Miracle-dz/UniFAR-FTUs huggingface dataset:
Thai EFL Learners’ Attitudes and Motivation Towards Learning English Through Content-based Instruction

This study examined EFL learners’ attitudes and motivation (CBI) towards learning English through content-based instruction at a university in Thailand. Eighty-one university students, majority sophomores, answered a 6-Likert scale questionnaire on attitudes and motivation. Statistical analysis showed that students in general held a positive attitude towards the course and their English learning motivation was at a moderate level. Significant differences in attitudes between students from faculty of nursing and those from faculty of medicine and that between females and males were found. However, their overall attitude towards the course was not significantly correlated with their learning motivation. It is recommended that CBI teachers adopt motivational strategies to enhance both students’ instrumental motivation and integrative motivation. It is advisable that further study may investigate learning environment, learner identity and learner engagement in the CBI classroom.
A study on Effects of Intrinsic Characteristics of Datasets on Classification Performance

The classification performance of standard machine learning classification algorithms is highly affected by some properties of the datasets like imbalance in the class distribution of non-linearly separable datasets. The problem of class imbalance becomes more severe in the cases of datasets with class overlap and when there is lack of density in the training set. In this paper the effect of intrinsic characteristics of datasets on the classification performance of various standard algorithms has been analyzed.
A framework for printing, detection, tracking, and registration of deformable grids

ABSTRACTThis paper proposes a novel framework for high resolution printing, detection, tracking, and registration of deformable grids. The undeformed dot feature grid is created on the target surface using a flat bed inkjet printer. After deformation, the surface is imaged using close-range monocular vision, and the dots are detected and localized to sub-pixel accuracy. A novel, deformation invariant grid registration topological map and associated algorithms are used to track features between subsequent video frames. By matching the map to the undeformed grid geometry, stretching or compression of the distance between the dots can be used to compute the material strains. Accuracy, repeatability, and computation time for several image based dot feature detectors are compared, and experimental laboratory results demonstrate consistent robustness of the method under practical imaging conditions.
Social Media Activism

This book focuses on the referendums against water privatization in Italy and explores how activists took to social media, ultimately convincing twenty-seven million citizens to vote. Investigating the relationship between social movements and internet-related activism during complex campaigns, this book examines how a technological evolution — the increased relevance of social media platforms — affected in very different ways organizations with divergent characteristics, promoting at the same time decentralized communication practices, and new ways of coordinating dispersed communities of people. Matteo Cernison combines and adapts a wide set of methods, from social network analysis to digital ethnography, in order to explore in detail how digital activism and face-to-face initiatives interact and overlap. He argues that the geographical scale of actions, the role played by external media professionals, and the activists’ perceptions of digital technologies are key elements that contribute in a significant way to shape the very different communication practices often described as online activism.
Gerenciamento de roteadores virtuais em ambientes de virtualização de redes heterogêneos

In network virtualization environments (NVEs), the physical infrastructure is shared among different users (or service providers) who create multiple virtual networks (VNs). As part of VN provisioning, virtual routers (VRs) are created inside physical routers supporting virtualization. Currently, the management of NVEs is mostly realized by proprietary solutions, usually based on Command Line Interfaces (CLI). Heterogeneous NVEs (i.e., with different equipment and technologies) are difficult to manage due to the lack of standardized management solutions. As a first step to achieve management interoperability, good performance, and high scalability, we implemented, evaluated, and compared five management interfaces for physical routers that host virtual ones. The interfaces are based on SNMP (v2c and v3), NETCONF, and RESTful Web Services (over HTTP and HTTPS), and are designed to perform three basic VR management operations: VR creation, VR information retrieval, and VR removal. We evaluate these interfaces with regard to the following metrics: response time, CPU time, memory consumption, and network usage. Results show that the SNMPv2c interface is the most suitable one for small NVEs without strict security requirements and NETCONF is the best choice to compose a management interface to be deployed in more realistic scenarios, where security and scalability are major concerns.
Designing with the Body - Somaesthetic Interaction Design

With the rise of ubiquitous technology, data-driven design and the Internet of Things, our interactions and our interfaces with technology will look radically different in the years ahead, incorporating changes like full body interaction, shape-changing interfaces, wearables and movement tracking apps. These changes offer an enormous opportunityindeed, a necessity-to reinvent the way we interact with the inanimate world. Once-familiar, everyday objects, from our phones to our vacuums, require novel interaction models not just typing text on screens, but, increasingly, movementbased, bodily communication. A qualitative shift is required in our design methods, from a predominantly symbolic, language-oriented design stance, to an experiential, felt, aesthetic stance permeating the whole design and use cycle.
Multiple minority stress: The role of proximal and distal stress on mental health outcomes among lesbian, gay, and bisexual people of color

Abstract Existing literature suggests that both sexual and racial minorities are at a heightened risk of developing symptoms of depression and anxiety. However, there is minimal research addressing the mental health outcomes of lesbian, gay, and bisexual people of color (LGB-POC). Meyer’s minority stress theory posits that the manifestation of depression and anxiety is related to both distal and proximal stress. The present study examines the predictive value of perceived distal stress (e.g., LGBT-POC microaggressions and daily heterosexist experiences) and internalized proximal stress processes (e.g., self-stigma, identity salience, and sexual orientation rumination) for self-reported depression/anxiety symptoms among LGB-POC. A sample of 88 cisgender LGB-POC was collected online. Regression analyses suggested that proximal stressors accounted for 15% more variance in reported mental health outcomes than distal stress factors alone. Additionally, distal and proximal minority stressors together accounted for 33% of the variance in participants’ mental health outcomes. Results also indicated that bisexual/plurisexual participants reported significantly higher instances of depression and anxiety symptoms in the past month than lesbian/gay or monosexual participants. Implications regarding mental health outcomes for racial and sexual minorities and for conceptualizing the unique prejudices and stigma that LGB people of color face are discussed.
Cresting the wave : proper motions of the Eastern Banded Structure.

We study the kinematic properties of the Eastern Banded Structure (EBS) and Hydra I overdensity using exquisite proper motions derived from the Sloan Digital Sky Survey (SDSS) and Gaia source catalogue. Main sequence turn-off stars in the vicinity of the EBS are identified from SDSS photometry; we use the proper motions and, where applicable, spectroscopic measurements of these stars to probe the kinematics of this apparent stream. We find that the EBS and Hydra I share common kinematic and chemical properties with the nearby Monoceros Ring. In particular, the proper motions of the EBS, like Monoceros, are indicative of prograde rotation (Vϕ ∼ 180–220 km s−1), which is similar to the Galactic thick disc. The kinematic structure of stars in the vicinity of the EBS suggests that it is not a distinct stellar stream, but rather marks the ‘edge’ of the Monoceros Ring. The EBS and Hydra I are the latest substructures to be linked with Monoceros, leaving the Galactic anti-centre a mess of interlinked overdensities which likely share a unified, Galactic disc origin.
Orthogonal Differential Evolution Algorithm for Solving System of Equations

First,nonlinear system of equations was transformed into unconstrained optimization problem by using the concept of surrogate constraints and amended maximum entropy function.Then,the concept of average similarity was introduced to design adaptive orthogonal crossover operator,and orthogonal design was used to generate initial population,and on the basis,adaptive orthogonal differential evolution algorithm was proposed for solving the maximum entropy function.Finally,using equations verified the algorithm.
Local multiagent control in large factored planning Problems

Local Multiagent Control in Large Factored Planning Problems by Philipp Robbel Submitted to the Program in Media Arts and Sciences on November 11, 2015 in partial fulûllment of the requirements for the degree of Doctor of Philosophy at theMassachusetts Institute of Technology Many problems of economic and societal interest in today’s world involve tasks that are inherently distributed in nature. Whether it be the eõcient control of robotic warehouses or delivery drones, distributed computing in the Internet ofhings, or battling a disease outbreak in a city, they all share a common settingwheremultiple agents collaborate to jointly solve a larger task. he ability to quickly ûnd eòective solutions in such multiagent systems (MASs) forms an important prerequisite for enabling applications that require exibility to changes in tasks or availability of agents. his thesis contributes to the understanding and eõcient exploitation of locality for the solution of general, cooperativemultiagent Markov Decision Processes (MDPs). To achieve this, the proposed approximation architectures assume that the solution of the overall system can be represented with sparsely interacting (i.e., local) value function components that—if found— approximate the global solution well. Locality takes on multiple interpretations, from its spatial sense to more general sparse interactions between subsets of agents, and the eõcient representation of local eòects in large planning problems. Developed in the thesis are computational methods for extracting sparse agent coordination structure automatically in general, cooperativeMDPs. Based on novel theoretical insights about factored value functions, the proposed algorithms automate the search for coordination via principled basis expansion in the approximate linear program (ALP). We show that the search maintains bounded solutions with respect to the optimal solution and that the bound improves monotonically. Introduced then are novel solution methods that exploit “anonymous in uence” in a particular class of factoredMDPs. We show how anonymity can lead to representational and computational eõciencies, both for general variable elimination in a factor graph but also for the ALP solution to factored MDPs. he latter allows to scale linear programming to MDPs that were previously unsolvable. Complex MAS applications require a principled trade-oò between complexity in agent coordination and solution quality. he thesis results enable bounded approximate solutions to largemultiagent control problems—e.g., disease controlwith up to 50 agents in graphswith 100 nodes—for which previously only empirical results were reported. hesis Supervisor: Cynthia Breazeal Title: Associate Professor ofMedia Arts and Sciences
Design of tool for analysis of speech development disorders using landmarks and other acoustic cues

Non-word repetition tasks have been used to diagnose children with various developmental difficulties with phonology, but these productions have not been phonetically analyzed to reveal the nature of the modifications produced by children diagnosed with SLI, autism spectrum disorder or dyslexia compared to those produced by typically-developing children. In this thesis, we compared the modification of predicted acoustic cues to distinctive features of manner, place and voicing for just under 30 children (ages 5-12), for the CN-Rep word inventory, in an extension of the earlier analysis in Levy et al. 2014. Feature cues, including abrupt acoustic landmarks (Stevens 2002) and other acoustic feature cues, were hand-labeled and analysis of factors that may influence feature cue modifications included position in the word, position in the syllable, word length measured in syllables, lexical stress, and manner type. Results suggest specific patterns of modification in specific contexts for specific clinical populations. These findings set the foundation for understanding how phonetic variation in speech arises in both typical and clinical populations, and for using this knowledge to develop tools to aid in more accurate and insightful diagnosis as well as improved intervention methods.
The Conference in Pedestrian and Evacuation Dynamics 2014 (PED2014) Optimization-based pedestrian model calibration for evaluation

The evaluation and comparison of crowd simulation algorithms (complex, high-dimensional, multi-scale systems) is an important question. ”Realism” being dependent on target applications, comparisons with real measurements are not easy. Promising solutions have been suggested for such evaluations (Guy et al. (2012)). Here, we address estimating simulation parameters before evaluating: what do evaluation results mean if the assessed model is not performing at its best? We propose an optimization-based approach encompassing: reference data, metrics, simulation algorithms and optimization techniques. We demonstrate finding good parameter values setting simulation results as close as possible to reference data, enabling fair and meaningful comparisons. c
Misremembrance of Things Past: Depression Is Associated With Difficulties in the Recollection of Both Specific and Categoric Autobiographical Memories

Impaired retrieval of specific, autobiographical memories of personally experienced events is characteristic of major depressive disorder (MDD). However, findings in subclinical samples suggest that the reduced specificity phenomenon may reflect a broader impairment in the deliberate retrieval of all autobiographical memory types. This experiment (N = 68) explored this possibility by requiring individuals with and without MDD to complete a cued-recall task that required retrieval of specific, single-incident memories to a block of cues; retrieval of categoric, general memories to a block of cues; and to alternate between retrieval of specific and general memories for a block of cues. Results demonstrated that relative to never-depressed controls, individuals with MDD experience reduced recall of both specific (d = 0.48) and general memories (d = 1.00) along with reduced flexibility in alternating between specific and general memories (d = 0.90). Findings support further development of autobiographical memory–based interventions that target a range of retrieval deficits rather than specificity alone.
Combining multiple features for color texture classification

Abstract. The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.
Neuropsychological Evaluation and Rehabilitation after Acquired Brain Injury

Symptoms of interhemispheric disconnection after acquired brain injury are unusual in the current clinical practice. Symptoms usually diminish spontaneously and may include tactile anomia, agraphia and left signs of competition between the hemispheres, such as “alien hand sign”. This work presents a case report of a 33 year old patient who developed symptoms of interhemispheric disconnection and executive function defi cits after surgery for removal of a tumor in the pituitary region, as well as the evaluation of the applicability of the neuropsychological rehabilitation program for the cognitive complaints presented. The patient was evaluated with standardized neuropsychological instruments before starting rehabilitation and again close to the discharge from treatment. The case study was conducted by analyzing intervention data and the clinical outcome of the patient in the institutional records (results of neuropsychological evaluation examination and records of interventions in rehabilitation sessions). Throughout his evolution, the patient showed improvement in cognitive symptoms and demonstrated benefi ts from the training and strategies used for his adaptation and return to work and other previous activities. The study highlights the importance of conducting more studies of neuropsychological interventions for individuals with cognitive defi cits.
The Analysis of Cultural and Intercultural Elements in Mandarin as a Foreign Language Textbooks from Selected Malaysian Public Higher Education Institutions.

Language cannot be taught outside the culture of the society. A foreign language learner needs to learn the context and the cultural elements to avoid conflicts and misunderstanding arising from differing values, beliefs, and customs. Hence, foreign language instruction needs to emphasise intercultural communicative competence and include cultural and intercultural elements. Teaching materials such as textbooks and learning modules should also include cultural elements to develop students’ intercultural communicative competence. Mandarin as a foreign language (MFL) courses has gained popularity among non-native speakers in higher education institutes (HEI) as the enrolment for this course has risen. However, there does not seem to be studies determining whether cultural elements are incorporated in the course and in the textbooks used. This study investigates whether the cultural and intercultural are incorporated elements in MFL textbooks from seven selected public HEI in Malaysia. Directed content analysis is used based on the cultural and intercultural framework for communication. The findings indicate that there were insufficient cultural and intercultural elements in most of the sampled textbooks. This means that in future, textbooks written for MFL should include cultural and intercultural elements for better understanding of the language.
Human Brain Anatomy In Computerized Images

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Analyse automatisée des risques sur la vie privée dans les réseaux sociaux

Cette these vise a comprendre le risque de fuite d’informations personnelles sur un reseau social. Nous etudions les violations potentielles de la vie privee, concevons des attaques, prouvons leur faisabilite et analysons leur precision. Cette approche nous aide a identifier l’origine des menaces et constitue un premier pas vers la conception de contre-mesures efficaces. Nous avons d’abord introduit une mesure de sensibilite des sujets a travers une enquete par questionnaire. Puis, nous avons concu des attaques de divulgation (avec certitude) des liens d’amitie et des liens d’appartenance aux groupes sur “Facebook”. Ces attaques permettent de decouvrir le reseau local d’une cible en utilisant uniquement des requetes legitimes. Nous avons egalement concu une technique d’echantillonnage pour collecter rapidement des donnees utiles autour d’une cible. Les donnees collectees sont ensuite representees par des graphes et utilisees pour effectuer des inferences d’attributs (avec incertitude). Pour augmenter la precision des attaques, nous avons concu des algorithmes de nettoyage. Ces algorithmes quantifient la correlation entre les sujets, selectionnent les plus pertinents et permettent de gerer la rarete (sparsity) des donnees. Enfin, nous avons utilise un reseau de neurones pour classer les donnees et deduire les valeurs secretes d’un attribut sensible d’une cible donnee avec une precision elevee mesuree par AUC sur des donnees reelles. Les algorithmes proposes dans ce travail sont inclus dans un systeme appele SONSAI qui aide les utilisateurs finaux a controler la collecte d’informations sur leur vie privee