Miracle-dz/UniFAR-FTUs huggingface dataset:
Vegetation Detection in UAV Imagery for Railway Monitoring

Vegetation management on and alongside the railway tracks is very crucial for safe railway operations. The railway industry, therefore, needs to regularly monitor the growth of vegetation on railway tracks and embankments and mostly relies on human inspectors for the inspection and monitoring. This manual process being prohibitively time-consuming and cost-ineffective, there is a growing need to automate the process of vegetation detection. Aerial imagery collected using Unmanned Aerial Vehicles (UAVs) is becoming increasingly popular for automated inspection and monitoring. On the other hand, due to their recent success, Deep Convolutional Neural Networks (DCNNs) have seen rapid deployment in a wide array of image understanding tasks. In this work, we therefore, investigate the effectiveness of DCNNs for automating the vegetation detection task using UAV imagery. We further propose simple yet effective modification to an existing DCNN architecture and demonstrate its efficacy for vegetation detection using publicly available dataset.
SVW-UCF Dataset for Video Domain Adaptation

Unsupervised video domain adaptation (DA) has recently seen a lot of success, achieving almost if not perfect results on the majority of various benchmark datasets. Therefore, the next natural step for the field is to come up with new, more challenging problems that call for creative solutions. By combining two well known sets of data SVW and UCF, we propose a large-scale video domain adaptation dataset that is not only larger in terms of samples and average video length, but also presents additional obstacles, such as orientation and intra-class variations, differences in resolution, and greater domain discrepancy, both in terms of content and capturing conditions. We perform an accuracy gap comparison which shows that both SVW→UCF and UCF→SVW are empirically more difficult to solve than existing adaptation paths. Finally, we evaluate two state of the art video DA algorithms on the dataset to present the benchmark results and provide a discussion on the properties which create the most confusion for modern video domain adaptation methods.
Improving the Modelling of Human-centric Aspects of Software Systems: A Case Study of Modelling End User Age in Wirefame Designs

Taking into account the diverse human aspects – gender, age, emotions, personality, language, culture, physical and mental challenges, etc – is critical towards achieving more human-centric design of software systems. Human-centric aspects affecting software have long been underestimated or even ignored as a result of the lack of in-depth capture and understanding during development. The use of technology has become the norm and the range of users has increased from just adults to children as well as seniors. Modelling frameworks are methods to represent the way a software system should be defined, and to date, little research has been done on age-related issues within modelling frameworks. In this paper, we investigate how human-centric aspects regarding age can be better modelled by extending these modelling frameworks. We introduce an extension to wireframe-based designs so that they can cater for decisions regarding age within the modelling framework. We have evaluated this modelling extension using multiple questionnaires as well as usability testing by using the extended age-modelling wireframe approach to design a news app. Questionnaires were used to evaluate the requirements of the users and developers for the extended wireframes. Our analysis shows that when using our extended wireframes, developers can cater for different user types and their accessibility needs easily and therefore users can use the prototypes with more ease.
A Systematic Mapping of Serious Games for Oral Health

Oral health in the daily lives of individuals allows health promotion and prevention. Oral health literacy is fundamental to enable people to have the knowledge and be aware of its importance, enabling them to process, evaluate and apply information on this topic. Serious games, in the educational domain, can be used to assist in this literacy. This article carries out systematic mapping of these games in the field of oral health. The mapping included work carried out in the last 5 years and indexed in four repositories. As result it was found that there are few serious games for oral health, and few have a well-defined process for learning.
Integration of Image Processing Techniques into the Unity Game Engine

: This paper describes an approach for using the Unity game engine for image processing by integrating a custom GPU-based image processor. For it, it describes different application levels and integration approaches for extending the Unity game engine. It further documents the respective software components and implementation details required, and demonstrates use cases such as scene post-processing and material-map processing.
Comparative Performance Study of Lightweight Hypervisors Used in Container Environment

: Virtual Machines (VMs) are used extensively in cloud computing. The underlying hypervisor allows hardware resources to be split into multiple virtual units which enhances resource utilization. However, VMs with traditional architecture introduce heavy overhead and reduce application performance. Containers have been introduced to overcome this drawback, yet such a solution raises security concerns due to poor isolation. Lightweight hypervisors have been leveraged to strike a balance between performance and isolation. However, there has been no comprehensive performance comparison among them. To identify the best ﬁt use case, we investigate the performance characteristics of Docker container, Kata containers, gVisor, Firecracker and QEMU/KVM by measuring the performance on disk storage, main memory, CPU, network, system call and startup time. In addition, we evaluate their performance of running Nginx web server and MySQL database management system. We use QEMU/KVM as an example of traditional VM, Docker as the standard container and the rest as the representatives of lightweight hypervisors. We compare and analyze the benchmarking results, discuss the possible implications, explain the trade-off each organization made and elaborate on the pros and cons of each architecture.
Entwicklung und Nutzung interdisziplinärer Repositorien für historische textbasierte Korpora

