Miracle-dz/UniFAR-FTUs huggingface dataset:
An Industrial Study on the Challenges and Effects of Diversity-Based Testing in Continuous Integration

Many test prioritisation techniques have been proposed in order to improve test effectiveness of Continuous Integration (CI) pipelines. Particularly, diversity-based testing (DBT) has shown promising and competitive results to improve test effectiveness. However, the technical and practical challenges of introducing test prioritisation in CI pipelines are rarely discussed, thus hindering the applicability and adoption of those proposed techniques. This research builds on our prior work in which we evaluated diversity-based techniques in an industrial setting. This work investigates the factors that influence the adoption of DBT both in connection to improvements in test cost-effectiveness, as well as the process and human related challenges to transfer and use DBT prioritisation in CI pipelines. We report on a case study considering the CI pipeline of Axis Communications in Sweden. We performed a thematic analysis of a focus group interview with senior practitioners at the company to identify the challenges and perceived benefits of using test prioritisation in their test process. Our thematic analysis reveals a list of ten challenges and seven perceived effects of introducing test prioritisation in CI cycles. For instance, our participants emphasized the importance of introducing comprehensible and transparent techniques that instill trust in its users. Moreover, practitioners prefer techniques compatible with their current test infrastructure (e.g., test framework and environments) in order to reduce instrumentation efforts and avoid disrupting their current setup. In conclusion, we have identified tradeoffs between different test prioritisation techniques pertaining to the technical, process and human aspects of regression testing in CI. We summarize those findings in a list of seven advantages that refer to specific stakeholder interests and describe the effects of adopting DBT in CI pipelines.
Optimization and Control of Autonomous UAV Swarm for Object Tracking

Unmanned Aerial Vehicles (UAVs) swarms are emerging technologies with the ability to carry out a wide range of military missions aiming to avoid human casualties. The UAVs' ability to access remote and inaccessible territories while gathering critical intelligence can be critical. Therefore, swarm real-time coordination on the fly is vital for information exchange and dynamically updating the flying hierarchy without failing the mission. Nevertheless, coordinating UAV swarms to execute maneuvers poses a complex challenge. In our Demonstration, we execute an on-the-fly swarm update based on unexpected or urgent events during the missions to avoid collisions and ensure mission safety. This demo shows a " find and rescue" military mission for security operations using a swarm of UAVs to track other adversaries' UAVs in a restricted area, which requires advanced swarm synchronization and on-the-fly decisions. For this demo, we conducted a successful flying swarming coordination for dynamic on-the-fly maneuvers with a reliable collision avoidance mechanism.
Enhancing Autism Education: Exploring Interactive Videos and AI Integration for Effective Teaching

This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the condition and employ tailored instructional strategies, including adapting assignments, aiding those with language difficulties, and utilizing visual aids for better organization and focus. Traditional teacher training methods can be both expensive and time-consuming. In contrast, interactive videos provide a proactive and flexible way to access training content, empowering teachers to engage with the material dynamically and take control of their learning experiences. Future work will explore the integration of AI-driven ChatGPT to offer personalized support and create a dynamic training program, with the goal of improving inclusivity and educational quality in autism settings while benefiting teachers, students, and the education system at large.
On the Discrete Concavity of Packet Reception Ratio Utility Functions

The wireless channel often shows high complexity in the attempt to analyze theoretically the network layer parameters. A parameter of this layer is the Packet Reception Ratio (PRR). The PRR is a practical metric, which when analyzed theoretically, exhibits high complexity. Its optimization results in high throughput and a reduction in the interference and collisions of transmissions. In theory, PRR is described with a sigmoid function, which conveys a very hard problem to solve. In this paper, we will reduce this complexity by transforming PRR and transmission power function to a discrete concave-up one; thus, allowing fast algorithms, which locate the globally optimal solutions, to be applied. We do this transformation utilizing networking terms, which come into play to reduce the complexity of a PRR optimization process.
Numerical and Experimental Anaysis of Massive MIMO Channel Characteristics in a Rectangular Highway Tunnel at 5.9 GHz

