing deep learning inference with AMI stream data. It leverages Apache Kafka to support stream data and Kubernetes to support the cloud environment. StreamDL considers the specific requirements for stream data, which supports stream parsing to fit any DL model especially recurrent network and continual training to alleviate accuracy degradation by the change of stream distribution. In this paper, we introduce the detail of the StreamDL platform and its use-cases using real AMI data.
Automatic Device-Location Association based on Received Signal Strength Measurements

Sensory data is only meaningful if correctly associated with originating locations. Received signal strength (RSS) is a cost-effective option to locate low-cost sensors due to its universal availability, but yet to be practical because of its coarse ranging accuracy with multiplicative errors. This paper presents a new RSS-based approach to the localization and association of a large number of low-cost sensors given their possible 3D installation locations. The approach addresses the mathematically challenging combinatorial optimization of association by first co-operatively locating the sensors in the continuous 3D spaces, then associating the continuous location estimates to the installation points, and finally refining the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous 3D spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local 3D regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated through both computer simulation and lab test. It achieves close-to-100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly.
Fuzzy Neural Network-Based Assessment of Road Traffic Situations Using Extracted Information Obtained from Optical High-Resolution Satellite Remote Sensing Images

This study proposes a comprehensive fuzzy neural network (FNN) traffic assessment method using the optical high-resolution remote sensing image (RSI) to process a non-quantified relationship between traffic information and the assessment result. Using the classic road extraction and vehicle detection method, the number of lanes and vehicle density and velocity are obtained as the model input variables. The FNN traffic assessment model is established using the Takagi-Sugeno-Kang network structure, which is constructed using the BP network based on four types of traffic situation. Using QuickBird and WorldView2 satellite 0.5 m resolution panchromatic images, the experimental results show a reasonable traffic assessment result.
Secure Short-Packet Communications in Cognitive Internet of Things

To deal with the problem of spectrum scarcity in the Internet of Things (IoT), cognitive radio technology is introduced into IoT, namely, cognitive IoT. This paper investigates the secrecy performance of secondary wiretap networks in cognitive IoT. Specifically, the low-latency requirement and the security demand in cognitive IoT are guaranteed by using short-packet communications and maximal ratio transmission (MRT) scheme, respectively. Considering the impact of blocklength on transmission delay and secrecy performance, we derive the approximations for secrecy throughput in a closed-form under the secrecy constraints and decoding error probability. In addition, we further analyze the secrecy throughput of the system from two scenarios of the high signal-to-noise ratio (SNR) regime and the infinite blocklength. The numerical results verify the accuracy of the proposed approximations and illustrate the impact of the system parameters on the tradeoff between transmission delay and reliability under a secrecy constraint.
Application of Python Parallel Computing in Online Identification of Thevenin Equivalent Parameters

The premise of analysing static voltage stability of large power grids online is identifying Thevenin equivalent parameters rapidly and accurately. And based on the current reaearch in this field, this paper analyses its formation mechanism and the feasibility of parallel programming in Python. The simulation test and analysis are carried out on the actual system. The results demonstrate that parallel programs can reduce the calculating time of Thevenin equivalent parameters significantly, make full use of computer performance and improve CPU utilization futher.
Simulation Model of Bluetooth Passive Scanning for Vehicular Traffic Monitoring

We propose a simulation model of Bluetooth (BT) passive scanning for vehicular traffic monitoring. The model is designed to simulate packet detection in the physical and Medium Access Control (MAC) layers for fixed BT sensors. At the physical layer, we consider the radio propagation effects of path loss, small- and large-scale fading. At the MAC layer, we model channel hopping and packet collisions.Associated to a vehicular traffic simulator, our proposal provides a cost-efficient way to generate large-scale BT traffic measurement datasets including both sensor and ground-truth data, avoiding deployment costs.We validate the model by comparing the simulation output to experimental data and we show a simulation use-case of vehicular traffic monitoring in a complex urban context.
Second Law Analysis of Laminar Flow in a Circular Pipe Immersed in an Isothermal Fluid

