Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Analysis of the Relevance of Features and Effects on Gender Classification Models for Social Media Author Profiling

Automatic user profiling from social networks has become a popular task due to its commercial applications (targeted advertising, market studies.). Automatic profiling models infer demographic characteristics of social network users from their generated content or interactions. Users’ demographic information is also precious for more social worrying tasks such as automatic early detection of mental disorders. For this type of users’ analysis tasks, it has been shown that the way how they use language is an important indicator which contributes to the effectiveness of the models. Therefore, we also consider that for identifying aspects such as gender, age or user’s origin, it is interesting to consider the use of the language both from psycho-linguistic and semantic features. A good selection of features will be vital for the performance of retrieval, classification, and decision-making software systems. In this paper, we will address gender classification as a part of the automatic profiling task. We show an experimental analysis of the performance of existing gender classification models based on external corpus and baselines for automatic profiling. We analyse in-depth the influence of the linguistic features in the classification accuracy of the model. After that analysis, we have put together a feature set for gender classification models in social networks with an accuracy performance above existing
Urban Traffic Incident Detection for Organic Traffic Control: A Density-based Clustering Approach

The traffic demands in urban road networks can fluctuate immensely. The Organic Traffic Control (OTC) offers a resilient traffic management to control such traffic demands. An additional challenge is the detection of unforeseen traffic incidents. To enhance the capabilities of OTC accordingly, we outline a traffic incident algorithm based on DBSCAN, a density-based clustering algorithm: In a simulated urban road network, equipped with traffic light controllers at intersections, vehicle detectors are used to gather traffic flow data. The clustering of this time series data to detect simulated road blockages is expanded using various filters. This extension of the initial clustering is the result of an manual evaluation process, which shows the principal applicability of this approach.
Deployment Service for Scalable Distributed Deep Learning Training on Multiple Clouds

This paper introduces a platform based on open-source tools to automatically deploy and provision a distributed set of nodes that conduct the training of a deep learning model. To this end, the deep learning framework TensorFlow will be used, as well as the Infrastructure Manager service to deploy complex infrastructures programmatically. The provisioned infrastructure addresses: data handling, model training using these data, and the persistence of the trained model. For this purpose, public Cloud platforms such as Amazon Web Services (AWS) and General-Purpose Computing on Graphics Processing Units (GPGPU) are employed to dynamically and efficiently perform the workflow of tasks related to training deep learning models. This approach has been applied to real-world use cases to compare local training versus distributed training on the Cloud. The results indicate that the dynamic provisioning of GPU-enabled distributed virtual clusters in the Cloud introduces great flexibility to cost-effectively train deep learning models.
Dramen als small worlds? Netzwerkdaten zur Geschichte und Typologie deutschsprachiger Dramen 1730-1930

Neben dem ›klassischen‹ strukturalistischen Paradigma, das sich wesentlich an Theoremen der Linguistik orientiert (u. a. Lotman 1972; Titzmann 1977), gibt es in der Literaturwissenschaft bereits seit Jahrzehnten Ansätze zu einer Strukturanalyse, die sich auf die empirische Soziologie – insbesondere auf die Social Network Analysis – bezieht und Struktur entsprechend nicht über basale semantische Relationen (etwa als Opposition oder Äquivalenz) definiert, sondern über soziale Interaktionen (Marcus 1973; Stiller et al. 2003; de Nooy 2006; Stiller / Hudson 2005; Elson et al. 2010; Agarwal et al. 2012). Im Kontext der Digital Humanities haben diese Ansätze zu einer literaturwissenschaftlichen Netzwerkanalyse (Trilcke 2013) in den letzten Jahren eine neue Dynamik gewonnen (Moretti 2011; Rydberg-Cox 2011; Park et al. 2013). Aus literaturwissenschaftlicher Sicht versprechen diese Analyseverfahren dabei auf umfangreichen Korpora basierende, von quantitativen Daten gestützte Erkenntnisse über die Literaturgeschichte wie auch über die generischen Eigenarten literarischer Texte. Im Projekt dlina. Digital Literary Network Analysis haben wir einen Workflow zur Extraktion, Analyse und Visualisierung von Netzwerkdaten aus dramatischen Texten mit rudimentärer TEI-Auszeichnung entwickelt (Fischer et al. 2015). Der hier projektierte Vortrag wird Ergebnisse der netzwerkanalytischen Auswertung dieser Daten präsentieren und vor dem Hintergrund etablierter fachwissenschaftlicher Fragestellungen diskutieren.
Developing Evaluation Metrics for Active Reading Support