To increase channel capacity, MIMO techniques are often used, and now, with the development of the 5G technique, massive transmitting arrays become to be widely implemented. For vehicular communication, the dedicated frequency band is around 5.9 GHz, and, to our knowledge, there are very few contributions to massive MIMO in tunnels and especially in road tunnels. In this paper, the case of a highway rectangular tunnel is treated, being its width much larger than its height. The Tx array is a square array of 64 elements and the correlation between array elements, deduced from simulation and measurements, is first presented. Due to the shape of the tunnel cross-section, it appears that the correlation between vertically aligned elements is much greater than that occurring between elements situated on a horizontal line, and, as an example, the correlation between elements 7.5 cm apart, can be equal to 0.9 and 0.7, respectively. Consequently, the way of partitioning the massive array is studied to find the best compromise between channel capacity and complexity of the transmission scheme, taking the number of radio frequency chains into account. Illustrations are given for transmission techniques based either on beamforming or singular values decomposition of the transfer matrix. Lastly, for a MIMO transmission, the precoding matrices must be periodically updated when the mobile moves along the tunnel, due to changes in the transfer matrix. The longitudinal correlation distance and the channel stationarity are thus calculated for correlation values equal to 0.7 or 0.9.
Semantic Motif Segmentation of Archaeological Fresco Fragments

Archaeological fragment processing is crucial to support the analysis of pictorial contents of broken artifacts. In this paper, we focus on the unexplored task of semantic segmentation of fresco fragments. This task enables the extraction of semantic information from a fragment, facilitating subsequent tasks like fragment classification or reassembly. We introduce a semantic segmentation dataset of fresco fragments acquired at the Pompeii Archeological Site, accompanied by baseline models. Additionally, we introduce a supplementary task of fragment cleaning, providing a dataset with the detection of manual annotations of archaeological marks that require restoration before further analysis. Our experiments, using standard metrics and state-of-the-art baselines, demonstrate that semantic segmentation of fresco fragments is feasible, paving the way toward more complex activities that require a semantic understanding of fragmented artifacts. Dataset with annotations, and code will be released at https://repairproject.github. io/fragment-restoration/
Patient-Specific Atrial Fibrillation Simulation Prediction Depend on Rhythm Used for Calibration

Personalised therapies using patient-specific models of atrial fibrillation (AF) can enhance treatment outcomes. Electroanatomic data collected during an ablation procedure can be used to calibrate patient-specific activation and repolarisation properties, which affect AF properties and therapy outcomes. However, it is unknown how calibration data rhythm, pacing location, and choice of calibration technique affects prediction. In this study, we aim to compare AF properties and predicted therapy responses for patient-specific models calibrated using electroanatomic mapping data collected at different pacing rates. Initially, six patients underwent electroanatomic mapping during catheter ablation therapy, with pacing from a catheter in the coronary sinus at cycle lengths of 250ms and 600ms. Personalised anatomical models were constructed and calibrated to conduction velocity maps at each of the pacing rates. Simulations of AF were performed and pulmonary vein isolation was applied. Across the simulation models, all of the 6 cases had a greater number of occurrences of rotational activity for the models calibrated to 250ms than models calibrated to 600ms. This was most evident in the inferior wall, which had an average of 1.33 number of occurrences per patient at 250ms and an average of 0.17 number of occurrences per patient at 600ms. Thus, AF properties depend on the pacing rate used for model calibration.
Orbit Propagation and Determination using Genetic Algorithms

In order to operate effectively, the orbital parameters of the satellite are essential, to estimate satellite position over the previous and in the near future. For the planning of satellite missions and for linking satellite location data to accurate geographical locations, such knowledge is essential. However, determining satellite orbits poses a significant challenge for small to medium satellite operators lacking the necessary tracking infrastructure. These organizations often rely on third-party services like Celestrak, which offer orbital information but may not provide it with the required frequency. Moreover, in the initial stages of a mission, especially when multiple satellites are launched together, it becomes challenging to attribute specific orbital parameters to individual satellites within tshe group. This ambiguity hampers mission planning and monitoring efforts. To address these challenges, this research presents an approach to tackle the problem of orbital parameter determination by leveraging Global Positioning System (GPS) data, and artificial intelligence, specifically genetic algorithms, to enhance the accuracy and efficiency of orbit determination algorithms and orbit propagation techniques, thus enabling small and medium satellite operators to reliably obtain vital orbital parameters.
IoT Systems Development Using Upcycled Mobile Phones: A Survey