Der Workshop setzt sich das Ziel, die Möglichkeiten, Aufgaben und Herausforderungen bei der Wiederverwendung von historischen Korpora zu identifizieren und zu diskutieren. Insbesondere sollen dabei deren Architektur, Dokumentation, Veröffentlichung und Speicherung betrachtet werden. So wollen wir versuchen, Methoden und Strategien für das interdisziplinäre Forschungsparadigma der Digital Humanities zu entwickeln und diese in den Fragestellungen der Konferenz der DHd 2016 zu verorten. Der Fokus wird auf die spezifischen und fächerübergreifenden Anforderungen historischer Texte in Bezug auf deren Aufbereitung und Speicherung in Repositorien zum Zweck der Wiederverwendung gelegt. Damit richtet sich der Workshop gleichermaßen an Korpuserstellende, Entwickelnde und an Betreiber_innen von Repositorien und deren Nutzer_innen.
Water Hazard Depth Estimation for Safe Navigation of Intelligent Vehicles

This paper proposes a method to provide depth information about water hazards for ground vehicles. We can estimate underwater depth even with a moving mono camera. Besides the physical principles of refraction, the method is based on the theory of multiple-view geometry and basic point cloud processing techniques. We use the information gathered from the surroundings of the hazard to simplify underwater shape estimation. We detect water hazards, estimate its surface and calculate real depth of underwater shape based on matched points using refraction principle. Our pipeline was tested on real-life experiments, on-board cameras and a detailed evaluation of the measurements is presented in the paper.
A Method of Deep Reinforcement Learning for Simulation of Autonomous Vehicle Control

Nowadays autonomous driving is expected to revolutionize the transportation sector. Carmakers, researchers, and administrators have been working on this field for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, regarding not only complex technologies but also human awareness, culture, current traffic infrastructure. In terms of technical perspective, the accurate detection of obstacles, avoiding adjacent obstacles, and automatic navigation through the environment are some of the difficult problems. In this paper, an approach for solving those problems is proposed by using of Policy Gradient to control a simulated car via reinforcement learning. The proposed method is worked effectively to train an agent to control the simulated car in Unity ML-agents Highway, which is a simulating environment. This environment is chosen from some criteria of an environment simulating autonomous vehicle. The testing of the proposed method got positive results. Beside the average speed was well, the agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway
Machine Learning Techniques for Knowledge Tracing: A Systematic Literature Review

Machine Learning (ML) techniques are being intensively applied in educational settings. They are employed to predict competences and skills, grade exams, recognize behavioural academic patterns, evaluate open answers, suggest appropriate educational resources, and group or associate students with similar learning characteristics or academic interests. Knowledge Tracing (KT) allows modelling the learner's mastery of skill and to meaningfully predict student’s performance, as it tracks within the Learner Model (LM) the knowledge state of students based on observed outcomes from their previous educational practices, such as answers, grades and/or behaviours. In this study, we survey commonly used ML techniques for KT figuring in 51 papers on the topic, out of an original search pool of 628 articles from 5 renowned academic sources, encompassing the latest research, based on the PRISMA method. We identify and review relevant aspects of ML for KT in LM that help paint a more accurate panorama on the topic and hence, contribute to alleviate the difficulty of choosing an appropriate ML technique for KT in LM. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who need an overview of existing ML techniques for KT in LM.
A Method for Estimating Potential Knowledge Increase after Updating Ontology Mapping

In the modern days, users cannot expect that ontologies in their initial states won’t remain static throughout the lifespan of their application, therefore a tool for managing appearing alterations is necessary. In our previous work, we have prepared a solid, formal, and flexible foundation that can be used to express changes that appear while maintained ontologies evolve. This paper contains a description of the process of constructing a method assessing knowledge increase after an ontology alignment update. Our developed measure estimates how ontology evolution influenced the increase of knowledge for two input ontologies. The developed method has been experimentally verified by simulating random ontology evolutions ad the obtained results have been statistically analyzed. Due to the limitation of this paper, we focus only on the concept level.
Limitations of Local-minima Gaze Prediction

