Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchical Architecture for the Future Internet of Vehicles

Recent advances in wireless communication, sensing, computation and control technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Demanded by the requirements of information-centric and data-driven intelligent transportation systems (ITS), it is of great significance to explore new paradigms of IoV in supporting large-scale, real-time, and reliable information services. In this article, we propose a hierarchical system architecture, which aims at synthesizing the paradigms of software defined networking and fog computing in IoV and best exploiting their synergistic effects on information services. Specifically, a four-layer architecture is designed, comprising the application layer, the control layer, the virtualization layer, and the data layer, with objectives of enabling logically centralized control via the separation of the control plane and the data plane; facilitating adaptive resource allocation and QoS oriented services based on network functions virtualization and network slicing, and enhancing system scalability, responsiveness, and reliability by exploiting the networking, computation, communication, and storage capacities of fog-based services. On this basis, we further analyze newly arising challenges and discuss future research directions by presenting a cross-layer protocol stack. Finally, for the proof of concept, we implement the system prototype and give two case studies in real-world IoV environments. The results of field tests not only demonstrate the great potential of the new architecture, but also give insight into the development of future ITS.
Low-Cost System for Early Detection and Deployment of Countermeasures Against Wild Fires

This article introduces a novel low-cost smart system of networked nodes, based of narrow beam Far Infrared (FIR) sensors, which can detect and locate strong heat sources, such as fire, in real-time. This system is able to detect approaching wildfires, and can be used in combination with active fire protection mechanisms, such as water sprinklers, in order to attempt to sustain the advance of the flame front. The prime objective of this work was to develop a solution to proactively protect small areas of interest: isolated homes, small villages, camping sites, music festivals, etc. Experiments conducted with real fire validated the proposed solution.
Fault Diagnosis of Reciprocating Compressor Using Component Estimating Empirical Mode Decomposition and De-Dimension Template With Double-Loop Correction Algorithm

This paper presents an approach to implement multi-parameter (i.e., pressure, temperature, vibration, current, and liquid level) signals for fault diagnosis of the reciprocating compressor (RC). Due to the complexity of structure and motion of such compressor, the acquired signals involve transient impacts and noises. This causes the useful information to be corrupted and makes it difficult to diagnose the fault patterns accurately. A component estimating empirical mode decomposition (CEEMD) method is proposed to remove the random noise and improve data quality. Furthermore, a new template matching algorithm called de-dimension template with double-loop correction (DDT-DLC) is applied to diagnose the fault pattern contained in the time series signals. The DDT employs a judging criterion for key characterization parameters extraction and a multicellular parameter fusion method to reduce the dimension of the matching template, and then, the DLC supplies a double-loop correction algorithm to build a parameter state array computing model of the time series data by adjusting the dynamic factors. The proposed approach is validated with three fault patterns and the healthy pattern in a two-stage reciprocating air compressor. To confirm the superiority of the proposed method, its performance is compared with that of the traditional methods. The results have indicated that the proposed approach is of highly diagnostic accuracy and shortly computing time in the fault diagnosis.
A Soft Exoglove Equipped With a Wearable Muscle-Machine Interface Based on Forcemyography and Electromyography

Soft, lightweight, underactuated assistive gloves (exogloves) can be useful for enhancing the capabilities of a healthy individual or to assist the rehabilitation of patients who suffer from conditions that limit the mobility of their fingers. However, most solutions found in the literature do not offer individual control of the fingers, hindering the execution of different types of grasps. In this letter, we focus on the development of a soft, underactuated, tendon-driven exo-glove that is equipped with a muscle-machine interface combining Electromyography and Forcemyography sensors to decode the user intent and allow the execution of specific grasp types. The device is experimentally tested and evaluated using different types of experiments: first, grasp experiments to assess the capability of the proposed muscle machine interface to discriminate between different grasp types and second, force exertion capability experiments, which evaluate the maximum forces that can be applied for different grasp types. The proposed device weighs 1150 g and costs $\sim$ 1000 USD (in parts). The exoglove is capable of considerably improving the grasping capabilities of the user, facilitating the execution of different types of grasps and exerting forces up to 20 N.
IoT solutions for Sustainable Cities: An Online Adaptation for the Driver Intent Inference Algorithm

