Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient Anomalous Diffusion in a Heterogeneous Environment

This work provides an analytical model for the diffusive motion of particles in a heterogeneous environment where the diffusivity varies with position. The model for diffusivity describes the environment as being homogeneous with randomly positioned pockets of larger diffusivity. This general framework for heterogeneity is amenable to a systematic expansion of the Green's function, and we employ a diagrammatic approach to identify common terms in this expansion. Upon collecting a common family of these diagrams, we arrive at an analytical expression for the particle Green's function that captures the spatially varying diffusivity. The resulting Green's function is used to analyze anomalous diffusion and kurtosis for varying levels of heterogeneity, and we compare these results with numerical simulations to confirm their validity. These results act as a basis for analysis of a range of diffusive phenomena in heterogeneous materials and living cells.
Osaka University MT Systems for WAT 2018: Rewarding, Preordering, and Domain Adaptation

In this paper, we present Osaka University MT systems submitted to WAT 2018 shared translation tasks and analysis of their performances. For the ASPEC Japanese-English task, we use our rewarding model on neural machine translation (NMT) and preorder-ing model on phrase-based statistical machine translation (PBSMT). For the Myanmar-English task, we further apply our mixed ﬁne tuning method for domain adaptation on NMT. We report the translation results on these two tasks, where the rewarding model performs the best.
A Predictive-Adaptive Algorithm for a Web Switch

A high performance and high reliable web server system is the key to the success of all Internet services. This is the main reason for Internet service providers (ISPs) to choose, more and more, running their service through a cluster of servers. Some of the current commercial products rely on constant checking of servers' health that can itself be a significant overload. Our paper proposes an algorithm for a web switch that manages a cluster of web servers, evaluated through discrete event simulation as the first step in the construction of a new web switch. The web switch model is based on the estimation of the throughput that the servers would have in the next future to dynamically balance the workload. In order to reduce the checking time, estimations are only computed in a variable slot scheduling. Simulation results have shown that a commitment between overhead and performance should be established to obtain good results in a stressed web cluster of servers Key-Words: Adaptive Switching, QoS, Web cluster, Load balancing, Overhead
Detection and localization of attacks on satellite-based navigation systems

The worldwide coverage of satellite-based navigation systems, such as the Global Positioning System (GPS), facilitates self-localization and time synchronization in outdoor environments. Location and time awareness are integral components of a wide field of applications including, but not limited to, emergency localization, autonomous vehicles, and aviation. However, the strong dependence on the integrity of GPS makes systems susceptible to signal outage, or even more severe, to deliberate manipulation. The latter is referred to as spoofing attacks, a powerful attack class against GPS-dependent systems and challenging to protect against. In addition, the tools available to attackers get increasingly more sophisticated and affordable. As a consequence, we perceive a discrepancy between how critical systems are protected and the feasibility of attacks. In order to overcome this discrepancy, we propose countermeasures to harden GPS-dependent systems against spoofing attacks. Moreover, our targeted domains, in particular aviation, impose strict requirements on possible modifications to avoid prolonged (re)certification processes. We address demanding real-world requirements and design lightweight countermeasures that can be realized with commercial hardware or can even be implemented with the already existing infrastructure. For instance, we develop effective mechanisms for the detection of GPS spoofing attacks. Further, we tackle the challenge of spoofer localization and propose Crowd-GPS-Sec as a system for pinpointing an attacker via Automatic Dependent SurveillanceBroadcast (ADS-B) aircraft messages. Furthermore, we design a verification scheme based on wireless witnessing to assess the trustworthiness of ADS-B aircraft reports. In conclusion, we evaluate and implement different security solutions for the detection and localization of attacks on satellite-based navigation systems. We theoretically analyze the viability of our proposals and develop prototypes demonstrating their effectiveness. These solutions can be implemented today to improve the security of GPS-dependent systems immediately.
Adaptive Approaches to Natural Language Processing in Annotation and Application