Reading academic literature in digital formats is becoming more and more of a normalcy for students, but designers of reading support tools do not share common, metrics for evaluating such tools. This paper introduces our work in developing an evaluation form which we call the aRSX (active Reading Support IndeX). The aRSX-form is a quantitative means for evaluating whether a specific software or hardware tool supports active, academic reading in a way that resonates with personal user experience and learning preferences in other words; whether the tool is practical and pleasant to use for the student who consumes academic literature. The paper presents the first and second iterations of the aRSX evaluation survey based on a preliminary exploratory experiment with 50 university students. The paper also describes how the evaluation form can be developed and used by designers of reading support tools.
Sonidos Telemáticos: Network Remote Performance for Compositional Paradigm Shifting in Peruvian Musical Learning Practice

This paper explores the implementation of network data transmission for real-time remote musical performance as part of a set of strategies for paradigm shifting in Peruvian musical education. It describes the use of the [netsend] / [netreceive] objects in the Pure Data (Pd) visual programming language by the Ensamble de Laptops de la Universidad Nacional de Música – ELUNM, for the composition and execution of musical pieces developed by the creation of programs (patches) design for network distant performance interactivity through a server computer. This practice aims at bridging a technological and conceptual gap, in musical composition and performance, that has accompanied the history of musical education in the country, keeping computational thinking from becoming a tool for the construction of new creative expressions and the extension/reconfiguration of the musical arts.
Three Dimensional Protein Localization Using High Voltage Electron Microscopy.

Three-dimensional (3D) protein localization at the electron microscopic level is of great benefit when determining the precise distribution of a given protein relative to complex subcellular structures. We have developed several 3D ultrastructural labeling approaches for use with higher voltage electron microscopy. High voltage electron microscopy allows for the use of much thicker sections than conventional transmission electron microscopy. Many of our staining protocols are based on the photooxidation of diaminobenzidine (DAB) by fluorescent markers. As a fluorescent dye is excited in the presence of DAB, the reactive oxygen generated gradually oxidizes DAB into an insoluble polymer which can be rendered electron dense. We previously demonstrated that photooxidation by the fluorophore eosin can be used for high resolution immunolocalization. Because of its small size, eosin-conjugated reagents penetrate well into tissue slices, providing labeling through a reasonable depth of tissue. When combined with high voltage electron microscopy using thick sections, the 3D distribution of a variety of proteins can be visualized. Analysis and interpretation of these 3D labeling patterns is further facilitated by the use of electron tomography, which allows for the derivation of 3D structure from a series of images taken at different orientations. In this review, several examples of 3D protein localization using these technologies are presented.
A Digital Application to Assist Basic Education Teachers in the Interdisciplinary Development of Computational Thinking Skills on the Math Discipline in Brazilian Learning Context

: Computational Thinking (CT) has skills that can be explored in Math questions and can improve student’s capability of solving problems. Approaches have been proposed in the literature to support teachers in elab-orating, sharing, and searching Math questions that stimulated CT skills. However, the wide dissemination of these approaches has problems to put in practice by teachers. To ﬁll this gap, in this paper, we present a digital application that aims to help the teachers to work in CT skills through activities of creation, cataloging, and utilization of Math questions. The validation of the application propose here was conducted with undergraduate students in Math, who reported that the solution is easy to use and supports and helps teachers put in practice creating, cataloging, and utilizing Math questions to stimulate CT skills. Also, the application able the searching and sharing Math questions between teachers easily.
Modelling Energy Consumption of IoT Devices in DISSECT-CF-Fog

: The continuously evolving information technology carries requirements to foster cost, resource and energy-aware systems. The Internet of Things is considered as one of the most trending technology, which is often coupled with Cloud or Fog Computing resources that manage the possibly big data generated by smart devices in an effective way. To reduce the carbon footprint of such IoT-Fog-Cloud infrastructures, planning and optimisation of their energy consumption is necessary to realise sustainable solutions. It is also inevitable to use simulation in the design phase of such complex systems, hence it would by hardly feasible and rather costly to evaluate numerous settings effecting the energy use. In this paper, we design an IoT energy model based on real world measurements, and propose an extension of the energy model of the DISSECT-CF-Fog simulator to enable the energy usage monitoring of complex, IoT-Fog-Cloud infrastructures. We also present a validation of the extension with a weather forecasting use case to exemplify its conﬁguration possibilities that meet the design requirements of the energy sector.
Food Recommendation in a Worksite Canteen

