Miracle-dz/UniFAR-FTUs huggingface dataset:
Aircraft Disruption Management: Increasing Performance with Machine Learning Predictions

Airlines experience schedule disruptions on a daily basis. Poor weather conditions, unscheduled aircraft maintenance and congested air spaces are just a few of the causes that prevent airlines from operating their flight schedules as planned. In the third quarter of 2017 over 20% of all scheduled flights in Europe suffered from delays. Operation Research based decision support systems (DSS) help airlines with their disruption management processes and provide suggestions for recovery options. For large hub-and-spoke carriers, with an extensive network and a large number of aircraft and flights, the computation time required to find the optimal recovery solution after a disruption increases rapidly. As airlines require fast recovery solutions when disruptions occur, there is an ongoing trade-off between computation time and system sophistication. In the majority of disruption cases, no or a limited number of undisrupted aircraft are required to find the optimal recovery solution. By selecting a limited number of aircraft, flights and airports used to find a recovery solution, the computation time is reduced exponentially. The challenge is determining which aircraft and flights should be selected. This research aims to develop a decision support system for the schedule and aircraft recovery process that is able to present a feasible solution to a disruption in less than 120 seconds. An aircraft recovery model will be developed based on the integer linear programming model that was created by Vink et al. (2019) and Vos et al. (2015). Crew and passenger recovery are not considered. To recover disruptions, the optimization model can delay and cancel flights as well as perform tails swaps, where the flights from two aircraft are switched. The novelty of the work is that machine learning is used to predict which undisrupted aircraft will help recover a disruption. Based on those predictions a sub-network selection algorithm will select the subset of aircraft to be included in the optimization instead of the entire aircraft fleet. The performance of the system is tested on a case study for the domestic hub-and-spoke network of Delta Airlines. The dataset for the study consists of 2200 daily flights, 147 airports and 827 aircraft in 8 aircraft families. The results of the system are compared with the optimal solution, where no aircraft selections were made. The case study shows that the system is able to make an aircraft selection where 50% of the fleet is discarded, while still finding the optimal solution in 98.9% of the 556 disruptions tested. Furthermore, the system reduces the computation time by 45%, resulting in an average time of 48 seconds. For the disruptions, the computation time varied between 9 and 180 seconds.
A Key Agreement Scheme for Smart Homes Using the Secret Mismatch Problem

To secure the communications of home area networks, cryptographic keys need to be established between smart home devices. Though many key agreement schemes have been proposed, they cannot be directly implemented, due to the reason that in some applications, smart home devices have no access to the online trusted third party. Moreover, in certain scenarios, the smart home devices are manufactured by different factories. As a result, it is a challenge to securely establish the communication keys using the secrets preloaded by different factories. A few key agreement schemes are designed without using the online trusted third party and the preloaded secrets. However, there are some problems in these schemes. In this article, we improve these schemes and design a key agreement scheme for smart homes such that the devices can automatically and securely establish the communication key. Specifically, our scheme is designed by making use of the secret mismatch problem (exists in the physical layer secret extraction schemes). We prove the security of the proposed scheme and present a prototype implementation. Additionally, we analyze the performance of our scheme and compare it with that of other related works. The comparison shows that our key agreement scheme can fix the problems in the related works, however, it introduces some extra communication consumptions.
Concit-Corpus: Context Citation Analysis to learn Function, Polarity and Influence

Citation Context Analysis serves not only to classify citations according to qualitative criteria for obtaining information that can be used to improve impact assessment, but also has other applications as summary generation and development of better information retrieval techniques, among others. In the present study, we focus mainly in the application first mentioned. In the Introduction section, we refer to many authors that have exposed the weakness of impact assessment using approaches with only citation counting, although these methods have proved to be very useful to valuate author and publishing media impact, they could be improved taking into account results from citation context analysis. Not all citations should have equal weight in the calculation of the impact, which is why we could use context citation analysis for considering quality criteria, such as the purpose and polarity, to differentiate citations. Some authors have commented on the distortions that can occur when are used only counting methods to valuate impact. Marder et al. (2010) showed that controversial articles with incorrect or incomplete data get higher citation counts. This situation could generate unethical incentives to publish, considering that there is a lot of pressure over the academics, pressure produced by the importance of impact assessment on the researchers’ career and on the image and relevance of the different scientific media. Therefore, it is necessary to differentiate the nature of each citation with complete citation criteria from citation context analysis. Articles and other scientific documents are types of texts that have specific characteristics. We can mention the following: authors are not explicit about citation purpose; likewise, rarely they are clear about polarity, but rather they avoid criticism in order to evade adverse reactions from colleagues, and conceal negative feelings about work of others through language resources called “hedges”; many citations are mentioned for reasons outside research interest; and, finally there is specialized lexicon for each knowledge area (Verlic et al., 2008). Context citation analysis must take into account these features. We can see that this is a task with specific and complex challenges.
A paradigm description of virtual reality and its possible applications in psychiatry