Most applications in natural language processing (NLP) are based on machine learning components. Most of these components need to be trained in a supervised way and require a substantial amount of training data. These training data have to be manually labeled or annotated by domain experts. Creating such dataset is a challenging task: first, domain experts 1) need to know what to annotate and how to annotate (compiling an annotation guideline), 2) study if existing annotation tools are adequate or develop a new annotation tool, and 3) assess the quality of annotated data. Second, the annotation task is expensive mainly to compensate the annotators. Third, the annotation task takes much time to collect enough data. Finally, the collected data might get obsolete if the requirement of the target NLP application changes over time, which is known as concept drift or semantic drift. Thiswork tackles threemain questions: how tobuild rapid annotation tools, how tobuild and integrate resources to NLP applications, and how to integrate adaptive models into NLP applications. A rapid annotation approach focuses on completing the task much quicker by employing different strategies within the annotation tool. For the development of an adaptive and personalizedNLP application, we embed an adaptivemachine learningmodel into the application that can continuously learn and update its model from usage data. The thesis comprises of five parts. Part I describes the problem statements, research problems, andbackgroundof the study, Part II presents the annotation andNLP toolswehave developed during the thesis work, Part III discusses the approaches used to collect semantic-aware NLP resources, Part IV describes the experimental setups and results, and finally Part V concludes with main findings and results of the thesis. InPart I, we present themain goals of the thesis (seeChapter 1) and discuss the foundations of the rapid annotations and adaptive NLP technologies (see Chapter 2). Part II of the thesis presents the different tools we design for rapid, adaptive, and personalized annotations. This part describes the technological basis, which needs to be especially
Bridging the Gap: Improve Part-of-speech Tagging for Chinese Social Media Texts with Foreign Words

Multilingual speakers often switch between languages and generate enormous quantities of cross-language data. This phenomenon is more frequent observed in social media texts, where a large body of user generated data is produced every day. Such mix-lingual and informal texts lead to a challenge for partof-speech (POS) tagging, which is one fundamental task in natural language processing. In this paper, we propose a language-agnostic POS tagger for social media texts, which is able to learn from heterogeneous data with different genre and language type. Particularly, in order to comprehensively evaluate POS tagging performance, we propose a new tagging scheme including exclusive tags for special symbols in social media texts, and a humanannotated dataset of Chinese-English mixed social media texts is also developed. Experiments on both synthetic and real datasets show the validity and effectiveness of our model on social media texts where it outperforms stateof-the-art language-specific taggers.
Oocyte Cryopreservation in Oncological Patients: Eighteen Years Experience of a Tertiary Care Referral Center

Objective: The aim of the present study is to report our experience on elective women fertility preservation before cancer treatment. Study Design: This is a single-center retrospective observational study, including all patients who underwent elective fertility preservation before oncological treatment between January 2001 and March 2019 at our Institute. Results: Of a total of 568 women who received fertility counseling, 244 (42.9%) underwent 252 oocyte retrieval cycles after controlled ovarian stimulation for cryopreservation. The majority of patients were diagnosed with breast cancer (59.9%), followed by women affected by Hodgkin's and non-Hodgkin's lymphoma (27.4%). A minority comprised patients diagnosed with other malignancies that affected soft tissues (2.8%), ovary borderline type (2.4%), digestive system (1.6%), leukemia (1.6%), uterine cervix (1.2%). The remaining 3.1% were affected by other cancer types. The mean age of the cohort was 31.3 ± 6.4 years and the mean oocyte retrieval was 13.5± 8.4. Of 11 women who returned to attempt a pregnancy, three performed two thawed cycles. We obtained four pregnancies from 24 embryo transfers (Pregnancy Rate 36.4% for couple): two miscarriages and two live births. Overall, 95.7% of oocytes are still in storage. Conclusions: A close collaboration between Cancer and Fertility Center in a tertiary care hospital is essential to provide a good health service in oncological patients. Offering fertility preservation is no longer considered optional and must be included in every therapeutic program for women who receive an oncological diagnosis in their reproductive age. Oocyte cryopreservation appears to be a good opportunity for fertility preservation. Our results, although they are obtained in a small sample, are encouraging, even if only 4.5% of patients returned to use their gametes.
Pleasantness Ratings for Harmonic Intervals With Acoustic and Electric Hearing in Unilaterally Deaf Cochlear Implant Patients

Background Harmony is an important part of tonal music that conveys context, form and emotion. Two notes sounded simultaneously form a harmonic interval. In normal-hearing (NH) listeners, some harmonic intervals (e.g., minor 2nd, tritone, major 7th) typically sound more dissonant than others (e.g., octave, major 3rd, 4th). Because of the limited spectro-temporal resolution afforded by cochlear implants (CIs), music perception is generally poor. However, CI users may still be sensitive to relative dissonance across intervals. In this study, dissonance ratings for harmonic intervals were measured in 11 unilaterally deaf CI patients, in whom ratings from the CI could be compared to those from the normal ear. Methods Stimuli consisted of pairs of equal amplitude MIDI piano tones. Intervals spanned a range of two octaves relative to two root notes (F3 or C4). Dissonance was assessed in terms of subjective pleasantness ratings for intervals presented to the NH ear alone, the CI ear alone, and both ears together (NH + CI). Ratings were collected for both root notes for within- and across-octave intervals (1–12 and 13–24 semitones). Participants rated the pleasantness of each interval by clicking on a line anchored with “least pleasant” and “most pleasant. ” A follow-up experiment repeated the task with a smaller stimulus set. Results With NH-only listening, within-octave intervals minor 2nd, major 2nd, and major 7th were rated least pleasant; major 3rd, 5th, and octave were rated most pleasant. Across-octave counterparts were similarly rated. With CI-only listening, ratings were consistently lower and showed a reduced range. Mean ratings were highly correlated between NH-only and CI-only listening (r = 0.845, p < 0.001). Ratings were similar between NH-only and NH + CI listening, with no significant binaural enhancement/interference. The follow-up tests showed that ratings were reliable for the least and most pleasant intervals. Discussion Although pleasantness ratings were less differentiated for the CI ear than the NH ear, there were similarities between the two listening modes. Given the lack of spectro-temporal detail needed for harmonicity-based distinctions, temporal envelope interactions (within and across channels) associated with a perception of roughness may contribute to dissonance perception for harmonic intervals with CI-only listening.
Bacterial Valorization of Lignin: Strains, Enzymes, Conversion Pathways, Biosensors, and Perspectives

