Miracle-dz/UniFAR-FTUs huggingface dataset:
Research and Implementation on Key Information Security Algorithm Based on GOOSE Message

In order to solve the contradiction between the security and real-time of GOOSE message from the IEC 61850, the paper proposes a communication security model, which is based on the AES_CBC_MAC algorithm and key information encryption, and the communication security implementation process of GOOSE message based on the model, after analyzing the structure of the GOOSE message, comparing the requirements of the security and the comparison of the experiment. By analyzing of confidentiality and integrity, exampling of the star network intelligent substation as the model, using Modelsim and OPNET as simulation software for different lengths of GOOSE messages to carryout security line processing and communication delay, we obtain the total delay of the communication security model. Experiments show that the intelligent substation GOOSE message communication security processing program has both good security and real-time.
A Robust Online Multi-Face Tracking System

This paper presents a system for robust online multi-face tracking in video sequences recorded by a stationary camera. Several components and algorithms are combined in the system architecture: an NPD robust face detector, a DSST tracker augmented with FIFO long- and short-term memories (LTMs/STMs), trajectory memories (TMs), a PSR-based tracking failure detector and a Hungarian algorithm for trajectory assignment. The paper gives the preliminary experimental results, expressed by MOTA, MOTP and IDS metrics, obtained on the benchmark dataset consisting of three videos.
Precision Health Care for Sustainable Patient Centric Solutions

The quest for sustainable healthcare amidst the rising cost and shrinking resources has led to the industrial uptake of essential technology that has impacted on the provision of healthcare services, subsequently triggering the mobility of patients seeking specialized healthcare services. Precision Health Care (PHC) seeks to harness technology in its delivery of bespoke patient centered diagnosis and treatment. This entails utilizing an individual's medical history and advanced decision support systems to tailor treatment prescriptions. With the increasing availability of healthcare data, PHC has the potential to break down the walls in realizing substantial benefits to all its stakeholders by providing valuable information integral for the delivery of personalized health support services to the patients and improved clinical decision support for the service provider. Understanding the role of a precise healthcare system paves way for a more intelligent healthcare ecosystem that's focused on prevention, early disease detection and personalized treatments through health data integration. This paper seeks to deconstruct the viability PHC in the context of knowledge discovery, privacy, data re-use and governance in the context of patient centric treatment solutions.
Experience in the implementation of projects in professional environment in a 1st cycle of studies of Civil Engineering

This paper describes the experience in the implementation of projects in a professional environment on the first cycle of studies on Civil Engineering from the School of Engineering - Polytechnic of Porto. This initiative began in the academic year of 2015/16 with 3 students, developed to 8 students on 2016/17 and grew furthermore to the participation of 16 students on the current academic year of 2017/18. The adopted approach has guaranteed a significative mobilization of several intervenient, particularly students, teachers and supervisors at the participating companies. The evaluation of the success of the initiative was monitored by online surveys assessing the satisfaction degree of all intervenient and by the analysis of the grades of the participating students. All parties involved gave a very positive assessment of the initiative and its importance on the students' training.
Epidemiology of amyotrophic lateral sclerosis in Friuli-Venezia Giulia, North-Eastern Italy, 2002–2014: a retrospective population-based study

Abstract Objectives: To describe the epidemiology of Amyotrophic Lateral Sclerosis (ALS) in Friuli-Venezia Giulia (FVG) region, Italy, over a 13-year period (2002–2014), estimating ALS (a) incidence, prevalence, and clinical features; (b) mortality, also comparing Udine municipality to the rest of FVG. Methods: We conducted a retrospective population-based study. ALS incident cases were ascertained using multiple sources and validated through expert review. We calculated crude and standardized incidence rate (IR), point prevalence and mortality rate (MR), each with 95% confidence interval. Standardized incidence (SIR) and mortality (SMR) ratio were calculated to compare Udine to FVG. Results: Among 444 incident cases (50.0% men, median age 68.5 years), onset was bulbar in 30.2%, spinal in 59.9%, mixed in 9.9%; 3.6% had familial ALS. Crude and 2000 European population standardized IR was respectively 2.81 (2.56–3.09) and 2.09 (1.89–2.29) per 100,000 person-years. Standardized male-to-female incidence ratio was 1.05. IR peaked at age 65–74 years (men: 9.93, 8.04–12.32; women: 7.74, 6.18–9.67) and decreased thereafter. Prevalence was 8.36 (6.74–9.97) cases per 100,000 inhabitants on 30 June 2009 and 7.98 (6.40–9.56) on 30 June 2014. SIR was 1.20 and SMR 1.11. Conclusions: When assessed over a long period, incidence of ALS was in the range of Italian and European population-based registries and showed a consistent pattern by age and sex. IR and MR were only slightly higher in Udine vs. FVG.
Three-dimensional (3D) Reconstruction of Range Gated Imaging