Purpose: Virtual reality (VR) is a relatively new technology that has been used in psychiatric research on various types of mental disorders, including psychotic disorders, for over 25 years. The authors of this article present the aims and assumptions of virtual reality use, its most important theoretical concepts, key concepts, technology characteristics, as well as virtual environments, social interactions that occur within them, ethical aspects, safety issues and adverse effects related to VR. Views: The authors discuss basic concepts of the VR environment and its impact on the users. They remain sensitive to ethical aspects related to research that use this technology. They describe certain specificities of virtual environments, the impact of their quality on the recipients and the conditions that have to be met in order to have an effective impact on them. They analyse research on the safety of VR for healthy individuals and psychiatric patients and present possible side effects. They discuss virtual embodiment techniques, the characteristics of social interactions in the virtual world and the possibilities of conducting research with the use of this environment. Conclusions: Virtual reality is a relatively safe method, not causing many undesirable effects. However, ethical aspects related to long-term threats require further research. It enables ecologically valid research to be conducted while maintaining a high level of standardization. The mapping of simple social interactions is relatively accurate, even with the use of non-photorealistic virtual environments. The possibility of creating virtual embodiment illusions opens new research perspectives.
Real-time patch-based stylization of portraits using generative adversarial network

We present a learning-based style transfer algorithm for human portraits which significantly outperforms current state-of-the-art in computational overhead while still maintaining comparable visual quality. We show how to design a conditional generative adversarial network capable to reproduce the output of Fišer et al.'s patch-based method [FJS* 17] that is slow to compute but can deliver state-of-the-art visual quality. Since the resulting end-to-end network can be evaluated quickly on current consumer GPUs, our solution enables first real-time high-quality style transfer to facial videos that runs at interactive frame rates. Moreover, in cases when the original algorithmic approach of Fišer et al. fails our network can provide a more visually pleasing result thanks to generalization. We demonstrate the practical utility of our approach on a variety of different styles and target subjects.
The visual attention span as a measure of orthographic grain size: effects of orthograpic depth and morphological complexity

This thesis studies some of the factors modulating the size of the orthographic units (or grains) that are used in reading. Two of these factors are reading expertise and word familiarity that are both related to whether the individual can link the orthographic units of the word to its orthographic and subsequently phonological and semantic representations in the lexicon. The thesis studies how this skill develops and the possible influence of the mappings between lexical and sub-lexical orthographic grains at the semantic/morphological and phonological level by studying the influence of orthographic depth and morphological complexity. The main goal of this thesis was to specifically study the modulation of orthographic grain size in reading, focusing on the visual aspects of orthographic processing and using the visual attention span as an indirect measure of orthographic grain size in reading. In particular, we studied the effect of a language's orthographic depth and morphological characteristics on orthographic grain size with two cross-linguistic studies (in readers of Basque, Spanish and French), and the effect of morphological structure at the word level (morphological complexity) on orthographic grain size with two studies in Basque. Our results provided support for the modulation of orthographic grain size based on orthographic depth, language morphology and morphological complexity, and for the adequacy of the visual attention span as a measure of orthographic grain size. Overall, the present thesis suggests a new perspective through which to study the visual demands and limitations imposed on orthographic processing during reading development in alphabetic orthographies.
Bit Allocation Algorithm based on SSIM for 3D Video Coding

The 3D video system has broad application prospects and has become a new research hotspot at home and abroad in the video field. However, there are still many problems in multi-view video coding rate control in a three-dimensional video system. Therefore, this paper proposes a rate allocation algorithm based on structural similarity index measurement (SSIM) for 3D video coding. In this paper, we first analyze the correspondence between the weights of inter-view rate and the correlation between viewpoints, and then we establish the multi-view video main view and non-main view bit allocation calculation model. Finally, the view layer, frame layer, and macro block layer respectively perform bit allocation and rate control. The experimental results show that the proposed method can effectively control the bit rate of multi-view video coding while maintaining the multi-view video coding quality under limited bandwidth compared with the existing view layer fixed ratio allocation rate.
Burst-Deletion-Correcting Codes for Permutations and Multipermutations

