Reviewerly/exHarmony huggingface dataset:
Semantic Scene Completion with Cleaner Self

Semantic Scene Completion (SSC) transforms an image of single-view depth and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a well-known ill-posed problem as the prediction model has to “imagine” what is behind the visible surface, which is usually represented by Truncated Signed Distance Function (TSDF). Due to the sensory imperfection of the depth camera, most existing methods based on the noisy TSDF estimated from depth values suffer from 1) incomplete volumetric predictions and 2) confused semantic labels. To this end, we use the ground-truth 3D voxels to generate a perfect visible surface, called TSDF-CAD, and then train a “cleaner” SSC model. As the model is noise-free, it is expected to focus more on the “imagination” of unseen voxels. Then, we propose to distill the intermediate “cleaner” knowledge into another model with noisy TSDF input. In particular, we use the 3D occupancy feature and the semantic relations of the “cleaner self” to supervise the counterparts of the “noisy self” to respectively address the above two incorrect predictions. Experimental results validate that our method improves the noisy counterparts with 3.1% IoU and 2.2% mIoU for measuring scene completion and SSC, and also achieves new state-of-the-art accuracy on the popular NYU dataset. The code is available at https://github.com/fereenwong/CleanerS.
sRGB Real Noise Synthesizing with Neighboring Correlation-Aware Noise Model

Modeling and synthesizing real noise in the standard RGB (sRGB) domain is challenging due to the complicated noise distribution. While most of the deep noise generators proposed to synthesize sRGB real noise using an end-to-end trained model, the lack of explicit noise modeling degrades the quality of their synthesized noise. In this work, we propose to model the real noise as not only dependent on the underlying clean image pixel intensity, but also highly correlated to its neighboring noise realization within the local region. Correspondingly, we propose a novel noise synthesizing framework by explicitly learning its neighboring correlation on top of the signal dependency. With the proposed noise model, our framework greatly bridges the distribution gap between synthetic noise and real noise. We show that our generated “real” sRGB noisy images can be used for training supervised deep denoisers, thus to improve their real denoising results with a large margin, comparing to the popular classic denoisers or the deep denoisers that are trained on other sRGB noise generators. The code will be available at https://github.com/xuan611/sRGB-Real-Noise-Synthesizing.
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning

We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different devices. We observe that direct adaptation of self-supervised learning (SSL) to universally slimmable networks misbehaves as the training process frequently collapses. We then discover that temporal consistent guidance is the key to the success of SSL for universally slimmable networks, and we propose three guidelines for the loss design to ensure this temporal consistency from a unified gradient perspective. Moreover, we propose dynamic sampling and group regularization strategies to simultaneously improve training efficiency and accuracy. Our US3L method has been empirically validated on both convolutional neural networks and vision transformers. With only once training and one copy of weights, our method outperforms various state-of-the-art methods (individually trained or not) on benchmarks including recognition, object detection and instance segmentation.
Behaviour coding approach for assessing pitfalls in a questionnaire instrument towards assessing healthcare security practice

Developing a reliable questionnaire instrument to understand security practices in a typical hospital is often challenging. This is so because hospitals are characterized by different questionnaire respondents with varying domain knowledge such as knowledge in information security, information communication technology, and the domain knowledge of healthcare. Having assessed various pretesting methods, a synergy of conventional pretesting and behaviour coding were adopted to pretest an instrument that was intended for empirical assessment of security practice in typical hospitals
A Quantitative Case Study on Rampant Online Ordering Scams in the Philippines

Scams are fraudulent acts that have become more common in the Philippines as a result of the growth of Ride-Hailing, Delivery, and Food Application Service platforms. Constant loss of profit and consumer services are severely impacted; as a result, both parties are impacted by the Malicious Acts committed by these individuals in order to disrupt and hurt the users of the applications. Rampancy in Scams, particularly in certain specific places, violates the terms and conditions of the apps by committing heinous activities, but also in terms of privacy of information (e.g., Able to Access Customer Address; Access/Contact Customer Phone Number) and criminal acts in the nation. According to the statistics, two-thirds of the dataset interviewers have not yet encountered
Research Trends in Creative Performance for Business Organization: A Bibliometric Study

