Miracle-dz/UniFAR-FTUs huggingface dataset:
Five-dimensional articulography

Current developments in the use of five-dimensional electromagnetic articulography for speech research are reviewed. Obvious advantages are the higher information density per sensor (three Cartesian coordinates, two spherical coordinates) compared to traditional 2D EMMA systems, and removal of the necessity to constrain the subject’s head. The drawbacks are equally related to this higher dimensional space: position calculation involves solving a non-linear optimization problem. In some cases, unstable solutions are encountered, resulting in mistrackings. On the positive side, we illustrate how the higher information density allows particularly succinct and robust characterizations of tongue configuration. Discussion also focuses on monitoring of head movement. This is crucial for accurate recovery of articulator movements themselves, but is also intrinsically interesting as part of speech motor activity. In addition to improving the naturalness of the speaking situation, the freedom of head movement also means that subjects tolerate longer recording sessions. This can facilitate new experimental paradigms. Regarding drawbacks (and ways around them), instabilities in position calculation are illustrated and it is shown how a first estimate of the measured positions can be used as a starting point for a more robust estimate, taking the continuity of speech movements into account. Diagnostics for assessing the reliability of the final solution are outlined. While work remains to be done to ensure the same accuracy over the whole 5D-measurement space, it is concluded that the system already offers unparalleled scope for large-scale acquisition of flesh-point data.
Enhancing a mobile and personalized learning platform through facial analytics and interactive quizzes

Learning analytics techniques to capture learners’ real-time responses can be computationally intensive for mobile devices. Yet with the diversity of many online educational videos such as the Ted Talks, YouTube or other open educational resources, the mobile learning platform intrinsically presents an opportunity for learners and instructors to analyze the pace and quality of learning. In a previous work, the PErsonalized Teaching And Learning (PETAL) platform was developed to monitor the learner’s progress in viewing educational videos through a carefully designed facial analytics technique to detect learners’ attention levels and their proximity to the screen of the mobile device. The PETAL application will alert a learner if (s)he is not paying attention while viewing an educational video. In this work, we propose to further improve the original PETAL by using online and interactive quizzes to more precisely evaluate the learner’s level of understanding while viewing the educational videos. The interactive quizzes can be flexibly added by an instructor by modifying a simple configuration file. When a learner gives an incorrect answer to a particular quiz question, (s)he will be requested to revise a specific section of the concerned video. All the data about the learner’s progress is uploaded and stored securely on a password-protected cloud platform to guarantee data privacy for future analyzes. There were some initial and positive students’ feedback collected from an empirical evaluation of the enhanced PETAL system. Clearly, the enhanced PETAL platform provides many promising directions for future extensions.
Identifying users and activities from brain wave signals recorded from a wearable headband

Identifying Users and Activities from Brain Wave Signals Recorded from a Wearable Headband by Glavin B. Wiechert This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable headband. Such wearable devices generate a large amount of data and due to their attractive pricing structure are becoming increasingly commonplace. As a result, the data generated from such wearables will increase exponentially, leading to many interesting data mining opportunities. This paper proposes a representation that reduces variable length signals to more manageable and uniformly fixed length distributions, and then explores the effectiveness of a variety of data mining techniques on the biometric signals. The proposed approach is demonstrated through data collected from a wearable headband that recorded EEG brain signals. The brain signals are recorded for a number of participants performing various tasks. The experiments use a number of classification and clustering techniques, including decision trees, SVM, neural networks, random forests, K-means clustering, and semi-supervised crisp and rough K-medoid clustering. The results show that it is possible to identify both the persons and the activities with a reasonable degree of precision. Furthermore, for identifying persons the evolutionary semi-supervised crisp and rough K-medoid clustering is shown to favourably compare with the conventional unsupervised algorithms such as K-means.
Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization

Research on the single objective optimization algorithms is the basis of the research on the more complex optimization algorithms such as multi-objective optimizations algorithms, niching algorithms, constrained optimization algorithms and so on. All new evolutionary and swarm algorithms are tested on single objective benchmark problems. In addition, these single objective benchmark problems can be transformed into dynamic, niching composition, computationally expensive and many other classes of problems. In the recent years various kinds of novel optimization algorithms have been proposed to solve real-parameter optimization problems, including the CEC'05 and CEC'13 Special Session on Real-Parameter Optimization [1][2]. Considering the comments on the CEC'13 test suite, we organize a new competition on real parameter single objective optimization. For this competition, we are developing benchmark problems with several novel features such as novel basic problems, composing test problems by extracting features dimension-wise from several problems, graded level of linkages, rotated trap problems, and so on. This competition excludes usage of surrogates or meta-models. There is a sub-competition to test the algorithms with a very small number of function evaluations in order emulate the computationally expensive optimization scenario. This sub-competition encourages the usage of surrogates and other approximation approaches. This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions. We encourage all researchers to test their algorithms on the CEC'14 test suite which includes 30 benchmark functions. The participants are required to send the final results in the format specified in the technical report to the organizers. The organizers will present an overall analysis and comparison based on these results. We will also use statistical tests on convergence performance to compare algorithms that generate similar final solutions eventually. Papers on novel concepts that help us in understanding problem characteristics are also welcome.
Multimodal Communication in the 21 st century : Professional and Academic Challenges The role of cognitive operations in the translation of film titles

The translation of film titles is not simply a linguistic matter, i.e. a transfer of words from one language to another language that looks for the most appropriate equivalent. It is determined by other factors such as the cultural component and the aim to catch the audience’s attention, which explain the use of translation techniques other than literal translation and zero translation. The present paper seeks to show the role of cognitive operations such as those proposed by Ruiz de Mendoza and Galera (2014) in the translation of English film titles into Spanish and French. We have gathered a corpus of 100 English titles and the corresponding Spanish and French titles. The comparative analysis suggests that, although the original titles are often kept in the other languages or literally translated, sometimes the translation is accounted for in terms of five cognitive operations: expansion, reduction, strengthening, mitigation or parametrization. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of Universidad Pablo de Olavide.
How can document analysis help in capturing five million pages?

This paper describes how document analysis techniques like OCR, layout analysis, model based recognition and interpretation can be fruitfully applied in the field of high-volume, high-accuracy document capturing with very hard time constraints. We describe the way we set up a workflow that enables reliable capturing of real-estate registration documents. Techniques from document analysis are used to speed up the archiving process and to raise its quality. In particular an automatic determination of the positions for input of new text in partially filled text columns is described. This enables to bridge the gap between the non-coded archived documents and the coded information which is used to update the documents later.
Altered mean apparent propagator-based microstructure and the corresponding functional connectivity of the parahippocampus and thalamus in Crohn’s disease

Crohn’s disease (CD) is a chronic and relapsing inflammatory bowel disorder that has been shown to generate neurological impairments, which has the potential to signify disease activity in an underlying neurological manner. The objective of this study was to investigate the abnormalities of brain microstructure and the corresponding functional connectivity (FC) in patients with CD, as well as their associations with disease condition. Twenty-two patients with CD and 22 age-, gender-, and education-matched healthy controls (HCs) were enrolled in this study. All subjects underwent mean apparent propagator (MAP)-MRI and resting-state functional magnetic resonance imaging (MRI) (rs-fMRI) data collection. Each patient was evaluated clinically for the condition and duration of the disease. The MAP metrics were extracted and compared between two groups. Pearson’s correlation analysis was conducted to determine the relationship between disease characteristics and significantly abnormal MAP metrics in the CD group. Regions of interest (ROIs) for ROI-wise FC analysis were selected based on their correlation with MAP metrics. Results showed that multiple brain regions, including the parahippocampus and thalamus, exhibited statistically significant differences in MAP metrics between CD patients and HCs. Additionally, CD patients exhibited decreased FC between the left parahippocampus and bilateral thalamus, as well as the right parahippocampus and bilateral thalamus. The findings of this work provide preliminary evidence that structural abnormalities in the parahippocampal gyrus (PHG) and thalamus, as well as decreased FC between them, may reflect the degree of inflammatory of the disease and serve as brain biomarkers for evaluating CD activity.
Constructive synthesis of optimized cryptographic primitives