In this paper we introduce an adaptation to the "driver intent inference algorithm for urban intersections". This algorithm has been proven to detect potential right turns of vehicles by estimating the probability of a driver to turn right, and we propose to use it for reducing the number of cycling deaths at an intersection. We extend this algorithm following the IoT design principles and thus, with this approach, cyclists’ safety no longer depends only on actions taken inside the vehicles, but also can use additional safety solutions based on standards and available information shared about the vehicles and drivers in vehicular networks. Our approach proposes to process the inference algorithm outside the vehicle, considering cloud and edge computing. We use predicting models for identifying driver’s intention of turning right at intersections and the use of edge connected devices running our algorithm for alerting cyclists of possible collisions, thus preventing as many collisions as possible in intersections.
Data Simulation as a Service: The I3T Paradigm on the IoT

This paper describes the architecture of a new cloud-based tool that focuses on the simulation of data generation from CPS/IoT devices and services. The paper defines the problem statement that the tool addresses and positions itself with respect to the current state of the art. The architectural overview of the tool and the adopted technologies are presented in detail. Finally, the status of the work in progress is presented along with the forthcoming steps.
Location Privacy Preservation for Mobile Users in Location-Based Services

Because location-based cyber services are increasingly found in mobile applications (e.g., social networking and maps), user location privacy preservation is essential and remains one of the several ongoing research challenges. In this paper, we propose a region-of-interest division-based algorithm to Preserve the location Privacy of mobile device users in location-based Cyber Services (PPCS). Unlike existing methods, our proposed PPCS approach generates dummy locations while considering the semantic information of those locations. The PPCS algorithm enables the generated locations to exclude or reduce the exposure of a user’s real location. In our analysis, we demonstrate that PPCS is resilient to both colluding attacks and inference attacks. We also evaluate the efficiency and demonstrate the utility of our proposed approach through extensive simulations.
Sensorless Estimation of the Planar Distal Shape of a Tip-Actuated Endoscope

Traditional endoscopes consist of a flexible body and a steerable tip with therapeutic capability. Although prior endoscopes have relied on operator pushing for actuation, recent robotic concepts have relied on the application of a tip force for guidance. In such case, the body of the endoscope can be passive and compliant; however, the body can have significant effect on mechanics of motion and may require modeling. As the endoscope body's shape is often unknown, we have developed an estimation method to recover the approximate distal shape, local to the endoscope's tip, where the tip position and orientation are the only sensed parameters in the system. We leverage a planar dynamic model and extended Kalman filter to obtain a constant-curvature shape estimate of a magnetically guided endoscope. We validated this estimator in both dynamic simulations and on a physical platform. We then used this estimate in a feed-forward control scheme and demonstrated improved trajectory following. This methodology can enable the use of inverse-dynamic control for the tip-based actuation of an endoscope, without the need for shape sensing.
A Systematic Mapping Study on Testing of Machine Learning Programs

We aim to conduct a systematic mapping in the area of testing ML programs. We identify, analyze and classify the existing literature to provide an overview of the area. We followed well-established guidelines of systematic mapping to develop a systematic protocol to identify and review the existing literature. We formulate three sets of research questions, define inclusion and exclusion criteria and systematically identify themes for the classification of existing techniques. We also report the quality of the published works using established assessment criteria. we finally selected 37 papers out of 1654 based on our selection criteria up to January 2019. We analyze trends such as contribution facet, research facet, test approach, type of ML and the kind of testing with several other attributes. We also discuss the empirical evidence and reporting quality of selected papers. The data from the study is made publicly available for other researchers and practitioners. We present an overview of the area by answering several research questions. The area is growing rapidly, however, there is lack of enough empirical evidence to compare and assess the effectiveness of the techniques. More publicly available tools are required for use of practitioners and researchers. Further attention is needed on non-functional testing and testing of ML programs using reinforcement learning. We believe that this study can help researchers and practitioners to obtain an overview of the area and identify several sub-areas where more research is required
Creating and experiencing Flipped Learning in Multivariable Calculus for Engineering