In this paper, several approaches are applied for 3D reconstruction of range gated imaging with the objective to improve the 3D performance in terms of accuracy and resolution. Feasibility of the proposed image processing, 3D reconstruction model, super-resolution technique, and mesh refinement is demonstrated. Our test case shows that the depth error of 3D reconstruction is improved from 2.26% to 1.65% after mesh refinement. This gives a constructive idea to provide optimised 3D solution and benefit various applications. The proposed approaches and the findings presented may stimulate further interest in the relevant fields such as machine vision and 3D active imaging.
Analysis of Organelle Positioning Using Patterned Microdevices

The consequences of alterations in the distribution of intracellular organelles, observed in many diseases, are often not clear. Intracellular organelles alter their morphology and positioning to regulate cell homeostasis and function. We outline how organelle positioning can be studied employing a density‐based analysis of 3D images applied to cells that show similar cellular geometries. Quantification is facilitated by the use of single cells seeded on micropatterned substrates that provide cues for controlled cell spreading. This minimal system mimics the reproducible distribution of organelles typically observed in tissues, simplifying image analysis and minimizing the number of cells required for the observation of robust phenotypes. Here we provide guidelines for how the majority of organelles can be efficiently analyzed in cells seeded on adhesive micropatterns. We exemplify how alterations in the positioning of different organelles as a result of the perturbation of the cytoskeleton or associated motor proteins can be efficiently quantified. © 2018 by John Wiley & Sons, Inc.
A Degree-based Distributed Label Propagation Algorithm for Community Detection in Networks

Community detection is an important way to understand the functions and characteristics of complex systems. The Label Propagation Algorithm (LPA) is a community detection algorithm of which complexity is close to linear. Due to the randomness and instability of the algorithm, it is difficult to get good results with this algorithm. This paper proposes a degree-based label propagation algorithm and parallelizes the algorithm based on the GraphX component in Spark platform (D-disLPA). Using WeChat empirical network data, this paper adopts modularity as an evaluation index to compare D-disLPA with traditional label propagation algorithms. The experimental results show that the D-disLPA can effectively solve the non-convergence problem in traditional algorithms and improve the stability and accuracy of community detection. At the same time, this distributed algorithm is able to satisfy the requirements of large-scale community detection.
CDM: Compound Dissimilarity Measure and an Application to Fingerprinting-Based Positioning

A non-vector-based dissimilarity measure is proposed by combining vector-based distance metrics and set operations. This proposed compound dissimilarity measure (CDM) is applicable to quantify the similarity of collections of attribute/feature pairs where not all attributes are present in all collections. This is a typical challenge in the context of e.g., fingerprinting-based positioning (FbP). Compared to vector-based distance metrics (e.g., Minkowski), the merits of the proposed CDM are i) the data do not need to be converted to vectors of equal dimension, ii) shared and unshared attributes can be weighted differently within the assessment, and iii) additional degrees of freedom within the measure allow to adapt its properties to application needs in a data-driven way. We indicate the validity of the proposed CDM by demonstrating the improvements of the positioning performance of fingerprinting-based WLAN indoor positioning using four different datasets, three of them publicly available. When processing these datasets using CDM instead of conventional distance metrics the accuracy of identifying buildings and floors improves by about 5% on average. The 2d positioning errors in terms of root mean squared error (RMSE) are reduced by a factor of two, and the percentage of position solutions with less than 2m error improves by over 10%.
The visual inheritance structure to support the design of visual notations

—It is a common practice in modelling languages to provide their users with a set of visual notations as a representation of semantic constructs. The use of visual notation is believed to help communicate complex information, especially when communicating with non-technical users. Therefore, research in the design of visual notation continues to evolve, e.g. research to provide an effective and efﬁcient design approach. There are approaches exist to support the designer in designing the visual notation such as the Cognitive Dimensions and the Physics of Notations. As the current metamodeling approach is widely adopted as a mechanism for improving standardisation and interoperability in modelling languages, it is important to provide a guideline that focus on the design of visual notation for a predeﬁned metamodel. In this paper, we address the visual inheritance structure to support the design of visual notations for a predeﬁned metamodel. This approach emphasises the design coherence between classes and sub-classes. To demonstrate that it is possible to apply our approach, we use part of the OMG Structured Assurance Case Metamodel as a case study.
3-D Polarized Modulation: System Analysis and Performance

