Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Model-based estimation for UAV tracking

This work presents a system to perform autonomous landing of a small size fixed-wing Unmanned Aerial Vehicle (UAV) on a Fast Patrol Boat (FPB). We propose a ground-based vision system with the camera, image capture and processing equipment installed in the ship, thus reducing the UAV size, weight and power requirements. The system observes the UAV and computes the control commands to send to the UAV via radio. This approach makes it also possible to use standard UAVs equipped with commercial autopilots. The developed system uses the captured image as input and a Particle Filter (PF) structure to estimate the UAV trajectory. It is also used an Unscented Kalman Filter (UKF) for the translational motion filtering and an Unscented Bingham Filter (UBiF) for the rotational motion filtering. This filtering structure is reminiscent of the Unscented Particle Filter (UPF). The obtained tracking error is compatible with automatic landing requirements.
A 0.65-V 10-bit 320-kS/s SAR-ADC with Charge Average and Skip Switching Algorithm

A high resolution and low power fully differential 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) is presented in this work aiming at neural signal data acquisition. By utilizing surplus energy restored by MSB-split capacitors of alternative switching structure both in conversion phase and reset phase, the proposed charge average and skip switching algorithm achieves superior DAC switching energy efficiency without the requirement of an external Vcm reference. Implemented in 180nm CMOS process, the proposed chip occupied a 0.0564-mm2 core area. Operating at 320-kS/s sampling rate with 0.65-V supply voltage, it achieved an ENOB of 9.66-bit and FoM of 6.26 fJ/conv. -step. The measured DNL and INL results are within 0.29 LSB and 0.38 LSB, respectively.
The Analysis and Optimization of Broadband Fiber Optical Parametric Amplifiers Pumped by Chirped Pulses

The broadband gain properties of fiber optical parametric amplifiers pumped both by one and two chirped pump pulses are investigated for the amplification of ultrashort pulses, analytically with the gain spectrum calculated in both the anti-Stokes’ and Stokes’ side. The influence of chirp rate and time delay on the gain spectrum of fiber-based optical parametric chirped pulse amplification is mainly explored. It is shown that the chirp rate and time delay distinctly affect the second order dispersion and phase mismatching, leading to the gain variation; the two pump scheme provides broader gain bandwidth, especially under the condition of the two chirp rate with low absolute value; and a genetic algorithm is employed to optimize the gain characteristics for realizing a flatter and wider, high gain that is useful for the ultrashort pulse system.
Measurement of Component Performance (Sensor) on Internet of Thing (IoT)

This study presents the testing of several devices (sensors) in obtaining sensor performance, there are several experiments and evaluations of the results obtained in the topology. Each sensor must be able to provide some results in the form of accuracy, reliability, range, and resolution. The accuracy and reliability have very important role in producing accurate data. With several explanations and analysis, it is expected to produce a reference for advanced development and policies making in the deployment of IoT system, especially in multi-sensing IoT systems. This work obtain the dataset through several stages, namely building topology (system design), data capture, and feature extraction. Wi-Fi and XBee communication protocols are used. In Wi-Fi protocol, the TCP traffic gives the greatest value compared to other traffic on normal data as well as attack data. In XBee protocol, the Low Rate Wireless PAN IEEE 802.15. 4 protocol has an average of 83.96 percent for normal data and 98.73 percent for attack data, respectively. The results of attribute reading experiments, the XBee protocol achieves eighteen attributes whereas the Wi-Fi protocol only seventeen attributes.
Cold Start Influencing Factors in Function as a Service

Function as a Service (FaaS) is a young and rapidly evolving cloud paradigm. Due to its hardware abstraction, inherent virtualization problems come into play and need an assessment from the FaaS point of view. Especially avoidance of idling and scaling on demand cause a lot of container starts and as a consequence a lot of cold starts for FaaS users. The aim of this paper is to address the cold start problem in a benchmark and investigate influential factors on the duration of the perceived cold start. We conducted a benchmark on AWS Lambda and Microsoft Azure Functions with 49500 cloud function executions. Formulated as hypotheses, the influence of the chosen programming language, platform, memory size for the cloud function, and size of the deployed artifact are the dimensions of our benchmark. Cold starts on the platform as well as the cold starts for users were measured and compared to each other. Our results show that there is an enormous difference for the overhead the user perceives compared to the billed duration. In our benchmark, the average cold start overheads on the user's side ranged from 300ms to 24s for the chosen configurations.
A Hybrid Feature Selection Approach for Applying to Patients with Diabetes Mellitus: KNHANES 2013-2015

