Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Design Pattern Support Easy Change Impact Analysis? - Focusing on the Command Pattern

Design patterns, a solution for the specific context and the problem, give benefits to us what a good design creates if we adequately use them in accordance with their purposes and principles. However, if we do not use them with inadequate purposes, using design patterns can be causes of increasing costs and decreasing maintainability and system performance. An industrial robot completes its given task through a series of hardware device’s primitive movements, controlled by an application using the APIs provided by specific hardware vendors. However, required movements of a robot change frequently because the given task changes often. This requires changes on hardware devices. As a consequence of this, APIs, controlling the hardware device change, and we also should modify the corresponding application. Therefore, an industrial robot software application should be designed such that it can easily trace hardware device’s change impacts on the application as well as it defines ordered composite primitive movements for completing the required task. This paper implements parts of an application that use hardware dependent APIs by using the command pattern and validates whether it support each change impact analysis.
A Survey on Edge Computing Systems and Tools

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage, and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. At present, the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open-source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this paper.
Daylight measurement in Milan.

In Climate Sensitive Buildings attention is often focused on thermal performance. This is done forgetting that illuminance and luminance distribution in daylight interiors not only affects the acceptance of architectural spaces [visual well-being] but above all the energy balance in the building [for example wide and transparent surfaces might cause excessive light resulting in glare and high thermal loads in summertime]. Regarding lighting aspects, the parameter for evaluating the natural light availability in an environment is the Daylight Factor. This parameter depends on the geometry of the environments, the dimensions and positioning of fenestration, the optic properties of the surfaces. The aim of this document is to define the real sky conditions in Milan, in fact the illuminance in a given point in a building depends on the value of outside illuminance for a given DF.
Master thesis : Simulating LISP with NS3

The Locator/Identi er Separation Protocol (LISP) is a novel routing architecture for the Internet that is based on the locator/identi er separation paradigm. The principle is to split the current address space into the identi er address space, which is composed of locally routable addresses used for identi cation; and the locator address space, composed of globally routable addresses used to route tra c in the network. The main objective is to solve the current Internet’s routing scalability problems threatening the network performance. Besides that, LISP also brings several new interesting bene ts, in particular for Tra c Engineering (TE), and multi-homing. Originally, LISP architecture had no support for node mobility. LISP Mobile Node (LISP-MN) has thus been developed to introduce mobility extensions to LISP and provide scalable and fast mobility. LISP-MN allows a node to roam from one site to another (whether the site is a LISP site or not), while maintaining ongoing communications at the transport-level. However, there was still no support for sites using Network Address Translation (NAT). Therefore, NAT extensions (LISP+NAT) have been established to de ne a NAT traversal mechanism for LISP mobile nodes. These extensions enable a node to send and receive LISP tra c, even though it is situated behind a NAT. So far, little is known about the performance of LISP-MN, and even less about the impact of NAT traversal for LISP tra c. Indeed, both propositions are mainly limited to the theoretical level and lack any experimentation. We aim to provide a rst look at this aspect of LISP through simulations on the ns-3 Network Simulator. To do so, we adapted the existing LISP implementation in ns-3 to add several functionalities to the model. Our main contribution consisted into adding a NAT model, proxy features (interworking mechanism used for communication between LISP sites and non-LISP sites), as well as the NAT extensions (LISP+NAT) to the LISP model. Additionally, we wrote a LISP-MN Helper, meant to help the script writer to easily setup a simulation scenario with mobile nodes and handovers. Finally, several unit tests have been integrated into the ns-3 testing framework for the NAT and LISP models. After investigation, we saw that all works on NAT traversal for LISP only focused on static scenarios, i.e., scenarios with no roaming and no handover. As such, some important aspects of mobility (the update of remote nodes’ state after a roaming event) have been left completely unspeci ed. We thus propose an extension of the protocol to take handovers into account and de ne a novel procedure for those cases. Our results con rm the intuition that NAT traversal has a negative impact on path stretch and on the handover delay. Indeed, most of the time, the handover delay when roaming into a non-LISP site behind a NAT is superior to the handover delay when roaming into a non-LISP site with no NAT deployment.
Copy-paste image forgery blind detection based on mean shift

