Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Monolingual Speech Corpora for Code-Mixed Speech Recognition

One of the main challenges in building code-mixed ASR systems is the lack of annotated speech data. Often, however, monolingual speech corpora are available in abundance for the languages in the code-mixed speech. In this paper, we explore different techniques that use monolingual speech to create synthetic code-mixed speech and examine their effect on training models for code-mixed ASR. We assume access to a small amount of real code-mixed text, from which we extract probability distributions that govern the transition of phones across languages at code-switch boundaries and the span lengths corresponding to a particular language. We extract segments from monolingual data and concatenate them to form code-mixed utterances such that these probability distributions are preserved. Using this synthetic speech, we show significant improvements in Hindi-English code-mixed ASR performance compared to using synthetic speech naively constructed from complete utterances in different languages. We also present language modelling experiments that use synthetically constructed codemixed text and discuss their benefits.
FINAL YEAR PROJECT Semantic Video Segmentation INDIVIDUAL FINAL REPORT

Computer vision plays a major role in the field of artificial intelligence. As a preliminary step of many computer vision tasks, semantic segmentation attracts massive research attention. Current methods lack accuracy, temporal cohesion and computational efficiency, and thus are not sufficient for real time video analysis with high throughput. This project aims to improve the performance of aforementioned aspects by modifying and integrating existing methods. Our work includes exploring per-frame prediction networks and integrating with extra information like optical flow. A new metric for evaluating temporal coherence is defined. And our results show an increased temporal coherence with compromised accuracy.
Automatic Detection of the Temporal Segmentation of Hand Movements in British English Cued Speech

Cued Speech (CS) is a multi-modal system, which complements the lip reading with manual hand cues in the phonetic level to make the spoken language visible. It has been found that lip and hand movements are asynchronous in CS, and thus the study of hand temporal organization is very important for the multi-modal CS feature fusion. In this work, we propose a novel diphthong-hand preceding model (D-HPM) by investigating the relationship between hand preceding time (HPT) and diphthong time instants in sentences for British English CS. Besides, we demonstrate that HPT of the first and second parts of diphthongs has a very strong correlation. Combining the monophthong-HPM (M-HPM) and D-HPM, we present a hybrid temporal segmentation detection algorithm (HTSDA) for the hand movement in CS. The evaluation of the proposed algorithm is carried out by a hand position recognition experiment using the multi-Gaussian classifier as well as the longshort term memory (LSTM). The results show that the HTSDA significantly improves the recognition performance compared with the baseline (i.e., audio-based segmentation) and the stateof-the-art M-HPM. To the best of our knowledge, this is the first work to study the temporal organization of hand movements in British English CS.
Fluid inclusion measurements by laser ablation sector

A state-of-the-art, highly sensitive sector-field inductively coupled plasmamass spectrometer (SF-ICP-MS) was evaluated for analyzing fluid inclusions in combination with a 193 nm excimer laser ablation, with the aim of expanding the technique to smaller inclusions and/or lower element concentrations. A well-defined assemblage of numerous co-genetic fluid inclusions and thin layers (50 nm) of chromium and copper containing traces of gold were studied regarding reproducibility and detection limits. For comparison, aliquots were analyzed with a quadrupole (Elan 6100 DRC) and the SF-ICP-MS instrument (Element XR). While using an element menu of 21 elements covering the mass range from lithium (7 amu) to lead (208 amu), approximately ten times lower detection limits were found for the SF-ICP-MS, whereby the cycle time increased by only 20% compared with the quadrupole instrument. An absolute detection limit for e.g. gold of 1 femtogram (10 15 g) was found for fluid inclusions analysis. Detection efficiencies of 2.6 10 5 counts per ablated atom for copper and 3.1 10 5 for chromium were determined by ablating the thin layers. The detection efficiency for sodium in the fluid inclusions was 2 10 , about five times lower than that expected from the sensitivity obtained on NIST SRM 610 glass.
Nonlinear structural modeling using multivariate adaptive regression splines

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.
Drug and biological development : from molecule to product and beyond

Health Care and Industry Overview and Challenges. - R&D Planning and Governance. - R&D Outcomes. - Discovery and Nonclinical Development. - Types of Clinical Studies. - Metabolism and Pharmacokinetics. - Laws and Regulations: The Discipline of Regulatory Affairs. - Clinical Trial Operations. - Formulation and Manufacturing. - Commercial Division. - Medical Affairs and Professional Services. - Special Considerations in Research.
Towards Affective Drone Swarms in Public Spaces