In this thesis, I designed and implemented a formally verified compiler for programs over arbitrary-width integers in Parametric Higher-Order Abstract Syntax. This was part of a larger project, Fiat-crypto, which seeks to produce formally verified machinecode implementations of Elliptic Curve Cryptography. My implementation outputs Qhasm, a high-level assembly language developed for the implementation of highly optimised cryptography, and maintains platform independence by being totally parametric over the width of system words. Thesis Supervisor: Adam Chlipala Title: Associate Professor
Improving e‐learning recommendation by using background knowledge

There is currently a large amount of e‐Learning resources available to learners on the Web. However, learners often have difficulty finding and retrieving relevant materials to support their learning goals because they lack the domain knowledge to craft effective queries that convey what they wish to learn. In addition, the unfamiliar vocabulary often used by domain experts makes it difficult to map a learner's query to a relevant learning material. We address these challenges by introducing an innovative method that automatically builds background knowledge for a learning domain. In creating our method, we exploit a structured collection of teaching materials as a guide for identifying the important domain concepts. We enrich the identified concepts with discovered text from an encyclopedia, thereby increasing the richness of our acquired knowledge. We employ the developed background knowledge for influencing the representation and retrieval of learning resources to improve e‐Learning recommendation. The effectiveness of our method is evaluated using a collection of Machine Learning and Data Mining papers. Our method outperforms the benchmark, demonstrating the advantage of using background knowledge for improving the representation and recommendation of e‐Learning materials.
Open source software: a gendered technology?

Exploiting the data gathered from Open Source Software contributors through in-depth interviews and Delphi method, this paper examines to what extend does gender variations affect the process of open source software development. Regardless of the rising literature on open source software process, information about gender and its issues in open source software is still limited, though the gender issues seem to be persisting issues on women in software technology industries. The extremely scarce numbers of women in open source software process reveals broader issues on the construction of open source software in its design and usage thus lead to the question of whether open source software is a gendered technology. This paper discussed that appropriate feminist approach through the lens of social constructivist’s theory are important in understanding open source software development process as whether it is a gendered technology. © 2015 Published by ISICO
A novel very wideband 2-port S-parameter calibration technique

In this article explicit formulas are presented permitting the de-embedding of two-port S-parameter data in the case of the through-reflect family of calibration techniques. The readily programmable algorithm developed is applied to a novel through-attenuate-reflect method (TAR) which, unlike the through-reflect-line method, has no inherent bandwidth limitation.
Frequent pattern space maintenance : theories and algorithms

This Thesis explores the theories and algorithms for frequent pattern space maintenance. Frequent pattern maintenance is essential for various data mining applications, ranging from database management to hypothetical query answering and interactive trend analysis. Through our survey, we observe that most existing maintenance algorithms are proposed as an extension of certain pattern discovery algorithms or the data structures they used. But, we believe that, to develop effective maintenance algorithms, it is necessary to understand how the space of frequent patterns evolves under the updates. We investigate the evolution of frequent pattern space using the concept of equivalence classes. This space evolution analysis lays a theoretical foundation for the construction of efficient algorithms. Based on the space evolution analysis, novel “maintainers” for the frequent pattern space, “Transaction Removal Update Maintainer” (TRUM) and “Pattern Space Maintainer” (PSM), are proposed. TRUM effectively addresses the decremental maintenance of frequent pattern space. PSM is a “complete maintainer” that effectively maintains the space of frequent patterns for incremental updates, decremental updates and support threshold adjustments. Experimental results demonstrate that both TRUM and PSM outperform the state-of-the-art discovery and maintenance algorithms by significant margins.
Managing and Querying Multi-versioned Documents using a Distributed Key-Value Store

We address the problem of compactly storing a large number of versions (snapshots) of a collection of keyed documents or records in a distributed environment, while efficiently answering a variety of retrieval queries over those, including retrieving full or partial versions, and evolution histories for specific keys. We motivate the increasing need for such a system in a variety of application domains, carefully explore the design space for building such a system and the various storage-computation-retrieval trade-offs, and discuss how different storage layouts influence those trade-offs. We propose a novel system architecture that satisfies the key desiderata for such a system, and offers simple tuning knobs that allow adapting to a specific data and query workload. Our system is intended to act as a layer on top of a distributed key-value store that houses the raw data as well as any indexes. We design novel off-line storage layout algorithms for efficiently partitioning the data to minimize the storage costs while keeping the retrieval costs low. We also present an online algorithm to handle new versions being added to system. Using extensive experiments on large datasets, we demonstrate that our system operates at the scale required in most practical scenarios and often outperforms standard baselines, including a delta-based storage engine, by orders-of-magnitude.
Integration of Reversing Voltage Multilevel Inverter Topology with High Voltage Gain boost Converter for Distributed Generation