: We describe a minimal gaze prediction system which is straightforward to implement, can run on everyday hardware, and does not require high-quality video images. We determine head pose and eye gaze from four facial landmarks (nose tip, nose bridge, and eye pupils) which can be expressed as local minima of simple pixel-intensity operations. We assess its stability to: variation of subject’s anatomy; facial landmark outliers; and facial landmark small systematic errors.
Effectiveness of Gamification in Undergraduate Education

: What is the quantitative effect of gamiﬁed learning/teaching on the performance of students? This basic question has not received as much attention as it deserves in the related literature, despite the many works that analyze the beneﬁts and design principles of gamiﬁed activities across many domains. We present in this paper an analysis of this question focused on identifying the network structure variables that quantitatively explain the improvements reported when using gamiﬁcation integrated in the course design. We compare the results with those from a group that does not follow gamiﬁcation at all, hence we are able to ascertain the strength and impact that gamiﬁcation has on numerical grades or scores.
A Systematic Mapping on the Use of Data Mining for the Face-to-Face School Dropout Problem

Dropout is a critical problem that affects institutions worldwide. Data mining is an analytical solution that has been used to deal with it. Typically, data mining follows a structured process containing the following general steps: data collection, pre-processing, pattern extraction, post-processing (validation). Until know, it is not known how data mining has been used to address the dropout problem in face-to-face education considering all steps of the process. For that, a Systematic Literature Mapping was conducted to identify and analyze the primary studies available in the literature to address some research questions. The aim was to provide an overview of the aspects related to data mining steps in the presented context, without going into details about specific techniques, but about the solutions themselves (for example, imbalanced techniques, instead of SMOTE). 118 papers were selected considering a period of 10 years (01/01/2010 to 31/12/2020).
RT-MongoDB: A NoSQL Database with Differentiated Performance

The advent of Cloud Computing and Big Data brought several changes and innovations in the landscape of database management systems. Nowadays, a cloud-friendly storage system is required to reliably support data that is in continuous motion and of previously unthinkable magnitude, while guaranteeing high availability and optimal performance to thousands of clients. In particular, NoSQL database services are taking momentum as a key technology thanks to their relaxed requirements with respect to their relational counterparts, that are not designed to scale massively on distributed systems. Most research papers on performance of cloud storage systems propose solutions that aim to achieve the highest possible throughput, while neglecting the problem of controlling the response latency for specific users or queries. The latter research topic is particularly important for distributed real-time applications, where task completion is bounded by precise timing constraints. In this paper, the popular MongoDB NoSQL database software is modified introducing a per-client/request prioritization mechanism within the request processing engine, allowing for a better control of the temporal interference among competing requests with different priorities. Extensive experimentation with synthetic stress workloads demonstrates that the proposed solution is able to assure differentiated per-client/request performance in a shared MongoDB instance. Namely, requests with higher priorities achieve reduced and significantly more stable response times, with respect to lower priorities ones. This constitutes a basic but fundamental brick in providing assured performance to distributed real-time applications making use of NoSQL
An Approach to Reduce Network Effects in an Industrial Control and Edge Computing Scenario

The cloud-based nature of Industry 4.0 enhances its flexibility and scalability features. To support timesensitive and mission-critical applications, whereby low latency and fast response are essential requirements, usually cloud computing resources should be placed closer to the industry. The Edge Computing concept combined with next-generation networks, such as 5G, may fulfill those requirements. This paper presents an experimental system setup that combines a Model Predictive Control approach with a compensation strategy to mitigate network delay and packet loss. The experimental system has two sides, namely, the edge and the local side. The former executes the controller and connects to the local side through a network. The latter is attached to the application and has lower computing capabilities. In our setup, the application under control is a two-wheeled mobile robot, which could act as an Automated Guided Vehicle. We defined two control objectives, the Point Stabilization, and the Trajectory Tracking, which ran through distinct network conditions, including delays and packet losses. These control objectives are only validation scenarios of the proposed approach but could be replaced by a real case path planner. The obtained results suggest that the
Using MDE for Teaching Database Query Optimizer