Recent years, the diabetes mellitus is an important public health problem and has been the top 10 leading causes of death in lower-middle-income countries and upper-middle-income countries in the world. In this study, we tried to use a hybrid feature selection approach to find proper and optimal feature subsets to classify and predict the diabetes mellitus patients in Korea based on the data from Korea National Health and Nutrient Examination Survey (KNHANES). We used the information gain feature selection approach as the filter phase and used the support vector machine with sequential search method as the wrapper phase. To validate the efficiency of the proposed approach, we also compared our proposed approach with several popular feature selection approaches. The results showed that our proposed approach can significantly improve the classification accuracy and outperformed other feature selection approaches.
Deep Learning versus Traditional Classifiers on Vietnamese Students’ Feedback Corpus

Student’s feedback is an important source of collecting students’ opinions to improve quality of training activities. Implementing sentiment analysis into student feedback data, we can determine sentiments polarities which express all problems in the institution since changes necessary will be applied to improve the quality of teaching and learning. This study focused on the machine learning and natural language processing techniques (Naive Bayes, Maximum Entropy, Long Short-Term Memory, Bi-Directional Long Short-Term Memory) on the Vietnamese Students’ Feedback Corpus collected from a university. The final results were compared and evaluated to find the most effective model based on different evaluation criteria. The experimental results show that Bi-Directional Long Short-Term Memory algorithm outperformed than three other algorithms in term of the F1-score measurement with 92.0% on the sentiment classification task and 89.6% on the topic classification task. In addition, we developed a sentiment analysis application analyzing student feedback. The application will help the institution to recognize students’ opinions about a problem and identify shortcomings that still exist. With the use of this application, the institution can propose an appropriate method to improve the quality of training activities in the future.
Chameleon: A Scalable and Adaptive Permissioned Blockchain Architecture

Designing blockchain architecture is still an open question and encounters a lot of challenges such as scalability, security, high utilization and so on. In this paper, we propose Chameleon, a scalable and adaptive permissioned blockchain architecture. We adopt the principles of non-forking, high security, scalable and high utilization to design the Chameleon to be suitable for next generation blockchain architecture. In Chameleon, we introduce credit value which can only be acquired through honest behavior to enhance the security of the consensus algorithm. We also introduce the QoS of transactions to meet the various needs of different users. While previous work using sharding to improve the scalability, they either store all transactions in every area or just work independently, it either causes too much redundant data or low resource utilization. Combined with the cloud storage, Chameleon partition the nodes into different area according to different scenario, every area process their own transactions and can also cooperate with each other dynamically to improve transaction throughput and resource utilization.
STANN: A Spatio–Temporal Attentive Neural Network for Traffic Prediction

Recently, traffic prediction based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, dynamic spatio-temporal dependences among network-wide links and long-term traffic prediction for the next few hours. To address these two challenges, this paper proposes a spatio-temporal attentive neural network (STANN) for the network-wide and long-term traffic prediction. The STANN captures the spatial–temporal dependences based on the encoder–decoder architecture with the attention mechanisms. In the encoder, the STANN learns the spatio-temporal dependences from historical traffic series using a recurrent neural network (RNN) with long short-term memory (LSTM) units, in which a new spatial attention model is developed to consider the contribution of each link to the network-wide prediction. In the decoder, the STANN exploits another RNN with LSTM units and a temporal attention model to select the relevant and important historical spatio-temporal dependences from the encoder for long-term traffic prediction. Finally, we conduct extensive experiments to evaluate STANN on three real-world traffic datasets. The experimental results show that the STANN is significantly better than other state-of-the-art models.
Complex Field Mapping of Large Direct Detector Focal Plane Arrays