Drones are an exciting kind of robot having recently found their way into the commercial mainstream. The idea of using drones in public spaces has gained momentum and been implemented in some recent public events, such as the 2018 Winter Olympics’ opening ceremonies. As drones are designed to be directly observed and possibly interacted with by an ordinary audience, some fundamental challenges remain for such interactions to be carried out successfully. Our work focuses on bridging the psychological gap between front-end technologies (drones) and the human observers’ emotional perceptions. For interactions to be effective, the observers need to feel safe and empathized with the artificial entities. To that end, we identify and discuss some open technical challenges to address this gap.
ScienceDirect Computational design of antibod ies

Antibody design aims to create new antibodies with biological activity that can be used in therapy and research. Traditional methods for antibody discovery, such as animal immunization and large-scale library screening, generate antibodies that bind to the target of interest, but do not necessarily have the desired functional effect. Computational methods can be utilized as a means to guide the search for biologically relevant antibodies, focusing on specificity and affinity determinants to target a particular region of the antigen. Such an approach would allow for the design of epitope-specific antibodies that will have the desired effect on the function of the targeted protein.
Phrase-level Quality Estimation for Machine Translation

The paper presents the first attempt to perform quality estimation (QE) of machine translation (MT) at the level of phrases. Automatically translated sentences directly or indirectly labelled by humans for quality at the word level are used to devise phrase-level quality labels. We suggest methods of segmenting sentences into phrases which mimic the actual segmentation that generated the translations. For the prediction models, we apply two sets of phrase-level features: (1) features used in sentence-level QE work, (2) features based on word vector representations. Our experiments show that the phrase-level models can improve over wordlevel models in terms of how well they detect errors.
Dogs are highly susceptible to H 5 N 1 avian in fl uenza virus

a Animal Influenza Laboratory of the Ministry of Agriculture and National Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, 427 Maduan Street, Harbin 150001, People's Republic of China b Institute of Medical Sciences, University of Tokyo, Tokyo 108-8639, Japan c Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, 2015 Linden Drive, Madison, WI 53706, USA d ERATO infection-induced Host Responses Project, Japan Science and Technology Agency, Saitama 332-0012, Japan
Conceptualizing and explaining cosmopolitan coverage

Research on international news flows has mostly aimed to explain why certain countries and regions are more reported on than others. There are few studies, however, on the reasons why some media outlets cover foreign affairs more intensively than others. This article thus extends our current knowledge by mapping different degrees of cosmopolitan coverage and identifying key conditions that help to explain these differences. Analysing foreign reporting and transnational debate in 12 newspapers from six European countries the study then employs FsQCA as the method for identifying the best ‘recipes’ – defined as the most relevant constellations of conditions for explaining cosmopolitan coverage. These causal recipes combine conditions at the level of the media outlet and conditions related to the country where the respective outlet is situated.
Dynamic Causal Model of FMRI Images for Early Detection in People at Risk of Alzheimer ’ s Disease

Elderly people with Mild Cognitive Impairment (MCI) are considered at heightened risk to develop Alzheimer’s disease (AD). This research was an attempt to develop an AD diagnostic tool under a hypothesis: Mirror Neuron System (MNS) decreases its function due to the disease pathology. Nowadays, Magnetic Resonance Imaging (MRI) is an effective tool for Alzheimer’s disease diagnosis. However, it is very hard to detect the disease in patients without pathological symptoms or in early stages. Therefore, this research aims to extend the use of functional Magnetic Resonance Imaging (fMRI) to study the functional connectivity among brain regions and effects of stimuli through modeling neuronal interactions from fMRI time-series using Dynamic Causal Modeling (DCM) method. Objectives were to build model of brain regions in three groups (20 normal control, 5 MCI, 7 AD: matched by age 60-70 years old) and investigate connectivity changes from all groups. All 32 subjects were MRI scanned with visual stimuli to study a functional MNS network. Results revealed major activated areas including precentral gyrus, inferior parietal lobule, inferior occipital gyrus, and supplementary motor area. Moreover, results from DCM analyses showed functional connectivity at all connections were different in all groups and significantly different in inferior occipital gyrus to precentral gyrus and inferior occipital gyrus to SMA. The connection strength of normal group was stronger than those of MCI and AD groups. With further larger group study, the DCM connection strength information may be helpful in evaluating the memory impairment state allowing physicians to detect the diseases in early states, and then intervening the memory deterioration. Manuscript received on July 26, 2014 ; revised on November 25, 2014. 1Advanced Diagnostic Imaging Center (AIMC) and the Department of Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. email: nrh@ecs.soton. ac.uk 2 Biological Engineering Program, Faculty of Engineering, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand.
Grid-Connected PV-Wind-Battery Based Multi-Input Transformer Coupled Bidirectional DC-DC Converter with Multilevel Inverter Fed AC Load