Entropy generation and pumping power to heat transfer ratio (PPR) of a laminar flow, for a circular tube immersed in an isothermal fluid, are studied analytically in this paper. Two different fluids, namely, water and ethylene glycol, are chosen to study the influence of fluid properties on entropy generation and PPR. The expressions for dimensionless entropy generation, Bejan number and PPR are derived in a detailed way and their variations with Reynolds number, external Biot number, and the dimensionless temperature difference are illustrated. The results of the analysis are compared with those for a laminar flow in a circular tube with uniform wall temperature boundary condition. Finally, a criterion is established to determine which type of thermal boundary conditions is more suitable for a particular fluid, with respect to its influence on entropy generation.
Quantitative carbon-13 NMR study of urea-formaldehyde resins in relation to the formaldehyde emission levels

On etudie l'application de la RMN du carbone 13 aux resines uree-formaldehyde aux divers stades de leur synthese et aux resines fabriquees avec divers rapports formaldehyde/uree. On relie la structure chimique aux niveaux d'emission de formaldehyde quand ces resines sont utilisees comme liants pour panneaux de particules
MAV-DTLS toward Security Enhancement of the UAV-GCS Communication

Currently, Unmanned Aerial Vehicles (UAVs) are gaining a lot of attention due to their different potentialities and use cases. We can use UAVs to track intruders, to capture images and videos in harsh areas and to boost the coverage of existing cellular systems. For all these context and scenario security of the communication system between UAV and its ground control station (GCS) is primordial. The standardized point-to-point communication protocol between UAV and the GCS named MAVLINK has several vulnerabilities. MAVLINK used to carry telemetry, to control, and to command small UAVs. UAV-GCS communication link could be attacked. In this context, this paper proposes MAV-DTLS mechanism to enhance the security of the communication links between UAVs and the GCS. We have described the main issues of our proposed solution that empowers secure communication between UAV and GCS. The obtained result proof that MAV-DTLS resist to the different studied attacks (DOS, GPS spoofing, MItM, Data modification).
A DQN-Based Handover Management for SDN-Enabled Ultra-Dense Networks

Software defined network (SDN) is considered as one of the most promising network architectures in the next generation mobile networks. SDN-enabled ultra dense network (UDN) has a simpler and more flexible network architecture, but its mobility management is still a challenging task. The major problem is the occurrence of frequent handover (FHO). Therefore, a SDN-enabled UDN architecture is firstly proposed to make the network more agile. Then, a deep Q-learning (DQN) method is used to control the handover (HO) procedure of the user equipments (UEs) by well capturing the characteristics of wireless signals/interferences and network load. In details, we use the SINR and the access rate per node to characterize the state of the UE. Thanks to the generalization ability of deep neural network (DNN), newly arrived UEs can use the trained neural network to avoid possible bad initial points. Experimental results show that the proposed scheme can reduce HO rate and guarantee the system throughput, which is better than the traditional HO scheme.
Fast 3D Point Cloud Segmentation Using Deep Neural Network

Accurate segmentation of entity categories is the key step for 3D scene understanding. In this paper, we present a novel fast Deep Neural Network (DNN) model with Dense Conditional Random Field (DCRF) as a post-processing step, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on the labels of semantics, a novel DCRF model is elaborated to refine the result of segmentation. Besides, we apply optimization to the original point cloud, allowing the network to handle fewer points without any sacrifice to accuracy. In the experiment, our proposed method is comprehensively evaluated through four indicators, and achieves state-of-art performance.
Analog Radio-over-Fiber-Aided Optical-Domain MIMO Signal Processing for High-Performance Low-Cost Radio Access Networks