The conventional energy sources available to us are on the verge of depletion. This depletion of conventional energy source leads to concentrate more on alternative energy sources. In this research, the focus is on the role of renewable energy sources (RES) in electrical power generation. Even though, the RES based plants produce power, we cannot directly connect it to the grid or loads. Because, the voltage ratings and nature supply of RES plants would not match with the load. Hence, this is a major issue for connecting RES plants to load or other utility. The power electronic converters are extensively being used as a link between load and supply. As most of the renewable energy power generation is DC in nature, the DC-DC converter is used to increase the voltage level and this DC must be converted to AC for grid connection. Therefore, inverters are used for DC to AC conversion. In this paper, the DC supply of renewable energy is connected to load by using cascade DC-DC converters along with a proposed reversing voltage (RV) multilevel inverter (MLI). The first DC-DC converter is used to enhance the voltage level with high gain and second converter is used to split the DC supply for inverter convenience. In this paper, proposed RV symmetrical and asymmetrical MLI generates 7, 9, 11, 13 and 15 levels with only ten power switches. In-phase level-shifted triangular carrier type sine pulse width modulation (PWM) technique is employed to trigger the commutating switches of proposed RV MLI. Switches of H-Bridge for reverse voltage appearance across the load are triggered by simple pulse generator. The circuits are modeled and simulated in MATLAB/SIMULINK software. Results are presented and discussed.
IMPROVING THE RELEVANCY OF DOCUMENT SEARCH USING THE MULTI-TERM ADJACENCY KEYWORD-ORDER MODEL

This paper presents an enhanced vector space model, Multi-Term Adjacency Keyword-Order Model, to improve the relevancy of search results, specifically document search. Our model is based on the concept of keyword grouping. The keyword-order relationship in the adjacency terms is taken into consideration in measuring a term’s weight. Assigning more weights to adjacency terms in a query order results in the document vector being moved closer to the query vector, and hence increases the relevancy between the two vectors and thus eventually results in documents with better relevancy being retrieved. The performance of our model is measured based on precision metrics against the performance of a classic vector space model and the performance of a Multi-Term Vector Space Model. Results show that our model performs better in retrieving more relevant results based on a particular search query compared to both the other models.
The cognitive responses to UK railway signals during train driving

When a train driver’s error results in a red, stop signal being passed without authority there is the potential for disaster. These events are termed a “SPAD”, signal passed at danger without authority. Occasionally these incidents have led to tragedies such as the Ladbroke Grove accident in 1999. This accident led to 31 fatalities and over 523 injuries. Investigations of incidents have resulted in safety advancements that decreased the number of incidents and accidents. Despite all these efforts SPADs still occur and very little is known about the cognitive effects that the track-side signalling system has on the driver during normal operational conditions. The motivation for understanding the cognitive and behavioural effects of the routine patterns of railway signalling is to identify potential high and low risk situations. The identification of neural correlates that predict the driver’s state of readiness prior to a potentially dangerous situation. The combination of knowledge of these events and the insights into their causes could allow better systems, operational methods and logistics to be designed. This is an Electroencephalograph, (EEG) study to identify neural correlates that are used to identify high and low risk response and perceptual accuracy situations. The behavioural data is recorded from the keyboard responses and used to guide the EEG analysis. The tools applied to solving the research problem are artefact detection and removal from the EEG data, followed by analysis for patterns and features. The phase-locking functional-connectivity reveals repetition priming, antipriming, and neural precursors to correct and erroneous responses. The phase-locking for certain graph metrics are found to vary significantly prior to response errors. The EEG analysis reveals that multiple cortical region coordinated cognitive activity is required to successfully perform multiple paradigm tasks. Certain channels and regions of the brain are important in creating a cognitive state that facilitated future correct responses. Different states are required to promote response accuracy for different forthcoming events.