The main problem in the electrical system is the load demand by using the renewable and nonrenewable sources we are satisfying the load demand. The power flow between the load and renewable energy sources is difficult so we gone for multi-input transformer coupled bidirectional dcdc converter. This maintains the power flow between the load, grid and renewable sources. But the harmonics is main problem in the conventional model to overcome that problem we are going for the multilevel transformer in this paper, which can be observed in matlab/simulink software.
DESINING BIMATRIX GAME THOERY BASED NASH EQUILIBRIUM FOR INFORMATION SECURITY BASED ON RISK ANALYSIS IN CLOUD

With the mushroom growth of Cloud Service Providers (CSPs) offering services of similar functionality, makes a challenge for cloud users to choose an appropriate service provider from cloud marketplace. This unique paradigm brings about new risk related issues in Cloud Computing (CC) environment. This is because it is indispensable from a customer’s aspect to establish trust with a CSP with high risk of outsourced data to be misused. Though trustworthiness and competence to estimate risk of interaction and risk for customer’s privacy were addressed in many research works, the way through which the risk of outsourced data/ file can be minimized in the presence of malicious service providers pose challenging issues. The challenging issue of handling risk in cloud environment is addressed in this work through Risk Evaluation using Nash Equilibrium-based Bi-Matrix Decision (RE-NEBMD) model. RE-NEBMD model studies the problem of ensuring security where the possible risk of outsourced data is brought to an absolute minimum in Cloud Computing in the presence of malicious CSPs. Nash Equilibrium-based Trust Evaluation model performs trust evaluation model on CSPs for outsourced data based on strategy and utility function. Next, risk evaluation is measured using Bi-Matrix Decision model on the evaluated trust and risk is measured accordingly. Extensive analytical and experimental results are presented which show minimized cost during risk analysis, computation overhead and improving true positive rate or detected threats of our proposed model.
Distributed Representation of Chinese Collocation

We provide computational linguistics an unsupervised algorithm for Collocation Representing in vector space using segmented corpora. By collocation, we mean a directed association of head word and dependency word that constructed by fully random combine in a sentence. Our algorithm represents dependency words by dense vectors that are trained to predict contexts of the head word. We show that these vectors provide high performance for extracting collocation similarities in syntactic and semantic. The quality of work results performs effective on our test set, which is measured in a verb-verb phrase collocation prediction task.
Comparative Analysis among Single-Stage, Dual-Stage, and Triple-Stage Actuator Systems Applied to a Hard Disk Drive Servo System

In modern times, the design and optimization of different actuator systems for controlling a high-precision position control system represent a popular interdisciplinary research area. Initially, only single-stage actuator systems were used to control most of the motion control applications. Currently, dual-stage actuation systems are widely applied to high-precision position control systems such as hard disk drive (HDD) servo systems. In the dual-stage system, a voice coil motor (VCM) actuator is used as the primary stage and a piezoelectric micro-actuator is applied as the secondary stage. However, a dual-stage control architecture does not show significant performance improvements to achieve the next-generation high-capacity HDD servo system. Research continues on how to fabricate a tertiary actuator for a triple-stage HDD servo system. A thermal positioning controller (TPC) actuator is considered promising as the tertiary stage. The triple-stage system aims to achieve greater bandwidth, track density, and disk speed, with minimum sensitivity and greater error minimization. In this work, these three actuation systems with different combinations of proportional plus integral (PI), proportional plus derivative (PD), and proportional plus integral plus derivative (PID) controller, lag-lead controller, lag filter, and inverse lead plus a PI controller were designed and analyzed through simulation to achieve high-precision positioning. The comparative analyses were done on the MATLAB/Simulink simulation platform.
Detecting Depression with Word-Level Multimodal Fusion

Semi-structured clinical interviews are frequently used diagnostic tools for identifying depression during an assessment phase. In addition to the lexical content of a patient’s responses, multi-modal cues concurrent with the responses are indicators of their motor and cognitive state, including those derivable from their voice quality and gestural behaviour. In this paper, we use information from different modalities in order to train a classiﬁer capable of detecting the binary state of a subject (clinically depressed or not), as well as the level of their depression. We propose a model that is able to perform modality fusion incrementally after each word in an utterance using a time-dependent recurrent approach in a deep learning set-up. To mitigate noisy modalities, we utilize fusion gates that control the degree to which the audio or visual modality contributes to the ﬁnal prediction. Our results show the effectiveness of word-level multimodal fusion, achieving state-of-the-art results in depression detection and outperforming early feature-level and late fusion techniques.
On Learning Interpretable CNNs with Parametric Modulated Kernel-Based Filters