Wireless communication is facing an ever increasing thirst for high-quality data transmission. However, this imposes high demands on the radio access networks (RANs), where optical fiber has been widely used for both the backhaul and fronthaul. However, the explosive escalation of wireless tele-traffic tests the limits of the RAN's revenue vs. cost trade-off, which motivates low-cost designs. Hence, we present a cost-efficient but high-performance radio over fiber (RoF)-aided RAN concept. We commence by introducing this motivation, followed by a brief introduction to analog RoF (A-RoF) and digitized RoF (D-RoF), as well as to wireless multiple-input multiple-output (MIMO) techniques. Then we present the centralized RAN (C-RAN) concept relying on A-RoF-aid-ed MIMO signal processing, where the MIMO signal is carried by fiber and processed optically in a central unit. Subsequently, we focus our attention on a C-RAN small cell application followed by our performance vs. cost analysis of the A-RoF system compared to that of its baseband counterparts, demonstrating that the A-RoF design is capable of reducing the RAN's total cost while meeting the Third Generation Partnership Proj-ect's requirements.
A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

Industrial Internet of Things (IIoTs) are the extensions of the Internet of Things (IoTs) and have paved the way towards the industry revolution 4.0. IIoT accelerates the industry automation of internal and external working process including transport, manufacturing, and marketing units with a number of connected devices. Being the extension of IoT, it inherits the insecurities of the technology; however, this sensor-configured infrastructure of IIoT needs some extra effort to customize the existing security solutions. In spite of the reconstructions of security models, the scope of improved developments is open to detect unknown attacks. The present study helps the researchers to understand the cause for intrusion by classifying and comparing various attacks of each IIoT layers. The main focus of this survey is to analyze various security issues faced by IIoT and provide a comparative analysis on the available solutions to enhance the industrial IoTs’ protection systems. This study also notifies some open research problems for academia, technologists, and researchers to flourish the IIoT domain and its security aspects.
Multi-person device-free gesture recognition using mmWave signals

Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals, it may empower wireless networks with the augmented sensing ability. Researchers have made great achievements for single-person device-free gesture recognition. However, when multiple persons conduct gestures simultaneously, the received signals will be mixed together, and thus traditional methods would not work well any-more. Moreover, the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch, and thus the recognition accuracy would degrade remarkably. To address these problems, we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy. Meanwhile, we also present a deep-learning based robust device free gesture recognition framework, which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment. Furthermore, we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively. Experimental results reveal that the proposed system can achieve average accuracies of 93% and 84% when 10 gestures are conducted in different environments by two and four persons simultaneously, respectively.
Volatility Modeling for EMCALI Electricity Prices

The term "price volatility" is used to describe price fluctuations in asset prices. Prices of basic energy (natural gas, electricity, heating oil) are generally more volatile than prices of other commodities. Volatility indicates a measure of price uncertainty in markets. When volatility rises, firms may delay investment and other decisions or increase their risk management activities such as hedging. The costs associated with such activities tend to increase the costs of supplying and consuming energy. The main goal of this paper is to build a volatility model and demonstrate a mechanism to forecast electricity price volatility using electricity distribution of EMCALI ("Empresses Municipals de Cali") in Colombian electricity market. The objectives of this study are threefold: 1) First we begin our approach by introducing the energy market in Colombia and EMCALI’s role in generation and distribution of electricity. 2) Next we discuss some of the more widely used time series techniques and also show how we can utilize generalized autoregressive conditional heteroscedastic (GARCH) volatility model as a volatility forecasting model for the energy price in Colombia. 3) Finally, we apply this model to estimate the behavior of the electricity price volatility and discuss the results obtained. The paper ends with conclusions.
Multiuser Robust Transceiver Design for Indoor VLC with Noisy Channel State Information

In this paper, we focus on a multiple-input single-output (MISO) visible light communication (VLC) system with noisy channel state information (CSI). A robust transceiver is developed for the system based on noisy CSI to control interference. The mean square error (MSE) is used to measure the level of interference. To achieve a fair users’ performance, we formulate the transceiver design to maximize the minimum MSE of user. Then the problem is solved by the transformation with a convex optimization problem. Simulation results show the proposed transceiver has a robust performance.
Link Adaptation for Multi-connectivity Enabled 5G URLLC: Challenges and Solutions

