pwc-archive/papers-with-abstracts huggingface dataset:
Smartajweed Automatic Recognition of Arabic Quranic Recitation Rules
Authors: Ali M. Alagrami, Maged M. Eljazzar
Date: 2020-12-26 00:00:00
Tasks: Rhythm

Tajweed is a set of rules to read the Quran in a correct Pronunciation of the letters with all its Qualities, while Reciting the Quran. which means you have to give every letter in the Quran its due of characteristics and apply it to this particular letter in this specific situation while reading, which may differ in other times. These characteristics include melodic rules, like where to stop and for how long, when to merge two letters in pronunciation or when to stretch some, or even when to put more strength on some letters over other. Most of the papers focus mainly on the main recitation rules and the pronunciation but not (Ahkam AL Tajweed) which give different rhythm and different melody to the pronunciation with every different rule of (Tajweed). Which is also considered very important and essential in Reading the Quran as it can give different meanings to the words. In this paper we discuss in detail full system for automatic recognition of Quran Recitation Rules (Tajweed) by using support vector machine and threshold scoring system
The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention
Authors: Sumit Jha, Mohamed F. Marzban, Tiancheng Hu, Mohamed H. Mahmoud, Naofal Al-Dhahir Carlos Busso
Date: 2020-12-23 00:00:00

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behaviors and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi- Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver's facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver's activities are recorded with high definition RGB cameras and time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.
Solving the robot-world hand-eye(s) calibration problem with iterative methods
Authors: Tabb, Amy Ahmad Yousef, Khalil M.
Date: 2017-05-02 00:00:00
Tasks: Robot Manipulation

Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as AX= ZB, where X and Z are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and simulated datasets and compare to seven other state-of-the-art methods. Our collection of methods returns greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple-eye calibration, and results are shown with two and three cameras mounted on the same robot.
DSNet: A Flexible Detect-to-Summarize Network for Video Summarization
Authors: Wencheng Zhu, Jiwen Lu, Jiahao Li, and Jie Zhou
Date: 2020-12-01 00:00:00
Tasks: regression, Supervised Video Summarization, Video Summarization

In this paper, we propose a Detect-to-Summarize network (DSNet) framework for supervised video summarization. Our DSNet contains anchor-based and anchor-free counterparts. The anchor-based method generates temporal interest proposals to determine and localize the representative contents of video sequences, while the anchor-free method eliminates the pre-defined temporal proposals and directly predicts the importance scores and segment locations. Different from existing supervised video summarization methods which formulate video summarization as a regression problem without temporal consistency and integrity constraints, our interest detection framework is the first attempt to leverage temporal consistency via the temporal interest detection formulation. Specifically, in the anchor-based approach, we first provide a dense sampling of temporal interest proposals with multi-scale intervals that accommodate interest variations in length, and then extract their long-range temporal features for interest proposal location regression and importance prediction. Notably, positive and negative segments are both assigned for the correctness and completeness information of the generated summaries. In the anchor-free approach, we alleviate drawbacks of temporal proposals by directly predicting importance scores of video frames and segment locations. Particularly, the interest detection framework can be flexibly plugged into off-the-shelf supervised video summarization methods. We evaluate the anchor-based and anchor-free approaches on the SumMe and TVSum datasets. Experimental results clearly validate the effectiveness of the anchor-based and anchor-free approaches.
Spoken Language Identification with Phonotactics Methods on Minangkabau, Sundanese, and Javanese Languages
Authors: Nur Endah Safitri, Amalia Zahra, and Mirna Adriani
Date: 2016-10-01 00:00:00
Tasks: Language Identification, Language Modelling, Spoken language identification