In this paper, we present a novel modulation technique for dual-polarization communication systems, which reduces the error rate compared with the existent schemes. This modulation places the symbols in a 3-D constellation, rather than the classic approach of 2-D. Adjusting the phase of these symbols depending on the information bits, we are able to increase the bit rate. Hence, the proposed scheme conveys information by selecting both polarization state and the phase of radiated electromagnetic wave. We also analyses the performance of the 3-D polarized modulation (PMod) for different constellation sizes and we obtain a curve of rate adaptation. Finally, we compare the proposed 3-D PMod with the other existing schemes, such as single polarization phase shift keying and double polarization vertical Bell Laboratories layer space–time, both carrying the same number of information bits. The results show that the 3-D PMod always outperforms all other schemes, except for low-order modulation. Therefore, we can conclude that the 3-D PMod is an excellent candidate for medium- and high-modulation-order transmissions.
Towards flexible object and class modeling tools: an experience report

Recently, we have proposed an approach for class modeling starting from imperfect object models. The underlying process puts emphasis on the free expression of ideas of the developer through allowing to formulate incomplete and even inconsistent object diagrams. From these object diagrams, class diagrams are deduced. This paper reports on experiences with a supporting tool grouped into two categories. One category involves students at a university and how they get along with the general concept and the tool. The other category shows how researchers work with our proposed ideas.
Physical, social, and psychological characteristics of community-dwelling elderly Japanese dog and cat owners

Objectives Previous studies examined the physical characteristics of older dog owners. However, associations of health-related factors with dog/cat ownership have not been comprehensively evaluated. This cross-sectional study examined physical function, physical activity, social function, and psychological function of a population of community-dwelling older Japanese dog and cat owners after controlling for important confounders. Methods The analysis included data from 11,233 community-dwelling adults aged 65 years or older (51.5% women; 52.3% aged 75–84), in Ota City, Tokyo, Japan. Pet ownership experience and pet species owned were determined by self-reported questionnaire, and current, past, and never dog/cat ownership was analyzed. Results Analysis of variables related to physical function and physical activity showed that motor fitness scale and walking activity were significantly associated with experience of dog ownership, after adjustment for important sociodemographic and health characteristics. Analysis of social function showed that interaction with neighbors, social isolation, and trust in neighbors were significantly associated with experience of dog ownership and cat ownership. Conclusions As compared with respondents with no history of pet ownership, motor fitness and walking activity are greater for dog owners and social function is higher for dog and cat owners. Caring for a dog or cat might be an effective health promotion strategy to increase physical activity and facilitate social participation among older adults.
Resource Management for Cognitive IoT Systems With RF Energy Harvesting in Smart Cities

The number of Internet of Things (IoT) nodes is increasing in modern cities which demands spectrum and energy efficiency. Fifth-generation (5G) networks are considered as a key paradigm for the realization of future IoT applications. Particularly, cognitive radio and non-orthogonal multiple access are candidate technologies for 5G networks that can improve spectral efficiency and accommodate a large number of IoT devices. Furthermore, radio frequency (RF) energy harvesting can increase the energy efficiency of IoT networks. In this paper, we propose a resource management scheme for cognitive IoT network with RF energy harvesting in 5G networks. The objective is to maximize the throughput while assuring quality-of-service requirements in terms of data rate and minimum residual energy constraint on each IoT node. We use mixed integer linear programming and greedy approaches to solve the optimization problem. We then present the simulation results of the proposed scheme to exhibit the significant positive impact on the performance of the IoT network.
Millimeter- And Terahertz-Wave Radio Systems Enabled by Photonics Technology for 5G/IoT and Beyond

In the upcoming 5G era, throughput and capacity of connections will be drastically increased as comparable to an optical network. In the scenario, seamless convergence between radio and optical technologies is a key for the realization of low latency and high-reliable networks. In the talk, we discuss a radio over fiber technology utilized as a seamless conversion technique between optical signals and millimeter-wave signals, even terahertz-wave signals.
Increase of confidence for the solution of problems in preuniversity students through Computational Thinking

The paper describes the initial research on the integration of Computational Thinking, through the dissemination of skills and practice of exercises in a context of Gamification, within the framework of a pilot program called Apadrinamiento, aimed to students of high school who visit the facilities of the Universidad Tecnológica de Puebla, and that are considering studying in the Division of Information and Communication Technologies. The main objective is to increase the confidence of students in the solution of problems through the skills that exercise Computational Thinking. This report details the planning of the activities, the indicators to be measured and the scenarios where the interventions will be carried out that allow, in a second stage, the creation of strategies to avoid desertion. The main conclusion of the paper is to have created an academic strategy to benefit the understanding of pre-university students in the current computer world through the exercise of Computational Thinking.
Internet of Too Many Things in Smart Transport: The Problem, The Side Effects and The Solution