This article discusses the process of creating, implementing and experiencing Flipped Learning in a Multivariable Calculus course for second year engineering students. We describe the construction of the teaching material, consisting of short videos for pre-class preparation and aligned worksheets for in-class dynamics, and the activities that were conducted. We discuss difficulties and key aspects to be considered while creating this material and during implementation of Flipped Learning. We present how students reacted to pre-class preparation and how in-class dynamics developed during implementation. We show results on students performance and perception when enrolling in a flipped classroom section. We present comparative results on students performance of a section taught with Flipped Learning vs a parallel section thought in the traditional expository way. We could conclude that flipped courses show similar results in passing percentage than traditionally taught courses, that student's perceptions are generally mixed, and we perceived that students repeating the course preferably do not choose flipped classes. Finally, we discussed the methodological evolution of this course converging to a mixed methodology throughout a four year period, observing that the instructors evaluation decreases in classes that were flipped. Mixed methodologies on the other hand, increased the learning experience of students resulting in an increased instructors evaluation score and higher students enrollment in the course.
Systematic Transmission Method of Industrial IEEE 802.15.4 for Real-time Mixed Traffic

In this paper, dynamic GTS scheduling method based on IEEE 802.15. 4 is proposed for wireless control system considering reliability and real-time property. The proposed methods can guarantee a transmission of real-time periodic and sporadic data within the limited time frame in factory environment. The superframe of IEEE 802.15. 4 is used for the dynamic transmission method of real-time mixed traffic (periodic data, sporadic data, and non real-time message). By separating CFP and CAP properly, the periodic, sporadic, and non real-time messages are transmitted effectively and guarantee real-time transmission within a deadline. The simulation results show the improvement of real-time performance of periodic and sporadic data at the same time.
Leveraging knowledge bases and parallel annotations for music genre translation

Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and subjectivity of music genres. Accounting for these has, though, many benefits for aligning knowledge sources, integrating data and enriching musical items with tags. Here, we choose a new angle for the genre study by seeking to predict what would be the genres of musical items in a target tag system, knowing the genres assigned to them within source tag systems. We call this a translation task and identify three cases: 1) no common annotated corpus between source and target tag systems exists, 2) such a large corpus exists, 3) only few common annotations exist. We propose the related solutions: a knowledge-based translation modeled as taxonomy mapping, a statistical translation modeled with maximum likelihood logistic regression; a hybrid translation modeled with maximum a posteriori logistic regression with priors given by the knowledge-based translation. During evaluation, the solutions fit well the identified cases and the hybrid translation is systematically the most effective w.r. t. multilabel classification metrics. This is a first attempt to unify genre tag systems by leveraging both representation and interpretation diversity.
Software Engineering, Testing, and Quality Assurance for Natural Language Processing

Software engineering in general is a first-class research object in computer science, but generally has not been treated as such within the natural language processing community. This is despite the fact that natural language as an input type has unique characteristics that present special problems for software testing, quality assurance, and even requirements specification. The goals of this workshop included raising awareness of the need for good software engineering practices in NLP, stimulating research on same, and disseminating the results of current work in this area.
Topological Methods for Characterising Spatial Networks: A Case Study in Tumour Vasculature

Understanding how the spatial structure of blood vessel networks relates to their function in healthy and abnormal biological tissues could improve diagnosis and treatment for diseases such as cancer. New imaging techniques can generate multiple, high-resolution images of the same tissue region, and show how vessel networks evolve during disease onset and treatment. Such experimental advances have created an exciting opportunity for discovering new links between vessel structure and disease through the development of mathematical tools that can analyse these rich datasets. Here we explain how topological data analysis (TDA) can be used to study vessel network structures. TDA is a growing field in the mathematical and computational sciences, that consists of algorithmic methods for identifying global and multi-scale structures in high-dimensional data sets that may be noisy and incomplete. TDA has identified the effect of ageing on vessel networks in the brain and more recently proposed to study blood flow and stenosis. Here we present preliminary work which shows how TDA of spatial network structure can be used to characterise tumour vasculature.
Logical Segmentation of Source Code

Many software analysis methods have come to rely on machine learning approaches. Code segmentation - the process of decomposing source code into meaningful blocks - can augment these methods by featurizing code, reducing noise, and limiting the problem space. Traditionally, code segmentation has been done using syntactic cues; current approaches do not intentionally capture logical content. We develop a novel deep learning approach to generate logical code segments regardless of the language or syntactic correctness of the code. Due to the lack of logically segmented source code, we introduce a unique data set construction technique to approximate ground truth for logically segmented code. Logical code segmentation can improve tasks such as automatically commenting code, detecting software vulnerabilities, repairing bugs, labeling code functionality, and synthesizing new code.
Hardware Constraints in Compressive Sensing Based Antenna Array

