Miracle-dz/UniFAR-FTUs huggingface dataset:
Prototyping a Traffic Light Recognition Device with Expert Knowledge

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.
Monitoring norms: a multi-disciplinary perspective

Abstract The concept of a norm is found widely across fields including artificial intelligence, biology, computer security, cultural studies, economics, law, organizational behaviour and psychology. The concept is studied with different terminology and perspectives, including individual, social, legal and philosophical. If a norm is an expected behaviour in a social setting, then this article considers how it can be determined whether an individual is adhering to this expected behaviour. We call this process monitoring, and again it is a concept known with different terminology in different fields. Monitoring of norms is foundational for processes of accountability, enforcement, regulation and sanctioning. Starting with a broad focus and narrowing to the multi-agent systems literature, this survey addresses four key questions: what is monitoring, what is monitored, who does the monitoring and how the monitoring is accomplished.
Irony Detection in Tweets: X2Check at Ironita 2018 (Short Paper)

English. In this paper we describe and show the results of the two systems that we have specifically developed to participate at Ironita 2018 for the irony detection task. We scored as the third team in the official ranking of the competition, thanks to the X2C-B system, at a distance of just 0.027 of F1 score from the best system. Italiano. In questo report descriviamo i due sistemi che abbiamo sviluppato ad hoc per partecipare ad Ironita 2018, nello specifico al task di irony detection. Il nostro team è risultato essere il terzo classificato nella classifica ufficiale della competizione, grazie al nostro sistema X2C-B, che ha ottenuto un F1 score solo 0.027 inferiore rispetto al primo classificato.
Curve fitting based shared cache partitioning scheme for energy saving

A linear and exponential (sqrt2 rule) combined curve fitting (CF) is proposed for low-energy shared cache partitioning. Considering cache’s inherent characteristics between cache miss rate and its size, function with an exponent of (1-sqrt2) fits the region of non-linear high-utility cache size, while linear function fits both regions of linear high-utility and low-utility cache size. Using the fitted functions, we proposed a scheme with purely mathematical formulization of energy consumption, which helps fast and efficient shared cache partition. Experimental results show that CF based shared cache partitioning scheme achieves up to 34.5% energy savings compared with other traditional techniques. Moreover, our approach has a high prediction accuracy for the shared cache miss rate and the energy.
Parametric Blending of Triangular Meshes

We present a new method for constructing a parametric blending surface that smoothly connects two triangular meshes. A user selects a subregion on each triangular mesh. The local parameterizations of two selected regions are found by using geodesic polar coordinates, and a base surface on each triangular mesh is constructed by two boundary curves on parametric domain. Finally, two base surfaces are smoothly blended for generating a blending surface. The shape of a blending surface can easily be controlled by several shape parameters or by directly manipulating surface point. We demonstrate the effectiveness of our technique by showing several modeling examples.
Semi-Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods

Accurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the CC from magnetic resonance (MR) images of the brain, namely: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method and the Distance Regularized Level Set Evolution (DRLSE) method. The pre-processing methods applied during research work were to improve the contrast, reduce noise and thus help segment the CC better. In this project, 3D CC models reconstructed based on the segmentations of cross-sections of MR images were also visualised. The results, as measured by quantitative tests of the similarity indice (SI) and overlap value (OV) are the best for the EM model (SI = 92%, OV = 82%) and are comparable to or better than those for other methods taken from a literature review. Furthermore, the properties of the EM model consisting in its ability to both expand and shrink at the same time allow segmentations to be better fitted in subsequent CC slices then in state-of-the art ACMs such as DRLSE or SBGFRLS. The CC contours from previous and subsequent iterations produced by the EM model can be used for initiation in subsequent or previous frames of MR images, which makes the segmentation process easier, particularly as the CC area can increase or decrease in subsequent MR image frames.
Effect of Cobalt Catalyst Confinement in Carbon Nanotubes Support on Fischer-Tropsch Synthesis Performance