The Internet of Things (IoT) involves embedding electronics, software, sensors, and actuators into physical devices, such as vehicles, buildings, and a wide range of smart devices. Network connectivity allows IoT devices to collect and exchange data. The prevalence of IoT devices has increased rapidly in last five years, driven by cheaper electronics and a desire to monitor and control the physical world. We introduce the concept of IoT flood to describe the increased use of IoT devices. Just like the data deluge, the IoT flood has potential benefits and risks. This paper focuses on the hidden side effects of the increased usage of IoT, such as energy consumption, physical pollution, radiation, and health issues. We indicate that an evaluation system with carefully designed metrics reflecting the impact of IoT flood with input from academic, industry, and government is required. We propose some practical measures that can reduce the IoT flood, such as common platforms and data sharing to reduce the side effects. This paper demonstrates the IoT flood problem and potential solutions by examining the intelligent transport system domain where IoT is being deployed to solve problems related to time efficiency and energy consumption through smart mobility.
Comparative analysis of Cuk and bl-csc PFC converter dynamic performance operating a BLDC motor

In this paper a comparative analysis is carried out on PFC converters. The output voltages and efficiencies of the converters are considered, and speed comparisons of BLDC motor are taken. For the comparison Cuk and bl-csc PFC converters are taken with common BLDC motor parameters. The analysis and comparison are modelled in MATLAB Simulink environment with graphical representations using GUI tool.
Point-of-Care Device and Internet of Things Platform for Chronic Disease Management

Access to healthcare is limited for the population in rural areas due to the shortage of medical workerns, insufficient facilities, and the long commute to the hospital. As a result, hospitals and staff are overwhelmed by a lot of patients, yet patients do not receive appropriate care, especially in this growing diabetes and other noncommunicable diseases pandemic. Point-of-care test with an IoT platform can be used to offer a higher quality yet affordable healthcare solution to the community and minimize the workflow of healthcare workers.
Approximate Entropy of Spiking Series of a Neuronal Network in Either Subcritical or Critical State

Spontaneous activity of neural networks depends on their stage of development. Computational performances of a network increase when the maturation leads to a self-organized criticality. Thus, an increasing complexity in the behavior of the network is expected when it enters in this developmental stage, called critical state. We tested this hypothesis investigating with a Micro-Electrodes Array of 60 electrodes a neuronal culture that during maturation exhibited first a subcritical and then a critical state. We found that in the critical state the local complexity (measured in terms of Approximate Entropy) was larger than in subcritical conditions ($\mathbf{mean}\pm \mathbf{std}$, ApEn about $\mathbf{1.03}+\mathbf{0.10},\mathbf{0.77}+\mathbf{0.18}$ in critical and sub-critical states, respectively; differences statistically significant), but only if the embedding dimension is at least 3 and the tolerance is fixed (we considered it equal to 1 ms, which is close to the characteristic time of neural communications).
Plasmonic Eigenmodes on Regular Nano-Polyhedra: Case Study of Electrostatic and Dynamic Resonant Effects on Rounded Superquadric Solids

The use of the theory of characteristic modes for the study of plasmonic eigenstates of rounded hexahedral and octahedral nanoscatterers is proposed and presented. After a brief study of electrostatic modes in an hexahedral structure follows the corresponding characteristic mode analysis. The analysis reveals the existence, for the first time, of highly symmetric dark modes on the surface of a rounded cube, enhancing our views on the triggered scattering mechanisms for single nanoparticles.
A Joint Row and Column Action Method for Cone-Beam Computed Tomography

The inversion of linear systems is fundamental in computed tomography (CT) reconstruction. Computational challenges arise when trying to invert large linear systems, as limited computing resources mean that only a part of the system can be kept in computer memory at any one time. In linear tomographic inversion problems, such as X-ray tomography, even a standard scan can produce millions of individual measurements and the reconstruction of X-ray attenuation profiles typically requires the estimation of a million attenuation coefficients. To deal with the large data sets encountered in real applications and to efficiently utilize modern graphics processing unit based computing architectures, combinations of iterative reconstruction algorithms and parallel computing schemes are increasingly applied. Whilst different parallel methods have been proposed, individual computations currently need to access either the entire set of observations or estimated X-ray absorptions, which can be prohibitive in many realistic applications. We present a fully parallelizable CT image reconstruction algorithm where each computation node works on arbitrary partial subsets of the data and the reconstructed volume. We further develop a nonhomogeneously randomized selection criterion which guarantees that submatrices of the system matrix are selected more frequently if they are dense, thus maximizing information flow through the algorithm. We compare our algorithm with block alternating direction method of multipliers and show that our method is significantly faster for CT reconstruction.