The rapid advancement of technology has led to a significant increase in electronic waste, including discarded mobile phones. These old mobile phones can still be valuable resources for creating Internet of Things (IoT) systems through upcycling. In this survey we explore the use of upcycled mobile phones in IoT system development. We look at the sensors present on most mobile phones and their usage. These sensors are inbuilt, integrated, and ready for use. Using these saves efforts as compared to any system development from scratch, which involves procuring, assembling, and testing a bunch of sensors in isolation and together. We look at some efforts to create an operating system-based environment to aid application development on upcycled mobile phones. We look at some projects using mobile phone sensors and some of the applications developed as part of these efforts. We consider some successful deployments and look at these efforts' benefits. One benefit that arises is the possibility of crowd-sourcing-based data gathering using upcycled mobile phones. This is possible because of the availability of mobile phones with many participants. We look at use cases for the IoT systems using mobile phones in important fields like healthcare and agriculture. We examine the benefits, challenges, and potential avenues for future research on upcycled mobile phone use cases.
Towards the Development of Virtual Heart Technology for Creating Digital Twins of Cardiac Electrophysiology

Introduction: Virtual heart technologies (VHTs) promise to offer an detailed view on cardiac electrophysiology (EP). However, only very few VHTs simulating EP of four-chamber hearts have been reported so far and widespread use is limited due to labor intense workflows and the difficulty of ascertaining a close match between simulations and observations. This motivates the development of efficient workflows for both critical stages of creating a digital twin of cardiac EP: i) the generation of patient-specific anatomical models, and ii) the calibration of EP models to match clinical observations. Methods: A workflow for the generation of anatomically accurate models of the heart, with emphasis on the atria.from imaging data was developed. This includes, automated creation of multi-label segmentations using a convolutional neural network (CNN), a meshing step, annotation of tissue regions, and assignment of fibers. To speed up functional twinning, a prototype to run close to real-time EP simulations that can be used to find a good initial guess for model calibration methods that fit physiologically more accurate mono- or bidomain EP models to clinical data has been designed. Results: A semi-automated workflow to create atrial models at scale was developed, implemented and applied to a first clinical data set. A GUI based tool to plan and run whole heart EP simulations was prototyped.
Cognitive Reinforcement Learning For Autonomous Driving

Most existing reinforcement learning methods are combined with deep neural networks in autonomous driving. There is a well-known trouble named ‘black-box’ of deep neural networks, which lacks of interpretability, occurring in the decision-making process. That will affect the safety of autonomous vehicle. In this work, we propose a cognitive reinforcement learning framework. This framework illustrates a cognitive model that transfers the state space to cognitive signals. Subsequencely, based on these signals, the model simulates the human decision-making process. As a result, the outcome of decision provides reward to the reinforcement learning. The computational experiments conducted in CARLA demonstrate that our framework performs equally well as the conventional reinforcement learning methods and provides interpretability under the same circumstance in the reinforcement learning.
Framework and Key Technologies for High-speed Railway Comprehensive Transport Hub

The surge in urban population density in China has underscored the vital role of high-speed rail and subway systems in urban transit. Comprehensive transportation hubs, notably high-speed rail passenger terminals, have emerged as linchpins for seamless multimodal transit both within and beyond urban centers. China's railway sector vigorously champions the innovative concept of "station-city integration" and has articulated a forward-looking "16-word policy" for modern railway stations: "Seamless integration, environmental friendliness, economic viability, and intelligent accessibility". The judicious allocation of resources and the augmentation of coordination and control capabilities within comprehensive transportation hubs are imperative for the harmonious evolution of urban transit. The implementation of intelligent control systems for transportation hubs has emerged as a pivotal area of research. This study delves into the framework and critical technologies for high-speed railway comprehensive transport hubs, encompassing various components and their intricate interplay, including transport modalities, infrastructure, travelers, environmental factors, and information systems, thus forming a prototypical cyber-physical-social system. Addressing the prevalent and pressing imperative for the advancement of "station-city integration" high-speed railway comprehensive transport hubs, this research endeavor aims to surmount bottleneck challenges and refine the oversight of passenger flows at high-speed rail terminals. The findings hold noteworthy theoretical import and portend promising practical applications.
Gated Context Model with Embedded Priors for Deep Image Compression

A deep image compression scheme is proposed in this paper, offering the state-of-the-art compression efficiency, against the traditional JPEG, JPEG2000, BPG and those popular learning based methodologies. This is achieved by a novel conditional probably model with embedded priors which can accurately approximate the entropy rate for rate-distortion optimization. It utilizes three separable stacks to eliminate the blind spots in the receptive field for better probability prediction and computation reduction. Those embedded priors can be further used to help the image reconstruction when fused with latent features, after passing through the proposed information compensation network (ICN). Residual learning with generalized divisive normalization (GDN) based activation is used in our encoder and decoder with fast convergence rate and efficient performance. We have evaluated our model and other methods using rate-distortion criteria, where distortion is measured by multi-scale structural similarity (MS-SSIM). We have also discussed the impacts of various distortion metrics on the reconstructed image quality. Besides, a field study on perceptual quality is also given via a dedicated subjective assessment, to compare the efficiency of our proposed methods and other conventional image compression methods.
Genetic characterization of the Iberian populations of two invasive mollusks: zebra mussel and asiatic clam