Research in the field of spoken language identification (spoken LID) on local languages helps to extend the outreach of
technology to local language speakers. This research also contributes to the preservation of local languages. In this paper, we
report our work on identifying spoken data in three local Indonesian languages: Minangkabau, Sundanese and Javanese.
Statistical phonotactics models are created to map the speech signals into the language used by the speaker. We use two
phonotactics methods, namely Phone Recognition followed by Language Modelling (PRLM) and Parallel Phone Recognition
followed by Language Modelling (PPRLM). PRLM method shows the highest accuracy using the phone recognizer trained for
English and Russian with the average of 77.42% and 75.94% respectively.
Automatic language identification
Authors: Marc A. Zissman, Kay M. Berkling
Date: 2001-08-01 00:00:00
Tasks: Language Identification

Automatic language identification of speech is the process by which the language of a digitized speech utterance is recognized by a computer. In this paper, we will describe the set of available cues for language identification of speech and discuss the different approaches to building working systems. This overview includes a range of historical approaches, contemporary systems that have been evaluated on standard databases, and promising future approaches. Comparative results are also reported.
AUTOMATIC LANGUAGE IDENTIFICATION USING DEEP NEURAL NETWORKS
Authors: Ignacio Lopez-Moreno, Javier Gonzalez-Dominguez, Oldrich Plchot, David Martinez, Joaquin Gonzalez-Rodriguez, Pedro Moreno
Date: 2014-05-04 00:00:00
Tasks: Acoustic Modelling, Language Identification

This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared to state-of-the-art i-vector based acoustic systems on two  different datasets: Google 5M LID corpus and NIST LRE 2009. Results show how LID can largely benefit from using DNNs, especially when a large amount of training data is available. We found relative improvements up to 70%, in Cavg, over the baseline system.
Methods for Spoken Language Identification
Authors: Julien Boussard, Andrew Deveau, Justin Pyron
Date: 2017-12-16 00:00:00
Tasks: BIG-bench Machine Learning, Language Identification, Spoken language identification

In this paper, we explore several machine learning techniques for classifying spoken language. In particular, we construct algorithms which utilize various spectral features derived from English and Mandarin Chinese phone call audio to predict the language to
which the phone call belongs. We investigate multiple feature sets and modeling approaches, and find that Gaussian Mixture Models,
combined with shifted delta cepstra (SDC) features, achieve the best performance.
Automatic Spoken Language Identification Utilizing Acoustic and Phonetic Speech Information
Authors: Kim-Yung Eddie Wong, BEng(Hons), BIT
Date: 2004-06-01 00:00:00
Tasks: Language Identification, speech-recognition, Speech Recognition, Spoken language identification

Automatic spoken Language Identification (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identification systems provide. A prominent application arises in call centers dealing with speakers speaking different languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation.
Acoustic and phonetic speech information are targeted as the most suitable features for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Phonetic speech information is extracted using existing speech recognition technology. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by different speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech.
As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.
Do your Resources Sound Similar?
Authors: Abdullah Fathi Ahmed, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo
Date: 2019-11-19 00:00:00

An increasing number of heterogeneous datasets abiding by the Linked Data paradigm is published everyday. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Complex LS com-
bine similarity measures with thresholds to determine whether a given predicate holds between two resources. State of the art LD frameworks rely mostly on string-based similarity measures such as Levenshtein and Jaccard. However, string-based similarity measures often fail to catch the similarity of resources with phonetically similar property values when these property values are represented using different string representation (e.g., names and street labels). In this paper, we evaluate the impact of using phonetics-based similarities in the process of LD. Moreover, we evaluate the impact of phonetic-based similarity measures on a state-of-the-art machine learning approach used to generate LS. Our experiments suggest that the combination of string-based and phonetic-based measures can improve the F-measures achieved by LD frameworks on most datasets.
A Study on Spoken Language Identification using Deep Neural Networks
Authors: Alexandra Draghici, Jakob Abeßer, Hanna Lukashevich
Date: 2020-09-15 00:00:00
Tasks: Language Identification, Spoken language identification