The traditional blind detection methods of image copy-paste forgery are time consuming, of high computation cost and low detection precision. A blind detection algorithm of copy-paste image forgery based on Mean Shift( MS) was proposed in this paper, which extracted Speed Up Robust Feature( SURF) points and then performed feature matching utilizing the method of best bin first in order to filter redundant points and locate the copy-paste forgery regions preliminarily. Pixels with the same or similar attributes would be segmented in the same region after implementing MS. The copy-paste regions could be detected according to the position dependency between matched feature point with its segmented region of MS and the detection result would be further refined by comparing the similarity of edge histogram and HSV( Hue-SaturationValue) color histogram among the segmented regions of matched SURF and its neighborhood, and those regions with large similarity were included in the forged region. The experimental results show that the copy-paste forgery regions are detected accurately in the image with clear outline and rich details, and the proposed algorithm can robustly and efficiently detect the copy-paste forgery regions of image.
An Iterative Robust Kernel-Based Regression Method for Simultaneous Single Image Super-Resolution and Denoising

In this paper, we present a uniform mathematical framework based on a robust kernel-based regression for the task of simultaneous single-image super-resolution and denoising. The given model is formulated as a convex $\ell _{1}$ sparse optimization problem, which can be efficiently solved by the alternating direction method of multipliers (ADMM). Especially, the proposed method is applied to image patches to reduce computational time. Additionally, an iterative strategy is also incorporated into the approach to refine more image details. The extensive experiments on simulated natural images with additional sparse noise and real time-of-flight (ToF) images demonstrate the ability of simultaneously removing sparse noise and enhancing image resolution.
A Personalized Learning System Based on Semantic Web Technologies

a ABSTRACT In this study, a personalized learning system framework that is KOULearn (Kocaeli University Learning System) has been proposed. It is based on learning object and produces a personalized learning content according to learning style and cognitive knowledge level of an individual. The proposed framework is situated in advanced learning technologies according to educational perspective and is a semantic web application according to development technologies. The proposed framework is aimed to form a personalized learning environment. Three models are included in this system: domain model, user model and adaptation model, respectively. Components of the system have been realized by ontology based knowledge modeling approach. Execution of the personalized learning content structure and learning environment of the system, related to electric education, has been examined by resonance learning module which is constituted considering personalization requirements of the system
Study of Uncertainties and Objective Function Modeling Effects on Probabilistic Optimization Results

In this work, we study the effect of uncertainties modeling and the choice of objective function on the results of optimization design problems in deterministic and probabilistic contexts. Uncertainties modeling are studied in two cases identified in the literature. The results show how the choice of two different objective functions, which lead to the same results in deterministic case, may lead to opposite results in probabilistic case. Also, the results show how the uncertainties modeling type can affect the antagonism between mean and standard deviation in the reliability-based robust design optimization (RBRDO) problems. Three mechanical applications chosen from the literature are used to illustrate these cases.
Validation of the Dutch Version of the DN4 Diagnostic Questionnaire for Neuropathic Pain

Difficulties in diagnosing neuropathic pain in routine clinical practice support the need for validated and easy‐to‐use diagnostic tools. The DN4 neuropathic pain diagnostic questionnaire aims to discriminate neuropathic pain from nociceptive pain, but needs clinical validation. A total of 269 patients with chronic pain in three pain clinics were included in the study of which 248 had analyzable data. The mean duration of pain was 4.9 years. The most frequent etiologies were posttraumatic (36%), (pseudo) radicular (14%), and mechanical back pain (12%). The mean intensity of pain at visit was 5.6 on a 0–10 scale. Hundred and ninety‐six of 248 patients had an identical pain diagnosis from both physicians: 85 had neuropathic pain, 57 had nociceptive pain, and 54 had mixed pain. Among patients with identical diagnoses of neuropathic or nociceptive pain, using a receiver operating characteristic curve analysis, the area under the curve (AUC) was 0.81 for the DN4 7‐item and 0.82 for the 10‐item version. A cutoff point of 5/10 for the full questionnaire resulted in a sensitivity of 75% and a specificity of 79%, while a cutoff point of 4/7 for the partial questionnaire resulted in a sensitivity of 74% and a specificity of 79%. The items “brushing,” “painful cold,” and “numbness” were most discriminating. The DN4 is an easy‐to‐use screening tool that is reliable for discriminating between neuropathic and nociceptive pain conditions in daily practice. Item‐specific scores provide important information in addition to the total score.
Prediction of Students’ Performance in E-Learning Environment Using Random Forest

The need for advancement in e-learning technology causes educational data to become very huge and increase rapidly. The data is generated on daily basis as a result of students’ interaction with learning management systems. The data contains hidden information about participation of students in various activities of e-learning which when revealed can be used to associate with the students’ performance. Predicting the performance of students based on the use of e-learning system in educational institutions is a major concern and has become very important for education managements to better understand why so many students perform poorly or even fail in their studies. However, it is difficult to do the prediction due to the diverse factors or characteristics that influence their performance. This paper is aimed at predicting students’ performance by considering the students interaction in e-learning environment, their assessment marks and prerequisite knowledge as prediction features. Random Forest algorithm has been used for the prediction. Results show that the algorithm outperforms the popular decision tree and K-Nearest Neighbor algorithms. In addition to the performance prediction, the research findings also revealed most significant attributes that influences students’ performance.
Planning for active mapping of crop fields using UAVs

As more and more farmers are turning to drones to monitor the health of their crops and to assist them in decision making, there is an increasing interest in efficient mapping algorithms. Nowadays, the coverage path planning problem for crop fields using unmanned aerial vehicles (UAVs) is often solved offline with a pre-planned trajectory and the acquisitions are then compiled at the end of the flight. Such a non-adaptive approach may yield, for some reasons, sub-optimal maps which do not cover the whole target area or with quality defects. In this research, we propose a novel method based on metaheuristics to produce an optimised flight time trajectory to survey non-convex polygon areas in wind with a fixed-wing UAV. While offering better results than classical greedy approaches, this algorithm also proves to greatly reduce execution time. Thus, it can be used for online replanning tasks where the plan goals are deduced from a vision-based simultaneous localisation and mapping (SLAM), which exhibits areas where additional data must be gathered. This work is developed and validated within a realistic simulation framework.
Etude non invasive des réserves cardio-respiratoires, hémodynamiques et musculaires au cours de l'effort du patient handicapé respiratoire

La capacite musculaire et la performance a l’effort des patients sont devenues des enjeux prioritaires dans la prise en soins des pathologies respiratoires aigues ou chroniques. Il est maintenant bien etabli que dans les secteurs de reanimation ou de l’insuffisance respiratoire chronique obstructive, la fonction musculaire est un facteur independamment associe au pronostic vital des patients. Les investigations pour l’etude des reserves cardio-respiratoires et musculaires sont primordiales pour optimiser la prise en soins des patients. Dans le cadre de cette these, nous avons recherche a explorer de facon non invasive les capacites des patients a deployer leur reserve cardio-circulatoire et respiratoire au cours de differentes situations d’effort (du sevrage de la ventilation mecanique au reentrainement a l’effort). Dans la premiere partie nous avons observe sur 124 patients intubes en cours de sevrage de la ventilation mecanique leur force musculaire inspiratoire. Nous avons defini la faiblesse des muscles respiratoires par une pression inspiratoire maximale inferieure a 30 cmH2O. Nous avons observe que la faiblesse musculaire inspiratoire etait un facteur independamment associe a la mortalite a un an de l’extubation. Ensuite nous avons montre sur 90 patients que la mesure des pressions respiratoires maximales effectuee via une sonde d’intubation etaient fiables. Pour finir nous avons differencies l’atteinte musculaire inspiratoire et l’atteinte musculaire peripherique sur 99 patients et nous avons observes qu’une atteinte combinee etait associee a une augmentation de la mortalite a court terme. Dans la seconde partie nous avons evaluer la reponse cardio-vasculaire et la microcirculation musculaire de 20 patients intubes et sedates au cours de 4 techniques de rehabilitation precoce. Nous avons constate que 3 techniques sur 4 augmentaient peu le metabolisme a l’effort. Seule une technique combinant mouvement et contraction musculaire induite par stimulation electrique (FES-Cycling) induisait une reponse physiologique suggerant une activite musculaire plus intense. Dans la derniere partie, nous avons etudie la FES-Cycling dans le cadre d’une seance de reentrainement a l’effort d’intensite moderee (50% de la VO2pic) sur 25 patients BPCO. Comparativement a un placebo, la FES-Cycling entrainait une augmentation de la VO2 au cours de l’effort sans augmentation de la dyspnee ressentie, suggerant une intensite de travail atteinte plus importante. Ainsi, nous avons utilise plusieurs methodes non invasives pour etudier et optimiser la condition musculaire des patients handicapes respiratoire au cours de l’effort.
Modelització de dades òmiques amb autoencoders.