Complex field mapping is a powerful tool to characterize the optical performance of astronomical instruments, and has become the standard for characterizing heterodyne array cameras. Recently, an adaptation of the heterodyne beam mapping technique was demonstrated on a single pixel of a direct detector instrument. We present a novel measurement apparatus and data acquisition techniques to efficiently reconstruct the complex field pattern of individual pixels across a direct detector focal plane array. These techniques are scalable to high pixel counts as the technology maturation and scientific requirements push to larger arrays. For this demonstration, we used an engineering model of the low-frequency band of the APEX microwave kinetic inductance detector camera with a center frequency of $\nu = {\text{350}}$ GHz. Amplitude and phase radiation patterns were measured from all 880 pixels of the test array in two orthogonal polarizations. We also discuss an updated postprocessing pipeline using the complex field data to characterize the optical performance of the array. Using the measured complex field pattern, we extract the co- and cross-polarization patterns and Gaussian beam parameters, and propagate the beam from the measurement plane to additional planes of interest across all pixels in the test array. Complex field measurements of direct detectors allow more precise characterization of beam parameters when compared to thermal measurements, particularly for individualized fitting in postprocessing not reliant on the accuracy of the probe system alignment. These techniques enable high-precision characterization of individualized beam parameters as well as the overall optical system to very large format arrays with modest computational processing power. These results demonstrate the diagnostic power of the presented measurement and analysis techniques.
Hand-eye calibration of arc welding robot and laser vision sensor through semidefinite programming

Purpose This paper aims to propose a hand–eye calibration method of arc welding robot and laser vision sensor by using semidefinite programming (SDP). Design/methodology/approach The conversion relationship between the pixel coordinate system and laser plane coordinate system is established on the basis of the mathematical model of three-dimensional measurement of laser vision sensor. In addition, the conversion relationship between the arc welding robot coordinate system and the laser vision sensor measurement coordinate system is also established on the basis of the hand–eye calibration model. The ordinary least square (OLS) is used to calculate the rotation matrix, and the SDP is used to identify the direction vectors of the rotation matrix to ensure their orthogonality. Findings The feasibility identification can reduce the calibration error, and ensure the orthogonality of the calibration results. More accurate calibration results can be obtained by combining OLS + SDP. Originality/value A set of advanced calibration methods is systematically established, which includes parameters calibration of laser vision sensor and hand–eye calibration of robots and sensors. For the hand–eye calibration, the physics feasibility problem of rotating matrix is creatively put forward, and is solved through SDP algorithm. High-precision calibration results provide a good foundation for future research on seam tracking.
Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization

Geographically weighted regression (GWR) is an inherently exploratory technique for examining process non-stationarity in data relationships. This paper develops and applies a hyper-local GWR which extends such investigations further. The hyper-local GWR simultaneously optimizes both local model selection (which covariates to include in each local regression) and local kernel bandwidth specification (how much data should be included locally). These are evaluated using a measure of model fit. The hyper-local GWR approach evaluates different kernel bandwidths at each location and selects the most parsimonious local regression model. By allowing models and bandwidths to vary locally, this approach extends and refines the one-size-fits-all "whole map model" and "constant bandwidth calibration" under standard GWR. The results provide an alternative, complementary and more nuanced interpretation of localized regression. The method is illustrated using a case study modeling soil total nitrogen (STN) and soil total phosphorus (STP) from data collected at 689 locations in a watershed in Northern China. The analysis compares linear regression, standard GWR, and hyper-local GWR models of STN and STP and highlights the different locations at which covariates are identified as significant predictors of STN and STP by the different GWR approaches and the spatial variation in bandwidths. The hyper-local GWR results indicate that the STN processes are more non-stationary and localized than found via a standard application of GWR. By contrast, the results for STP are more confirmatory (i.e., similar) between the two GWR approaches providing extra assurance about the nature of the moderate non-stationary relationships observed. That is, a standard GWR may underestimate localized spatial heterogeneity where it is strongly present (as in the STN case study) and may overestimate it where spatial homogeneity is present (as in the STP case study). The overall benefits of hyper-local GWR are discussed, particularly in the context of the original investigative aims of GWR. A hyper-local approach provides a useful counter view of local regression modeling to that found with standard GWR. Where spatial non-stationarity exists, the hyper-local GWR provides a more spatially nuanced indication of the localization than a standard GWR analysis and can be used to suggest the direction of further analyses and investigations. Some areas of further work are suggested.
A Prototype Mixer to Improve Cross-Modal Attention During Audio Mixing

The Channel Strip mixer found on physical mixing desks is the primary Graphical User Interface design for most Digital Audio Workstations. While this metaphor provides transferable knowledge from hardware, there may be a risk that it does not always translate well into screen-based mixers. For example, the need to search through several windows of mix information may inhibit the engagement and 'flow' of the mixing process, and the subsequent screen management required to access the mixer across multiple windows can place high cognitive load on working memory and overload the limited capacity of the visual mechanism. This paper trials an eight-channel proto-type mixer which uses a novel approach to the mixer design to address these issues. The mixer uses an overview of the visual interface and employs multivariate data objects for channel parameters which can be filtered by the user. Our results suggest that this design, by reducing both the complexity of visual search and the amount of visual feedback on the screen at any one time, leads to improved results in terms of visual search, critical listening and mixing workflow.
Planification automatique de chemins à l'intérieur de bâtiments basée sur un modèle BIM. (Automatic indoor path planning based on BIM model)