Permutation codes and multipermutation codes are widely studied due to various applications in information theory. Designing codes correcting deletion errors has been the main subject of works in the literature and to the best of our knowledge, there exist only optimal codes capable of correcting a single deletion in a permutation. In this paper, we construct several classes of permutation and multipermutation codes that are capable of correcting a burst deletion of length <inline-formula> <tex-math notation="LaTeX">$s \geq 2$ </tex-math></inline-formula>, for both stable and unstable models. Efficient error decoders are provided to show the correctness of our constructions.
Sparse Representations of Hyperspectral Images

Sparse Representations of Hyperspectral Images Robin Joseph Swanson Hyperspectral image data has long been an important tool for many areas of science. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Additionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.
Crystal Structure of the Ferroelectric Phase of Triglycine Sulfate under an Electric Field

Crystal structure analysis of a triglycine sulfate single crystal under an electric field has been carried out in the ferroelectric phase by X-ray diffraction with anomalous scattering effect. Both crystal structure in positive polarity and that in negative polarity were clarified. The structural change in polarization reversal has been disclosed by indication of flip-flop motion of the hydrogen atoms between oxygen atoms, by rotation of the glycine I group and by changing of the hydrogen bonds connected to nitrogen atoms of the glycines II and III.
A 2D CRF Model for Sentence Alignment

The identification of parallel segments in parallel or comparable corpora can be performed at various levels. Alignments at the sentence level are useful for many downstream tasks, and also simplify the identification of finer grain correspondences. Most state-of-the-art sentence aligners are unsupervised, and attempt to infer endogenous alignment clues based on the analysis of the sole bitext. The computation of alignments typically relies on multiple simplifying assumptions, so that efficient dynamic programming techniques can be used. Because of these assumptions, high-precision sentence alignment remains difficult for certain types of corpora, in particular for literary texts. In this paper, we propose to learn a supervised alignment model, which represents the alignment matrix as two-dimensional Conditional Random Fields (2D CRF), converting sentence alignment into a structured prediction problem. This formalism enables us to take advantage of a rich set of overlapping features. Furthermore, it also allows us to relax some assumptions in decoding.
Development of Traffic Volume Forecasting Using Multiple Regression Analysis and Artificial Neural Network

The purpose of this study is to develop a model for traffic volume forecasting of the road network in Anamorava Region. The description of the current traffic volumes is enabled using PTV Visum software, which is used as an input data gained through manual and automatic counting of vehicles and interviewing traffic participants. In order to develop the forecasting model, there has been the necessity to establish a data set relying on time series which enables interface between demographic, socio-economic variables and traffic volumes. At the beginning models have been developed by MLR and ANN methods using original data on variables. In order to eliminate high correlation between variables appeared by individual models, PCA method, which transforms variables to principal components (PCs), has been employed. These PCs are used as input in order to develop combined models PCA-MLR and PCA-RBF in which the minimization of errors in traffic volumes forecasting is significantly confirmed. The obtained results are compared to performance indicators such R2, MAE, MSE and MAPE and the outcome of this undertaking is that the model PCA-RBF provides minor errors in forecasting.
The Reciprocal Relationship Between Passive Social Networking Site (SNS) Usage and Users’ Subjective Well-Being

Prior studies have found an inconclusive relationship between social networking site (SNS) usage and users’ subjective well-being. Passive SNS usage may be detrimental to subjective well-being, because it cannot provide social support and may evoke envy and jealousy. Conversely, it is also possible that lower subjective well-being may predict higher passive SNS usage, which can be used as a means to relieve stress. To examine this reciprocal process, a two-wave study among a sample of Chinese college students was conducted (N = 350 at Time 1, 265 at Time 2). Data were analyzed with structural modeling. Cross-lagged analysis indicated that passive SNS usage at Time 1 predicted a decrease in subjective well-being at Time 2. Lower subjective well-being at Time 1 also predicted an increase in passive SNS usage at Time 2. These findings deepen our understanding of the complicated association between SNS usage and well-being and has implications for how to help individuals use SNS healthily.
The role of sense-makingin working across internal interfacesin a multi-national organisation

In a world of accelerated pace and increased globalisation, managers are responding with organisation designs that are often matrix managed, geographically diverse and reliant on interdependency between groups. Interfaces are a fundamental part of that design. The ability to work across both internal and external interfaces has become increasingly important, but rather than utilising diversity and collaboration to the organisations advantage, it is often an inhibiting tension; a place where teams get “stuck”. In today‟s complex environment managers are interested in identifying the drivers which enable effective action across internal interfaces. This thesis seeks to identify the components of an effective interface and explores the role of sense-making as an enabler or inhibitor in working between groups and creating effective movement across internal interfaces. In exploring the topic it asks three key questions: Why do teams get stuck at the interface? What holds them there? What might help create movement? Considered from a complexity perspective, which acknowledges the messy, unpredictable and non-linear nature of organisations; it takes a phenomenological and interpretive approach. It employed a „bricolage‟ approach as a means of researching in this context; which pragmatically uses multiple tools and methodologies. The primary methodology used was that of a Case Study, where three case studies were conducted in the same multi national organisation. These provide an empirical view of different types of interfaces in their day to day context. It took an Action Research orientation as the author worked with teams at organisational interfaces in exploring how they might work together more effectively. A semi Grounded Theory approach was taken for data analysis. The thesis presents six core findings. The first two findings relate to interface effectiveness; namely that: the components of inter- team effectiveness have the same basis as team effectiveness, and secondly that interface effectiveness might be considered a variant of team effectiveness but can be differentiated from „boundary spanning‟ These two findings enabled the development of a framework to consider interface effectiveness. A further four findings related to sense- making at the interface; these were that there are a number of key triggers at the interface where sense giving and sense making occur; that there is a critical interplay between sense giving and sense-making at the interface which can enable or impede action. This can be seen both in terms of hierarchical sense giving and peer to peer sense giving and making. The fifth finding concluded that although sense-making is a live and dynamic process happening in the moment; there is a time lag between sense-making and sense-giving which needs to be appreciated. Finally this research supports findings elsewhere that skills in dialogue and narrative are key enablers in the sense-making process. The thesis concludes with a discussion of the study limitations and identifies areas for future research.
Mining and characterizing cross-platform apps

Smartphones and the applications (apps), which run on them, have grown tremendously over the past few years. To capitalize on this growth and attract more users, developing the same app for different platforms has become a common industry practice. However, each mobile platform has its own development language, Application program interfaces (APIs), software development kits (SDKs) and online stores for distributing apps to users. To understand the characteristics of and differences in how users perceive the same app implemented for and distributed through different platforms, we present a large-scale comparative study of cross-platform apps. We mine the characteristics of 80,000 app-pairs (160K apps in total) from a corpus of 2.4 million apps collected from the Apple and Google Play app stores. We quantitatively compare their app-store attributes, such as stars, versions, and prices. We measure the aggregated user-perceived ratings and find many differences across the platforms. Further, we employ machine learning to classify 1.7 million textual user reviews obtained from 2,000 of the mined app-pairs. We analyze discrepancies and root causes of user complaints to understand cross-platform development challenges that impact cross-platform user-perceived ratings. We also follow up with the developers to understand the reasons behind identified differences. Further, we take a closer look at a special category of cross-platform apps, which are built using Cross Platform Tools (CPTs). CPTs allow developers to use a common code-base to simultaneously create apps for multiple platforms. Apps created using these CPTs are called hybrid apps. We mine 15,512 hybrid apps; measure their aggregated user-perceived ratings and compare them to native apps of the same category. ii Preface This thesis presents two large-scale empirical studies on cross-platform apps. The work presented was conducted by myself in collaboration with my supervisor, Professor Ali Mesbah. Chapter 2 of this thesis was done with equal collaboration from Mona Erfani Joorabchi. I was responsible for devising the approach and collecting the data, running the experiments, analyzing the results and writing the manuscript. My collaborators guided me with the creation of the methodology, analysis of results, editing and writing portions of the manuscript. The results described in Chapter 2 are submitted as a full paper to an ACM SIGSOFT conference and are currently under review. The work presented in Chapter 3 has been published as a workshop paper in November 2016 in the Proceedings of the 1st International Workshop on App Market Analytics (WAMA) [4].