This study aims to systematically review and analyze the literature on creative performance research trends in business organizations through a holistic approach to previous studies so that gaps in the field can be identified clearly and further research plans can be formulated. In this context, bibliometric analysis methods uncover information yet to be fully captured or evaluated, including information regarding important sources, documents, countries, and keywords used in the field. Three hundred forty-three research articles produced between 1964 and 2022 regarding creative performance in business organizations were analyzed, and the insights obtained were poured into scientific mapping. The findings show that research and publications in the field of creative performance in business organizations have continued to increase from 1964-2022, but the highest publication only reached 9.22% in 2022. The total number of citations from all publications reached 14,871, with an average citation per year of 256.4 and citations per paper of 43.36. Much research on creative performance in business organizations is conducted in business, management, and accounting. It is published in journal articles, where the Creativity Research Journal is the most substantial document source in the creative performance business organization literature. From the author's point of view, Karatepe, O.M. is the most productive writer in creative performance by producing six articles. In contrast, the article with the highest number of citations was written by Oldham, G.R., and Cummings, A, entitled "Employee creativity: Personal and contextual factors at work," with a total of 2,330 citations. Then the country that contributed the most to the spread of creative performance knowledge in business organizations was the United States. Map of the development of creative performance research in business organizations based on the co-occurrence of author keywords grouped into 11 clusters, where creative performance and creativity are the keywords that most often appear in creative performance research in business organizations.
Pelatihan Penelitian Lapangan Mahasiswa ABA and STIBA Persada Bunda Pekanbaru

This training was carried out in response to the needs of students from Akademi Bahasa Asing (ABA) and Sekolah Tinggi Ilmu Bahasa Asing (STIBA) Persada Bunda Pekanbaru to conduct field research. That said, students require knowledge of field research; what is field research and its various types, how to determine a research topic, collect data, proceed with data, analyze data, and make conclusions and recommendations. Based on this, the training activities aimed at improving the abilities and broadening the knowledge of students to be capable of conducting and making a report of field research. This activity was completed over the course of one day, lasting four hours, and divided into two sessions. Students were trained on how to conduct field research in the first session. Furthermore, in the second session, participants had the opportunity to ask questions and proposed field research topics related to foreign language issues.
AR技術を用いたシミュレータによるメカトロニクス教育支援

With the increased interest of manufacturing education and creative education, the number of education programs for experiential learning on mechatronics is on the rise. Most programs introduce the experiment and practical training with actual robots into education for the purpose of enhancing educational effects. However, the burden of preparing and maintaining the robot is not small and sharing an actual robot with some students increases the risk of infection from Coronavirus (COVID-19) in recent years. In this research we have developed a new simulator that supports education of mechatronics using AR technology. By using the simulation, the cost of preparing and managing the actual robot can be significantly reduced and the risk of infection disease is also reduced. In addition the AR technology makes it possible for students to make experience as they control the actual robots.
ParaDiom – A Parallel Corpus of Idiomatic Texts

This paper present ParaDiom – a parallel corpus with 2000 Slovene and English text segments. The text segments are rich with manually annotated idiomatic expressions, which poses a challenge for machine translation systems. We describe the definition of idiomatic expressions, the sampling of the corpus sentences, the annotation scheme, and the general characteristics of the finished corpus. The motivation for this corpus is to have a test set for machine translation systems to evaluate their performance on figurative language. In the last part of the paper, we demonstrate an example use of the corpus in a machine translation experiment.
Multilingual TTS Accent Impressions for Accented ASR

Automatic Speech Recognition (ASR) for high-resource languages like English is often considered a solved problem. However, most high-resource ASR systems favor socioeconomically advantaged dialects. In the case of English, this leaves behind many L2 speakers and speakers of low-resource accents (a majority of English speakers). One way to mitigate this is to fine-tune a pre-trained English ASR model for a desired low-resource accent. However, collecting transcribed accented audio is costly and time-consuming. In this work, we present a method to produce synthetic L2-English speech via pre-trained text-to-speech (TTS) in an L1 language (target accent). This can be produced at a much larger scale and lower cost than authentic speech collection. We present initial experiments applying this augmentation method. Our results suggest that success of TTS augmentation relies on access to more than one hour of authentic training data and a diversity of target-domain prompts for speech synthesis.
Japanese How-to Tip Machine Reading Comprehension by Multi-task Learning Based on Generative Model

In the research of machine reading comprehension of Japanese how-to tip QA tasks, conventional extractive machine reading comprehension methods have difficulty in dealing with cases in which the answer string spans multiple locations in the context. In this paper, we trained a generative machine reading comprehension model of Japanese how-to tip by constructing a generative dataset based on the website “wikihow” as a source of information. We proposed two methods for multi-task learning to fine-tune the generative model, i.e., i) multi-task learning with generative and extractive hybrid training dataset, where both generative and extractive datasets are simultaneously trained on a single model, and ii) multi-task learning with inter-sentence semantic similarity and answer generation, where, drawing upon the answer generation task, the model additionally learns the distance between the sentences of question/context and the answer in the training examples. Evaluation experimental results showed that both of the multi-task learning models significantly outperformed that of the single-task learning model on the generative QA dataset. Especially, that with generative and extractive hybrid training dataset performed the best in terms of the manual evaluation result.
Mono- and Multilingual GPT-3 Models for Hungarian

In recent years, the growth in size of Transformer-based language models has accelerated significantly. Global technology companies are training larger and larger models that require enormous resources and training data. With these experiments, they aim to demonstrate that sufficiently large models with abundant training data can solve any natural language processing task even without fine-tuning. It may not be feasible to compete directly in this race, but there is an opportunity to conduct experiments in the direction of larger models in their shadow. Our aim is to train large language models for Hungarian. According to the knowledge transfer researches, a language model can adapt valuable knowledge from other languages. Furthermore, in order for the model to be able to solve translation tasks, it also needs multilingual knowledge. In our research, we trained a Hungarian monolingual and a Hungarian-English-Chinese trilingual 6.7 billion parameter GPT language model with more than 1TB text data. In our experiments, we also fine-tuned our model with the prompts provided by the Stanford Alpaca dataset. Thus, employing this methodology, an instruct GPT was built, which, as far as we know, is the first multilingual large language model in this region that can follow instructions.
Voice Cloning for Voice Disorders: Impact of Phonetic Content

Organic dysphonia can lead to vocal impairments. Recording patients’ impaired voice could allow them to use voice cloning systems. Voice cloning, being the process of producing speech matching a target speaker voice, given textual input and an audio sample from the speaker, can be used in such a context. However, dysphonic patients may only produce speech with specific or limited phonetic content. Considering a complete voice cloning process, we investigate the relation between the phonetic content, the length of samples and their impact on the output quality and speaker similarity through the use of phonetically limited artificial voices. The analysis of the speakers embedding which are used to capture voices shows an impact of the phonetic content. However, we were not able to observe those variations in the final generated speech.
Unified Modeling of Multi-Domain Multi-Device ASR Systems

Modern Automatic Speech Recognition (ASR) technology is typically fine-tuned for a targeted domain or application to obtain the best recognition results. This requires training and maintaining a dedicated ASR model for each domain, which increases the overall cost. Moreover, fine-tuned model might not be the most optimal way of sharing knowledge across domains. To address this, we propose a novel unified RNN-T based ASR technology that leverages domain embeddings and attention based mixture of experts architecture. Further, the proposed unified neural architecture allows for sharing of data and parameters seamlessly across domains. Our experiments show that the proposed approach outperforms a carefully fine-tuned domain-specific ASR model, yielding up to 10% relative word error rate (WER) improvement and 30% reduction in overall training cost.
A German Parallel Clausal Coordinate Ellipsis Corpus that Aligns Sentences from the TüBa-D/Z Treebank with Reconstructed Canonical Forms

This paper presents a new German resource for coordinated sentences, including asyndetons. The aim is to align cases of Clausal Coordinate Ellipsis (CCE) with the ellipsis-reconstructed sentences. The latter are called canonical forms. CCE is a challenging linguistic phenomenon in which constituents can be omitted under certain conditions. Often, several elision phenomena occur simultaneously. Even state-of-the-art constituency parsers have difficulties with CCE sentences. Although CCE examples occur in sufficient numbers in both written and spoken corpora, they are often among those with the lowest F1 scores. We surmise that elided verbforms, in particular, lead to incorrect hypotheses about phrase boundaries. Our new parallel corpus is designed to support the development of effective models for machine learning or natural language processing components that can automatically reconstruct CCE phenomena.
Morphological Tagging and Lemmatization of Spoken Corpora of Czech

We describe the annotation of corpora of spoken Czech according to a new annotation standard valid since the publication of the SYN2020 corpus of written Czech. The standard distinguishes lemmas and sublemmas, assigns a new attribute to verb forms, deals with multi-word tokens in an appropriate way. In order to annotate the corpora of spoken Czech by the same standard, new training data for the annotation of spoken text was created and experiments with using both written and spoken data for training a neural tagger were performed.
Unsupervised Learning for Automatic Speech Recognition in Air Traffic Control Environment

This paper addresses the enduring challenge of domain-specific automatic speech recognition (ASR) with limited training data, particularly in air traffic control (ATC) communications involving highly accented speakers. While state-of-the-art models like wav2vec have achieved significant progress when fine-tuned on smaller, specialized datasets, the issue of having little or no transcribed data for specific domains like ATC remains unresolved. We present our findings using the wav2vec-U 2.0 model, an advanced self-supervised ASR framework that learns from raw audio and unpaired text without the need for transcriptions. By fine-tuning wav2vec-U 2.0 on the domain-specific ATCOSIM dataset, we explore its effectiveness in handling domain-specific ASR tasks with scarce or non-existent transcribed data. Our results demonstrate promising speech recognition accuracy, suggesting that wav2vec-U 2.0 can effectively address the problem with small amounts of transcribed data posed by highly specific ASR-domains such as ATC communications. Furthermore, we discuss the implications of our findings for the broader ASR research community and provide suggestions into potential future directions for improving ASR accuracy in ATC communications and other specialized domains with limited training data.
Developing State-of-the-Art End-to-End ASR for Norwegian

We present the process of developing a modern end-to-end (E2E) automatic speech recognition (ASR) system for Norwegian (NO), which is a challenging language with many dialects and two written standards (Bokmål and Nynorsk). Since the existing speech corpora for this language are severely limited, we have had to acquire large amounts of additional data. This acquisition has been done by automatic processing of publicly accessible broadcast and parliament archives, YouTube and podcast channels, and also audiobooks. The data-harvesting process has been controlled by the ASR system, whose model has continuously been updated on the extracted chunks of speech. The final model has been trained on 1,246 h of Norwegian and further enhanced by transfer learning from an existing Swedish model. The performance of the ASR system has been evaluated on an 18-h collection of test sets (most of them publicly available) representing different application areas. Our best word error rate (WER) achieved on this collection is 7.6%, which is better than the results obtained from Google and Microsoft cloud services.
Resolving Hungarian Anaphora with ChatGPT

This paper presents an experiment in which we investigated how ChatGPT performs in resolving ambiguous pronominal anaphoras in Hungarian sentences. We used the chat function of ChatGPT to gain insight into its response strategy. We used four different experimental setups to find out which questioning strategy gives the best results. We also looked at how consistent the responses were. In addition to evaluating the results, we looked for reasons why the model could or could not solve certain problems.
Evaluation of Speech Representations for MOS Prediction

In this paper, we evaluate feature extraction models for predicting speech quality. We also propose a model architecture to compare embeddings of supervised learning and self-supervised learning models with embeddings of speaker verification models to predict the metric MOS. Our experiments were performed on the VCC2018 dataset and a Brazilian-Portuguese dataset called BRSpeechMOS, which was created for this work. The results show that the Whisper model is appropriate in all scenarios: with both the VCC2018 and BRSpeechMOS datasets. Among the supervised and self-supervised learning models using BRSpeechMOS, Whisper-Small achieved the best linear correlation of 0.6980, and the speaker verification model, SpeakerNet, had linear correlation of 0.6963. Using VCC2018, the best supervised and self-supervised learning model, Whisper-Large, achieved linear correlation of 0.7274, and the best model speaker verification, TitaNet, achieved a linear correlation of 0.6933. Although the results of the speaker verification models are slightly lower, the SpeakerNet model has only 5M parameters, making it suitable for real-time applications, and the TitaNet model produces an embedding of size 192, the smallest among all the evaluated models. The experiment results are reproducible with publicly available source-code.
Searching for Reasons of Transformers’ Success: Memorization vs Generalization

The Transformer architecture has, since its conception, led to numerous breakthrough advancements in natural language processing. We are interested in finding out whether its success is primarily due to its capacity to learn the various generic language rules, or whether the architecture leverages some memorized constructs without understanding their structure. We conduct a series of experiments in which we modify the training dataset to prevent the model from memorizing bigrams of words that are needed by the test data. We find out that while such a model performs worse than its unrestricted counterpart, the findings do not indicate that the Transformers’ success is solely due to its memorization capacity. In a small qualitative analysis, we demonstrate that a human translator lacking the necessary terminological knowledge would likely struggle in a similar way.
CML-TTS: A Multilingual Dataset for Speech Synthesis in Low-Resource Languages

In this paper, we present CML-TTS, a recursive acronym for CML-Multi-Lingual-TTS, a new Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is based on Multilingual LibriSpeech (MLS) and adapted for training TTS models, consisting of audiobooks in seven languages: Dutch, French, German, Italian, Portuguese, Polish, and Spanish. Additionally, we provide the YourTTS model, a multi-lingual TTS model, trained using 3,176.13 h from CML-TTS and also with 245.07 h from LibriTTS, in English. Our purpose in creating this dataset is to open up new research possibilities in the TTS area for multi-lingual models. The dataset is publicly available under the CC-BY 4.0 license ( https://freds0.github.io/CML-TTS-Dataset ).