Plus de la moitie de la population mondiale vit aujourd’hui en zone urbaine et passe plus de 90 % de son temps a l’interieur de bâtiments. Cette these propose un systeme, nomme BiMov, de planification automatique de chemin a l’interieur de bâtiments, base sur leur maquette numerique (un BIM au format IFC). Le processus consiste a exploiter les caracteristiques semantiques, geometriques et topologiques des constituants du BIM afin de generer des graphes de navigation possible, en fonction du profil du navigant et de l’etat conjoncturel d’accessibilite des espaces et transitions, dans lesquels le plus court chemin d’un point a un autre puisse etre determine. BiMov s’appuie sur quatre modeles de donnees (1) un modele de bâtiment deduit du BIM, qui represente et structure les caracteristiques essentielles du bâtiment en vue de la mobilite interieure (2) un modele de navigant a meme de representer ses caracteristiques d’encombrement, ses aptitudes aux deplacements horizontaux et verticaux ainsi que ses habilitations (3) un modele de calendrier permettant de connaitre l’etat d’accessibilite des espaces et des transitions (4) un modele de graphe sur trois niveaux de details. Le niveau Macro represente un simple graphe de connectivite entre les espaces interieurs voisins ; il permet aux architectes de verifier leur conception architecturale en termes d’accessibilite. Le niveau Externe permet de connecter les espaces accessibles via leurs transitions horizontales ou verticales. Il est destine aux navigants qui n’exigent pas un chemin detaille pour se deplacer. Le niveau Interne integre un maillage des espaces, en 2D pour la navigation au sol, en 3D pour la navigation de drones. Il est concu pour considerer les obstacles interieurs comme les meubles, les machines ou les equipements. Ce niveau est destine aux navigants devant fiabiliser leur deplacement a l’interieur des espaces, comme les manutentionnaires d’objets encombrants ou les robots mobiles. L’approche proposee a fait l’objet d’un developpement informatique qui permet d’illustrer quelques scenarios de planification de chemin dans des modeles BIM d’origine externe a la these.
Jam with Jamendo: Querying a Large Music Collection by Chords from a Learner's Perspective

Nowadays, a number of online music databases are available under Creative Commons licenses (e.g. Jamendo, ccMixter). Typically, it is possible to navigate and play their content through search interfaces based on metadata and file-wide tags. However, because this music is largely unknown, additional methods of discovery need to be explored. In this paper, we focus on a use case for music learners. We present a web app prototype that allows novice and expert musicians to discover songs in Jamendo's music collection by specifying a set of chords. Its purpose is to provide a more pleasurable practice experience by suggesting novel songs to play along with, instead of practising isolated chords or with the same song over and over again. To handle less chord-oriented songs and transcription errors that inevitably arise from the automatic chord estimation used to populate the database, query results are ranked according to a computational confidence measure. In order to assess the validity of the confidence ranked system, we conducted a small pilot user study to assess its usefulness. Drawing on those preliminary findings, we identify some design recommendations for future applications of music learning and music search engines focusing on the user experience when interacting with sound.
Developing a Quality of Experience (QoE) model for Web Applications

Web‐based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers' Quality of Experience (QoE). Although models exist that assess the QoE of Web Application, significant challenges remain in: (1) Defining QoE factors from a Web engineering perspective, (2) quantifying the relationship between so‐called “objective” and “subjective” factors of relevance, and (3) dealing with limited data available in relation to subjective factors. In response, the work here presents a novel model (and associated software instantiation) that integrates factors through Key Performance Indicators and Key Quality Indicators. The mapping is incorporated into a correlation model that assesses the QoE of Web Applications, with a consideration of defining the factors in term of quality requirements derived from web architecture. The data resulting from the mapping is used as input of the proposed model to develop artefacts that quantify and predict QoE using Machine Learning. The development of proposed model is framed and guided by Design Science Research approach with the purpose of enabling providers to more informed decisions regarding QoE and/or to optimise resources accordingly. Although the work is oriented at developing an artefact that has clear utility for practice, the nascent design theory underpinning the work is developed and discussed.
A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods.