Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiword expressions in multilingual applications within the Grammatical Framework

The main focus of Grammatical Framework (GF) is in multilingual applications where the same type of content is produced and analyzed in several languages at once. This is achieved by joining the grammars for all languages with a shared interlingual representation. In designing the interlingua, multiword expressions are an important factor that must be considered. Here, I adopt the broader definition where everything that translates non-compositionally accross languages is considered an expression. In this chapter I present multiword expressions from a cross-lingual perspective in relation to an interlingual grammar.
Video Stitching for Linear Camera Arrays Supplementary Material

In this supplementary document, we present additional results to complement the paper. First, we provide the implementation and training details of the proposed method. Second, we analyze the robustness of the proposed method on the variation of the camera settings. Finally, we show some failure cases of our model. The video results are available in our project website at http://vllab.ucmerced. edu/wlai24/video_stitching/.
Phonetic and phonological vowel reduction in Brazilian Portuguese

Abstract The present study examines the phonetic and phonological status of vowel reduction in Brazilian Portuguese. In order to compare the effects of duration and metrical structure, we tested the influence of duration on the realization of /a/ in five prosodic positions: word-initial pretonic, word-medial pretonic, tonic, word-medial posttonic, and word-final posttonic. The results revealed that, while both phonetic duration and prosodic position had effects on F1 values for /a/, the categorical effect of prosodic position was much stronger and more reliable. In particular, F1 values for /a/ were best predicted by a two-way distinction between posttonic and non-posttonic syllable positions. Correlations between a vowel’s duration and its F1 frequency were statistically significant but generally weak in all positions. We argue that these findings suggest that vowel reduction in Brazilian Portuguese primarily reflects phonological patterning rather than phonetic undershoot, although there was also evidence for some amount of undershoot. Brazilian Portuguese can therefore be said to have a mixed system of phonological and phonetic reduction. The present study discusses the results in the context of Brazilian Portuguese metrical organization, sound change, and the relation between phonetics and phonology.
Computational Intelligence to aid Text File Format Identification

One of the challenges faced in digital preservation is to identify the file types when the files can be opened with simple text editors and their extensions are unknown. The problem gets complicated when the file passes through the test of human readability, but would not make sense how to put to use! The Text File Format Identification (TFFI) project was initiated at The National Archives to identify file types from plain text file contents with the help of computing intelligence models. A methodology that takes help of AI and machine learning to automate the process was successfully tested and implemented on the test data. The prototype developed as a proof of concept has achieved up to 98.58% of accuracy in detecting five file formats.
A ( PARTIAL ) TAXONOMY OF MACHINE LEARNING FEATURES

The two most labor-intensive steps in machine learning for natural language are: 1) feature engineering; and 2) labeling of training sets. Supervised machine learning uses these training sets where every point is an inputoutput pair, mapping an input, which is a feature, to an output, which is the label. The machine learning consists of inferring (“learning”) a function that maps between these inputs and outputs with acceptable predictive power. This learned function can then be applied to previously unseen inputs in order to predict the output label. The technique is particularly suited to problems of regression or of classification.
Optical nano-antennas as a key-connection between the nanoscale and optical fibers

The wavelength is a central parameter in optics. Be sides its direct relationship with colors and photo n energy, it defines the limit between two different optical regimes. Optics at scales larger than the w avelength is directly perceived with our eyes. It invo lves free space propagating light (under the form o f plane waves, spherical waves, etc.) and is nowadays involved in an extremely broad panel of scientific , medical and industrial researches and applications ra ging from astronomy to microscopy. Optics at sub wavelength scale involves another type of optical w ves, called “evanescent waves”, which are bound to object surfaces and cannot leave them to propagate in fr e space. They are thus undetectable with lens es and cameras. These non-radiative waves originate a large panel of extraordinary optical phenomena tha t only exist at subwavelength scale [1].
Motion Description Based on Histograms of Sparse Trajectories Juiz de Fora 2016

Motion description has been a challenging and popular theme over many years within computer vision and signal processing, but also very closely related to machine learning and pattern recognition. Very frequently, to address this task, one extracts motion information from image sequences and encodes this information into a descriptor. This work presents a simple and fast computing method to extract this information and encode it into descriptors based on histograms of relative displacements. Our descriptors are compact, global, meaning it aggregates information from whole frames, and what we call self-descriptors, meaning they do not depend on information from sequences other than the one we want to describe. To validate these descriptors and compare them to other works, we use them in the context of Human Action Recognition, where scenes are classified according to the action portrayed. In this validation, we achieve results that are comparable to those in the state-of-the-art for the KTH dataset. We also evaluate our method on the UCF11 and Hollywood2 datasets, with lower recognition rates, considering their higher complexity. Our approach is a promising one, due to the fairly good recognition rates we obtain with a much less complex method than those of the state-of-the-art, in terms of speed of computation and final descriptor compactness. Additionally, we experiment with the use of Metric Learning in the classification of our descriptors, aiming to improve the separability and compactness of the descriptors. Our results for Metric Learning show inferior recognition rates, but great improvement for the compactness of the descriptors.
The JHU Speaker Recognition System for the VOiCES 2019 Challenge

This paper describes the systems developed by the JHU team for the speaker recognition track of the 2019 VOiCES from a Distance Challenge. On this far-field task, we achieved good performance using systems based on state-of-the-art deep neural network (DNN) embeddings. In this paradigm, a DNN maps variable-length speech segments to speaker embeddings, called x-vectors, that are then classified using probabilistic linear discriminant analysis (PLDA). Our submissions were composed of three x-vector-based systems that differed primarily in the DNN architecture, temporal pooling mechanism, and training objective function. On the evaluation set, our best single-system submission used an extended time-delay architecture, and achieved 0.435 in actual DCF, the primary evaluation metric. A fusion of all three x-vector systems was our primary submission, and it obtained an actual DCF of 0.362.
Register-Bridge Architecture and its Application to Multiprocessor Systems

The interconnection delay in data transfer is becoming the dominant factor to restrain the improvement of the maximum clock frequency of LSIs. The regular distributed register (RDR) architecture is proposed where data transfer between the islands is separated from the computation and local data access, and distant data transfer is done using multiple clock cycles. In this paper a novel register-bridge (RB) architecture is proposed so that data transfer between adjacent islands is done through bridge registers in between the islands, thereby the necessary number of clock cycles for data transfer would be reduced. The experimental results show about 11 % reduction in the latency on average when example procedures are implemented on a multiprocessor system based on the RB architecture.
Detection and Recovery of OOVs for Improved English Broadcast News Captioning

In this paper we present a study on building various deep neural network-based speech recognition systems for automatic caption generation that can deal with out-of-vocabulary (OOV) words. We develop several kinds of systems using various acoustic (hybrid, CTC, attention-based neural networks) and language modeling (n-gram and RNN-based neural networks) techniques on broadcast news. We discuss various limitations that the proposed systems have and introduce methods to effectively use them to detect OOVs. For automatic OOV recovery, we compare the use of different kinds of phonetic and graphemic sub-word units, that can be synthesized into word outputs. On an experimental three hour broadcast news test set with a 4% OOV rate, the proposed CTC and attention-based systems are capable of reliably detecting OOVs much better (0.52 F-score) than a traditional hybrid baseline system (0.21 F-score). These improved detection gains translate further to better WER performance. With reference to a non-OOV oracle baseline, the proposed systems at just 12% relative (1.4% absolute) loss in word error rate (WER), perform signiﬁcantly better than the traditional hybrid system (with close to 50% relative loss), by recovering OOVs using their sub-word outputs.
COMPUTING MECHANICAL PROPERTIES FROM ORIENTATION TENSORS FOR FIBER FILLED POLYMERS IN AXISYMMETRIC FLOW AND PLANAR DEPOSITION FLOW

Fused Filament Fabrication (FFF) is quickly becoming an industrially viable Additive Manufacturing (AM) method that produces economical and intricate three-dimensional parts. The addition of discrete carbon fibers to the polymer feedstock has been shown to improve mechanical properties and the quality of the printed part. The improvement in mechanical properties is directly dependent on the fiber orientation state in the deposited polymer. To calculate the decoupled fiber orientation state, the flow field must be evaluated for the extrusion process. The flow fields of an axisymmetric nozzle and a two dimensional planar nozzle flow are modeled using Stokes’ flow of a Newtonian fluid. The models include the effects of extrudate swell at the die exit, including material redistribution when the extruded polymer contacts a moving surface. Fiber orientation tensors are calculated throughout the flow field using the Orthotropic Fitted Closure and the Folgar-Tucker isotropic rotary diffusion fiber interaction model. Fiber orientation tensors are used to evaluate mechanical properties using the orientation homogenization method of Jack and Smith. Fiber alignment is shown to increase in the convergence zone then decrease significantly in the extrudate swell. It is also shown that deposition onto a moving platform directly outside the die exit effects the velocity gradients within the melt flow and resulting fiber orientation. As a result, the mechanical properties of the extruded fiber filled composite are shown to be substantially effected by the abrupt changes in the flow field due to extrudate swell and melt deposition. Introduction Fused Filament Fabrication (FFF) is currently transitioning from small scale rapid prototyping to large scale Additive Manufacturing (AM) of industrially viable parts and tooling. One of the driving forces behind this transition is the addition of chopped carbon fiber to the polymer feedstock. The addition of chopped carbon fiber to ABS feedstock has recently been shown to increase the mechanical properties of the polymer and reduces the coefficient of thermal expansion of the composite [1]. The decrease in the composite CTE improves dimensional stability while decreasing the amount of warping and delamination’s [1]. The improvement in strength and toughness is directly dependent on the orientation of the chopped fibers suspended in the molten polymer, thus, there is a need for the accurate prediction of fiber orientation in the nozzle and immediately after the nozzle exit. An initial study by Nixon et al. [2] used Moldflow (Moldflow Corporation, Framingham, MA) to approximate the orientation of fibers suspended in a molten polymer that is being injected into a large open cavity. Heller et al. [3] studied the effects of extrudate swell and nozzle shape on fiber orientation using an axisymmetric model of an FFF nozzle. The effects of deposition flow on fiber orientation in the FFF process has yet to be properly addressed.
Item Response Theory ( IRT ) analysis of the SKT Short Cognitive Performance Test

In this article, the SKT Short Cognitive Performance Test (SKT = Syndrom Kurztest; Erzigkeit, 2001) was tested with models of IRT. Here, the SKT test functions as an example on how IRTanalyses with any cognitive test, which results in a summary score, can be applied. The SKT was newly scaled in 2015 (Stemmler, Lehfeld & Horn, 2015) using a regression-based continuous norming approach (Crawford & Garthwaite, 2006) while leaving the testing material unchanged. The data were from a sample of 811 older adults ( x  73.3 years; SD=7.2) assessed at multiple testing sites in Germany. The sample consisted of cognitively healthy older adults (n =250), of older adults suffering from mild cognitive impairment (MCI; n=290) as well as adults suffering from dementia (n=271). Analyses to check the psychometric validity of the SKT encompass the Partial Credit Model (PCM), the Graded Response Model (GRM) and Multidimensional Response Models (MRM). The strict assumptions of the one parametric Rasch model with regard to specific objectivity were not met. The two parametric GRM provided a better goodness-of-fit. A two-dimensional GRM revealed that the subtests assessing Memory and those assessing Attention can be distinguished as two separate dimensions. However, the use of a total score seems still valid, but, next to a careful inspection of the total score the scores for Memory and Attention should be taken into account and considered separately.
Late dead space fraction (fDlate) before and after pulmonary embolectomy

This report presents data on late dead space fraction (fDlate) of a patient submitted to surgical pulmonary embolectomy. Pulmonary thromboembolism (PTE) was diagnosed by echo-Doppler ultrasound of the lower limbs, lung scintigraphy, computerized helical tomography and angiography. The fDlate was calculated based on volumetric capnography as well as on arterial blood gases according to ERIKSSON et al. [1]. Preoperative fDlate value was 0.16, and was considered positive for the diagnosis of PTE, because it was higher than the cut-off point of 0.12. Postoperative fDlate value was 0.04, which was below 0.12 and was characterized as negative. The agreement of fDlate with the image results confirms the validity of this new, noninvasive diagnostic tool. Descriptors: Pulmonary embolism. Pulmonary gas exchange. Capnography. Resumo Este relato de caso apresenta os resultados da fDlate (fração tardia de espaço morto) em um paciente submetido a embolectomia por tromboembolismo pulmonar (TEP). O TEP foi diagnosticado por ultrassonografia ecodoppler de membros inferiores, cintilografia pulmonar, tomografia helicoidal computadorizada e arteriografia pulmonar. O cálculo da fDlate se baseou na capnografia volumétrica e na gasometria arterial de acordo com ERIKSSON et al. [1]. A fDlate préoperatória foi de 0,16 e foi considerada positiva por estar acima do valor de corte de 0,12. A fDlate pós-operatória foi de 0,04, um valor inferior ao valor de corte de 0,12 e foi caracterizada como negativa. A correlação da fDlate com os resultados de imagem confirma a validade desta nova ferramenta diagnóstica não-invasiva. Descritores: Embolia pulmonar. Troca gasosa pulmonar. Capnografia. Fração tardia do espaço morto (fDlate) antes e após embolectomia pulmonar Late dead space fraction (fDlate) before and after pulmonary embolectomy
Human Subjects Experiment Data Collection for Validating an Agent-based Model

The difficulties of validating an agent-based model (ABM) are well documented. Due to the complexities of the phenomenon that are traditionally modeled using ABM, it is difficult to obtain the necessary data to conduct an empirical validation of the resultant simulation. This difficulty means that a modeler is forced to validate through other means, either sensitivity analysis or white-box validation (also known as expert validation). In this paper, we describe a methodology to obtain human subjects data through an experiment for use in empirical validation of an ABM. The experiment will be conducted using a web-based version of a market economy game. These data are collected from a situation where a software agent from the simulation is replaced by a human agent. The remaining agents are independent and autonomous utility-maximizing software agents. Data collected will comprise the actions and decisions of the human agent during a series of game iterations. The proposed experiment will include just one participant per game to allow more control over potential confounders associated with actions from other players. The proposed modeling context is considered a variation of the market economy game known as the glove game. Agents in the game try to form coalitions that maximizing their revenue from selling pairs of gloves; with each agent starting with a different number of left and right gloves. The experiment was web-deployed in JavaScript. This paper discusses the methodology, issues encountered, and initial results from the prototype experiment.
Towards a Robust and Accurate Screening Tool for Dyslexia with Data Augmentation using GANs

Eye movements during text reading can provide insights about reading disorders. We developed the DysLexML, a screening tool for developmental dyslexia, based on various ML algorithms that analyze gaze points recorded via eye-tracking during silent reading of children. We comparatively evaluated its performance using measurements collected from two systematic field studies with 221 participants in total. This work presents DysLexML and its performance. It identifies the features with prominent predictive power and performs dimensionality reduction. Specifically, it achieves its best performance using linear SVM, with an accuracy of 97% and 84% respectively, using a small feature set. We show that DysLexML is also robust in the presence of noise. These encouraging results set the basis for developing screening tools in less controlled, larger-scale environments, with inexpensive eye-trackers, potentially reaching a larger population for early intervention. Unlike other related studies, DysLexML achieves the aforementioned performance by employing only a small number of selected features, that have been identified with prominent predictive power. Finally, we developed a new data augmentation/substitution technique based on GANs for generating synthetic data similar to the original distributions.
Acoustic Model Bootstrapping Using Semi-Supervised Learning

This work aims at bootstrapping acoustic model training for automatic speech recognition with small amounts of human-labeled speech data and large amounts of machine-labeled speech data.Semi-supervised learning is investigated to select the machine-transcribed training samples.Two semi-supervised learning methods are proposed: one is the local-global uncertainty based method which introduces both the local uncertainty from the current utterance and the global uncertainty from the whole data pool into the data selection; the other is the margin based data selection, which selects the utterances near to the decision boundary through language model tuning. The experimental results based on a Japanese far-ﬁeld automatic speech recognition system indicate that the acoustic model trained by automatically transcribed speech data achieve about 17% relative gain when in-domain human annotated data was not available for initialization. While 3.7% relative gain was obtained when the initial acoustic model was trained by small amount of in-domain data.
KL-Divergence Regularized Deep Neural Network Adaptation for Low-Resource Speaker-Dependent Speech Enhancement

In this paper, we propose a Kullback-Leibler divergence (KLD) regularized approach to adapting speaker-independent (SI) speech enhancement model based on regression deep neural networks (DNNs) to another speaker-dependent (SD) model using a tiny amount of speaker-specific adaptation data. This algorithm adapts the DNN model conservatively by forcing the conditional target distribution estimated from the SD model to be close to that from the SI model. The constraint is realized by adding KLD regularization to our previously proposed maximum likelihood objective function. Experimental results demonstrate that, even with only 10 seconds of SD adaptation data, the proposed framework consistently achieves speech intelligibility improvements under all 15 unseen noise types evaluated and at all signal-to-noise ratio levels for all 8 test speakers from the WSJ0 evaluation set.
Correct Program : Instantiation Execution Denotation : 0 Spurious Programs : Inconsistent Program

Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterancedenotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial “abstract program” and (2) refining it while modeling structured alignments with differential dynamic programming. We obtain state-of-the-art performance on the WIKITABLEQUESTIONS and WIKISQL datasets. When compared to a standard attention baseline, we observe that the proposed structuredalignment mechanism is highly beneficial.