Recommendation systems tackle with information overload to assist people in finding their best choice according to their preferences and past behaviour. This occurred in many contexts, including the food sector where culinary inspiration, sales increase or healthy advice motivate the adoption of such a system. In this paper we propose a canteen food recommendation system for workers operating at an innovation hub including more than 20 companies. The system leverages a 30 months data set of past choices, and adopts a content based and a collaborative filtering approach for canteen users, suggesting them with dishes chosen by other similar users. First results for frequent as well as occasional canteen visitors are encouraging to validate the proposed
Integrating Distributed Tracing into the Narayana Transaction Manager

ACID transactions have proven to be a very useful mechanism of ensuring reliability of applications. Guaranteeing transactional properties effectively and correctly is a challenging task by itself. Furthermore, investigating transaction issues in a distributed environment is at least equally complex and requires systematic data collection and analysis. In this paper, we present mechanisms and concepts of distributed tracing with focus on the OpenTracing API and showcase our integration of tracing capabilities into the Narayana transaction manager. We show that the performance impact of tracing does not drastically decrease user application performance while providing useful information for the analysis of running transactions.
Mining Students' Comments to Build an Automated Feedback System

: Giving the appropriate feedback to students is an important step toward helping them improve and get the most out of the course. Most of the time, students receive this feedback during the lesson time, or when there is a physical interaction with their professors. However, it is considerably time-consuming for the professors to provide individualized feedback to students. In an attempt to address this issue, we prepared a questionnaire and asked students to ﬁll it using their freely written comments. We used these comments to generate the appropriate feedback according to each comment and build an automated feedback system. In this paper, we describe the data collection and compare different machine learning and natural language processing techniques to build the models. Experimental results show that our proposed models achieved promising results while applying one of the most recent language models signiﬁcantly improved the performances, attaining 0.81 accuracy.
The Influence of Context on Value Proposition Co-creation of Online Shopping Platform in China

Service-Dominant (S-D) logic emphasizes contextual conceptualization of value, while relatively little attention has been paid on context within value proposition co-creation research. This paper contributes to value proposition (VP) co-creation within S-D logic by providing the empirical evidence of how context, defined as stakeholders with unique reciprocal links among them, influencing this process of the online shopping platform. Synthesizing S-D logic and social network theory, the VP co-creation is conceptualized as an operant resource integration process that is influenced by the tie strength among the consumer and three core stakeholder groups, namely core service providers, supporting service providers and other consumers, of the platform. Data collected from 380 consumers of major online shopping platforms in China provide support for the study and demonstrate the influence on VP co-creation brought by stakeholder context is mediated by quality of shared information and trust among stakeholders.
Car Drivers Do Not Choose Their Speed in Urban Environments: Speed Models in Tangent Streets

The performance-based design approach is one way to deal with speeding in the streets. Under this approach, the geometric elements of roadways can influence on the desired operating speeds. Thus, several studies have investigated the relationship between geometric elements and light vehicle speeds; however, no conclusive results have been reached at this stage. In this context, this article aims to investigate the influence of several characteristics from urban street tangents, car driver, and vehicle on their speed in free-flow conditions. Three tangents scenarios were set out: before stop-controlled intersections, before signal-controlled intersections, and before roundabout intersections. Speeds of light vehicles were measured at 34 streets. Speeds were collected with in-vehicle GPS equipment. Thirty-five car drivers participated in the study with their vehicles. Street geometric characteristics, street environment variables, driver and vehicle characteristics were also collected. As a result, 15 regression models were calibrated and validated. Street length and objects density were the most influential variables in those models, and not the driver and vehicle characteristics as would suppose. This comprehensive research extends the knowledge of the most influential variables on speed in several urban scenarios, offering useful information for urban planners and street designers.
A Novel Approach of Environment Impact Assessment and Emission Measurement on the Inter-city Transportation in the Greater Bay Area (GBA) of China using a Modified Gravity Model

The Guangdong-Hong Kong-Macau Greater Bay Area (GD-HK-MO) also referred as Greater Bay Area (GBA), is a megalopolis, consisting of nine cities and two special administrative regions, i.e., Hong Kong and Macao in South China. GBA has a total population of approximately 71.2 million people representing about 5% of China’s total population with a combined regional GDP at USD 1642 billion in 2018, i.e. about 12% of GDP for the whole mainland China. Hong Kong acting as a window of China, plays a critical role in contributing to the growth of the GDP. Given the enormous scale of this regional economy and increasing collaboration among these GBA cities, it is utmost important to design a novel environmental impact assessment and emission measurements of the cross-border transportation among Hong Kong and various GBA cities with the aim of proposing countermeasures on carbon emissions of vehicles in the transport and logistics sector of the GBA. In the study, two modified gravity models are designed by considering social, economic, and other variables affecting the carbon emission of vehicles travelling within and across cities in the GBA. Further study will be pursued using decomposition analysis based on the modified gravity model to analyse the crucial contributors and determinants of carbon emission among the GBA cities.
Tracing Gold Nanoparticle Charge by Electrolyte−Insulator−Semiconductor Devices

A capacitive field-effect electrolyte−insulator−semiconductor (EIS) device was applied for the first time to trace the charge of supported gold nanoparticles (Au-NPs) induced by oxygen plasma treatment or due to storing in aqueous oxidation and reduction solutions. In addition, X-ray photoelectron spectroscopy (XPS) has been used as a reference method to establish the various charge states of the Au-NPs resulting from the different treatment steps. After the oxygen-plasma treatment, a shift of the capacitance−voltage (C−V) curve (and flatband potential) of the Au-NP-covered p-Si−SiO2 EIS structure by about −300 mV was found. The exposure of the EIS sensor surface to an oxidative and a reductive solution resulted in a shift of the C−V curve for −85 and +81 mV, respectively. These observations correlate well with corresponding binding energy shifts in Au 4f core spectra in XPS experiments. The obtained results may open new opportunities for biosensing and biochips based on nanoparticle-charge-gated field-ef.
An Improved Approach for Effective Describing Geometric Data in ifcOWL through WKT High Order Expressions

Building Information Models (BIM) are considered as building digital representations, including comprehensive geometric and non-geometric information. For improving BIM interoperability, the semantic related technologies have been the one of main approaches for processing BIM data. Currently, ifcOWL is a recommended Web Ontology Language (OWL) representation of the Industry Foundation Classes (IFC) schema. When BIM geometric data is translated into ifcOWL representations, the excessive number of triples will be produced, and the generated Resource Description Framework (RDF) file will also be extremely bigger than the IFC original file. For generating concise geometric representation in Semantic Web context, Wellknown text (WKT) has been widely used to describe BIM geometry data in ifcOWL. However, to avoid losing semantic information, only some simple pre-existing WKT expressions (Point or LineString) are used to describe BIM geometric aggregated data in semantics context. For solving this issue, we propose an improved approach that can represent BIM geometric data in ifcOWL ontology through WKT high order expressions. This representation can not only take full advantage of pre-existing WKT expressions to generate a more concise RDF representation, but also reduce the loss of semantic information.
Investigating Student Insight in Software Engineering Team Projects

Preparing software engineering students for industry jobs is a difficult task. Employers often ask for a combination of technical and soft skills. In this paper, we describe an exploratory study focused on 47 teams of 3rd year Computer Science students who were each involved in the development of a medium sized software application. We employed open coding on free-text student feedback and focused on key areas of working process, planning, challenges and lessons learned. We evaluated how student perception aligned with feedback received from mentors with industry experience, as well as with the assessment from teaching staff experienced in software engineering. We open-sourced a replication package that allows reusing, repeating or extending our investigation. We showed that the course objectives were reached, that soft skills remain an important component of team projects as well as identified several action points that can further improve education in software engineering.
Cognitive-Driven Development: Preliminary Results on Software Refactorings

Refactoring is a maintenance activity intended to restructure code to improve different quality attributes without changing its observable behavior. However, if this activity is not guided by a clear purpose such as reducing complexity and the coupling between objects, there is a risk that the source code can become worse than the previous version. Developers often lose sight of the business problems being solved and forget the importance of managing complexity. As a result, after refactorings many software parts continue to have low readability levels. Cognitive-Driven Development (CDD) is our recent strategy for reducing cognitive overload during development when improving the code design. This paper provides an experimental study carried out in an industrial context to evaluate refactorings through the use of conventional practices guided by a cognitive constraint for complexity, a principle pointed out by CDD. Eighteen experienced participants took part in this experiment. Different software metrics were employed through static analysis, such as CBO (Coupling between objects), WMC (Weight Method Class), RFC (Response for a Class), LCOM (Lack of Cohesion of Methods) and LOC (Lines of Code). The result suggests that CDD can guide the restructuring process since it is designed to obtain a coherent and balanced separation of concerns.
A Holistic Machine Learning-based Autoscaling Approach for Microservice Applications

Microservice architecture is the mainstream pattern for developing large-scale cloud applications as it allows for scaling application components on demand and independently. By designing and utilizing autoscalers for microservice applications, it is possible to improve their availability and reduce the cost when the traffic load is low. In this paper, we propose a novel predictive autoscaling approach for microservice applications which leverages machine learning models to predict the number of required replicas for each microservice and the effect of scaling a microservice on other microservices under a given workload. Our experimental results show that the proposed approach in this work offers better performance in terms of response time and throughput than HPA, the state-of-the-art autoscaler in the industry, and it takes fewer actions to maintain a desirable performance and quality of service level for the target application.