Query optimization is considered as an important part of Data Base Management Systems (DBMS) and plays a major role in database research community (e.g. SQL, Spark, Map-reduce). Generally, this optimization is done using Cost Base Optimization (CBO), which is difficult to understand due to the complexity of platform, database, query and DBMS. Moreover, database query optimizer is usually a very complex process, with difficult concepts depending on the behaviour of the query engine of DBMS. Therefore, users (e.g. novice user, developer, DB administrator) have difficulties to understand and build a mental image of database query optimizer in order to produce more efficient queries. In this paper, we propose a Framework based on the model-driven engineering (MDE) paradigm to facilitate understanding and improving query performance. Indeed, MDE approach has been proven useful for developing new software applications, and its adoption for a teaching perspective presents a major challenge. We illustrate our proposal with use case and proof-ofconcept prototype for the whole provided process.
The Effect of Resistance to Change on Students' Acceptance in a Flipped Classroom Course

Digital technologies are increasingly used in higher education in so-called blended learning courses. A growing popular course concept is the Flipped Classroom (FC). In an FC, knowledge is acquired at home and deepened in the in-class time with the teacher. Compared to traditional teaching concepts, FC courses are considered particularly effective in terms of learning success. However, the transformation to FC-concepts is a big change for learners and students and often combines with a resistance to change. In this study we investigate the effect of resistance to change (inertia) on the acceptance of Flipped Classroom courses from the students' perspective. Teachers can use this knowledge to increase the attractiveness of FC.
Comparison of rumen in vitro fermentation of temperate pastures using different batch culture systems

In vitro batch culture systems are popular because they are relatively inexpensive and allow the screening and testing of large amounts of samples in a short time. Most of the batch culture systems have been designed for the evaluation of gas produced during fermentation of substrates and different designs have been compared between laboratories, but very little work is published where methane production or volatile fatty acid production is compared. The aim of this study was to determine the degree of agreement between two different in vitro batch culture systems, from different laboratories when measuring in vitro fermentation kinetics and end products using pasture samples as substrates. The two systems were a manual and a fully automated pressure-based system. Duplicates of pasture samples were incubated in three consecutive runs. Concordance correlation coefficients between systems and estimates of variance components (pasture, incubation run and random error) for each system were determined for all measured variables. There were poor correlations between systems for most of the variables except for time to produce half of the asymptotic gas production and acetate molar proportion of volatile fatty acids. However, for both systems most of variance was due to pasture sample and then incubation run. The poor agreement between systems might be explained by the different laboratory protocols. Therefore, comparisons of absolute values from different batch culture systems or experiments must be done carefully. Alternatively, more standardisation in terms of sample preparation and incubation procedure may be needed to compare in vitro fermentation products among systems.
A Reinforcement Learning Approach for Traffic Control

Intelligent traffic control is a key tool to achieve and to realize resource-efficient and sustainable mobility solutions. In this contribution, we study a promising data-based control approach, reinforcement learning (RL), and its applicability to traffic flow problems in a virtual environment. We model different traffic networks using the microscopic traffic simulation software SUMO. RL-methods are used to teach controllers, so called RL agents, to guide certain vehicles or to control a traffic light system. The agents obtain real-time information from other vehicles and learn to improve the traffic flow by repetitive observation and algorithmic optimization. As controller models, we consider both simple linear models and non-linear radial basis function networks. The latter allow to include prior knowledge from the training data and a two-step training procedure leading to an efficient controller training.
Towards a Framework to Scaffold Problem-solving Skills in Learning Computer Programming

Developing problem solving skills through learning programming has become a real challenge. Problemsolving skills are fundamental to learning computer programming and can be developed during learning. Teachers focus more on the syntax of the languages than on the development of problem solving skills. We present a conceptual framework to promote problem-solving skills in learning computer programming. This framework is based on an IDE which integrates two components. The first one is an explicit guidance to support the acquisition of skills related to different stages of a problem-solving method. It consists in explicitly following the steps of the process with activities that develop related skills. The second one is a semantic feedback system to develop problem-solving skills.
Modeling the e-Inclusion Prediction System

e-Inclusion aims to provide the benefits of digital technology for every member of society. Digital skills and their meaningful use are a prerequisite for everyone to be e-included. The improvement of learning outputs of online and blended courses on digital skills is therefore an important aspect of ensuring an e-included society. Due to the use of learning management systems and their ability to collect data on students, different types of student data become available for analysis. We proposed the data-driven approach which uses student data and machine learning algorithms to predict learning outcomes. The goal of this article is to present the conceptual architecture and prototype of the e-inclusion prediction system which is based on a combination of several algorithms and uses a machine learning approach.