HuBERT-TR: Reviving Turkish Automatic Speech Recognition with Self-supervised Speech Representation Learning

While the Turkish language is listed among low-resource languages, literature on Turkish automatic speech recognition (ASR) is relatively old. In this paper, we present HuBERT-TR, a speech representation model for Turkish, based on HuBERT. HuBERT-TR achieves state-of-the-art results on several Turkish ASR datasets. We investigate pre-training HuBERT for Turkish with large-scale data curated from online resources. We pre-train HuBERT-TR using over 6,500 hours of speech data curated from YouTube that includes extensive variability in terms of quality and genre. We show that language-specific models are superior to other pre-trained models, where our Turkish model HuBERT-TR/base performs better than the x10 times larger state-of-the-art multilingual XLS-R-1b model in low-resource settings. Moreover, we study the effect of scaling on ASR performance by scaling our models up to 1B parameters. Our best model yields a state-of-the-art word error rate of 4.97% on the Turkish Broadcast News dataset. Models are available at https://huggingface.co/asafayaComment: Submitted to ICASSP202

Short sales and trade classification algorithms

This paper demonstrates that short sales are often misclassified as buyer-initiated by the Lee–Ready and other commonly used trade classification algorithms. This result is due in part to regulations which require that short sales be executed on an uptick or zero-uptick. In addition, while the literature considers “immediacy premiums” in determining trade direction, it ignores the often larger borrowing premiums that short sellers must pay. Since short sales constitute approximately 30% of all trade volume on U.S. exchanges, these results are important to the empirical market microstructure literature, as well as to measures that rely upon trade classification, such as the probability of informed trading (PIN) metric

Environmental citizen science and action in Hong Kong schools

My research investigates the impact of citizen science experiences on Hong Kong students' values, attitudes, knowledge and behaviour towards the natural environment. This study intersects citizen science, environmental and experiential education, environmental behavioural psychology, and citizenship action. My aim was to examine citizen science as a pedagogical tool to influence greater youth agency to tackle environmental issues. This investigation evaluates the impact of school-based citizen science, addressing known research gaps. Increased environmental knowledge from citizen science is well established in the literature from western studies, though less is conclusively known about its impact on values, attitudes and behaviours, especially from an Asian perspective. Using a quasi-experimental mixed methods approach, I engaged with citizen science organisers, and teachers and students from eight schools in Hong Kong. Informed by environmental behaviour psychology, and environmental and experiential education theories, I modified an environmental behaviour model as my theoretical framework to guide the design of survey and semi-structured interview questions. My analysis is based on pre- and post-surveys from 187 students, and interviews with 46 students, 18 teachers and four citizen science organisers. My findings suggest that citizen science experiences lead to increased environmental knowledge and self-reported pro-environmental behaviours, with a moderate positive correlation between behavioural intention and behaviour. Pro-environmental behaviour is most influenced by field trips and personal experiences in natural environments, one's connection to nature, and being exposed to environmentally positive actions. This illuminates the importance of 'nurture in nature for nurture of nature'. Teachers, students and citizen science organisers shared similar impressions about the value of environmental education and citizen science but differed about where and how citizenship action was incorporated in environmental education. My findings provide evidence about how to use citizen science as a catalyst to enhance environmental education and narrow the value-action gaps in Hong Kong youth

Go Green Initiative from Google: A Study of Evolution in Teaching and Learning Environment by Google Classroom

Google provides Classroom which is a free web-based platform that integrates Google Apps for Education account with all your Google Apps services, including Google Docs, Gmail, and Google Calendar. In its Go Green initiative, Google Classroom saves time and paper, and makes it easy to create classes, distribute assignments, communicate, and stay organized in very effective manner. In present paper many functions of Google Classroom are evaluated.Using Google Classroom Teachers can quickly see who has or hasn\u27t completed the work, and provide direct, real-time feedback and grades right in Google Classroom. From the study we can say that this is the best Go Green Initiative from google via its Google Classroom platform

Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report

We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020). We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages. The event consists of regular research papers and a shared task, which is about event sentence coreference identification (ESCI), tracks. All submissions were reviewed by five members of the program committee. The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection. It has shown us the volume and variety of both the data sources and event information collection approaches related to socio-political events and the need to fill the gap between automated text processing techniques and requirements of social and political sciences

Veni Vidi Dixi: Reliable Wireless Communication with Depth Images

The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance cameras in the communication environment and utilizes Convolutional Neural Networks CNNs to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.Comment: Accepted for publication in CoNext 2019 with reproducibility badges. The measurements and the processing codes are available at https://gitlab.lrz.de/lkn_measurements/vvd_measurements for your evaluatio