In this paper, we investigate a previously proposed algorithm for spoken language identification based on convolutional neural networks and convolutional recurrent neural networks. We improve the algorithm by modifying the training strategy to ensure equal class distribution and efficient memory usage. We successfully repli cate previous experimental findings using a modified set of languages. Our findings confirm that both a convolutional neural network as well as convolutional recurrent neural networks are capable to learn language-specific patterns in mel spectrogram representations of speech recordings.
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task Learning
Authors: Wenxin Hou, Yue Dong, Bairong Zhuang, Longfei Yang, Jiatong Shi, Takahiro Shinozaki
Date: 2020-10-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, Multi-Task Learning, speech-recognition, Speech Recognition

In this paper, we report a large-scale end-to-end language-independent multilingual model for joint automatic speech recognition (ASR) and language identification (LID). This model adopts hybrid CTC/attention architecture and achieves word error rate (WER) of 52.8 and LID accuracy of 93.5 on 42 languages with around 5000 hours of training data. We also compare the effects of using subword-level or character-level vocabulary for large-scale multilingual tasks. Furthermore, we transfer the pre-trained model to 14 low-resource languages. Results show that the pre-trained model achieves significantly  better results than non-pretrained baselines on both language specific and multilingual low-resource ASR tasks in terms of WER, which is reduced by 28.1% and 11.4% respectively.
SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy
Authors: Nicola Parolini, Luca Dede', Paola F. Antonietti, Giovanni Ardenghi, Andrea Manzoni, Edie Miglio, Andrea Pugliese, Marco Verani, Alfio Quarteroni
Date: 2021-01-09 00:00:00

The COVID-19 epidemic is the last of a long list of pandemics that have affected humankind in the last century. In this paper, we propose a novel mathematical epidemiological model named SUIHTER from the names of the seven compartments that it comprises: susceptible uninfected individuals (S), undetected (both asymptomatic and symptomatic) infected (U), isolated (I), hospitalized (H), threatened (T), extinct (E), and recovered (R). A suitable parameter calibration that is based on the combined use of least squares method and Markov Chain Monte Carlo (MCMC) method is proposed with the aim of reproducing the past history of the epidemic in Italy, surfaced in late February and still ongoing to date, and of validating SUIHTER in terms of its predicting capabilities. A distinctive feature of the new model is that it allows a one-to-one calibration strategy between the model compartments and the data that are daily made available from the Italian Civil Protection. The new model is then applied to the analysis of the Italian epidemic with emphasis on the second outbreak emerged in Fall 2020. In particular, we show that the epidemiological model SUIHTER can be suitably used in a predictive manner to perform scenario analysis at national level.
Model-based cellular kinetic analysis of SARS-CoV-2 infection: different immune response modes and treatment strategies
Authors: Zhengqing Zhou, Zhiheng Zhao, Shuyu Shi, Jianghua Wu, Dianjie Li, Jianwei Li, Jingpeng Zhang, Ke Gui, Yu Zhang, Heng Mei, Yu Hu, Qi Ouyang, Fangting Li
Date: 2021-01-12 00:00:00

Increasing number in global COVID-19 cases demands for mathematical model to analyze the interaction between the virus dynamics and the response of innate and adaptive immunity. Here, based on the assumption of a weak and delayed response of the innate and adaptive immunity in SARS-CoV-2 infection, we constructed a mathematical model to describe the dynamic processes of immune system. Integrating theoretical results with clinical COVID-19 patients' data, we classified the COVID-19 development processes into three typical modes of immune responses, correlated with the clinical classification of mild & moderate, severe and critical patients. We found that the immune efficacy (the ability of host to clear virus and kill infected cells) and the lymphocyte supply (the abundance and pool of na\"ive T and B cell) play important roles in the dynamic process and determine the clinical outcome, especially for the severe and critical patients. Furthermore, we put forward possible treatment strategies for the three typical modes of immune response. We hope our results can help to understand the dynamical mechanism of the immune response against SARS-CoV-2 infection, and to be useful for the treatment strategies and vaccine design.
Learning and Upgrading in Global Value Chains: An Analysis of India's Manufacturing Sector
Authors: Sourish Dutta
Date: 2021-01-12 00:00:00
Tasks: Position