The zebra mussel and the Asian clam are two invasive aquatic species worldwide. Both species are present in the Iberian Peninsula for several years. However, introduction history and colonization routes of these two invasive species in the Iberian Peninsula remain mainly unknown. We have developed new molecular markers for each species to be used in population genetics studies. Then, we characterized the genetic structure of the Iberian populations in order to infer their possible colonization routes. Our results show how the zebra mussel invasion into the Iberian Peninsula involves a single invasion episode with a separate colonization history than the rest of Europe. In Asian clam, our results revealed that at least two colonization episodes have occurred in the Iberian Peninsula. Finally, we have developed a genetic method based on environmental DNA for dreissenid larvae detection in waterbody samples, increasing the sensibility of the routinely zebra mussel larvae detection methods El mejillon cebra y la almeja asiatica son dos especies acuaticas invasoras, presentes en la Peninsula Iberica desde hace muchos anos. Sin embargo, no se conocen de manera cierta ni la historia de su invasion ni sus rutas de colonizacion. Desarrollamos nuevos marcadores moleculares para usarlos en estudios de genetica de poblaciones. Seguidamente, caracterizamos la estructura genetica de las poblaciones ibericas para inferir posibles rutas de invasion. Nuestros resultados muestran que la invasion del mejillon cebra en la Peninsula Iberica se basa en un unico y reciente episodio de invasion, diferente al resto de Europa. Respecto a la almeja asiatica, nuestros resultados mostraron que ha habido al menos dos episodios de colonizacion en la Peninsula Iberica. Finalmente, desarrollamos un metodo genetico basado en DNA ambiental para detectar larvas de especies de dreissenidos en masas de agua, incrementando la sensibilidad de los metodos rudimentarios de deteccion de larvas de mejillon cebra
Wearable Sensors Based Human Core Body Temperature Computing Method

Measuring core body temperature has significant implications in various fields, including healthcare, sports medicine, and military operations. Existing core body temperature measurement, such as rectal and esophageal measurements, are invasive, costly, and may not provide continuous monitoring, highlighting the need for non-invasive and reliable alternatives. With the development of wearable and computer technology, predicting core body temperature based on continuous measurement of skin temperature, ambient temperature, and other physiological parameters has become a trend for future development. In this study, we proposed a novel non-invasive method for predicting core body temperature using wearable sensors. A stacking fusion model was proposed to improve the core temperature prediction accuracy. The skin temperature and ambient temperature were collected by a wearable device and processed to remove outliers. Simultaneously, the core body temperature collected by a capsule temperature sensor was used as the gold standard. Then, we used the preprocessed data to train a machine learning algorithm, which was validated on collecting human data. The results showed that the algorithm based on stacking model fusion performed best, with an RMSE of 0.0448, MAE of 0.0214, and MAPE of 0.0582%, indicating high precision in predicting core body temperature, which can be used for continuous monitoring and accurate diagnosis, effective medical treatment.
Tactile Internet Security System Architecture for Mobile Networks

The deficiencies of the Fifth Generation (5G) mobile networks are stimulating vendors to focus on the specifications of the next generation of wireless systems. The efficient integration of the Tactile Internet with mobile networks, like the emerging Sixth Generation (6G) wireless systems, is anticipated to achieve global ubiquitous accessibility, extremely low latency, high reliability, and elevated security for haptic communication. Based on the characteristics of the new generation of mobile networks, a novel system architecture is proposed along with a security framework for the utilization of Tactile Internet applications.
Detection of Driver Behaviours using Convolutional Neural Network with Modified Inception Module

Drivers who are distracted are the significant causes of traffic accidents. Traffic accidents could be minimized with a system that gives an early warning about their conditions. This study aims to develop detection systems for driver conditions. The proposed method consists of a conventional convolutional layer followed by two inception modules with each depth of two layers, comprising five layers. The Inception module is modified by adding larger $7\times7$ kernels. We collect a dataset from four users, each comprising ten driver conditions. Our experiments for user-dependent systems, i.e., conditions where all users are used in training and testing data, show an accuracy of 99.33%. However, the performance drops for user-independent systems, i.e., conditions where users for training are different from that of the testing. Nevertheless, our systems perform better than a conventional CNN in both evaluation conditions.