Pre-treating the multi-walled carbon nanotubes (CNTs) support by refluxing in 35 vol% nitric acid followed by heating at the temperature of 600 to 900 °C resulted in the formation of defects on the CNTs. Increasing the temperature of the pre-treatment of the CNTs from 600 °C to 900 °C, enhanced the fraction of cobalt-oxide nanoparticles encapsulated in the channels of CNTs from 31% to 70%. The performance of Co/CNTs in Fischer-Tropsch synthesis (FTS) was evaluated in a fixed-bed micro-reactor at a temperature of 240 °C and a pressure of 2.0 MPa. The highest CO conversion obtained over Co/CNTs.A. 900 was 59% and it dropped by ~3% after 130 h of time-on-stream. However, maximum CO conversion using Co/CNTs.A. 600 catalysts was 28% and it decreased rapidly by about 54% after 130 h of time-on-stream. These findings show that the combined acid and thermal pre-treatment of CNTs support at 900 °C has improved the stability and activity of the Co/CNTs catalyst in FTS.
A 39 GHz broadband high-isolation CMOS mixer using magnetic-coupling CG Gm stage for 5G applications

This paper presents a 39 GHz broadband high-isolation mixer for 5G applications in 65 nm CMOS process. By adopting a common-gate (CG) gm stage with magnetic coupling, the mixer obtains a wide RF bandwidth without extra input matching components, which is benefit for system-level design. Meanwhile, it can improve gain and noise performance. Besides, a fully symmetrical design method is employed to keep a minimum LO leakage. According to experimental results, the mixer operates from 27.6 to 57.8 GHz and achieves a gain of 7.3 dB under LO power of 0 dBm. The LO-RF and LO-IF isolations are 59.5 and 57 dB, respectively.
SeMoM, a semantic middleware for IoT healthcare applications. (SeMoM, middleware sémantique pour les applications de santé baséees sur l'internet des objets)

Nowadays, the adoption of the Internet of Things (IoT) has received a considerable interest from both academia and industry. It provides enhancements in quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these "Things" are made available with heterogeneous vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were originally set up. In this thesis, we address these challenges in the context of healthcare applications considering how we transform raw data to cognitive knowledge and ontology-based information shared between IoT system components. With respect to heterogeneity and integration challenges, our main contribution is an ontology-based IoT architecture allowing the deployment of semantic IoT applications. This approach allows sharing of sensors observations, contextualization of data and reusability of knowledge and processed information. Specific contributions include: * Design of the Cognitive Semantic Sensor Network ontology (CoSSN) ontology: CoSSN aims at overcoming the semantic interoperability challenges introduced by the variety of sensors potentially used. It also aims at describing expert knowledge related to a specific domain. * Design and implementation of SeMoM: SeMoM is a flexible IoT architecture built on top of CoSSN ontology. It relies on a message oriented middleware (MoM) following the publish/subscribe paradigm for a loosely coupled communication between system components that can exchange semantic observation data in a flexible way. From the applicative perspective, we focus on healthcare applications. Indeed, specific approaches and individual prototypes are preeminent solutions in healthcare which straighten the need of an interoperable solution especially for patients with multiple affections. With respect to these challenges, we elaborated two case studies 1) bedsore risk detection and 2) Activities of Daily Living (ADL) detection as follows: * We developed extensions of CoSSN to describe each domain concepts and we developed specific applications through SeMoM implementing expert knowledge rules and assessments of bedsore and human activities. * We implemented and evaluated the SeMoM framework in order to provide a proof of concept of our approach. Two experimentations have been realized for that target. The first is based on a deployment of a system targeting the detection of ADL activities in a real smart platform. The other one is based on ADLSim, a simulator of activities for ambient assisted living that can generate a massive amount of data related to the activities of a monitored person.
Overview of the Evalita 2018 itaLIan Speech acT labEliNg (iLISTEN) Task