Lignin, an aromatic polymer found in plants, has been studied for years in many biological fields. Initially, when biofuel was produced from lignocellulosic biomass, lignin was regarded as waste generated by the biorefinery and had to be removed, because of its inhibitory effects on fermentative bacteria. Although it has since proven to be a natural resource for bio-products with considerable potential, its utilization is confined by its complex structure. Hence, the microbial degradation of lignin has attracted researchers' interest to overcome this problem. From this perspective, the studies have primarily focused on fungal systems, such as extracellular peroxidase and laccase from white- and brown-rot fungi. However, recent reports have suggested that bacteria play an increasing role in breaking down lignin. This paper, therefore, reviews the role of bacteria in lignin and lignin-related research. Several reports on bacterial species in soil that can degrade lignin and their enzymes are included. In addition, a cellulolytic anaerobic bacterium capable of solubilizing lignin and carbohydrate simultaneously has recently been identified, even though the enzyme involved has not been discovered yet. The assimilation of lignin-derived small molecules and their conversion to renewable chemicals by bacteria, such as muconic acid and polyhydroxyalkanoates, including genetic modification to enhance their capability was discussed. This review also covers the indirect use of bacteria for lignin degradation, which is concerned with whole-cell biosensors designed to detect the aromatic chemicals released from lignin transformation.
A multicomponent real-fluid fully compressible four-equation model for two-phase flow with phase change

Electric field is the method of choice in microfluidic devices for precise transport and placement of particles via fluid electroosmosis and particle electrophoresis. However, current studies on particle electrophoresis in microchannels have been focused mainly upon Newtonian fluids though many of the chemical and biological fluids possess non-Newtonian characteristics. Especially lacking is the experimental study of particle electrophoresis in either type of fluids. We report in this work an unprecedented experimental observation that polystyrene particles migrate towards the walls of straight rectangular microchannels in the electroosmotic flow of shear-thinning xanthan gum (XG) solutions. This phenomenon is opposite to the particle focusing along the channel center in our control experiment with the Newtonian base fluid of the XG solutions. It is attributed to a fluid shear thinning-induced lift that overcomes the wall-induced repulsive electrical lift. The parametric effects of the fluid-particle-channel-(electric) field system are systematically investigated. Published under license by AIP Publishing. https://doi.org/10.1063/1.5085186
Modelling temporal patterns in user behaviour

Modelling sequential data is one of the most challenging problems in machine learning research. The object of the investigation can be records of user behaviour, which are analysed to uncover hidden temporal patterns. A broad range of solutions exist for this type of problem, including multi-space hidden Markov models (HMMs). The main strength of this technique is its ability to jointly model features on a discrete and continuous scale, which is a property that conventional HMMs do not possess; therefore, multi-space HMMs are well suited to model temporal patterns in combination with other features. However, so far,they have not been utilised to build temporal models of user behaviour. Based on a newly developed integrated framework for creating multi-space HMMs, user behaviour is modelled in two fields of research. By creating HMMs of two phases in user behaviour during a session search, prior qualitative information-seeking models are augmented by a quantitative component. In a series of experiments based on a search engine transaction log, it could be shown that approximately one out of three search sessions reached the second phase, which is characterised by heightened effectiveness and efficiency of user actions. Furthermore, how the search phase model can be used to estimate crucial parameters of a search session is demonstrated; for example, the expected time to find the next relevantdocument. In the second practical application, the HMM framework’s versatility is highlighted by utilising the models as a classifier to detect rumourous conversations on Twitter and to model their veracity. Thus, this work complements prior research by using tweet stance and time as the only features to build a high recall rumour detection system based on multi-space HMMs. Especially when modelling rumour veracity, the strength of the joint modelling of the temporal component is evident since the multi-space HMMs achieve state-of-the-art results. In further experiments, it is also shown that the models are robust to noise and can provide timely veracity classifications.
A Sequence Modeling Approach for Structured Data Extraction from Unstructured Text