Multi-connectivity with packet duplication across multiple links is a potential solution for the 5G ultra-reliable low latency communication (URLLC). However, the multi-link adaptation becomes a quite challenging problem of selecting not only the modulation and coding scheme (MCS) but also the appropriate links and per link decoding error targets, for efficient radio resource utilization while satisfying the users’ quality of service (QoS) requirements. We address these challenges in this work. Our proposed solution comprises: physical layer abstraction - a useful technique to adapt the MCS and links jointly - with more number of MCS values; short packet communication; 5G physical layer enablers like flexible numerology; and a novel time-frequency resource scheduling. Simulation results show that the proposed solution provides more efficient resource allocation, while satisfying the URLLC users’ QoS requirements, as compared to the existing schemes.
Perceived Image Decoding From Brain Activity Using Shared Information of Multi-Subject fMRI Data

Decoding a person’s cognitive contents from evoked brain activity is becoming important in the field of brain-computer interaction. Previous studies have decoded a perceived image from functional magnetic resonance imaging (fMRI) activity by constructing brain decoding models that were trained with a single subject’s fMRI data. However, accurate decoding is still challenging since fMRI data acquired from only a single subject have several disadvantageous characteristics such as small sample size, noisy nature, and high dimensionality. In this article, we propose a method to decode categories of perceived images from fMRI activity using shared information of multi-subject fMRI data. Specifically, by aggregating fMRI data of multiple subjects that contain a large number of samples, we extract a low-dimensional latent representation shared by multi-subject fMRI data. Then the latent representation is nonlinearly transformed into visual features and semantic features of the perceived images to identify categories from various candidate categories. Our approach leverages rich information obtained from multi-subject fMRI data and improves the decoding performance. Experimental results obtained by using two public fMRI datasets showed that the proposed method can more accurately decode categories of perceived images from fMRI activity than previous approaches using a single subject’s fMRI data.
Analysis of Magnetically-Coupled Loops Based On Characteristic Modes

A preliminary numerical magnetic field study of a wireless power transfer system composed of two interconnected source loops is addressed in this paper. The key feature of this study is to propose a new configuration capable of maintaining a uniform magnetic field density between the two loops. Furthermore, it has been shown that the Theory of Characteristic Modes is a helpful tool for investigating the near field of the magnetically-coupled loops.
Wavelet-based Artifact Removal Algorithm for EEG Data by Optimizing Mother Wavelet and Threshold Parameters

EEG recordings are usually affected by various artifact types come from non-neural sources and make it difficult for accurate signal classification in the later stage. Thus reliably detecting and removing artifacts from EEG by an automated signal processing algorithm is an active research area. In this paper we have developed a wavelet based artifact removal algorithm from EEG data that selects the best (optimal) threshold parameters, and hence consequently provides the best performance of artifact removal. In the proposed algorithm we choose to sweep both the wavelet filter parameter and threshold parameters until the best accuracy and/or least distortion is achieved by making a decision based on a reference dataset. The criteria for optimized selection are based on the metrics that quantify both amount of artifact removal and amount of distortion in the signal in both time and frequency domain. The algorithm is tested on synthesized EEG data that include different artifact templates and thus quantifies the performance based on several time and frequency domain measures. The achieved results prove that by selecting the optimum mother wavelet and parameter values adaptively would give the best performance both with regard to amount of artifact removal and least signal distortion compared with selecting any predefined mother wavelet and/or constant threshold parameter. This research would help the EEG signal analysis community a platform to work further in future on such problem to be able to properly select the wavelet parameters.
Kernel Rotational Network for Synthetic Aperture Radar Target Recognition

Convolutional Neural Networks (CNNs) have excellent ability in image recognition, however, the requirement of a large amount of labeled dataset limits its application in the field of synthetic aperture radar (SAR) image processing. In this paper, a kernel rotational network (KR-Net) for SAR target recognition is constructed. When the labeled dataset is small, the KR-net can achieve higher classification rate than standard CNNs benefit from its inherent rotational convolution units. Also, weights sharing strategy is introduced to increase network capacity without multiplying the number of weights parameters. Meanwhile, a simple and feasible multi-branch feature converging method for the KR-Net is proposed to fuse features of rotational convolution units. Experimental results show that our network can achieve state-of-art result in the MSTAR dataset, especially when the training set is small.