English. We describe the first edition of the “ itaLIan Speech acT labEliNg” (iLISTEN) task at the EVALITA 2018 campaign (Caselli et al., 2018). The task consists in automatically annotating dialogue turns with speech act labels, i.e. with the communicative intention of the speaker, such as statement, request for information, agreement, opinion expression, or general answer. The task is justified by the large number of applications that could benefit from automatic speech act annotation of natural language interactions such as tools for the intelligent information access, that is by relying on natural dialogues. We received two runs from two teams, one from academia and the other one from industry. In spite of the inherent complexity of the tasks, both systems largely outperformed the baseline. Italiano. Descriviamo la prima edizione del task di “itaLIan Speech acT labEliNg” (iLISTEN) organizzato nell’ambito della campagna di valutazione EVALITA 2018. Il task consiste nell’annotazione automatica di turni di dialogo con la label di speech act corrispondente. Ciascuna categoria di speech act denota l’intenzione comunicativa del parlante, ossia l’intenzione di formulare un’affermazione oggettiva, l’espressione di un’opinione, la richiesta di informazioni, una risposta, un’espressione di consenso. Riteniamo che il task sia rilevante per la il dominio della linguistica computazionale e non solo, alla luce del recente interesse da parte della comunità scentifica nei confronti dei paradigmi di interazione e accesso intelligente all’informazione basati su dialogo. Il task ha visto la partecipazione di due team, uno accademico e uno industriale. Nonostante la complessità del task proposto, entrabi i team hanno ampiamente superato la baseline.
Overview of the EVALITA 2018 Aspect-based Sentiment Analysis Task (ABSITA)

English. ABSITA is the Aspect-based Sentiment Analysis task at EVALITA 2018 (Caselli et al., 2018). This task aimed to foster research in the field of aspect-based sentiment analysis within the Italian language: the goal is to identify the aspects of given target entities and the sentiment expressed for each aspect. Two subtasks are defined, namely Aspect Category Detection (ACD) and Aspect Category Polarity (ACP). In total, 20 runs were submitted by 7 teams comprising 11 total individual participants. The best system achieved a micro F1-score of 0.810 for ACD and 0.767 for ACP. Italiano. ABSITA è l’esercizio di valutazione di aspect-based sentiment analysis di EVALITA 2018 (Caselli et al., 2018). Il compito ha l’obiettivo di promuovere la ricerca nel campo della sentiment analysis per lingua italiana: ai partecipanti è stato richiesto di identificare gli aspetti rilevanti per le entitá fornite come input e la sentiment espressa per ognuno di essi. In particolare abbiamo definito come sottotask l’Aspect Category Detection (ACD) e l’Aspect Category Polarity (ACP). In totale, sono state presentate 20 soluzioni di 7 team composti in totale da 11 singoli partecipanti. Il miglior sistema ha ottenuto un punteggio di micro F1 di 0,810 per ACD e 0,767 per ACP.
High efficiency broadband ellipse antenna array with tapered parallel-double transmission line feed network

: In this paper, an antenna element and array for broadband application are proposed. The original antenna element consists of a paral-lel-double transmission line (PDTL) fed ellipse dipole and a re ﬂ ecting plate for unidirectional radiation, high-gain, and high e ﬃ ciency operation. By introducing a circular patch as parasitic radiator, the voltage standing wave ratio (VSWR) ≤ 2 is achieved from 3 GHz to 9 GHz and the element shows a stable radiation pattern during the operating band. In order to reach a high gain performance, a 4 × 8 array is investigated. For the broadband performance of the proposed antenna array, a broadband and low loss tapered PDTL network is used to construct the array. Finally, to verify the feasibility of the proposed antenna, a prototype array is manufactured and measured. Experimental results are found in acceptable agreement with the simulated ones in terms of gain, radiation e ﬃ ciency, radiation pattern, and VSWR. The result indicates that the proposed antenna has an impedance bandwidth (IBW) for VSWR ≤ 2 of 100% ranging from 3.0 to 9.0 GHz. Meanwhile the radiation e ﬃ ciency is better than 82.7% and the gain varies from 14.8 to 20.9 dBi within the IBW.
Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service

Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms.
The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