New constraints based on practical hardware are introduced in compressive sensing (CS) based rectangular antenna array thinning technique. In a standard CS array sparsity enforcement, antenna elements are considered as ideal point sources which do not comply with the practical hardware. It also does not consider the impact of mutual coupling of neighbouring antenna elements on the impedance mismatch. In this work, we propose a combination of constraints based on physical antenna array specifications, mutual coupling and practical antenna element radiation performance in the CS based array thinning enforcement. Analytical modelling along with a design example is presented and discussed. Array performance based on full-wave electromagnetic simulations shows the reliability of the proposed approach.
A Single Image based Head Pose Estimation Method with Spherical Parameterization

Head pose estimation plays a vital role in various applications, e.g., driverassistance systems, human-computer interaction, virtual reality technology, and so on. We propose a novel geometry based algorithm for accurately estimating the head pose from a single 2D face image at a very low computational cost. Specifically, the rectangular coordinates of only four non-coplanar feature points from a predefined 3D facial model as well as the corresponding ones automatically/ manually extracted from a 2D face image are first normalized to exclude the effect of external factors (i.e., scale factor and translation parameters). Then, the four normalized 3D feature points are represented in spherical coordinates with reference to the uniquely determined sphere by themselves. Due to the spherical parameterization, the coordinates of feature points can then be morphed along all the three directions in the rectangular coordinates effectively. Finally, the rotation matrix indicating the head pose is obtained by minimizing the Euclidean distance between the normalized 2D feature points and the 2D re-projections of morphed 3D feature points. Comprehensive experimental results over two popular databases, i.e., Pointing'04 and Biwi Kinect, demonstrate that the proposed algorithm can estimate head poses with higher accuracy and lower run time than state-of-the-art geometry based methods. Even compared with start-of-the-art learning based methods or geometry based methods with additional depth information, our algorithm still produces comparable performance.
A bundle framework for observer design on smooth manifolds with symmetry

The article presents a bundle framework for nonlinear observer design on a manifold having a Lie group action. The group action on the manifold decomposes the manifold to a quotient structure and an orbit space, and the problem of observer design for the entire system gets decomposed to a design over the orbit (the group space) and a design over the quotient space. The emphasis throughout the article is on presenting an overarching geometric structure; the special case when the group action is free is given special emphasis. Gradient based observer design on a Lie group is given explicit attention. The concepts developed are illustrated by applying them on well known examples, which include the action of $\SOthree$ on $\R^3 \setminus \{0\}$ and the simultaneous localisation and mapping (SLAM) problem.
Standard CMMI Appraisal Method for Process Improvement (SCAMPI) A, Version 1.3: Method Definition Document

Abstract : The Standard CMMI Appraisal Method for Process Improvement (SCAMPI) is designed to provide benchmark quality ratings relative to Capability Maturity Model Integration (CMMI) models and the People CMM. The SCAMPI Method Definition Document (MDD) describes the requirements, activities, and practices associated with the processes that compose the SCAMPI method. The MDD also contains precise descriptions of the method's context, concepts, and architecture.
Radio Frequency Fingerprint Identification Based on Denoising Autoencoders

Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising AutoEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from −10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation

A Markov Decision Process (MDP) is a popular model for reinforcement learning. However, its commonly used assumption of stationary dynamics and rewards is too stringent and fails to hold in adversarial, nonstationary, or multi-agent problems. We study an episodic setting where the parameters of an MDP can differ across episodes. We learn a reliable policy of this potentially adversarial MDP by developing an Adversarial Reinforcement Learning (ARL) algorithm that reduces our MDP to a sequence of \emph{adversarial} bandit problems. ARL achieves $O(\sqrt{SATH^3})$ regret, which is optimal with respect to $S$, $A$, and $T$, and its dependence on $H$ is the best (even for the usual stationary MDP) among existing model-free methods.
Interacting systems of infinite spiking neurons with weights beyond uniform summability

We consider an infinite system of spiking neurons with a drift and both excitatory and inhibitory connections. We study conditions for non-explosiveness and the uniqueness of the invariant measure. In particular, we examine conditions that allow this infinite interacting system to go beyond the usual interactions of uniformly summable weights. As a result, we extend the Galves-L\"ocherbach model beyond the restrictive uniform summability of the model.