Extraction of structured information from unstructured text has always been a problem of interest for NLP community. Structured data is concise to store, search and retrieve; and it facilitates easier human & machine consumption. Traditionally, structured data extraction from text has been done by using various parsing methodologies, applying domain specific rules and heuristics. In this work, we leverage the developments in the space of sequence modeling for the problem of structured data extraction. Initially, we posed the problem as a machine translation problem and used the state-of-the-art machine translation model. Based on these initial results, we changed the approach to a sequence tagging one. We propose an extension of one of the attractive models for sequence tagging tailored and effective to our problem. This gave 4.4% improvement over the vanilla sequence tagging model. We also propose another variant of the sequence tagging model which can handle multiple labels of words. Experiments have been performed on Wikipedia Infobox Dataset of biographies and results are presented for both single and multi-label models. These models indicate an effective alternate deep learning technique based methods to extract structured data from raw text.
Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte

The usage of models within model-driven software development facilitates managing the complexity of the system under development and closes the gap between the problem and the solution domain. The usage and agile development of Model-Driven Development tools can itself increase the complexity within a project. The huge number of different artifacts and relations, the different artifact and relation kinds and the high degree of automation hinders the understanding, maintenance and evolution within MDD projects. A systematic approach to understand and manage MDD projects with a focus on its artifacts and corresponding relations is necessary to handle the complexity. This thesis presents such an approach for the analysis of artifact structures within MDD projects. The approach relies on appropriate modeling techniques and intends the usage of analysis tools to perform analyses using corresponding methodologies. The results of this thesis can be summarized as follows: • Development of a modeling technique based on the UML languages Class Diagrams, Object Constraint Language and Object Diagrams for the precise definition of possible project situations and the actual project situation as well as for the specification of analyses. • Modeling of a concrete artifact model, which defines the artifacts and relation kinds within MontiCore-based MDD projects. Related exemplary analyses are given in addition. • Development of an overall methodology consisting of a methodology to create project specific artifact models, a methodology for the specification of automated performed analyses and a methodology for the conduct of artifact-based analyses. • Development of a tool chain supporting artifact-based analyses and a methodology for its usage, configuration, adaption and integration. The application of the approach has been demonstrated in real projects of the Chair of Software Engineering of RWTH Aachen University, which led to the investigation of optimization potential of those projects. This thesis contributes to handling the growing complexity in big MDD projects by applying artifact-based analyses relying on appropriate modeling techniques, tools and methodologies.
Statistical Machine Translation Using 5-grams Word Segmentation in Decoding

This paper presents UCSMNLP’s submission to the WAT 2018 Translation Tasks focusing on the Myanmar-English translation for mixed domain tasks. In statistical machine translation (SMT), word segmentation is a necessary step to generate more fluent translation results. Myanmar is one of the low resource languages and many researches are supposed to develop word segmentation for Myanmar language. However, there are no public tools for Myanmar word segmentation. This paper addresses the problem of word segmentation for Myanmar language in SMT by developing syllable, 5-grams and longest matching word segmentation with monolingual lexicon. This paper describes the phrase-based statistical machine translation (PBSMT) with batch version of MIRA tuning using 5-grams word segmentation for English-Myanmar language pairs in both directions. The experimental results showed that the baseline SMT with 5-grams could outperform the baseline system in terms of BLEU, RIBES and AMFM scores.
Conversational UX Design for Kids : Toward Learning Companions

Kids’ use of conversational interfaces is growing along with the ubiquity of conversational agents in our pockets and on our countertops. Kids already use these interfaces for a variety of daily tasks, and supporting kids’ learning is a highly promising area for conversational interface design. In particular, companion conversational agents, which act as a peer to the student and not as an authority figure, can support learning while providing a fun, engaging experience. However, there are many open research questions around the design of companion conversational agents for kids. We discuss findings to date on companion agent dialogue with middle school students as we have investigated these agents within a challenging serious game. We also discuss important areas for future research related to companion conversational agents within the growing field of conversational UX design.
A Modeling Framework to Facilitate Schedule Synthesis of Time-Sensitive Networking

Time-Sensitive Networking (TSN) standards address hard timing requirements of Ethernet-based distributed applications. The traffic planning and verification of these networks require advanced expertise and are time-consuming. In this thesis, a modeling approach is presented based on logic programing to facilitate network scheduling and infrastructural verification and to enable tracing of design errors.