The continuous increase in deprived living conditions in many cities of the Global South contradicts efforts to make cities inclusive, safe, resilient, and sustainable places. Using examples of Asian, African, and Latin American cities, this study shows the scope and limits of earth observation (EO)-based mapping of deprived living conditions in support of providing consistent global information for the SDG indicator 11.1. 1 “proportion of urban population living in slums, informal settlements or inadequate housing”. At the technical level, we compare several EO-based methods and imagery for mapping deprived living conditions, discussing their ability to map such areas including differences in terms of accuracy and performance at the city scale. At the operational level, we compare available municipal maps showing identified deprived areas with the spatial extent of morphological mapped areas of deprived living conditions (using EO) at the city scale, discussing the reasons for inconsistencies between municipal and EO-based maps. We provide an outlook on how EO-based mapping of deprived living conditions could contribute to a global spatial information base to support targeting of deprived living conditions in support of the SDG Goal 11.1. 1 indicator, when uncertainties and ethical considerations on data provision are well addressed.
A survey of large-scale reasoning on the Web of data

Abstract As more and more data is being generated by sensor networks, social media and organizations, the Web interlinking this wealth of information becomes more complex. This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies. In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insights from the data. However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data. To address this problem, several high-performance reasoning systems, which mainly implement distributed or parallel algorithms, have been proposed in the last few years. These systems differ significantly; for instance in terms of reasoning expressivity, computational properties such as completeness, or reasoning objectives. In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the methods and over the conducted experiments. We highlight the shortcomings of these approaches and discuss some of the open problems related to performing scalable reasoning.
Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data

Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.
Multi-Criteria Decision Making (MCDM) Model for Seismic Vulnerability Assessment (SVA) of Urban Residential Buildings

Earthquakes are among the most catastrophic natural geo-hazards worldwide and endanger numerous lives annually. Therefore, it is vital to evaluate seismic vulnerability beforehand to decrease future fatalities. The aim of this research is to assess the seismic vulnerability of residential houses in an urban region on the basis of the Multi-Criteria Decision Making (MCDM) model, including the analytic hierarchy process (AHP) and geographical information system (GIS). Tabriz city located adjacent to the North Tabriz Fault (NTF) in North-West Iran was selected as a case study. The NTF is one of the major seismogenic faults in the north-western part of Iran. First, several parameters such as distance to fault, percent of slope, and geology layers were used to develop a geotechnical map. In addition, the structural construction materials, building materials, size of building blocks, quality of buildings and buildings-floors were used as key factors impacting on the building’s structural vulnerability in residential areas. Subsequently, the AHP technique was adopted to measure the priority ranking, criteria weight (layers), and alternatives (classes) of every criterion through pair-wise comparison at all levels. Lastly, the layers of geotechnical and spatial structures were superimposed to design the seismic vulnerability map of buildings in the residential area of Tabriz city. The results showed that South and Southeast areas of Tabriz city exhibit low to moderate vulnerability, while some regions of the north-eastern area are under severe vulnerability conditions. In conclusion, the suggested approach offers a practical and effective evaluation of Seismic Vulnerability Assessment (SVA) and provides valuable information that could assist urban planners during mitigation and preparatory phases of less examined areas in many other regions around the world.
Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks

Information-centric networking (ICN) technology matches many major requirements of vehicular ad hoc networks (VANETs) in terms of its connectionless networking paradigm accordant with the dynamic environments of VANETs and is increasingly being applied to VANETs. However, wireless transmissions of packets in VANETs using ICN mechanisms can lead to broadcast storms and channel contention, severely affecting the performance of data dissemination. At the same time, frequent changes of topology due to driving at high speeds and environmental obstacles can also lead to link interruptions when too few vehicles are involved in data forwarding. Hence, balancing the number of forwarding vehicular nodes and the number of copies of packets that are forwarded is essential for improving the performance of data dissemination in information-centric networking for vehicular ad-hoc networks. In this paper, we propose a context-aware packet-forwarding mechanism for ICN-based VANETs. The relative geographical position of vehicles, the density and relative distribution of vehicles, and the priority of content are considered during the packet forwarding. Simulation results show that the proposed mechanism can improve the performance of data dissemination in ICN-based VANET in terms of a successful data delivery ratio, packet loss rate, bandwidth usage, data response time, and traversed hops.