The topic of my research is "Learning and Upgrading in Global Value Chains: An Analysis of India's Manufacturing Sector". To analyse India's learning and upgrading through position, functions, specialisation & value addition of manufacturing GVCs, it is required to quantify the extent, drivers, and impacts of India's Manufacturing links in GVCs. I have transformed this overall broad objective into three fundamental questions: (1) What is the extent of India's Manufacturing Links in GVCs? (2) What are the determinants of India's Manufacturing Links in GVCs? (3) What are the impacts of India's Manufacturing Links in GVCs? These three objectives represent my three chapters in my PhD thesis.
Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems
Authors: Yanzhen Liu, Qiyu Hu, Yunlong Cai, Markku Juntti
Date: 2021-01-12 00:00:00

In this letter, we investigate an intelligent reflecting surface (IRS) aided device-to-device (D2D) offloading system, where an IRS is employed to assist in computation offloading from a group of users with intensive tasks to another group of idle users. We propose a new two-timescale joint passive beamforming and resource allocation algorithm based on stochastic successive convex approximation to minimize the system latency while cutting down the heavy overhead in exchange of channel state information (CSI). Specifically, the high-dimensional passive beamforming vector at the IRS is updated in a frame-based manner based on the channel statistics, where each frame consists of a number of time slots, while the offloading ratio and user matching strategy are optimized relied on the low-dimensional real-time effective channel coefficients in each time slot. The convergence property and the computational complexity of the proposed algorithm are also examined. Simulation results show that our proposed algorithm significantly outperforms the conventional benchmarks.
Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life
Authors: Samuel Greenbank, David A. Howey
Date: 2021-01-12 00:00:00
Tasks: feature selection

Lithium-ion cells may experience rapid degradation in later life, especially with more extreme usage protocols. The onset of rapid degradation is called the `knee point', and forecasting it is important for the safe and economically viable use for batteries. We propose a data-driven method that uses automated feature selection to produce inputs for a Gaussian process regression model that estimates changes in battery health, from which the entire capacity fade trajectory, knee point and end of life may be predicted. The feature selection procedure flexibly adapts to varying inputs and prioritises those that impact degradation. For the datasets considered, it was found that calendar time and time spent in specific voltage regions had a strong impact on degradation rate. The approach produced median root mean square errors on capacity estimates under 1\%, and also produced median knee point and end of life prediction errors of 2.6\% and 1.3\% respectively.
Evaluation of Performance Measures for Classifiers Comparison
Authors: Vincent Labatut, Hocine Cherifi
Date: 2011-12-18 00:00:00
Tasks: General Classification

The selection of the best classification algorithm for a given dataset is a very widespread problem, occuring each time one has to choose a classifier to solve a real-world problem. It is also a complex task with many important methodological decisions to make. Among those, one of the most crucial is the choice of an appropriate measure in order to properly assess the classification performance and rank the algorithms. In this article, we focus on this specific task. We present the most popular measures and compare their behavior through discrimination plots. We then discuss their properties from a more theoretical perspective. It turns out several of them are equivalent for classifiers comparison purposes. Futhermore. they can also lead to interpretation problems. Among the numerous measures proposed over the years, it appears that the classical overall success rate and marginal rates are the more suitable for classifier comparison task.
On a Model for Integrated Information
Authors: Alessandro Epasto, Enrico Nardelli
Date: 2009-12-31 00:00:00
Tasks: model

In this paper we give a thorough presentation of a model proposed by Tononi et al. for modeling \emph{integrated information}, i.e. how much information is generated in a system transitioning from one state to the next one by the causal interaction of its parts and \emph{above and beyond} the information given by the sum of its parts. We also provides a more general formulation of such a model, independent from the time chosen for the analysis and from the uniformity of the probability distribution at the initial time instant. Finally, we prove that integrated information is null for disconnected systems.