We investigate the problem of direct waveform modelling us-ing parametric kernel-based ﬁlters in a convolutional neural network (CNN) framework, building on SincNet, a CNN employing the cardinal sine ( sinc ) function to implement learnable bandpass ﬁlters. To this end, the general problem of learning a ﬁlterbank consisting of modulated kernel-based baseband ﬁl-ters is studied. Compared to standard CNNs, such models have fewer parameters, learn faster, and require less training data. They are also more amenable to human interpretation, paving the way to embedding some perceptual prior knowledge in the architecture. We have investigated the replacement of the rectangular ﬁlters of SincNet with triangular, gammatone and Gaussian ﬁlters, resulting in higher model ﬂexibility and a reduction to the phone error rate. We also explore the properties of the learned ﬁlters learned for TIMIT phone recognition from both perceptual and statistical standpoints. We ﬁnd that the ﬁlters in the ﬁrst layer, which directly operate on the waveform, are in accord with the prior knowledge utilised in designing and engineering standard ﬁlters such as mel-scale triangular ﬁlters. That is, the networks learn to pay more attention to perceptually signiﬁcant spectral neighbourhoods where the data centroid is located, and the variance and Shannon entropy are highest.
6 MYRIAPOD PHYLOGENY AND THE RELATIONSHIPS OF CHILOPODA

1 Australian Museum, 6 College Street, Sydney, NSW 2010, Australia. 2 Department of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, E.U.A. RESUMEN. Estudios recientes han propuesto que los Myriapoda constituyen un grupo monofilético, parafilético en relación con los Hexapoda, o incluso polifilético. Algunos caracteres morfológicos compartidos por los Chilopoda y los Progoneata son sinapomorfías potenciales de los Myriapoda. La monofilia de Myriapoda es más robusta cuando los hexápodos se unen con los crustáceos (las supuestas sinapomorfías de Atelocerata unen a los miriápodos). El análisis de la filogenia interna de los Chilopoda (ciempiés) basada en la combinación de secuencias de rRNA 18S y 28S y morfología soportan la monofilia de todos los órdenes, incluyendo a Lithobiomorpha, y de los clados supraordinales Pleurostigmophora, Epimorpha y Craterostigmus + Epimorpha. Los datos moleculares y morfológicos coinciden en la división de Lithobiomorpha en Lithobiidae y Henicopidae (= Anopsobiinae + Henicopinae), en la parafilia de las Cryptopidae en relación con las Scolopendridae, y en la división de los Geophilomorpha en Adesmata y Placodesmata.
Vocal Biomarker Assessment Following Pediatric Traumatic Brain Injury: A Retrospective Cohort Study

Recommendations following pediatric traumatic brain injury (TBI) support the integration of instrumental measurement to aid perceptual assessment in recovery and treatment plans. A comprehensive set of sensitive, robust and non-invasive measurements is therefore essential in assessing variations in speech characteristics over time following pediatric TBI. In this paper, we discuss a method for measuring changes in the speech patterns of a pediatric cohort of ten subjects diagnosed with severe TBI. We apply a diverse set of both well-known and novel feature measurements to child speech recorded throughout the year following diagnosis. We analyze these features individually and by speech subsystem for each subject as well as for the entire cohort. In children older than 72 months, we find highly significant (p < 0.01) increases in pitch variation and number of unique phonemes spoken, shortened pause length, and steadying articulation rate variability. Younger children exhibit similar steadied rate variability alongside an increase in articulation complexity. Nearly all speech features significantly change (p < 0.05) for the cohort as a whole, confirming that acoustic measures expanding upon perceptual assessment are needed to identify efficacious treatment targets for speech therapy following TBI.
Systematic Notes on the genus Hedychium ( Zingiberaceae ) in Myanmar

The genus Hedychium (Zingiberaceae) occurring in Myanmar was studied on the basis of the field explorations and specimen examinations in several large herbaria of the world housing the Myanmar specimens. As the results, four species of H. densiflorum Wall. , H. flavescens Carley ex Roscoe, H. gardnerianum Sheppard ex KerGawl. and H. griffithianum Wall. were newly recorded from Myanmar. Two undescribed taxa were also discovered as the result of field explorations. Molecular phylogenetic relationship based on nucleotide sequences of nuclear ribosomal ITS region supported that one is a putative natural hybrid and the other is closely related to H. villosum Wall. Hedychium×natmataungense Nob.Tanaka and H. villosum var. kachinense Nob. Tanaka are described and illustrated as new to science. The key to all taxa of Hedychium presently recorded in Myanmar is also provided.
Fertilizer source influence on antioxidant activity of lettuce

1 Programa de Pós-Graduação em Agricultura e Ambiente, Universidade Federal de Alagoas – Campus Arapiraca, Brazil. 2 Rede Nordeste de Biotecnologia/RENORBIO – Instituto de Química e Biotecnologia, Universidade Federal de Alagoas, Brazil. 3 Departamento de Mestrado Profissional em Análise de Sistemas Ambientais, Centro Universitário Cesmac, Brazil. 4 Empresa Brasileira de Pesquisa Agropecuária, Centro de Pesquisa Agropecuária dos Tabuleiros Costeiros, Embrapa Tabuleiros Costeiros. Campus Delza Gitai, Brazil.