in English, 250 words or less): Objective: replicate and explore the use of a stacked auto-encoder based artificial neural network to model omic data. Application context: omic data analysis has required the development of new analysis tools, given their great dimensionality. Deep learning tools have brought improvements to this analysis challenge. The work of Xie, et al. (1) showed the superiority of a multilayer perceptron based on stacked autoencoders model (MLP-SAE) to study the relationship between single nucleotide polymorphisms (SNP) and genetic expression (measured using high-throughput sequencing [HTSeq]), when compared with other available methods. Methodology: implementation, fine-tuning and evaluation of an MLP-SAE model based on Xie, et al. work. Keras and Tensorflow packages for R are used. The original paper data set were used for first evaluation of created model. A search for suitable alternative data set was planned to be performed. Results: The created model outcome showed to be worse than the original one. Despite having better mean squared error (0.1956 vs 0.2890), it failed to have a single gene prediction with an R2 over 0.2, compared with 890 genes over that value that got the original work. Efforts on improving and understanding the created model were prioritized, and no search for the new data set took place. Conclusion: Using autoencoder based artificial neural network to model omic data is feasible, but get a good performance as a predictor is complex and is resource demanding to the used hardware.
Learning via mobile phones - students' learning styles, needs, preferences and concerns

With increasing penetration of mobile devices into people's everyday life, mobile learning is regarded as the next generation of e-learning. Learning via mobile devices, especially mobile phones is widely practiced in educational areas all over the world. This paper describes a five-year ongoing mobile language learning project which creates and delivers English learning materials to Japanese university students. Based on the data collected from the project, we investigate Japanese students' mobile learning styles, learning needs and material preferences as well as their concerns about using mobile learning. The data is collected from various sources: online surveys, server logs, user registered personal information, interviews and actual quiz results. The findings obtained from this research reveal the status quo of learners in mobile learning and will provide useful information for future mobile learning project/system design.
Analyzing, Optimizing and Synthesizing Scenes by Reasoning About Relationships Between Objects

3D scene modeling has many applications, including virtual social worlds, massively multiplayer online games, and production of catalog images. However, scene modeling is extremely tedious and challenging, due to the requirement of realism and the involvement of large numbers of objects. Therefore, automated methods for 3D scene modeling are needed. A promising approach is to first analyze the existing 3D scenes, such as the ones in online repositories (e.g. Trimble 3D Warehouse), and then use the knowledge obtained from the analysis step to create new scenes. Although a significant amount of research has focused on developing algorithms for scene analysis and scene modeling, these processes still remain challenging for two reasons. First, the large variability of 3D scenes makes it hard to capture the commonality among scenes for scene analysis. Second, the highly constrained space of realistic 3D scenes makes it challenging to automatically create satisfactory scenes. This dissertation pushes the limits of existing efforts on analyzing, synthesizing, and optimizing 3D scenes by reasoning about relationships between objects. First, it describes an algorithm that segments and annotates 3D scenes by considering relationships between objects in a hierarchical representation. Second, it describes a tool that optimizes a 3D scene to produce compositions by considering relationships between objects in the image space and the scene space. Finally, it focuses on style compatibility between objects, which is a relationship that has never been considered in previous scene modeling tools, and it presents a method for learning to predict the stylistic compatibility between 3D furniture models from different object classes. In this dissertation, we find that relationships between objects are comparatively stable across scenes, and that they can serve as a strong cue for inferring annotation and segmentation of scenes. Furthermore, we also find that modeling relationships between objects helps ensure the realism of synthesized scenes. Therefore, reasoning about relationships between objects greatly facilitates scene analysis and synthesis. iii